Package: a4 Version: 1.42.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats License: GPL-3 MD5sum: 5addb55beeeb58bc6129e3a3cc8d2190 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: RELEASE_3_14 git_last_commit: fc26809 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/a4_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4_1.42.0.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: 82 Package: a4Base Version: 1.42.0 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: 3a0b384b18b6747b5df1860965635e57 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: RELEASE_3_14 git_last_commit: d7296e2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/a4Base_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Base_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Base_1.42.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 dependencyCount: 73 Package: a4Classif Version: 1.42.0 Depends: a4Core, a4Preproc Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils, graphics, stats Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: dbc9719a09436dec184ef5cf9461e125 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: RELEASE_3_14 git_last_commit: 820854a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/a4Classif_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Classif_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Classif_1.42.0.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: 1.42.0 Imports: Biobase, glmnet, methods, stats Suggests: knitr, rmarkdown License: GPL-3 MD5sum: c24c6808131117d34947f5dbb2e6dae4 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: RELEASE_3_14 git_last_commit: 6985950 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/a4Core_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Core_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Core_1.42.0.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: 1.42.0 Imports: BiocGenerics, Biobase Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 8bd01b1a25ba04f5b9567a30e6364c4d 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: RELEASE_3_14 git_last_commit: 773a91e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/a4Preproc_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Preproc_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Preproc_1.42.0.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: 1.42.0 Imports: methods, xtable Suggests: knitr, rmarkdown License: GPL-3 MD5sum: fc5f30815968540f6319dd6da7b52a4d 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: RELEASE_3_14 git_last_commit: b0d715b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/a4Reporting_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/a4Reporting_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/a4Reporting_1.42.0.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: ABAEnrichment Version: 1.24.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.11.5), gplots (>= 2.14.2), gtools (>= 3.5.0), ABAData (>= 0.99.2), data.table (>= 1.10.4), GOfuncR (>= 1.1.2), grDevices, stats, graphics, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 84cbf1d30fbf148af2e5549c1b090792 NeedsCompilation: yes Title: Gene expression enrichment in human brain regions Description: The package ABAEnrichment is designed to test for enrichment of user defined candidate genes in the set of expressed genes in different human brain regions. The core function 'aba_enrich' integrates the expression of the candidate gene set (averaged across donors) and the structural information of the brain using an ontology, both provided by the Allen Brain Atlas project. 'aba_enrich' interfaces the ontology enrichment software FUNC to perform the statistical analyses. Additional functions provided in this package like 'get_expression' and 'plot_expression' facilitate exploring the expression data, and besides the standard candidate vs. background gene set enrichment, also three additional tests are implemented, e.g. for cases when genes are ranked instead of divided into candidate and background. biocViews: GeneSetEnrichment, GeneExpression Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ABAEnrichment git_branch: RELEASE_3_14 git_last_commit: 5d20752 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ABAEnrichment_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ABAEnrichment_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ABAEnrichment_1.24.0.tgz vignettes: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.html vignetteTitles: Introduction to ABAEnrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.R suggestsMe: ABAData dependencyCount: 59 Package: ABarray Version: 1.62.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: 5c99a4ae2845b2aa6a05b2d5c87a895a 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: RELEASE_3_14 git_last_commit: b46ddfc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ABarray_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ABarray_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ABarray_1.62.0.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: 1.12.0 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: 7cf2a78c3c31300a1b720594490e7c64 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: RELEASE_3_14 git_last_commit: 2fdfd3e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/abseqR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/abseqR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/abseqR_1.12.0.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: 109 Package: ABSSeq Version: 1.48.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: 71d7538345ea09e601b33700f36556bd 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: RELEASE_3_14 git_last_commit: b237c96 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ABSSeq_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ABSSeq_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ABSSeq_1.48.0.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: 9 Package: acde Version: 1.24.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 452bc3d2690c46ce385e6011a52c4dd2 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: RELEASE_3_14 git_last_commit: 0c3c4d4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/acde_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/acde_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/acde_1.24.0.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: 1.12.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: e7974749e0956c0e311d2e15e77edd9e 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: RELEASE_3_14 git_last_commit: 543b422 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ACE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ACE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ACE_1.12.0.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: 80 Package: aCGH Version: 1.72.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, grDevices, graphics, methods, stats, splines, utils License: GPL-2 Archs: i386, x64 MD5sum: a130c807e7cbff5e526c4d345b635248 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: RELEASE_3_14 git_last_commit: b5d4022 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/aCGH_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/aCGH_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/aCGH_1.72.0.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, snapCGH suggestsMe: beadarraySNP dependencyCount: 16 Package: ACME Version: 2.50.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 6784c41228f5e42c8ca3ce1cd36f2f4a 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: RELEASE_3_14 git_last_commit: d55a19a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ACME_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ACME_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ACME_2.50.0.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: 2.34.0 Depends: R (>= 3.2.0), parallel, ff, GLAD Imports: bit, ffbase, DNAcopy, tilingArray, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: i386, x64 MD5sum: f6a3880393721afc5aec16f32ed9d9f7 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, BioHMM, 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 . Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: RELEASE_3_14 git_last_commit: 876bbc3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ADaCGH2_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ADaCGH2_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ADaCGH2_2.34.0.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: 100 Package: ADAM Version: 1.10.0 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 License: GPL (>= 2) Archs: i386, x64 MD5sum: 10d3290f7a6a7cfb593d67cc596d99b3 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: RELEASE_3_14 git_last_commit: 1b384b4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ADAM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ADAM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ADAM_1.10.0.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: 84 Package: ADAMgui Version: 1.10.0 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: cfc6278aa6dc6c4c6e4038e390f68ad4 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: RELEASE_3_14 git_last_commit: 8367d21 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ADAMgui_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ADAMgui_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ADAMgui_1.10.0.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: 159 Package: adductomicsR Version: 1.10.0 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: 56f5bfa28f2b5e8ef9aa7bf6fd6dd0a0 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: RELEASE_3_14 git_last_commit: 0caebfa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/adductomicsR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/adductomicsR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/adductomicsR_1.10.0.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: 1.4.0 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: e2d755939320f690c5d2d34d4d0f4d98 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: RELEASE_3_14 git_last_commit: ec8778a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ADImpute_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ADImpute_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ADImpute_1.4.0.tgz vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html vignetteTitles: ADImpute tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R dependencyCount: 53 Package: adSplit Version: 1.64.0 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: i386, x64 MD5sum: 91dee21b386cce5f69c8c2a2751a1507 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: RELEASE_3_14 git_last_commit: 32f150e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/adSplit_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/adSplit_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/adSplit_1.64.0.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: AffiXcan Version: 1.12.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 99995105a44789eafe223a39c24b240e 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: RELEASE_3_14 git_last_commit: 61ecd22 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AffiXcan_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AffiXcan_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AffiXcan_1.12.0.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: 54 Package: affxparser Version: 1.66.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: c85cc65aaacaad8d99a368889f71bda4 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: RELEASE_3_14 git_last_commit: 2ea72d4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/affxparser_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affxparser_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affxparser_1.66.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder importsMe: affyILM, cn.farms, crossmeta, EventPointer, GCSscore, GeneRegionScan, ITALICS, oligo suggestsMe: TIN, aroma.affymetrix, aroma.apd dependencyCount: 0 Package: affy Version: 1.72.0 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 License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 4cded407bb7fe51f64fa9d5a364e6048 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: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affy git_branch: RELEASE_3_14 git_last_commit: 3750b4e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/affy_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affy_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affy_1.72.0.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, farms, frmaTools, gcrma, logitT, maskBAD, panp, prebs, qpcrNorm, RefPlus, Risa, RPA, SCAN.UPC, sscore, webbioc, affydata, ALLMLL, AmpAffyExample, bronchialIL13, ccTutorial, CLL, curatedBladderData, curatedOvarianData, ecoliLeucine, Hiiragi2013, MAQCsubset, mvoutData, PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf, RobLoxBioC importsMe: affycoretools, affyILM, affylmGUI, arrayQualityMetrics, bnem, CAFE, ChIPXpress, Cormotif, crossmeta, Doscheda, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, iCheck, lumi, makecdfenv, mimager, MSnbase, PECA, plier, puma, pvac, Rnits, STATegRa, tilingArray, TurboNorm, vsn, rat2302frmavecs, DeSousa2013, signatureSearchData, IsoGene suggestsMe: AnnotationForge, ArrayExpress, autonomics, beadarray, beadarraySNP, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, factDesign, GeneRegionScan, limma, made4, piano, PREDA, qcmetrics, runibic, siggenes, TCGAbiolinks, estrogen, ffpeExampleData, arrays, aroma.affymetrix, hexbin, isatabr, maGUI dependencyCount: 11 Package: affycomp Version: 1.70.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 8b646d98764334f574c0c036d5c469e1 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: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affycomp git_branch: RELEASE_3_14 git_last_commit: 487f677 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/affycomp_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affycomp_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affycomp_1.70.0.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: AffyCompatible Version: 1.54.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: b04fd3ba6d4d4eca49de61a033445ec9 NeedsCompilation: no Title: Affymetrix GeneChip software compatibility Description: This package provides an interface to Affymetrix chip annotation and sample attribute files. The package allows an easy way for users to download and manage local data bases of Affynmetrix NetAffx annotation files. The package also provides access to GeneChip Operating System (GCOS) and GeneChip Command Console (AGCC)-compatible sample annotation files. biocViews: Infrastructure, Microarray, OneChannel Author: Martin Morgan, Robert Gentleman Maintainer: Martin Morgan git_url: https://git.bioconductor.org/packages/AffyCompatible git_branch: RELEASE_3_14 git_last_commit: fde7d86 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AffyCompatible_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AffyCompatible_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AffyCompatible_1.54.0.tgz vignettes: vignettes/AffyCompatible/inst/doc/MAGEAndARR.pdf, vignettes/AffyCompatible/inst/doc/NetAffxResource.pdf vignetteTitles: Retrieving MAGE and ARR sample attributes, Annotation retrieval with NetAffxResource hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyCompatible/inst/doc/MAGEAndARR.R, vignettes/AffyCompatible/inst/doc/NetAffxResource.R dependencyCount: 19 Package: affyContam Version: 1.52.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: e8bcd99c3f588a33abdc9729990b6179 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: RELEASE_3_14 git_last_commit: 47c1d86 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/affyContam_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affyContam_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affyContam_1.52.0.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: 1.66.0 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: 85fd667a7c26cd7bd02b0842539c9c69 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: RELEASE_3_14 git_last_commit: 6bf769d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/affycoretools_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affycoretools_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affycoretools_1.66.0.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: 188 Package: affyILM Version: 1.46.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: df97fa95c3887dfbb4ba388d1bfcfc86 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: RELEASE_3_14 git_last_commit: 67ffbfa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/affyILM_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affyILM_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affyILM_1.46.0.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: 26 Package: affyio Version: 1.64.0 Depends: R (>= 2.6.0) Imports: zlibbioc, methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: 4b7a85ff8136936a2c98ae58ac39a2e4 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: RELEASE_3_14 git_last_commit: aa7ce48 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/affyio_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affyio_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affyio_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: makecdfenv, SCAN.UPC, sscore importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods dependencyCount: 2 Package: affylmGUI Version: 1.68.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi, BiocManager, R2HTML, xtable License: GPL (>=2) MD5sum: 4f7213fc16e65cbb2c4e7c44abe7486e 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: RELEASE_3_14 git_last_commit: e6b1079 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/affylmGUI_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affylmGUI_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affylmGUI_1.68.0.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: 57 Package: affyPLM Version: 1.70.0 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 License: GPL (>= 2) Archs: i386, x64 MD5sum: cf0c6550f41606b7ef1c64a42fc8f27f 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: RELEASE_3_14 git_last_commit: 64abfec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/affyPLM_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/affyPLM_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/affyPLM_1.70.0.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: RefPlus importsMe: affylmGUI, arrayQualityMetrics, mimager suggestsMe: arrayMvout, BiocGenerics, frmaTools, metahdep, piano, aroma.affymetrix dependencyCount: 25 Package: AffyRNADegradation Version: 1.40.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: 1e98a63d50702cd7407e3340b175f7b8 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: RELEASE_3_14 git_last_commit: 8539a91 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AffyRNADegradation_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AffyRNADegradation_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AffyRNADegradation_1.40.0.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: 1.42.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 61f06ae2a701182ad373918d717876b4 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: RELEASE_3_14 git_last_commit: 175cf1b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AGDEX_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AGDEX_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AGDEX_1.42.0.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: 1.4.0 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: 003570ab174aa81dc3b275d99db6a526 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: RELEASE_3_14 git_last_commit: 0964b9a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/aggregateBioVar_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/aggregateBioVar_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/aggregateBioVar_1.4.0.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: 39 Package: agilp Version: 3.26.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 7f9a13fcd53d66fa459ec0d87f419351 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: RELEASE_3_14 git_last_commit: 3170fe2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/agilp_3.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/agilp_3.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/agilp_3.26.0.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: 2.44.0 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: 2c1d6b71572f5f9ccfea89f39be21c8e 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: RELEASE_3_14 git_last_commit: 8b308ba git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AgiMicroRna_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AgiMicroRna_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AgiMicroRna_2.44.0.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: 189 Package: AIMS Version: 1.26.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 5e93221b51b0117d354520b8d115b00d 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: RELEASE_3_14 git_last_commit: 5dcf60e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AIMS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AIMS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AIMS_1.26.0.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: 1.2.0 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 License: GPL-2 MD5sum: e0522aa9c2b7f632474fcad73b16daf9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/airpart git_branch: RELEASE_3_14 git_last_commit: 62f2b32 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/airpart_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/airpart_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/airpart_1.2.0.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: 123 Package: ALDEx2 Version: 1.26.0 Depends: methods, stats, zCompositions, Imports: Rfast, BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest Suggests: testthat, BiocStyle, knitr, rmarkdown License: file LICENSE MD5sum: 09a78b2f3d28b31e400eddd8ef1c776b NeedsCompilation: no Title: Analysis Of Differential Abundance Taking Sample 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 p-values and Benjamini-Hochberg corrected p-values. biocViews: DifferentialExpression, RNASeq, Transcriptomics, GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing, Software, Microbiome, Metagenomics, ImmunoOncology Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng 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: RELEASE_3_14 git_last_commit: 0876a2e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ALDEx2_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ALDEx2_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ALDEx2_1.26.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html vignetteTitles: ANOVA-Like Differential Expression tool for high throughput sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R dependsOnMe: omicplotR importsMe: benchdamic, microbiomeMarker suggestsMe: propr dependencyCount: 45 Package: alevinQC Version: 1.10.0 Depends: R (>= 4.0) Imports: rmarkdown (>= 2.5), tools, methods, ggplot2, GGally, dplyr, rjson, shiny, shinydashboard, DT, stats, utils, tximport (>= 1.17.4), cowplot, rlang Suggests: knitr, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 1b625c37e55a6c571220d14f532d7436 NeedsCompilation: no Title: Generate QC Reports For Alevin Output Description: Generate QC reports summarizing the output from an alevin 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] Maintainer: Charlotte Soneson URL: https://github.com/csoneson/alevinQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/alevinQC/issues git_url: https://git.bioconductor.org/packages/alevinQC git_branch: RELEASE_3_14 git_last_commit: 3b0466b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/alevinQC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/alevinQC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/alevinQC_1.10.0.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: 89 Package: AllelicImbalance Version: 1.32.0 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: 91acca23686a99782551a64ca4238913 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: RELEASE_3_14 git_last_commit: 428ab8c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AllelicImbalance_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AllelicImbalance_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AllelicImbalance_1.32.0.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: 147 Package: AlphaBeta Version: 1.8.0 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: fd05d1dde9c9348e6437bcb8d446356d 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: RELEASE_3_14 git_last_commit: da93a7f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AlphaBeta_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AlphaBeta_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AlphaBeta_1.8.0.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: 79 Package: alpine Version: 1.20.0 Depends: R (>= 3.3) Imports: Biostrings, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, SummarizedExperiment, GenomicFeatures, speedglm, splines, graph, RBGL, stringr, stats, methods, graphics, GenomeInfoDb, S4Vectors Suggests: knitr, testthat, markdown, alpineData, rtracklayer, ensembldb, BSgenome.Hsapiens.NCBI.GRCh38, RColorBrewer License: GPL (>=2) MD5sum: 78fd886e36d0e8ed817b88cf0acb5c25 NeedsCompilation: no Title: alpine Description: Fragment sequence bias modeling and correction for RNA-seq transcript abundance estimation. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, GeneExpression, Transcription, Coverage, BatchEffect, Normalization, Visualization, QualityControl Author: Michael Love, Rafael Irizarry Maintainer: Michael Love VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alpine git_branch: RELEASE_3_14 git_last_commit: 9348ef1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/alpine_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/alpine_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/alpine_1.20.0.tgz vignettes: vignettes/alpine/inst/doc/alpine.html vignetteTitles: alpine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alpine/inst/doc/alpine.R dependencyCount: 101 Package: AlpsNMR Version: 3.4.0 Depends: R (>= 4.0), dplyr (>= 0.7.5), future (>= 1.10.0), magrittr (>= 1.5) Imports: utils, graphics, stats, grDevices, signal (>= 0.7-6), assertthat (>= 0.2.0), rlang (>= 0.3.0.1), stringr (>= 1.3.1), tibble(>= 1.3.4), tidyr (>= 1.0.0), readxl (>= 1.1.0), plyr (>= 1.8.4), purrr (>= 0.2.5), glue (>= 1.2.0), reshape2 (>= 1.4.3), GGally (>= 1.4.0), mixOmics (>= 6.3.2), matrixStats (>= 0.54.0), writexl (>= 1.0), fs (>= 1.2.6), rmarkdown (>= 1.10), speaq (>= 2.4.0), htmltools (>= 0.3.6), ggrepel (>= 0.8.0), pcaPP (>= 1.9-73), furrr (>= 0.1.0), ggplot2 (>= 3.1.0), baseline (>= 1.2-1), zip (>= 2.0.4), tidyselect (>= 0.2.5), vctrs (>= 0.3.0), BiocParallel, SummarizedExperiment, S4Vectors Suggests: DT (>= 0.5), testthat (>= 2.0.0), plotly (>= 4.7.1), ChemoSpec, knitr License: MIT + file LICENSE MD5sum: f7babda4488f504f9b8b59fd48443c69 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] (), Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut] (), Nestlé Institute of Health Sciences [cph], Institute for Bioengineering of Catalonia [cph] Maintainer: Sergio Oller Moreno VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlpsNMR git_branch: RELEASE_3_14 git_last_commit: 8ef0246 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AlpsNMR_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AlpsNMR_3.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AlpsNMR_3.4.0.tgz vignettes: vignettes/AlpsNMR/inst/doc/introduction-to-alpsnmr.html vignetteTitles: Introduction to AlpsNMR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlpsNMR/inst/doc/introduction-to-alpsnmr.R dependencyCount: 153 Package: alsace Version: 1.30.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice, knitr License: GPL (>= 2) MD5sum: 88e899d53be19449ea236745dc4c35f8 NeedsCompilation: no Title: ALS for the Automatic Chemical Exploration of mixtures Description: Alternating Least Squares (or Multivariate Curve Resolution) for analytical chemical data, in particular hyphenated data where the first direction is a retention time axis, and the second a spectral axis. Package builds on the basic als function from the ALS package and adds functionality for high-throughput analysis, including definition of time windows, clustering of profiles, retention time correction, etcetera. Author: Ron Wehrens Maintainer: Ron Wehrens URL: https://github.com/rwehrens/alsace VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/alsace git_branch: RELEASE_3_14 git_last_commit: d0e09b2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/alsace_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/alsace_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/alsace_1.30.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: tofsims dependencyCount: 12 Package: altcdfenvs Version: 2.56.0 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: d8c8758b0452b334775a04a5a3066c43 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: RELEASE_3_14 git_last_commit: 941e00b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/altcdfenvs_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/altcdfenvs_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/altcdfenvs_2.56.0.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: 26 Package: AMARETTO Version: 1.10.0 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 License: Apache License (== 2.0) + file LICENSE MD5sum: f6f2c1b1035c211952e89f9e1392a083 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: RELEASE_3_14 git_last_commit: f2da2d8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AMARETTO_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AMARETTO_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AMARETTO_1.10.0.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: 156 Package: AMOUNTAIN Version: 1.20.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 86acb58c9cbe2bb11dae8bc315c29b96 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: RELEASE_3_14 git_last_commit: 0746fe7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AMOUNTAIN_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AMOUNTAIN_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AMOUNTAIN_1.20.0.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: 1.16.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings (>= 2.44.2), 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 (>= 2.2.0), 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), clusterCrit (>= 1.2.7) LinkingTo: Rcpp Suggests: testthat, BiocStyle, GenomicAlignments License: GPL-3 Archs: i386, x64 MD5sum: 9f5103e9c9569777fd7af4b1e2c5dee6 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: RELEASE_3_14 git_last_commit: afffd18 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/amplican_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/amplican_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/amplican_1.16.0.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: 103 Package: Anaquin Version: 2.18.0 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: 7bcbd15ae380957e048bf28cede1c7c8 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: RELEASE_3_14 git_last_commit: c8e3df3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Anaquin_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Anaquin_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Anaquin_2.18.0.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: 108 Package: ANCOMBC Version: 1.4.0 Imports: stats, MASS, nloptr, Rdpack, phyloseq, microbiome Suggests: knitr, tidyverse, testthat, DT, magrittr, qwraps2 (>= 0.5.0), rmarkdown License: Artistic-2.0 MD5sum: 132c4c21aac89f1bb73df02c0f55408b NeedsCompilation: no Title: Analysis of compositions of microbiomes with bias correction Description: ANCOMBC is a package for normalizing the microbial observed abundance data due to unequal sampling fractions across samples, and identifying taxa (e.g. phyla, families, genera, species, etc.) that are differentially abundant with respect to the covariate of interest (e.g. study groups) between two or more groups of multiple samples. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Software Author: Huang Lin [cre, aut] (), Shyamal Das Peddada [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: RELEASE_3_14 git_last_commit: b9a7fb1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ANCOMBC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ANCOMBC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ANCOMBC_1.4.0.tgz vignettes: vignettes/ANCOMBC/inst/doc/ANCOMBC.html vignetteTitles: ANCOMBC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANCOMBC/inst/doc/ANCOMBC.R importsMe: microbiomeMarker dependencyCount: 102 Package: AneuFinder Version: 1.22.0 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, ReorderCluster, mclust Suggests: knitr, BiocStyle, testthat, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 Archs: i386, x64 MD5sum: 4f97f996111071b1a17d4318485f38b2 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: RELEASE_3_14 git_last_commit: ea0beb3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AneuFinder_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AneuFinder_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AneuFinder_1.22.0.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: 89 Package: ANF Version: 1.16.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: ca848f42da9c71df56afebedb8eaf792 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: RELEASE_3_14 git_last_commit: 746a193 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ANF_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ANF_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ANF_1.16.0.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: 17 Package: animalcules Version: 1.10.0 Depends: R (>= 4.0.0) Imports: assertthat, shiny, shinyjs, DESeq2, caret, plotly, ggplot2, rentrez, reshape2, covr, ape, vegan, dplyr, magrittr, MultiAssayExperiment, SummarizedExperiment, S4Vectors (>= 0.23.19), XML, forcats, scales, lattice, glmnet, tsne, plotROC, DT, utils, limma, methods, stats, tibble, biomformat, umap, Matrix, GUniFrac Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: 3230e6ce1dc113a517ffa8b753d8aae8 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: Yue Zhao [aut, cre] (), Anthony Federico [aut] (), W. Evan Johnson [aut] () Maintainer: Yue Zhao URL: https://github.com/compbiomed/animalcules VignetteBuilder: knitr BugReports: https://github.com/compbiomed/animalcules/issues git_url: https://git.bioconductor.org/packages/animalcules git_branch: RELEASE_3_14 git_last_commit: 4c16173 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/animalcules_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/animalcules_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/animalcules_1.10.0.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: 1.66.0 Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 411909cf4556666ad78d64da0dbe2573 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: RELEASE_3_14 git_last_commit: aa1afa1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/annaffy_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/annaffy_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/annaffy_1.66.0.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: 1.36.99 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: 49ea90d16734271c8a28fa9bf7a6ef7d 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://gitlab.com/cruk-mi/annmap git_url: https://git.bioconductor.org/packages/annmap git_branch: RELEASE_3_14 git_last_commit: 959b765 git_last_commit_date: 2022-01-07 Date/Publication: 2022-01-09 source.ver: src/contrib/annmap_1.36.99.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/annmap_1.36.99.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: 67 Package: annotate Version: 1.72.0 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, License: Artistic-2.0 MD5sum: 6ebfd3beab54be64e2d97bef6a846efd NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/annotate git_branch: RELEASE_3_14 git_last_commit: 67ac76a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/annotate_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/annotate_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/annotate_1.72.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/chromLoc.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useDataPkgs.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf vignetteTitles: Annotation Overview, HowTo: use chromosomal information, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Data Packages, Using Affymetrix Probe Level Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLoc.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, GeneAnswers, geneplotter, GOSim, GSEABase, idiogram, macat, MineICA, MLInterfaces, phenoTest, PREDA, sampleClassifier, SemDist, Neve2006, PREDAsampledata importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DrugVsDisease, GeneAnswers, genefilter, GlobalAncova, globaltest, GOstats, lumi, methylumi, MGFR, phenoTest, qpgraph, RpsiXML, tigre, UMI4Cats, easyDifferentialGeneCoexpression, geneExpressionFromGEO, GOxploreR suggestsMe: BiocGenerics, GenomicRanges, GSAR, GSEAlm, hmdbQuery, maigesPack, metagenomeSeq, MLP, pageRank, pcxn, PhosR, RnBeads, siggenes, SummarizedExperiment, systemPipeR, adme16cod.db, ag.db, ath1121501.db, bovine.db, canine.db, canine2.db, celegans.db, chicken.db, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, drosgenome1.db, drosophila2.db, ecoli2.db, GGHumanMethCancerPanelv1.db, h10kcod.db, h20kcod.db, hcg110.db, hgfocus.db, hgu133a.db, hgu133a2.db, hgu133b.db, hgu133plus2.db, hgu219.db, hgu95a.db, hgu95av2.db, hgu95b.db, hgu95c.db, hgu95d.db, hgu95e.db, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133b.db, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, htmg430a.db, htmg430b.db, htmg430pm.db, htrat230pm.db, htratfocus.db, hu35ksuba.db, hu35ksubb.db, hu35ksubc.db, 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ragene10sttranscriptcluster.db, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rgu34a.db, rgu34b.db, rgu34c.db, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, ri16cod.db, RnAgilentDesign028282.db, rnu34.db, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rwgcod.db, SHDZ.db, u133x3p.db, xlaevis.db, yeast2.db, ygs98.db, zebrafish.db, clValid, limorhyde, maGUI, MOSS, optCluster dependencyCount: 47 Package: AnnotationDbi Version: 1.56.2 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges Imports: DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST Suggests: hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: 9cda13f36cd7223c1bf325c99ac60759 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: RELEASE_3_14 git_last_commit: 13fdc4a git_last_commit_date: 2021-11-09 Date/Publication: 2021-11-09 source.ver: src/contrib/AnnotationDbi_1.56.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationDbi_1.56.2.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationDbi_1.56.2.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db" annotation packages, 1. Introduction To Bioconductor Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.R, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R dependsOnMe: annotate, AnnotationForge, ASpli, attract, Category, ChromHeatMap, customProDB, deco, DEXSeq, EGSEA, EpiTxDb, GenomicFeatures, goProfiles, GSReg, ipdDb, miRNAtap, OrganismDbi, pathRender, proBAMr, safe, SemDist, topGO, adme16cod.db, ag.db, agprobe, anopheles.db0, arabidopsis.db0, ath1121501.db, ath1121501probe, barley1probe, bovine.db, bovine.db0, bovineprobe, bsubtilisprobe, canine.db, canine.db0, canine2.db, canine2probe, canineprobe, celegans.db, celegansprobe, chicken.db, chicken.db0, chickenprobe, chimp.db0, citrusprobe, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottonprobe, DO.db, drosgenome1.db, drosgenome1probe, drosophila2.db, drosophila2probe, ecoli2.db, ecoli2probe, ecoliasv2probe, ecoliK12.db0, ecoliprobe, ecoliSakai.db0, fly.db0, GGHumanMethCancerPanelv1.db, GO.db, h10kcod.db, h20kcod.db, hcg110.db, hcg110probe, hgfocus.db, hgfocusprobe, hgu133a.db, hgu133a2.db, hgu133a2probe, hgu133aprobe, hgu133atagprobe, hgu133b.db, hgu133bprobe, hgu133plus2.db, hgu133plus2probe, hgu219.db, hgu219probe, hgu95a.db, hgu95aprobe, hgu95av2.db, hgu95av2probe, hgu95b.db, hgu95bprobe, hgu95c.db, hgu95cprobe, hgu95d.db, hgu95dprobe, hgu95e.db, hgu95eprobe, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133aprobe, hthgu133b.db, hthgu133bprobe, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmprobe, htmg430a.db, htmg430aprobe, htmg430b.db, htmg430bprobe, htmg430pm.db, htmg430pmprobe, htrat230pm.db, htrat230pmprobe, htratfocus.db, htratfocusprobe, hu35ksuba.db, hu35ksubaprobe, hu35ksubb.db, hu35ksubbprobe, hu35ksubc.db, hu35ksubcprobe, hu35ksubd.db, hu35ksubdprobe, hu6800.db, hu6800probe, huex10stprobeset.db, huex10sttranscriptcluster.db, HuExExonProbesetLocation, HuExExonProbesetLocationHg18, HuExExonProbesetLocationHg19, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1probe, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, IlluminaHumanMethylation450kprobe, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizeprobe, malaria.db0, medicagoprobe, mgu74a.db, mgu74aprobe, mgu74av2.db, mgu74av2probe, mgu74b.db, mgu74bprobe, mgu74bv2.db, mgu74bv2probe, mgu74c.db, mgu74cprobe, mgu74cv2.db, mgu74cv2probe, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, mirna10probe, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430aprobe, moe430b.db, moe430bprobe, moex10stprobeset.db, moex10sttranscriptcluster.db, MoExExonProbesetLocation, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1probe, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db, mouse4302probe, mouse430a2.db, mouse430a2probe, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubaprobe, mu11ksubb.db, mu11ksubbprobe, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180probe, nugomm1a520177.db, nugomm1a520177probe, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Mxanthus.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, paeg1aprobe, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db, pig.db0, plasmodiumanophelesprobe, POCRCannotation.db, poplarprobe, porcine.db, porcineprobe, primeviewprobe, r10kcod.db, rae230a.db, rae230aprobe, rae230b.db, rae230bprobe, raex10stprobeset.db, raex10sttranscriptcluster.db, RaExExonProbesetLocation, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1probe, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0, rat2302.db, rat2302probe, rattoxfxprobe, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34aprobe, rgu34b.db, rgu34bprobe, rgu34c.db, rgu34cprobe, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesus.db0, rhesusprobe, ri16cod.db, riceprobe, RnAgilentDesign028282.db, rnu34.db, rnu34probe, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34probe, rwgcod.db, saureusprobe, SHDZ.db, soybeanprobe, sugarcaneprobe, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test3probe, tomatoprobe, u133x3p.db, u133x3pprobe, vitisviniferaprobe, wheatprobe, worm.db0, xenopus.db0, xenopuslaevisprobe, xlaevis.db, xlaevis2probe, xtropicalisprobe, yeast.db0, yeast2.db, yeast2probe, ygs98.db, ygs98probe, zebrafish.db, zebrafish.db0, zebrafishprobe, tinesath1probe, rnaseqGene, convertid importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, annotatr, artMS, beadarray, bioCancer, BiocSet, biomaRt, BioNet, biovizBase, bumphunter, BUSpaRse, categoryCompare, ccmap, cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, Cogito, conclus, consensusDE, cosmosR, crisprseekplus, CrispRVariants, crossmeta, cTRAP, DAPAR, debrowser, derfinder, DominoEffect, DOSE, EDASeq, eegc, EnrichmentBrowser, ensembldb, erma, esATAC, FRASER, GA4GHshiny, gage, GAPGOM, genefilter, geneplotter, GeneTonic, geneXtendeR, GenVisR, ggbio, GlobalAncova, globaltest, GmicR, GOfuncR, GOpro, GOSemSim, goseq, GOSim, goSTAG, GOstats, goTools, gpart, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, ideal, IMAS, InPAS, interactiveDisplay, IRISFGM, isomiRs, IVAS, karyoploteR, LRBaseDbi, lumi, mAPKL, MCbiclust, MeSHDbi, meshes, MesKit, MetaboSignal, methylGSA, methylumi, MIGSA, MineICA, MiRaGE, mirIntegrator, miRNAmeConverter, missMethyl, MLP, MSnID, multiGSEA, multiMiR, NanoMethViz, NanoStringQCPro, nanotatoR, netOmics, NetSAM, ontoProc, ORFik, Organism.dplyr, PADOG, pathview, pcaExplorer, phantasus, phenoTest, proActiv, psichomics, pwOmics, qpgraph, QuasR, ReactomePA, REDseq, regutools, restfulSE, rgsepd, ribosomeProfilingQC, RNAAgeCalc, RpsiXML, rrvgo, rTRM, SBGNview, scanMiRApp, scPipe, scruff, scTensor, SGSeq, signatureSearch, simplifyEnrichment, singleCellTK, SLGI, SMITE, SpidermiR, StarBioTrek, SubCellBarCode, TCGAutils, tenXplore, TFutils, tigre, trackViewer, trena, TRESS, tricycle, txcutr, tximeta, Ularcirc, UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO, adme16cod.db, ag.db, agcdf, ath1121501.db, ath1121501cdf, barley1cdf, bovine.db, bovinecdf, bsubtiliscdf, canine.db, canine2.db, canine2cdf, caninecdf, celegans.db, celeganscdf, chicken.db, chickencdf, citruscdf, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottoncdf, cyp450cdf, DO.db, drosgenome1.db, drosgenome1cdf, drosophila2.db, drosophila2cdf, ecoli2.db, ecoli2cdf, ecoliasv2cdf, ecolicdf, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, GGHumanMethCancerPanelv1.db, gp53cdf, h10kcod.db, h20kcod.db, hcg110.db, hcg110cdf, hgfocus.db, hgfocuscdf, hgu133a.db, hgu133a2.db, hgu133a2cdf, hgu133acdf, hgu133atagcdf, hgu133b.db, hgu133bcdf, hgu133plus2.db, hgu133plus2cdf, hgu219.db, hgu219cdf, hgu95a.db, hgu95acdf, hgu95av2.db, hgu95av2cdf, hgu95b.db, hgu95bcdf, hgu95c.db, hgu95ccdf, hgu95d.db, hgu95dcdf, hgu95e.db, hgu95ecdf, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hivprtplus2cdf, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, Hspec, hspeccdf, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133acdf, hthgu133b.db, hthgu133bcdf, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmcdf, htmg430a.db, htmg430acdf, htmg430b.db, htmg430bcdf, htmg430pm.db, htmg430pmcdf, htrat230pm.db, htrat230pmcdf, htratfocus.db, htratfocuscdf, hu35ksuba.db, hu35ksubacdf, hu35ksubb.db, hu35ksubbcdf, hu35ksubc.db, hu35ksubccdf, hu35ksubd.db, hu35ksubdcdf, hu6800.db, hu6800cdf, hu6800subacdf, hu6800subbcdf, hu6800subccdf, hu6800subdcdf, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1cdf, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizecdf, medicagocdf, mgu74a.db, mgu74acdf, mgu74av2.db, mgu74av2cdf, mgu74b.db, mgu74bcdf, mgu74bv2.db, mgu74bv2cdf, mgu74c.db, mgu74ccdf, mgu74cv2.db, mgu74cv2cdf, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, miRBaseVersions.db, mirna102xgaincdf, mirna10cdf, mirna20cdf, miRNAtap.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430acdf, moe430b.db, moe430bcdf, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1cdf, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse4302cdf, mouse430a2.db, mouse430a2cdf, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubacdf, mu11ksubb.db, mu11ksubbcdf, Mu15v1.db, mu19ksuba.db, mu19ksubacdf, mu19ksubb.db, mu19ksubbcdf, mu19ksubc.db, mu19ksubccdf, Mu22v3.db, mu6500subacdf, mu6500subbcdf, mu6500subccdf, mu6500subdcdf, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180cdf, nugomm1a520177.db, nugomm1a520177cdf, OperonHumanV3.db, paeg1acdf, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, plasmodiumanophelescdf, POCRCannotation.db, PolyPhen.Hsapiens.dbSNP131, poplarcdf, porcine.db, porcinecdf, primeviewcdf, r10kcod.db, rae230a.db, rae230acdf, rae230b.db, rae230bcdf, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1cdf, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rat2302cdf, rattoxfxcdf, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34acdf, rgu34b.db, rgu34bcdf, rgu34c.db, rgu34ccdf, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesuscdf, ri16cod.db, ricecdf, RmiR.Hs.miRNA, RmiR.hsa, RnAgilentDesign028282.db, rnu34.db, rnu34cdf, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34cdf, rwgcod.db, saureuscdf, SHDZ.db, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, soybeancdf, sugarcanecdf, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test1cdf, test2cdf, test3cdf, tomatocdf, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.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.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, u133aaofav2cdf, u133x3p.db, u133x3pcdf, vitisviniferacdf, wheatcdf, xenopuslaeviscdf, xlaevis.db, xlaevis2cdf, xtropicaliscdf, ye6100subacdf, ye6100subbcdf, ye6100subccdf, ye6100subdcdf, yeast2.db, yeast2cdf, ygs98.db, ygs98cdf, zebrafish.db, zebrafishcdf, celldex, chipenrich.data, DeSousa2013, msigdb, ppiData, scRNAseq, ExpHunterSuite, aliases2entrez, BiSEp, CAMML, DIscBIO, jetset, Mega2R, MetaIntegrator, netgsa, pathfindR, prioGene, pulseTD, RobLoxBioC, WGCNA suggestsMe: APAlyzer, autonomics, bambu, BiocGenerics, BiocOncoTK, BioPlex, CellTrails, cicero, cola, csaw, DEGreport, edgeR, eisaR, enrichplot, esetVis, FELLA, FGNet, fgsea, GA4GHclient, gCrisprTools, GeneAnswers, GeneRegionScan, GenomicRanges, iSEEu, limma, MutationalPatterns, oligo, OUTRIDER, piano, Pigengene, plotgardener, pRoloc, ProteoDisco, quantiseqr, R3CPET, recount, RGalaxy, RLSeq, sigPathway, sparrow, SummarizedExperiment, systemPipeR, tidybulk, topconfects, weitrix, wiggleplotr, BloodCancerMultiOmics2017, curatedAdipoChIP, RforProteomics, bulkAnalyseR, CALANGO, conos, cRegulome, DGCA, dnapath, easylabel, langevitour, pagoda2, Platypus, rliger, scITD dependencyCount: 44 Package: AnnotationFilter Version: 1.18.0 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: 3bd3d3b7f9f78f3297a0434d7d88afd5 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: RELEASE_3_14 git_last_commit: 60a9b66 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AnnotationFilter_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationFilter_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationFilter_1.18.0.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, ensembldb, Organism.dplyr importsMe: biovizBase, BUSpaRse, drugTargetInteractions, ggbio, QFeatures, scanMiRApp, TVTB, GenomicDistributionsData, RNAseqQC, utr.annotation suggestsMe: dasper, GenomicDistributions, TFutils, wiggleplotr dependencyCount: 17 Package: AnnotationForge Version: 1.36.0 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 License: Artistic-2.0 MD5sum: b888edfb1feaf1c930d0171d5cfb69e9 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, Hervé Pagès 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: RELEASE_3_14 git_last_commit: 523b5f0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AnnotationForge_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationForge_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationForge_1.36.0.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html vignetteTitles: Creating probe packages, AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema., 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: 46 Package: AnnotationHub Version: 3.2.2 Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 1.5.1) Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion, curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors, interactiveDisplayBase, httr, yaml, dplyr Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, GenomicFeatures, MSnbase, mzR, Biostrings, SummarizedExperiment, ExperimentHub, gdsfmt, rmarkdown, HubPub Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 1504583a31b71617e95c9c1793ed90d6 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: RELEASE_3_14 git_last_commit: 8e761e1 git_last_commit_date: 2022-02-28 Date/Publication: 2022-03-01 source.ver: src/contrib/AnnotationHub_3.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationHub_3.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationHub_3.2.2.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.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/TroubleshootingTheCache.R dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, hipathia, ipdDb, LRcell, ProteomicsAnnotationHubData, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState, org.Mxanthus.db, phastCons30way.UCSC.hg38, rGenomeTracksData, synaptome.data, MetaGxBreast, MetaGxOvarian, NestLink, sesameData, tartare, annotation, sequencing, OSCA.advanced, OSCA.basic, OSCA.workflows importsMe: annotatr, circRNAprofiler, cTRAP, customCMPdb, dmrseq, EWCE, GenomicScores, GSEABenchmarkeR, gwascat, MACSr, meshes, MSnID, NxtIRFcore, ontoProc, psichomics, pwOmics, regutools, REMP, restfulSE, RLSeq, scanMiRApp, scAnnotatR, scmeth, scTensor, TSRchitect, tximeta, Ularcirc, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, grasp2db, metaboliteIDmapping, synaptome.db, adductData, alpineData, BioImageDbs, biscuiteerData, celldex, chipseqDBData, curatedMetagenomicData, curatedTBData, curatedTCGAData, depmap, DropletTestFiles, easierData, FieldEffectCrc, GenomicDistributionsData, HCAData, HMP16SData, HMP2Data, mcsurvdata, MetaGxPancreas, msigdb, RLHub, scpdata, scRNAseq, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, TCGAWorkflow, RNAseqQC, utr.annotation suggestsMe: BgeeCall, BioPlex, Chicago, ChIPpeakAnno, CINdex, clusterProfiler, CNVRanger, COCOA, DNAshapeR, dupRadar, ensembldb, epiNEM, EpiTxDb, epivizrChart, epivizrData, GenomicRanges, Glimma, GOSemSim, maser, MIRA, MSnbase, multicrispr, nullranges, OrganismDbi, plotgardener, recountmethylation, satuRn, VariantAnnotation, AHEnsDbs, CTCF, ENCODExplorerData, excluderanges, gwascatData, ontoProcData, HarmonizedTCGAData, SingleRBook dependencyCount: 85 Package: AnnotationHubData Version: 1.24.2 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15) Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocManager, biocViews, BiocCheck, graph, AnnotationDbi, Biobase, Biostrings, DBI, GenomeInfoDb (>= 1.15.4), OrganismDbi, RSQLite, AnnotationForge, futile.logger (>= 1.3.0), XML, RCurl Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData, rmarkdown, HubPub License: Artistic-2.0 MD5sum: e8c3f8c5c50e5e3fb915c37e03f9f4d0 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: RELEASE_3_14 git_last_commit: fe8b582 git_last_commit_date: 2022-01-18 Date/Publication: 2022-01-20 source.ver: src/contrib/AnnotationHubData_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnnotationHubData_1.24.2.zip mac.binary.ver: bin/macosx/contrib/4.1/AnnotationHubData_1.24.2.tgz vignettes: vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html vignetteTitles: Introduction to AnnotationHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ExperimentHubData importsMe: AHEnsDbs, EuPathDB suggestsMe: HubPub, GenomicState dependencyCount: 133 Package: annotationTools Version: 1.68.0 Imports: Biobase, stats Suggests: BiocStyle License: GPL MD5sum: d4f77ca026cf1b013298f6495c0d518f 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: RELEASE_3_14 git_last_commit: 387174f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/annotationTools_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/annotationTools_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/annotationTools_1.68.0.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: 1.20.0 Depends: R (>= 3.4.0) 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: d68d24129d9abd101518a8a6095f6c57 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: RELEASE_3_14 git_last_commit: aa03096 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/annotatr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/annotatr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/annotatr_1.20.0.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 suggestsMe: ramr dependencyCount: 141 Package: anota Version: 1.42.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 0de2cfba8335adcb7094c9a62fd01b41 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: RELEASE_3_14 git_last_commit: 2ec6d93 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/anota_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/anota_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/anota_1.42.0.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: 50 Package: anota2seq Version: 1.16.0 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: 2cdb2b7795c9fb77f4f4271e9c82918c 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 , Julie Lorent VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/anota2seq git_branch: RELEASE_3_14 git_last_commit: 9340b34 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/anota2seq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/anota2seq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/anota2seq_1.16.0.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: 101 Package: antiProfiles Version: 1.34.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: bee9d260201493da5cfce9211adb0083 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: RELEASE_3_14 git_last_commit: b8869a9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/antiProfiles_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/antiProfiles_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/antiProfiles_1.34.0.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: 1.6.7 Depends: R (>= 3.6), dplyr Imports: stats, utils, methods, futile.logger, jsonlite, httr, rapiclient (>= 0.1.3), tibble, tidyselect, tidyr, rlang, BiocManager Suggests: parallel, knitr, rmarkdown, testthat, withr, readr, BiocStyle License: Artistic-2.0 MD5sum: eface3b7b5ee0d4c4f6e596de7bb3727 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, Dockstore, and Gen3 RESTful programming interface, including helper functions to transform JSON responses to formats more amenable to manipulation in R. biocViews: Infrastructure Author: Martin Morgan [aut, cre] (), Nitesh Turaga [aut], BJ Stubbs [ctb], Vincent Carey [ctb], Marcel Ramos [ctb], Sehyun Oh [ctb], Sweta Gopaulakrishnan [ctb], Valerie Obenchain [ctb] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVIL git_branch: RELEASE_3_14 git_last_commit: 906a401 git_last_commit_date: 2022-03-15 Date/Publication: 2022-03-29 source.ver: src/contrib/AnVIL_1.6.7.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnVIL_1.6.7.zip mac.binary.ver: bin/macosx/contrib/4.1/AnVIL_1.6.7.tgz vignettes: vignettes/AnVIL/inst/doc/BiocDockstore.html, vignettes/AnVIL/inst/doc/Introduction.html vignetteTitles: Dockstore and Bioconductor for AnVIL, Introduction to the AnVIL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVIL/inst/doc/BiocDockstore.R, vignettes/AnVIL/inst/doc/Introduction.R dependsOnMe: cBioPortalData importsMe: AnVILPublish dependencyCount: 39 Package: AnVILBilling Version: 1.4.0 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: e470efd69a1dbedc7b5f344e1cc0f718 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: RELEASE_3_14 git_last_commit: 54f05b3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AnVILBilling_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnVILBilling_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AnVILBilling_1.4.0.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: 90 Package: AnVILPublish Version: 1.4.1 Imports: AnVIL, httr, jsonlite, rmarkdown, yaml, readr, whisker, tools, utils, stats Suggests: knitr, BiocStyle, BiocManager, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: d5aa393993887854a11de91bfa159724 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: Martin Morgan [aut, cre] (), Vincent Carey [ctb] () Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVILPublish git_branch: RELEASE_3_14 git_last_commit: 9818879 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AnVILPublish_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/AnVILPublish_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/AnVILPublish_1.4.1.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: 69 Package: APAlyzer Version: 1.8.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq2, ggrepel, SummarizedExperiment, Rsubread, stats, ggplot2, methods, rtracklayer, ensembldb, VariantAnnotation, dplyr, tidyr, repmis, Rsamtools Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi, TBX20BamSubset, testthat, pasillaBamSubset License: LGPL-3 MD5sum: de9cc82a61e70ff944f4ab746b0a327e 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], Chuwei Zhong [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: RELEASE_3_14 git_last_commit: 4df8ba6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/APAlyzer_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/APAlyzer_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/APAlyzer_1.8.0.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: 135 Package: apComplex Version: 2.60.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: 8b8ffdf3d4310ac5f63259a759a1bd7d 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: RELEASE_3_14 git_last_commit: 2c08314 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/apComplex_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/apComplex_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/apComplex_2.60.0.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: 1.16.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 Archs: i386, x64 MD5sum: 38f07b23a3c15ae4c6fff24a4084db95 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: RELEASE_3_14 git_last_commit: 0d599df git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/apeglm_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/apeglm_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/apeglm_1.16.0.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 suggestsMe: bambu, BRGenomics, DESeq2, fishpond, NanoporeRNASeq dependencyCount: 36 Package: appreci8R Version: 1.12.1 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, rsnps, 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, utils, stats, GenomicRanges, S4Vectors, GenomicFeatures, IRanges, GenomicScores, SummarizedExperiment Suggests: GO.db, org.Hs.eg.db License: LGPL-3 MD5sum: c106ed2b77f7f1fbe3094f5cb94e61c2 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: RELEASE_3_14 git_last_commit: 727696d git_last_commit_date: 2022-03-02 Date/Publication: 2022-03-06 source.ver: src/contrib/appreci8R_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/appreci8R_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/appreci8R_1.12.1.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: 158 Package: aroma.light Version: 3.24.0 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: ffbcd0ceaf8177ce69f11685bda3635a 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: RELEASE_3_14 git_last_commit: d0f8f2b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/aroma.light_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/aroma.light_3.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/aroma.light_3.24.0.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: 1.54.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: 8ba02029431613cd191826dbc00df0b6 NeedsCompilation: no Title: Access the ArrayExpress Microarray Database at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Repository at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert Maintainer: Suhaib Mohammed git_url: https://git.bioconductor.org/packages/ArrayExpress git_branch: RELEASE_3_14 git_last_commit: 9a09ffb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ArrayExpress_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ArrayExpress_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ArrayExpress_1.54.0.tgz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease, maEndToEnd suggestsMe: Hiiragi2013 dependencyCount: 55 Package: ArrayExpressHTS Version: 1.44.0 Depends: sampling, Rsamtools (>= 1.99.0), snow Imports: Biobase, BiocGenerics, Biostrings, GenomicRanges, Hmisc, IRanges (>= 2.13.11), R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rhtslib (>= 1.15.3) License: Artistic License 2.0 MD5sum: ad64f697a56bfc479f6084cd7c48b4c6 NeedsCompilation: yes Title: ArrayExpress High Throughput Sequencing Processing Pipeline Description: RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets biocViews: ImmunoOncology, RNASeq, Sequencing Author: Angela Goncalves, Andrew Tikhonov Maintainer: Angela Goncalves , Andrew Tikhonov SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/ArrayExpressHTS git_branch: RELEASE_3_14 git_last_commit: bb650ce git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ArrayExpressHTS_1.44.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ArrayExpressHTS_1.44.0.tgz vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription profiling experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.R dependencyCount: 139 Package: arrayMvout Version: 1.52.0 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: 7292fb7cb93680120538ae9d546337db 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: RELEASE_3_14 git_last_commit: 963b432 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/arrayMvout_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/arrayMvout_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/arrayMvout_1.52.0.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: 168 Package: arrayQuality Version: 1.72.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: f5f8d28e2790d38b5917e634a72c27e0 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: RELEASE_3_14 git_last_commit: 38af9ec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/arrayQuality_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/arrayQuality_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/arrayQuality_1.72.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: arrayQualityMetrics Version: 3.50.0 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: a206e574b6ae22056924eeb641bb1665 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: RELEASE_3_14 git_last_commit: 784f433 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/arrayQualityMetrics_3.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/arrayQualityMetrics_3.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/arrayQualityMetrics_3.50.0.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: 123 Package: ARRmNormalization Version: 1.34.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 163b3a33a1a3f364829c68e37d2871f8 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: RELEASE_3_14 git_last_commit: e84ffab git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ARRmNormalization_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ARRmNormalization_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ARRmNormalization_1.34.0.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: 1.12.0 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: 834fe52996035e500cde130e4734f60d 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: RELEASE_3_14 git_last_commit: 58efc32 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/artMS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/artMS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/artMS_1.12.0.tgz vignettes: vignettes/artMS/inst/doc/artMS_vignette.html vignetteTitles: Learn to use artMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/artMS/inst/doc/artMS_vignette.R dependencyCount: 148 Package: ASAFE Version: 1.20.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 17e156e533cad0b9d29d2319c13cb7da 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: RELEASE_3_14 git_last_commit: a487569 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ASAFE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASAFE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASAFE_1.20.0.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: 1.38.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: d1c8fd8bd4e32f278936e21be75aeeff 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: RELEASE_3_14 git_last_commit: 7d346a8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ASEB_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASEB_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASEB_1.38.0.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: 1.28.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: 854e990a4b8e1e5828c9a26b7ee9dca0 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: RELEASE_3_14 git_last_commit: a84d4c2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ASGSCA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASGSCA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASGSCA_1.28.0.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 suggestsMe: matrixpls dependencyCount: 9 Package: ASICS Version: 2.10.0 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: e6864f2b12dfd84ac9307ccd3eda57b8 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: RELEASE_3_14 git_last_commit: f29bce8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ASICS_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASICS_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASICS_2.10.0.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: 89 Package: ASpediaFI Version: 1.8.0 Depends: R (>= 3.6.0), SummarizedExperiment, ROCR Imports: BiocParallel, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, IVAS, Rsamtools, biomaRt, limma, S4Vectors, stats, DRaWR, GenomeInfoDb, Gviz, Matrix, dplyr, fgsea, reshape2, igraph, graphics, e1071, methods, rtracklayer, scales, grid, ggplot2, mGSZ, utils Suggests: knitr License: GPL-3 MD5sum: 20dd8a644b0e5abb3cb8cc3d4c274101 NeedsCompilation: no Title: ASpedia-FI: Functional Interaction Analysis of Alternative Splicing Events Description: This package provides functionalities for a systematic and integrative analysis of alternative splicing events and their functional interactions. biocViews: AlternativeSplicing, Annotation, Coverage, GeneExpression, GeneSetEnrichment, GraphAndNetwork, KEGG, Network, NetworkInference, Pathways, Reactome, Transcription, Sequencing, Visualization Author: Doyeong Yu, Kyubin Lee, Daejin Hyung, Soo Young Cho, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr BugReports: https://github.com/nachoryu/ASpediaFI git_url: https://git.bioconductor.org/packages/ASpediaFI git_branch: RELEASE_3_14 git_last_commit: 4066ac0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ASpediaFI_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASpediaFI_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASpediaFI_1.8.0.tgz vignettes: vignettes/ASpediaFI/inst/doc/ASpediaFI.pdf vignetteTitles: ASpediaFI.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpediaFI/inst/doc/ASpediaFI.R dependencyCount: 185 Package: ASpli Version: 2.4.0 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 License: GPL MD5sum: fd540f5d6b45fc45cf0ecf5b51124480 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: Estefania Mancini git_url: https://git.bioconductor.org/packages/ASpli git_branch: RELEASE_3_14 git_last_commit: 9dad6f1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ASpli_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASpli_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASpli_2.4.0.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 dependencyCount: 164 Package: AssessORF Version: 1.12.0 Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0) Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices, methods, stats, utils Suggests: AssessORFData, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: e70abdf3cabd99549ed7f493ad8d427e 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: RELEASE_3_14 git_last_commit: 25d63b8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AssessORF_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AssessORF_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AssessORF_1.12.0.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: 36 Package: ASSET Version: 2.12.0 Depends: stats, graphics Imports: MASS, msm, rmeta Suggests: RUnit, BiocGenerics, knitr License: GPL-2 + file LICENSE MD5sum: 5da2760ddf2e864d06c3fc5084cad8ff 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], Nilanjan Chatterjee [aut], William Wheeler [aut] Maintainer: Samsiddhi Bhattacharjee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASSET git_branch: RELEASE_3_14 git_last_commit: 065a08f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ASSET_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASSET_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASSET_2.12.0.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: 1.30.0 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: 2dce89052b6b6dbc58afca57d751abd8 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: RELEASE_3_14 git_last_commit: d215c62 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ASSIGN_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ASSIGN_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ASSIGN_1.30.0.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: 98 Package: ATACseqQC Version: 1.18.1 Depends: R (>= 3.4), 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 Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, MotifDb, trackViewer, testthat, rmarkdown License: GPL (>= 2) MD5sum: 6cdec79cbd6ef0e5dec1098aa19a60e7 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: RELEASE_3_14 git_last_commit: 4319f1b git_last_commit_date: 2022-03-30 Date/Publication: 2022-03-31 source.ver: src/contrib/ATACseqQC_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ATACseqQC_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ATACseqQC_1.18.1.tgz vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html vignetteTitles: ATACseqQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R dependencyCount: 160 Package: atena Version: 1.0.5 Depends: R (>= 4.1), SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, BiocParallel, S4Vectors, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, SQUAREM, sparseMatrixStats Suggests: covr, BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 38237f016695a41e2f3833b648fa0bcd 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, cre], Robert Castelo [aut] Maintainer: Beatriz Calvo-Serra URL: https://github.com/functionalgenomics/atena VignetteBuilder: knitr BugReports: https://github.com/functionalgenomics/atena/issues git_url: https://git.bioconductor.org/packages/atena git_branch: RELEASE_3_14 git_last_commit: e5b05a6 git_last_commit_date: 2021-12-16 Date/Publication: 2022-02-20 source.ver: src/contrib/atena_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/atena_1.0.5.zip mac.binary.ver: bin/macosx/contrib/4.1/atena_1.0.5.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: 41 Package: atSNP Version: 1.10.0 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: i386, x64 MD5sum: 4eb15dc6592d985d73b4669eeaa37ab4 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: RELEASE_3_14 git_last_commit: b91e873 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/atSNP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/atSNP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/atSNP_1.10.0.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: 122 Package: attract Version: 1.46.0 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: 055b93462ff41340d5986d2936760aad 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: RELEASE_3_14 git_last_commit: 8500b7d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/attract_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/attract_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/attract_1.46.0.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: 67 Package: AUCell Version: 1.16.0 Imports: data.table, graphics, grDevices, GSEABase, methods, mixtools, R.utils, shiny, stats, SummarizedExperiment, BiocGenerics, S4Vectors, utils Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery, knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, rbokeh, Rtsne, testthat, zoo Enhances: doMC, doRNG, doParallel, foreach License: GPL-3 MD5sum: e4a79320ae56b828227795ad2b1dcf07 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: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: RELEASE_3_14 git_last_commit: dfde2b6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AUCell_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AUCell_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AUCell_1.16.0.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: RcisTarget suggestsMe: decoupleR dependencyCount: 87 Package: autonomics Version: 1.2.0 Depends: R (>= 4.0) Imports: abind, assertive, BiocFileCache, BiocGenerics, colorspace, data.table, edgeR, ggplot2, ggrepel, graphics, grDevices, grid, gridExtra, limma, magrittr, matrixStats, methods, MultiAssayExperiment, parallel, pcaMethods, rappdirs, rlang, R.utils, readxl, S4Vectors, scales, stats, stringi, SummarizedExperiment, tidyr, tools, utils Suggests: affy, AnnotationDbi, BiocManager, diagram, GenomicRanges, GEOquery, hgu95av2.db, ICSNP, knitr, lme4, lmerTest, MASS, mixOmics, mpm, nlme, org.Hs.eg.db, org.Mm.eg.db, RCurl, remotes, rmarkdown, ropls, Rsubread, rtracklayer, seqinr, statmod, testthat License: GPL-3 MD5sum: b80d6aeb59f6f01b57785f903fe37fd5 NeedsCompilation: no Title: Generifying and intuifying cross-platform omics analysis Description: This package offers a generic and intuitive solution for cross-platform omics data analysis. It has functions for import, preprocessing, exploration, contrast analysis and visualization of omics data. It follows a tidy, functional programming paradigm. biocViews: DataImport, DimensionReduction, GeneExpression, MassSpectrometry, Preprocessing, PrincipalComponent, RNASeq, Software, Transcription Author: Aditya Bhagwat [aut, cre], Shahina Hayat [aut], Anna Halama [ctb], Richard Cotton [ctb], Laure Cougnaud [ctb], Rudolf Engelke [ctb], Hinrich Goehlmann [sad], Karsten Suhre [sad], Johannes Graumann [aut, sad, rth] Maintainer: Aditya Bhagwat URL: https://github.com/bhagwataditya/autonomics VignetteBuilder: knitr BugReports: https://bitbucket.org/graumannlabtools/autonomics git_url: https://git.bioconductor.org/packages/autonomics git_branch: RELEASE_3_14 git_last_commit: f775f33 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/autonomics_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/autonomics_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/autonomics_1.2.0.tgz vignettes: vignettes/autonomics/inst/doc/using_autonomics.html vignetteTitles: using_autonomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/autonomics/inst/doc/using_autonomics.R dependencyCount: 126 Package: AWFisher Version: 1.8.0 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 Archs: i386, x64 MD5sum: 41663878babd8d30e84fd3637cf7f010 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: RELEASE_3_14 git_last_commit: fda6e38 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/AWFisher_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/AWFisher_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/AWFisher_1.8.0.tgz vignettes: vignettes/AWFisher/inst/doc/AWFisher.html vignetteTitles: AWFisher hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R dependencyCount: 11 Package: awst Version: 1.2.0 Imports: stats, methods, SummarizedExperiment Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown License: MIT + file LICENSE MD5sum: 906daea0377a7f0715eb401e4e2692b0 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: RELEASE_3_14 git_last_commit: 64612cd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/awst_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/awst_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/awst_1.2.0.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: 25 Package: BaalChIP Version: 1.20.0 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: b03782deab32260b31c7d8ae21cb51b9 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: RELEASE_3_14 git_last_commit: cd0f8f3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BaalChIP_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BaalChIP_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BaalChIP_1.20.0.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: 114 Package: BAC Version: 1.54.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: 5b81f3b2566b9318d9db02f4cda1ea2d NeedsCompilation: yes Title: Bayesian Analysis of Chip-chip experiment Description: This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments biocViews: Microarray, Transcription Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/BAC git_branch: RELEASE_3_14 git_last_commit: a404e5d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BAC_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BAC_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BAC_1.54.0.tgz vignettes: vignettes/BAC/inst/doc/BAC.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAC/inst/doc/BAC.R dependencyCount: 0 Package: bacon Version: 1.22.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: i386, x64 MD5sum: 837eb87e9f3c028d0a76bc3dc7db60f9 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: RELEASE_3_14 git_last_commit: 09c84ea git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bacon_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bacon_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bacon_1.22.0.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: 47 Package: BADER Version: 1.32.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: f92aeb190355401dda2019511205db5a 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: RELEASE_3_14 git_last_commit: 89ce506 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BADER_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BADER_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BADER_1.32.0.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: 1.22.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 055cb46d1bdb7b93c836e18a14e77aca 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: RELEASE_3_14 git_last_commit: e9d52d0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BadRegionFinder_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BadRegionFinder_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BadRegionFinder_1.22.0.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: 98 Package: BAGS Version: 2.34.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 218dd16af82e2ff8df22f4c05d25b7fa 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: RELEASE_3_14 git_last_commit: 4d95037 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BAGS_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BAGS_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BAGS_2.34.0.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: 2.26.0 Depends: R (>= 3.1.1), 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: f49ccb249e90ed72be192ec22ab64335 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: RELEASE_3_14 git_last_commit: 4f1dda8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ballgown_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ballgown_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ballgown_2.26.0.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 importsMe: RNASeqR suggestsMe: polyester, variancePartition dependencyCount: 82 Package: bambu Version: 2.0.6 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, apeglm, utils, DEXSeq Enhances: parallel License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 42b0f0f0bfd6a3d7559b90cb9365d98c NeedsCompilation: yes Title: Reference-guided isoform reconstruction and quantification for 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, 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: RELEASE_3_14 git_last_commit: e340a81 git_last_commit_date: 2022-02-23 Date/Publication: 2022-02-24 source.ver: src/contrib/bambu_2.0.6.tar.gz win.binary.ver: bin/windows/contrib/4.1/bambu_2.0.6.zip mac.binary.ver: bin/macosx/contrib/4.1/bambu_2.0.6.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 suggestsMe: NanoporeRNASeq dependencyCount: 102 Package: bamsignals Version: 1.26.0 Depends: R (>= 3.2.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: d66b8af0221a75630a5e1de8e2a283d3 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: RELEASE_3_14 git_last_commit: d576434 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bamsignals_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bamsignals_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bamsignals_1.26.0.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, epigraHMM, karyoploteR, normr, segmenter, hoardeR dependencyCount: 18 Package: BANDITS Version: 1.10.0 Depends: R (>= 3.6.0) 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: i386, x64 MD5sum: 383f816cd3938395e34fd4427985a0b6 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], Mark D. Robinson [aut]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/BANDITS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/BANDITS/issues git_url: https://git.bioconductor.org/packages/BANDITS git_branch: RELEASE_3_14 git_last_commit: 7c4fcee git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BANDITS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BANDITS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BANDITS_1.10.0.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 dependencyCount: 78 Package: banocc Version: 1.18.0 Depends: R (>= 3.5.1), rstan (>= 2.17.4) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat License: MIT + file LICENSE MD5sum: 81965d0645f880b1192ff559308e44f6 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: RELEASE_3_14 git_last_commit: 8884459 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/banocc_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/banocc_1.18.0.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: 64 Package: barcodetrackR Version: 1.2.0 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: af3cb40fb6dd0b0b3b7c065194ed5b1c 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: RELEASE_3_14 git_last_commit: 29ecd67 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/barcodetrackR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/barcodetrackR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/barcodetrackR_1.2.0.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: 94 Package: basecallQC Version: 1.18.0 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: d7719e04bd0ef597cbedbc4d1b7ea96b 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: RELEASE_3_14 git_last_commit: 503470b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/basecallQC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/basecallQC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/basecallQC_1.18.0.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: 109 Package: BaseSpaceR Version: 1.38.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: fc6a0f850254fa41e21dafd1970b26f7 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: RELEASE_3_14 git_last_commit: 755e639 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BaseSpaceR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BaseSpaceR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BaseSpaceR_1.38.0.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: 1.30.0 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: 69c8772f9e66bad707da070ca81186f6 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: RELEASE_3_14 git_last_commit: feb01cf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Basic4Cseq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Basic4Cseq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Basic4Cseq_1.30.0.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: 48 Package: BASiCS Version: 2.6.0 Depends: R (>= 4.0), 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, matrixStats, assertthat, reshape2, BiocParallel, hexbin LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, magick License: GPL (>= 2) Archs: i386, x64 MD5sum: 50fe6f6ad5d2a0012eaf341d8134e14a 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], Nils Eling [aut], Alan O'Callaghan [aut, cre], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Alan O'Callaghan 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: RELEASE_3_14 git_last_commit: 4c9d804 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BASiCS_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BASiCS_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BASiCS_2.6.0.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 suggestsMe: splatter dependencyCount: 122 Package: BasicSTARRseq Version: 1.22.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: 11c404fbe9ddfc33d4b7cedf45ad22f4 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: RELEASE_3_14 git_last_commit: 8cdecc2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BasicSTARRseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BasicSTARRseq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BasicSTARRseq_1.22.0.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: 38 Package: basilisk Version: 1.6.0 Imports: utils, methods, parallel, reticulate, dir.expiry, basilisk.utils Suggests: knitr, rmarkdown, BiocStyle, testthat, callr License: GPL-3 MD5sum: 7ed9c6bd536315612867e2e4c06ed5ad 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 git_url: https://git.bioconductor.org/packages/basilisk git_branch: RELEASE_3_14 git_last_commit: 8910ee2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/basilisk_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/basilisk_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/basilisk_1.6.0.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 importsMe: BiocSklearn, cbpManager, dasper, densvis, FLAMES, MACSr, MOFA2, Rcwl, snifter, spatialDE, velociraptor, zellkonverter dependencyCount: 22 Package: basilisk.utils Version: 1.6.0 Imports: utils, methods, tools, dir.expiry Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 5a4ec14005a8e96f7658af75bd785a7f 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: RELEASE_3_14 git_last_commit: f6bb193 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/basilisk.utils_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/basilisk.utils_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/basilisk.utils_1.6.0.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 dependencyCount: 5 Package: batchelor Version: 1.10.0 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 Archs: i386, x64 MD5sum: cee0bdff27501741dd34ce62bbe2a233 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: RELEASE_3_14 git_last_commit: fbbcf91 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/batchelor_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/batchelor_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/batchelor_1.10.0.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.advanced, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: ChromSCape, mumosa, singleCellTK suggestsMe: TSCAN, bcTSNE, RaceID dependencyCount: 49 Package: BatchQC Version: 1.22.0 Depends: R (>= 3.5.0) Imports: utils, rmarkdown, knitr, pander, gplots, MCMCpack, shiny, sva, corpcor, moments, matrixStats, ggvis, heatmaply, reshape2, limma, grDevices, graphics, stats, methods, Matrix Suggests: testthat License: GPL (>= 2) MD5sum: 98e26410d4d41184562b8d9222bdc45c 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, PrincipalComponent, Sequencing, Software, Visualization, QualityControl, RNASeq, Preprocessing, DifferentialExpression, ImmunoOncology Author: Solaiappan Manimaran , W. Evan Johnson , Heather Selby , Claire Ruberman , Kwame Okrah , Hector Corrada Bravo Maintainer: Solaiappan Manimaran URL: https://github.com/mani2012/BatchQC SystemRequirements: pandoc (http://pandoc.org/installing.html) for generating reports from markdown files. VignetteBuilder: knitr BugReports: https://github.com/mani2012/BatchQC/issues git_url: https://git.bioconductor.org/packages/BatchQC git_branch: RELEASE_3_14 git_last_commit: 5c5129c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BatchQC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BatchQC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BatchQC_1.22.0.tgz vignettes: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.pdf, vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQCIntro.html vignetteTitles: BatchQC_usage_advanced, BatchQC_examples, BatchQCIntro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.R dependencyCount: 159 Package: BayesKnockdown Version: 1.20.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: 023b3e8cb6fd20bb4c21e8f3a3fbe539 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: RELEASE_3_14 git_last_commit: a0dc6b6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BayesKnockdown_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BayesKnockdown_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BayesKnockdown_1.20.0.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: 1.4.1 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 Archs: i386, x64 MD5sum: f608fbb4a3c2b8d70d028fdd0a719422 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: RELEASE_3_14 git_last_commit: c421c9d git_last_commit_date: 2021-11-09 Date/Publication: 2021-11-11 source.ver: src/contrib/BayesSpace_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BayesSpace_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BayesSpace_1.4.1.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 dependencyCount: 135 Package: bayNorm Version: 1.12.0 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: i386, x64 MD5sum: db13998e1ac208d231242e9f1be05468 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: RELEASE_3_14 git_last_commit: 21f4d5b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bayNorm_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bayNorm_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bayNorm_1.12.0.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: 48 Package: baySeq Version: 2.28.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, parallel Imports: edgeR Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 30cda72226a6cad10ef4fc2e8fc0c58b 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 Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/baySeq git_branch: RELEASE_3_14 git_last_commit: 6793f6e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/baySeq_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/baySeq_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/baySeq_2.28.0.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, Rcade, segmentSeq, TCC importsMe: metaseqR2, riboSeqR, srnadiff suggestsMe: compcodeR dependencyCount: 25 Package: BBCAnalyzer Version: 1.24.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 4785bac863160c66f5cc9db0b1651fc9 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: RELEASE_3_14 git_last_commit: 6516fc9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BBCAnalyzer_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BBCAnalyzer_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BBCAnalyzer_1.24.0.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: 98 Package: BCRANK Version: 1.56.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: 0cb339e45cfe2653b499ef85cc43daea 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: RELEASE_3_14 git_last_commit: 56cfe7e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BCRANK_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BCRANK_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BCRANK_1.56.0.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: 18 Package: bcSeq Version: 1.16.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 8224f8027ed539b599179fd4b2b61909 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: RELEASE_3_14 git_last_commit: 7c51411 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bcSeq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bcSeq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bcSeq_1.16.0.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: 22 Package: BDMMAcorrect Version: 1.12.0 Depends: R (>= 3.5), vegan, ellipse, ggplot2, ape, SummarizedExperiment Imports: Rcpp (>= 0.12.12), RcppArmadillo, RcppEigen, stats LinkingTo: Rcpp, RcppArmadillo, RcppEigen Suggests: knitr, rmarkdown, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 1bb6fb88943030a4aeb05325d60d5fbd NeedsCompilation: yes Title: Meta-analysis for the metagenomic read counts data from different cohorts Description: Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables—microbial taxa in microbial studies—and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data. We develop a new method, Bayesian Dirichlet-multinomial regression meta-analysis (BDMMA), to simultaneously model the batch effects and detect the microbial taxa associated with phenotypes. BDMMA automatically models the dependence among microbial taxa and is robust to the high dimensionality of the microbiome and their association sparsity. biocViews: ImmunoOncology, BatchEffect, Microbiome, Bayesian Author: ZHENWEI DAI Maintainer: ZHENWEI DAI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BDMMAcorrect git_branch: RELEASE_3_14 git_last_commit: 35cd3dd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BDMMAcorrect_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BDMMAcorrect_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BDMMAcorrect_1.12.0.tgz vignettes: vignettes/BDMMAcorrect/inst/doc/Vignette.pdf vignetteTitles: BDMMAcorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BDMMAcorrect/inst/doc/Vignette.R dependencyCount: 64 Package: beachmat Version: 2.10.0 Imports: methods, DelayedArray (>= 0.15.14), BiocGenerics, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array License: GPL-3 Archs: i386, x64 MD5sum: 501d78705cd54ed29b38109923485939 NeedsCompilation: yes Title: Compiling Bioconductor to Handle Each Matrix Type Description: Provides a consistent C++ class interface for reading from and writing data to a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, third-party S4 classes may be supported by external linkage, while all other matrices are handled by DelayedArray block processing. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beachmat git_branch: RELEASE_3_14 git_last_commit: b7cc532 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/beachmat_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/beachmat_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/beachmat_2.10.0.tgz vignettes: vignettes/beachmat/inst/doc/external.html, vignettes/beachmat/inst/doc/input.html, vignettes/beachmat/inst/doc/linking.html, vignettes/beachmat/inst/doc/output.html vignetteTitles: 4. Supporting arbitrary matrix classes (v2), 2. Reading data from R matrices in C++ (v2), 1. Developer guide, 3. Writing data into R matrix objects (v2) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat/inst/doc/external.R, vignettes/beachmat/inst/doc/input.R, vignettes/beachmat/inst/doc/linking.R, vignettes/beachmat/inst/doc/output.R importsMe: batchelor, BiocSingular, DropletUtils, mumosa, scater, scran, scuttle, SingleR suggestsMe: bsseq, glmGamPoi, mbkmeans, PCAtools, scCB2 linksToMe: BiocSingular, bsseq, DropletUtils, glmGamPoi, mbkmeans, PCAtools, scran, scuttle, SingleR, BigDataStatMeth dependencyCount: 16 Package: beadarray Version: 2.44.0 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), hexbin Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio, methods, ggplot2 Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: MIT + file LICENSE Archs: i386, x64 MD5sum: 5a6a63797bf386a2b74f5799ff0ffb81 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: RELEASE_3_14 git_last_commit: c35a5ce git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/beadarray_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/beadarray_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/beadarray_2.44.0.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, beadarrayFilter importsMe: arrayQualityMetrics, blima, epigenomix, BeadArrayUseCases, RobLoxBioC suggestsMe: beadarraySNP, lumi, blimaTestingData, maGUI dependencyCount: 81 Package: beadarraySNP Version: 1.60.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: 2d7b1f5adddb4e6ca023a699c6d5ba26 NeedsCompilation: no Title: Normalization and reporting of Illumina SNP bead arrays Description: Importing data from Illumina SNP experiments and performing copy number calculations and reports. biocViews: CopyNumberVariation, SNP, GeneticVariability, TwoChannel, Preprocessing, DataImport Author: Jan Oosting Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/beadarraySNP git_branch: RELEASE_3_14 git_last_commit: baf3162 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/beadarraySNP_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/beadarraySNP_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/beadarraySNP_1.60.0.tgz vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf vignetteTitles: beadarraySNP.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarraySNP/inst/doc/beadarraySNP.R dependencyCount: 14 Package: BeadDataPackR Version: 1.46.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: 46c4ee616d1234e714cad46010ebe213 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: RELEASE_3_14 git_last_commit: 3dd6949 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BeadDataPackR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BeadDataPackR_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BeadDataPackR_1.46.0.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: 1.14.0 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: c32dfafe4bf88b44addcec071bd55799 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: RELEASE_3_14 git_last_commit: e44dfa3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BEARscc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BEARscc_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BEARscc_1.14.0.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: 58 Package: BEAT Version: 1.32.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: a1e585c5c566fd6041f31d495f7edd6d 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: RELEASE_3_14 git_last_commit: 606284b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BEAT_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BEAT_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BEAT_1.32.0.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: 51 Package: BEclear Version: 2.10.0 Depends: BiocParallel (>= 1.14.2) Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp, outliers, abind, stats, graphics, utils, methods LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, pander License: GPL-3 Archs: i386, x64 MD5sum: 7f2631255caa6701a071b0b6a08bb5ca 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: David Rasp [aut, cre] (), Markus Merl [aut], Ruslan Akulenko [aut] Maintainer: David 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: RELEASE_3_14 git_last_commit: 240fe18 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BEclear_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BEclear_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BEclear_2.10.0.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: 23 Package: benchdamic Version: 1.0.0 Depends: R (>= 4.1.0) Imports: stats, stats4, utils, methods, phyloseq, BiocParallel, zinbwave, edgeR, DESeq2, limma, ALDEx2, corncob, SummarizedExperiment, MAST, Seurat, metagenomeSeq, MGLM, ggplot2, RColorBrewer, plyr, ffpe, reshape2, ggdendro, graphics, cowplot Suggests: knitr, rmarkdown, HMP16SData, curatedMetagenomicData, BiocStyle, testthat License: Artistic-2.0 MD5sum: 42de3ee446e826c44255b4179cd23942 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] Maintainer: Matteo Calgaro VignetteBuilder: knitr BugReports: https://github.com/mcalgaro93/benchdamic/issues git_url: https://git.bioconductor.org/packages/benchdamic git_branch: RELEASE_3_14 git_last_commit: 5175773 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/benchdamic_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/benchdamic_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/benchdamic_1.0.0.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: 286 Package: BgeeCall Version: 1.10.0 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 MD5sum: 00b2a54bd6df99ff61c17dc4bf2d30fc 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: RELEASE_3_14 git_last_commit: 45c649c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BgeeCall_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BgeeCall_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BgeeCall_1.10.0.tgz vignettes: vignettes/BgeeCall/inst/doc/bgeecall-manual.html vignetteTitles: automatic RNA-Seq present/absent gene expression calls generation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R dependencyCount: 107 Package: BgeeDB Version: 2.20.1 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: 7353da8362f2b1dceea002d922c83de9 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: RELEASE_3_14 git_last_commit: 5d2f97b git_last_commit_date: 2022-03-11 Date/Publication: 2022-03-13 source.ver: src/contrib/BgeeDB_2.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BgeeDB_2.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BgeeDB_2.20.1.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 dependencyCount: 70 Package: BGmix Version: 1.54.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: 9bb1a765d1e134a462a03b54feaa775a NeedsCompilation: yes Title: Bayesian models for differential gene expression Description: Fully Bayesian mixture models for differential gene expression biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Alex Lewin, Natalia Bochkina Maintainer: Alex Lewin git_url: https://git.bioconductor.org/packages/BGmix git_branch: RELEASE_3_14 git_last_commit: 0750c98 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BGmix_1.54.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/BGmix_1.54.0.tgz vignettes: vignettes/BGmix/inst/doc/BGmix.pdf vignetteTitles: BGmix Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BGmix/inst/doc/BGmix.R dependencyCount: 2 Package: bgx Version: 1.60.0 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: i386, x64 MD5sum: 86bb3bbd52ff5189356d16063528fe33 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: RELEASE_3_14 git_last_commit: d7392b7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bgx_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bgx_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bgx_1.60.0.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: 26 Package: BHC Version: 1.46.0 License: GPL-3 Archs: i386, x64 MD5sum: 7182d4c4d0f4dd471560a4e4a331403f NeedsCompilation: yes Title: Bayesian Hierarchical Clustering Description: The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets. biocViews: Microarray, Clustering Author: Rich Savage, Emma Cooke, Robert Darkins, Yang Xu Maintainer: Rich Savage git_url: https://git.bioconductor.org/packages/BHC git_branch: RELEASE_3_14 git_last_commit: b3bffd8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BHC_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BHC_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BHC_1.46.0.tgz vignettes: vignettes/BHC/inst/doc/bhc.pdf vignetteTitles: Bayesian Hierarchical Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BHC/inst/doc/bhc.R dependencyCount: 0 Package: BicARE Version: 1.52.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: 20c7c4bfd4e143598dc3ebf1644ddefe 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: RELEASE_3_14 git_last_commit: 1c04ed8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BicARE_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BicARE_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BicARE_1.52.0.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: 57 Package: BiFET Version: 1.14.0 Imports: stats, poibin, GenomicRanges Suggests: rmarkdown, testthat, knitr License: GPL-3 MD5sum: 959e2b4b36a529e26786778800d37c58 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: RELEASE_3_14 git_last_commit: 13e7cc7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiFET_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiFET_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiFET_1.14.0.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: 17 Package: BiGGR Version: 1.30.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE MD5sum: eb716c092f4dbbacb66d58fd9057ea8d 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: RELEASE_3_14 git_last_commit: 5348640 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiGGR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiGGR_1.30.0.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: 25 Package: bigmelon Version: 1.20.0 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: cd52528d82c2a948c35be78e0bc1e56f 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 [cre, aut], Ayden Saffari [aut], Karim Malki [aut], Leonard C. Schalkwyk [aut] Maintainer: Tyler J. Gorrie-Stone git_url: https://git.bioconductor.org/packages/bigmelon git_branch: RELEASE_3_14 git_last_commit: 4fee9cc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bigmelon_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bigmelon_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bigmelon_1.20.0.tgz vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf vignetteTitles: The bigmelon Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R dependencyCount: 171 Package: bigPint Version: 1.10.0 Depends: R (>= 3.6.0) Imports: DelayedArray (>= 0.12.2), dplyr (>= 0.7.2), GGally (>= 1.3.2), ggplot2 (>= 2.2.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), grid (>= 3.5.0), gridExtra (>= 2.3), hexbin (>= 1.27.1), Hmisc (>= 4.0.3), htmlwidgets (>= 0.9), methods (>= 3.5.2), plotly (>= 4.7.1), plyr (>= 1.8.4), RColorBrewer (>= 1.1.2), reshape (>= 0.8.7), shiny (>= 1.0.5), shinycssloaders (>= 0.2.0), shinydashboard (>= 0.6.1), stats (>= 3.5.0), stringr (>= 1.3.1), SummarizedExperiment (>= 1.16.1), tidyr (>= 0.7.0), utils (>= 3.5.0) Suggests: BiocGenerics (>= 0.29.1), data.table (>= 1.11.8), EDASeq (>= 2.14.0), edgeR (>= 3.22.2), gtools (>= 3.5.0), knitr (>= 1.13), matrixStats (>= 0.53.1), rmarkdown (>= 1.10), roxygen2 (>= 3.0.0), RUnit (>= 0.4.32), tibble (>= 1.4.2), License: GPL-3 MD5sum: f077e9df2c51fc24150b85241695dc93 NeedsCompilation: no Title: Big multivariate data plotted interactively Description: Methods for visualizing large multivariate datasets using static and interactive scatterplot matrices, parallel coordinate plots, volcano plots, and litre plots. Includes examples for visualizing RNA-sequencing datasets and differentially expressed genes. biocViews: Clustering, DataImport, DifferentialExpression, GeneExpression, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Sequencing, Software, Transcription, Visualization Author: Lindsay Rutter [aut, cre], Dianne Cook [aut] Maintainer: Lindsay Rutter URL: https://github.com/lindsayrutter/bigPint VignetteBuilder: knitr BugReports: https://github.com/lindsayrutter/bigPint/issues git_url: https://git.bioconductor.org/packages/bigPint git_branch: RELEASE_3_14 git_last_commit: 8efcfe7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bigPint_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bigPint_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bigPint_1.10.0.tgz vignettes: vignettes/bigPint/inst/doc/bioconductor.html, vignettes/bigPint/inst/doc/manuscripts.html, vignettes/bigPint/inst/doc/summarizedExperiment.html vignetteTitles: "bigPint Vignette", "Recommended RNA-seq pipeline", "Data metrics object" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigPint/inst/doc/bioconductor.R, vignettes/bigPint/inst/doc/manuscripts.R, vignettes/bigPint/inst/doc/summarizedExperiment.R dependencyCount: 124 Package: BindingSiteFinder Version: 1.0.0 Depends: GenomicRanges, R (>= 4.1) Imports: tidyr, matrixStats, stats, ggplot2, methods, rtracklayer, S4Vectors, ggforce Suggests: testthat, BiocStyle, knitr, rmarkdown, dplyr, GenomicAlignments, ComplexHeatmap, GenomeInfoDb, forcats, scales License: Artistic-2.0 MD5sum: 5ae906c4a410e37d3fafc33e750531d6 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: RELEASE_3_14 git_last_commit: be6256c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BindingSiteFinder_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BindingSiteFinder_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BindingSiteFinder_1.0.0.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: 84 Package: bioassayR Version: 1.32.1 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: 11badc6c7cf3c40a9e06f2c9ab94b8b1 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: RELEASE_3_14 git_last_commit: 05a9464 git_last_commit_date: 2021-12-04 Date/Publication: 2021-12-05 source.ver: src/contrib/bioassayR_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/bioassayR_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.1/bioassayR_1.32.1.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.html vignetteTitles: bioassayR Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R dependencyCount: 71 Package: Biobase Version: 2.54.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets License: Artistic-2.0 Archs: i386, x64 MD5sum: bd29c9543315a628c30c2aca9f9bc4d9 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, V. Carey, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Biobase BugReports: https://github.com/Bioconductor/Biobase/issues git_url: https://git.bioconductor.org/packages/Biobase git_branch: RELEASE_3_14 git_last_commit: 8215d76 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Biobase_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Biobase_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Biobase_2.54.0.tgz vignettes: vignettes/Biobase/inst/doc/BiobaseDevelopment.pdf, vignettes/Biobase/inst/doc/esApply.pdf, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf vignetteTitles: Notes for eSet developers, esApply Introduction, An introduction to Biobase and ExpressionSets 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, beadarray, beadarraySNP, bgx, BicARE, bigmelon, BioMVCClass, BioQC, biosigner, BLMA, 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, fabia, factDesign, fastseg, flowBeads, frma, gaga, GeneAnswers, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, geNetClassifier, GeoDiff, GEOexplorer, GeomxTools, GEOquery, GOexpress, goProfiles, GOstats, GSEABase, GSEABenchmarkeR, GSEAlm, GWASTools, hapFabia, HELP, hopach, HTqPCR, HybridMTest, iCheck, IdeoViz, idiogram, InPAS, INSPEcT, isobar, iterativeBMA, IVAS, lumi, macat, made4, mAPKL, massiR, MEAL, metagenomeSeq, metavizr, MethPed, methylumi, Mfuzz, MiChip, microbiomeExplorer, mimager, MIMOSA, MineICA, MiRaGE, miRcomp, MLInterfaces, monocle, MSnbase, Mulcom, MultiDataSet, multtest, NanoStringDiff, NanoStringNCTools, NanoTube, NOISeq, nondetects, normalize450K, NormqPCR, oligo, omicRexposome, OrderedList, OTUbase, pandaR, panp, pcaMethods, pdInfoBuilder, pepStat, phenoTest, PLPE, POWSC, PREDA, pRolocGUI, PROMISE, qpcrNorm, qPLEXanalyzer, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, RefPlus, rexposome, Ringo, Risa, Rmagpie, Rnits, ropls, RTopper, RUVSeq, safe, SCAN.UPC, SeqGSEA, SigCheck, siggenes, singleCellTK, SpeCond, SPEM, spkTools, splineTimeR, STROMA4, SummarizedExperiment, TDARACNE, tigre, tilingArray, topGO, TPP, tRanslatome, tspair, twilight, UNDO, VegaMC, viper, vsn, wateRmelon, webbioc, XDE, yarn, EuPathDB, affycompData, ALL, bcellViper, beadarrayExampleData, bladderbatch, brgedata, cancerdata, CCl4, CLL, colonCA, CRCL18, curatedBreastData, davidTiling, diggitdata, DLBCL, dressCheck, etec16s, fabiaData, fibroEset, gaschYHS, golubEsets, GSE103322, GSE13015, GSE62944, GSVAdata, harbChIP, Hiiragi2013, HumanAffyData, humanStemCell, Iyer517, kidpack, leeBamViews, leukemiasEset, lumiBarnes, lungExpression, MAQCsubset, MAQCsubsetILM, 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, GExMap, GWASbyCluster, heatmapFlex, lmQCM, MM2Sdata, MMDvariance, propOverlap, statVisual importsMe: a4Base, a4Classif, a4Core, a4Preproc, ABarray, ACE, aCGH, adSplit, affyILM, AgiMicroRna, ANF, annmap, annotate, AnnotationHubData, annotationTools, ArrayExpressHTS, arrayQualityMetrics, attract, ballgown, BASiCS, BayesKnockdown, biobroom, bioCancer, biocViews, BioNet, biscuiteer, BiSeq, blima, bnem, bsseq, BubbleTree, CAFE, canceR, Cardinal, CellScore, CellTrails, CGHnormaliter, ChIPQC, ChIPXpress, ChromHeatMap, chromswitch, cicero, clipper, CluMSID, cn.mops, COCOA, cogena, combi, conclus, ConsensusClusterPlus, consensusDE, consensusOV, coRdon, CoreGx, crlmm, crossmeta, ctgGEM, cummeRbund, cyanoFilter, cycle, cydar, CytoML, CytoTree, DAPAR, ddCt, debCAM, deco, DEGreport, DESeq2, destiny, DExMA, diffloop, discordant, easyRNASeq, EBarrays, ecolitk, EGAD, ensembldb, erma, esetVis, ExiMiR, farms, ffpe, flowClust, flowCore, flowFP, flowMatch, flowMeans, flowSpecs, flowStats, flowUtils, flowViz, flowWorkspace, FRASER, frma, frmaTools, GAPGOM, gCrisprTools, gcrma, GCSscore, genbankr, geneClassifiers, GeneExpressionSignature, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GENESIS, GenomicFeatures, GenomicInteractions, GenomicScores, GenomicSuperSignature, GEOsubmission, gep2pep, gespeR, ggbio, girafe, GISPA, GlobalAncova, globaltest, gmapR, GSRI, GSVA, Gviz, Harshlight, HEM, HTqPCR, HTSFilter, imageHTS, ImmuneSpaceR, infinityFlow, IsoformSwitchAnalyzeR, IsoGeneGUI, isomiRs, iterClust, kissDE, lapmix, LiquidAssociation, LRBaseDbi, maanova, MAGeCKFlute, makecdfenv, maSigPro, MAST, mBPCR, MeSHDbi, metaseqR2, MethylAid, methylCC, methylclock, methylumi, mfa, MiChip, microbiomeDASim, microbiomeMarker, MIGSA, minfi, MinimumDistance, MiPP, MIRA, miRSM, missMethyl, MLSeq, MMAPPR2, mogsa, MoonlightR, MOSim, MSnID, MultiAssayExperiment, multiscan, mzR, NanoStringQCPro, NormalyzerDE, npGSEA, nucleR, oligoClasses, omicade4, ontoProc, oposSOM, oppar, OrganismDbi, panp, phantasus, PharmacoGx, phemd, phyloseq, piano, plethy, plgem, plier, podkat, ppiStats, prebs, PrInCE, proBatch, proFIA, progeny, pRoloc, PROMISE, PROPS, ProteomicsAnnotationHubData, PSEA, psygenet2r, ptairMS, puma, PureCN, pvac, pvca, pwOmics, qcmetrics, QDNAseq, QFeatures, qpgraph, quantiseqr, quantro, QuasR, qusage, RadioGx, randPack, RGalaxy, RIVER, Rmagpie, RNAinteract, rols, ROTS, RpsiXML, rqubic, rScudo, Rtpca, Rtreemix, RUVnormalize, scmap, scTGIF, SeqVarTools, ShortRead, SigsPack, sigsquared, SimBindProfiles, singscore, sitadela, SLGI, SomaticSignatures, SpatialDecon, spkTools, SPONGE, STATegRa, subSeq, TEQC, TFBSTools, timecourse, TMixClust, TnT, topdownr, ToxicoGx, tradeSeq, traviz, TTMap, twilight, uSORT, VanillaICE, variancePartition, VariantAnnotation, VariantFiltering, VariantTools, vidger, vulcan, wateRmelon, wpm, xcms, Xeva, BloodCancerMultiOmics2017, ccTutorial, DeSousa2013, DExMAdata, Fletcher2013a, GSE13015, hgu133plus2CellScore, IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, mcsurvdata, pRolocdata, RNAinteractMAPK, seqc, signatureSearchData, ExpHunterSuite, ExpressionNormalizationWorkflow, GeoMxWorkflows, AnnoProbe, BisqueRNA, CIARA, ClassComparison, ClassDiscovery, easyDifferentialGeneCoexpression, FMradio, geneExpressionFromGEO, HiResTEC, IntegratedJM, IsoGene, maGUI, MetaIntegrator, nlcv, NMF, PerseusR, pulseTD, ragt2ridges, RobLox, RobLoxBioC, RPPanalyzer, ssizeRNA, TailRank suggestsMe: AUCell, BiocCheck, BiocGenerics, BiocOncoTK, BSgenome, CellMapper, cellTree, clustComp, coseq, DART, dcanr, dearseq, edgeR, EnMCB, EpiDISH, epivizr, epivizrChart, epivizrStandalone, farms, genefu, GENIE3, GenomicRanges, GSAR, GSgalgoR, Heatplus, interactiveDisplay, kebabs, les, limma, M3Drop, mCSEA, messina, msa, multiClust, OSAT, PCAtools, pkgDepTools, POMA, RcisTarget, ReactomeGSA, ROC, RTCGA, scater, scmeth, scran, SeqArray, slinky, sparrow, spatialHeatmap, stageR, survcomp, TargetScore, TCGAbiolinks, TFutils, TimeSeriesExperiment, tkWidgets, TypeInfo, vbmp, widgetTools, biotmleData, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, ccTutorial, dorothea, dyebiasexamples, HMP16SData, HMP2Data, mammaPrintData, mAPKLData, RegParallel, rheumaticConditionWOLLBOLD, seventyGeneData, yeastExpData, yeastRNASeq, amap, aroma.affymetrix, BaseSet, clValid, CrossValidate, distrDoc, dnet, dplR, exp2flux, GenAlgo, hexbin, HTSCluster, isatabr, mi4p, Modeler, multiclassPairs, NACHO, optCluster, ordinalbayes, Patterns, pkgmaker, propr, rknn, Seurat, sigminer, SourceSet, tinyarray dependencyCount: 5 Package: biobroom Version: 1.26.0 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: 02ef65f90324cbdfb3c6d5710606d3e9 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: RELEASE_3_14 git_last_commit: ca6f1c4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biobroom_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biobroom_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biobroom_1.26.0.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: 51 Package: biobtreeR Version: 1.6.0 Imports: httr, httpuv, stringi,jsonlite,methods,utils Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown License: MIT + file LICENSE MD5sum: ec9176c397068bc31886f2d1f8336169 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: RELEASE_3_14 git_last_commit: 5ef2504 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biobtreeR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biobtreeR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biobtreeR_1.6.0.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: 19 Package: bioCancer Version: 1.22.0 Depends: R (>= 3.6.0), radiant.data (>= 0.9.1), cgdsr(>= 1.2.6), XML(>= 3.98) Imports: DT (>= 0.3), dplyr (>= 0.7.2), 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: fbde3ac7e44a9905d88a55ac5530ffa8 NeedsCompilation: no Title: Interactive Multi-Omics Cancers Data Visualization and Analysis Description: bioCancer 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: http://kmezhoud.github.io/bioCancer VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/bioCancer/issues git_url: https://git.bioconductor.org/packages/bioCancer git_branch: RELEASE_3_14 git_last_commit: bd61aa9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bioCancer_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bioCancer_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bioCancer_1.22.0.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: 225 Package: BiocCheck Version: 1.30.0 Depends: R (>= 3.5.0) Imports: biocViews (>= 1.33.7), BiocManager, stringdist, graph, httr, tools, optparse, codetools, methods, utils, knitr Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, rmarkdown, devtools (>= 1.4.1), usethis, BiocStyle Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: 796ea51b4441bf82f6e685f488a679cf NeedsCompilation: no Title: Bioconductor-specific package checks Description: Executes Bioconductor-specific package checks. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut, cre], Lori Shepherd [aut], Daniel von Twisk [ctb], Kevin Rue [ctb], Marcel Ramos [ctb], Leonardo Collado-Torres [ctb], Federico Marini [ctb] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocCheck/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocCheck git_branch: RELEASE_3_14 git_last_commit: 31109f8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocCheck_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocCheck_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocCheck_1.30.0.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 suggestsMe: GEOfastq, packFinder, preciseTAD, SpectralTAD, HMP16SData, HMP2Data, scpdata dependencyCount: 39 Package: BiocDockerManager Version: 1.6.0 Depends: R (>= 4.1) Imports: httr, whisker, readr, dplyr, utils, methods, memoise Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: ca01f07433ef1b65943ef325bbce3a72 NeedsCompilation: no Title: Access Bioconductor docker images Description: Package works analogous to BiocManager but for docker images. Use the BiocDockerManager package to install and manage docker images provided by the Bioconductor project. A convenient package to install images, update images and find which Bioconductor based docker images are available. biocViews: Software, Infrastructure, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Nitesh Turaga [aut] Maintainer: Bioconductor Package Maintainer SystemRequirements: docker VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocDockerManager/issues git_url: https://git.bioconductor.org/packages/BiocDockerManager git_branch: RELEASE_3_14 git_last_commit: 418643a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocDockerManager_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocDockerManager_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocDockerManager_1.6.0.tgz vignettes: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.html vignetteTitles: BiocDockerManager Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.R dependencyCount: 46 Package: BiocFileCache Version: 2.2.1 Depends: R (>= 3.4.0), dbplyr (>= 1.0.0) Imports: methods, stats, utils, dplyr, RSQLite, DBI, rappdirs, filelock, curl, httr Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer License: Artistic-2.0 MD5sum: 8d5d4ce09bf8564b2c8659ae59c41f1b 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. 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MethylSeqData, msigdb, scRNAseq, TENxBrainData, TENxPBMCData, chipseqDB, fluentGenomics, simpleSingleCell, SingleRBook dependencyCount: 46 Package: BiocGenerics Version: 0.40.0 Depends: R (>= 4.0.0), methods, utils, graphics, stats Imports: methods, utils, graphics, stats Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, DelayedArray, Biostrings, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, RUnit License: Artistic-2.0 MD5sum: cf429b6947086bea1bcafd166b7f8098 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: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/BiocGenerics BugReports: https://github.com/Bioconductor/BiocGenerics/issues git_url: https://git.bioconductor.org/packages/BiocGenerics git_branch: RELEASE_3_14 git_last_commit: 0bc1e0e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocGenerics_0.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocGenerics_0.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocGenerics_0.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, amplican, AnnotationDbi, AnnotationForge, AnnotationHub, ATACseqQC, beadarray, bioassayR, Biobase, Biostrings, bnbc, BSgenome, bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, clusterExperiment, codelink, consensusDE, consensusSeekeR, copynumber, CoreGx, CRISPRseek, cummeRbund, DelayedArray, ensembldb, ensemblVEP, ExperimentHub, ExperimentHubData, GDSArray, geneplotter, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, GenomicScores, ggbio, girafe, graph, GSEABase, GUIDEseq, HelloRanges, interactiveDisplay, interactiveDisplayBase, IRanges, MBASED, MIGSA, MineICA, minfi, MLInterfaces, MotifDb, mpra, MSnbase, multtest, NADfinder, ngsReports, oligo, OrganismDbi, plethy, plyranges, PoTRA, profileplyr, PSICQUIC, PWMEnrich, RareVariantVis, REDseq, Repitools, RnBeads, RPA, rsbml, S4Vectors, shinyMethyl, ShortRead, simplifyEnrichment, soGGi, spqn, StructuralVariantAnnotation, SummarizedBenchmark, svaNUMT, svaRetro, TEQC, tigre, topdownr, topGO, UNDO, UniProt.ws, VanillaICE, VariantAnnotation, VariantFiltering, VCFArray, XVector, yamss, liftOver importsMe: a4Preproc, affycoretools, affylmGUI, AllelicImbalance, AneuFinder, annmap, annotate, AnnotationHubData, ArrayExpressHTS, ASpli, atena, AUCell, autonomics, bambu, bamsignals, BASiCS, batchelor, beachmat, bigmelon, biocGraph, BiocIO, BiocSingular, biotmle, biovizBase, biscuiteer, BiSeq, blima, breakpointR, BrowserViz, BSgenome, BubbleTree, bumphunter, BUSpaRse, CAGEfightR, CAGEr, casper, celaref, CellaRepertorium, CellBench, cellHTS2, CellMixS, CellTrails, cghMCR, ChemmineOB, ChemmineR, ChIC, 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mirIntegrator, mnem, mosbi, motifStack, multiClust, MultiMed, multiOmicsViz, MungeSumstats, MWASTools, NBSplice, ncRNAtools, nempi, NetSAM, nondetects, nucleoSim, OMICsPCA, OncoScore, PAA, panelcn.mops, Path2PPI, PathNet, pathview, PCAtools, pepXMLTab, PhenStat, powerTCR, proBAMr, proFIA, pwrEWAS, qpgraph, quantro, QuartPAC, RBGL, rBiopaxParser, Rcade, rcellminer, rCGH, Rcpi, REBET, rfaRm, RGraph2js, Rgraphviz, rgsepd, riboSeqR, ROntoTools, ropls, ROSeq, RTN, RTNduals, RTNsurvival, rTRM, SAIGEgds, sangerseqR, SANTA, sarks, scDataviz, scmeth, scry, segmentSeq, SeqArray, seqPattern, seqTools, SICtools, sigFeature, sigsquared, SIMAT, similaRpeak, SIMLR, singleCellTK, SingleR, slingshot, SNPRelate, sojourner, SpacePAC, sparseDOSSA, SparseSignatures, spatialHeatmap, specL, STATegRa, STRINGdb, systemPipeTools, TCC, TFEA.ChIP, TIN, transcriptogramer, TraRe, traseR, TreeAndLeaf, trena, tripr, TRONCO, Uniquorn, variancePartition, VERSO, ENCODExplorerData, geneplast.data, ConnectivityMap, FieldEffectCrc, grndata, HarmanData, microRNAome, MIGSAdata, pwrEWAS.data, RegParallel, sesameData, adjclust, aroma.affymetrix, asteRisk, gkmSVM, MetaIntegrator, NutrienTrackeR, openSkies, pagoda2, polyRAD, Rediscover, Seurat dependencyCount: 4 Package: biocGraph Version: 1.56.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: 26952f54d007d6d26fdb4308f87fae6f 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: RELEASE_3_14 git_last_commit: 05ec694 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biocGraph_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biocGraph_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biocGraph_1.56.0.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: BiocIO Version: 1.4.0 Depends: R (>= 4.0) Imports: BiocGenerics, S4Vectors, methods, tools Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: d3ee8c939ee574850ace7aced331d1ed NeedsCompilation: no Title: Standard Input and Output for Bioconductor Packages Description: 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], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocIO/issues git_url: https://git.bioconductor.org/packages/BiocIO git_branch: RELEASE_3_14 git_last_commit: c335932 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocIO_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocIO_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocIO_1.4.0.tgz vignettes: vignettes/BiocIO/inst/doc/BiocIO.html vignetteTitles: BiocIO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocIO/inst/doc/BiocIO.R dependsOnMe: LoomExperiment importsMe: BiocSet, GenomicFeatures, rtracklayer dependencyCount: 8 Package: BiocNeighbors Version: 1.12.0 Imports: Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix LinkingTo: Rcpp, RcppHNSW Suggests: testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy, RcppHNSW License: GPL-3 Archs: i386, x64 MD5sum: 475e51c97b1b510153367b22e24bc64a 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: RELEASE_3_14 git_last_commit: 3c8a290 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocNeighbors_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocNeighbors_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocNeighbors_1.12.0.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.advanced, OSCA.workflows importsMe: batchelor, bluster, CellMixS, cydar, CytoTree, imcRtools, miloR, mumosa, scater, scDblFinder, SingleR suggestsMe: TrajectoryUtils, TSCAN, SingleRBook dependencyCount: 21 Package: BiocOncoTK Version: 1.14.0 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 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 License: Artistic-2.0 MD5sum: 2b5abf3ff33b30083010e0d3fd24cb9d NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/BiocOncoTK git_branch: RELEASE_3_14 git_last_commit: e958795 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocOncoTK_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocOncoTK_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocOncoTK_1.14.0.tgz vignettes: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.html, vignettes/BiocOncoTK/inst/doc/curatedMSIData.html, vignettes/BiocOncoTK/inst/doc/maptcga.html vignetteTitles: BiocOncoTK -- cancer oriented components for Bioconductor, curatedMSIData overview, "Mapping TCGA tumor codes to NCIT" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.R, vignettes/BiocOncoTK/inst/doc/curatedMSIData.R, vignettes/BiocOncoTK/inst/doc/maptcga.R dependencyCount: 203 Package: BioCor Version: 1.18.0 Depends: R (>= 3.4.0) Imports: BiocParallel, Matrix, methods, GSEABase Suggests: reactome.db, org.Hs.eg.db, WGCNA, GOSemSim, testthat, knitr, rmarkdown, BiocStyle, airway, DESeq2, boot, targetscan.Hs.eg.db, Hmisc, spelling License: MIT + file LICENSE MD5sum: fff8e729720808b2a6137936f8c9044e 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://llrs.github.io/BioCor/ VignetteBuilder: knitr BugReports: https://github.com/llrs/BioCor/issues git_url: https://git.bioconductor.org/packages/BioCor git_branch: RELEASE_3_14 git_last_commit: 867e619 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BioCor_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioCor_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioCor_1.18.0.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: 61 Package: BiocParallel Version: 1.28.3 Depends: methods, R (>= 3.5.0) Imports: stats, utils, futile.logger, parallel, snow LinkingTo: BH Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit, BiocStyle, knitr, batchtools, data.table License: GPL-2 | GPL-3 Archs: i386, x64 MD5sum: 22384ea953bd5c16dd6740307b18cd20 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: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Jiefei Wang [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut], Aaron Lun [ctb], Henrik Bengtsson [ctb] Maintainer: Bioconductor Package Maintainer 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: RELEASE_3_14 git_last_commit: 2f9d88a git_last_commit_date: 2021-12-07 Date/Publication: 2021-12-09 source.ver: src/contrib/BiocParallel_1.28.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocParallel_1.28.3.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocParallel_1.28.3.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.pdf, vignettes/BiocParallel/inst/doc/Random_Numbers.pdf 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, ClassifyR, clusterSeq, consensusSeekeR, CopywriteR, deco, DEWSeq, DEXSeq, DMCFB, DMCHMM, doppelgangR, DSS, FEAST, FRASER, GenomicFiles, hiReadsProcessor, INSPEcT, iPath, matter, MBASED, metagene, metagene2, metapone, ncGTW, Oscope, OUTRIDER, PCAN, periodicDNA, pRoloc, Rqc, ShortRead, SigCheck, Spectra, STROMA4, SummarizedBenchmark, sva, variancePartition, xcms, sequencing, OSCA.advanced, OSCA.workflows importsMe: abseqR, ADImpute, AffiXcan, ALDEx2, AlphaBeta, AlpsNMR, amplican, ASICS, ASpediaFI, atena, atSNP, bambu, BANDITS, BASiCS, batchelor, bayNorm, benchdamic, BiocNeighbors, BioCor, BiocSingular, BioMM, BioNERO, BioNetStat, biotmle, biscuiteer, bluster, brendaDb, bsseq, CAGEfightR, CAGEr, cellbaseR, CellBench, CelliD, CellMixS, censcyt, Cepo, ChIPexoQual, ChIPQC, ChromSCape, chromswitch, chromVAR, CNVRanger, CoGAPS, condiments, consensusDE, contiBAIT, CoreGx, coseq, cpvSNP, CrispRVariants, csaw, cydar, CytoGLMM, cytoKernel, cytomapper, dasper, dcGSA, debCAM, DEComplexDisease, derfinder, DEScan2, DESeq2, DEsingle, DiffBind, Dino, dmrseq, DOSE, DRIMSeq, DropletUtils, Dune, easier, easyRNASeq, EMDomics, enhancerHomologSearch, erma, ERSSA, escape, fgsea, FindIT2, flowcatchR, flowSpecs, GDCRNATools, GENESIS, GenomicAlignments, genotypeeval, gmapR, gscreend, GSEABenchmarkeR, GSVA, GUIDEseq, h5vc, HiCcompare, HTSeqGenie, HTSFilter, iasva, icetea, ideal, IMAS, imcRtools, InPAS, IntEREst, IONiseR, IPO, ISAnalytics, KinSwingR, LineagePulse, lisaClust, loci2path, LowMACA, LRcell, MACPET, mbkmeans, MCbiclust, metabomxtr, metaseqR2, MethylAid, methylGSA, methylInheritance, methylscaper, MetNet, mia, miaViz, MIGSA, miloR, minfi, mixOmics, MMAPPR2, MOGAMUN, monaLisa, motifbreakR, MPRAnalyze, MsBackendMassbank, MsBackendMgf, MsBackendRawFileReader, MSnbase, msqrob2, MSstatsSampleSize, multiHiCcompare, mumosa, muscat, NBAMSeq, NBSplice, NPARC, NxtIRFcore, OmicsLonDA, ORFik, OVESEG, PAIRADISE, PCAtools, PDATK, pengls, PharmacoGx, pipeComp, pram, PrecisionTrialDrawer, proActiv, proFIA, profileplyr, ProteoDisco, qpgraph, qsea, QuasR, RadioGx, Rcwl, recount, RegEnrich, REMP, RiboCrypt, RJMCMCNucleosomes, RNAmodR, Rsamtools, RUVcorr, satuRn, scanMiR, scanMiRApp, scater, scClassify, scDblFinder, scDD, scde, SCFA, scHOT, scMerge, SCnorm, scone, scoreInvHap, scPCA, scran, scRecover, scruff, scShapes, scTHI, scuttle, sesame, SEtools, sigFeature, signatureSearch, singleCellTK, SingleR, singscore, SNPhood, soGGi, sparrow, SpectralTAD, spicyR, splatter, SplicingGraphs, srnadiff, TAPseq, TarSeqQC, TBSignatureProfiler, ternarynet, TFBSTools, TMixClust, ToxicoGx, TPP2D, tradeSeq, TraRe, TreeSummarizedExperiment, Trendy, TSRchitect, TVTB, txcutr, VariantFiltering, VariantTools, velociraptor, waddR, weitrix, zinbwave, IHWpaper, ExpHunterSuite, DCLEAR, DysPIA, enviGCMS, minSNPs suggestsMe: beachmat, DelayedArray, DIAlignR, GenomicDataCommons, glmGamPoi, HDF5Array, netSmooth, omicsPrint, PureCN, randRotation, RcisTarget, rebook, scGPS, SeqArray, TFutils, TileDBArray, tofsims, TrajectoryUtils, trena, TSCAN, universalmotif, MethylAidData, Single.mTEC.Transcriptomes, TENxBrainData, TENxPBMCData, CAGEWorkflow, SingleRBook, conos, Corbi, digitalDLSorteR, pagoda2, phase1RMD, survBootOutliers, wrTopDownFrag dependencyCount: 10 Package: BiocPkgTools Version: 1.12.2 Depends: htmlwidgets Imports: BiocFileCache, BiocManager, biocViews, tibble, magrittr, methods, rlang, tidyselect, stringr, rvest, dplyr, xml2, readr, httr, htmltools, DT, tools, utils, igraph, tidyr, jsonlite, gh, RBGL, graph Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, SnowballC, visNetwork, clipr, blastula, kableExtra, DiagrammeR, SummarizedExperiment License: MIT + file LICENSE MD5sum: 0e708fd9eafe8ece8ed895d5c6a36a1c NeedsCompilation: no Title: Collection of simple tools for learning about Bioc 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 [ctb], Felix G.M. Ernst [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: RELEASE_3_14 git_last_commit: 19280c6 git_last_commit_date: 2021-11-04 Date/Publication: 2021-11-04 source.ver: src/contrib/BiocPkgTools_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocPkgTools_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocPkgTools_1.12.2.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 dependencyCount: 91 Package: BiocSet Version: 1.8.1 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: 1826837065cafea5bea3aeb7d464eed9 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: RELEASE_3_14 git_last_commit: 1f7d340 git_last_commit_date: 2021-11-02 Date/Publication: 2021-11-03 source.ver: src/contrib/BiocSet_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocSet_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocSet_1.8.1.tgz vignettes: vignettes/BiocSet/inst/doc/BiocSet.html vignetteTitles: BiocSet: Representing Element Sets in the Tidyverse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSet/inst/doc/BiocSet.R dependsOnMe: RegEnrich importsMe: sparrow suggestsMe: dearseq dependencyCount: 61 Package: BiocSingular Version: 1.10.0 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: i386, x64 MD5sum: 045e9602787cfb18b50e2e009827cca8 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++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/BiocSingular/issues git_url: https://git.bioconductor.org/packages/BiocSingular git_branch: RELEASE_3_14 git_last_commit: 6615ae8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocSingular_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocSingular_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocSingular_1.10.0.tgz vignettes: vignettes/BiocSingular/inst/doc/decomposition.html, vignettes/BiocSingular/inst/doc/representations.html vignetteTitles: 1. SVD and PCA, 2. Matrix classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSingular/inst/doc/decomposition.R, vignettes/BiocSingular/inst/doc/representations.R dependsOnMe: compartmap, OSCA.advanced, OSCA.basic, OSCA.multisample, OSCA.workflows importsMe: batchelor, BayesSpace, clusterExperiment, DelayedTensor, Dino, GSVA, miloR, mumosa, NanoMethViz, NewWave, PCAtools, scater, scDblFinder, scMerge, scran, scry, SingleR, velociraptor suggestsMe: ResidualMatrix, ScaledMatrix, spatialHeatmap, splatter, HCAData dependencyCount: 28 Package: BiocSklearn Version: 1.16.0 Depends: R (>= 4.0), reticulate, methods, SummarizedExperiment, knitr Imports: basilisk, Rcpp Suggests: testthat, restfulSE, HDF5Array, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 971f46c14b164db8ebc0f35298bcd4bb NeedsCompilation: no Title: interface to python sklearn via Rstudio reticulate Description: This package provides interfaces to selected sklearn elements, and demonstrates fault tolerant use of python modules requiring extensive iteration. biocViews: StatisticalMethod, DimensionReduction, Infrastructure Author: Vince Carey [cre, aut] Maintainer: Vince Carey SystemRequirements: python (>= 2.7), sklearn, numpy, pandas, h5py VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSklearn git_branch: RELEASE_3_14 git_last_commit: e86f9c8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocSklearn_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocSklearn_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocSklearn_1.16.0.tgz vignettes: vignettes/BiocSklearn/inst/doc/BiocSklearn.html vignetteTitles: BiocSklearn overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSklearn/inst/doc/BiocSklearn.R dependencyCount: 48 Package: BiocStyle Version: 2.22.0 Imports: bookdown, knitr (>= 1.30), rmarkdown (>= 1.2), stats, utils, yaml, BiocManager Suggests: BiocGenerics, RUnit, htmltools License: Artistic-2.0 MD5sum: f59f13ec6023b2aaf70c5376d8559bdf NeedsCompilation: no Title: Standard styles for vignettes and other Bioconductor documents Description: Provides standard formatting styles for Bioconductor PDF and HTML documents. Package vignettes illustrate use and functionality. biocViews: Software Author: Andrzej Oleś [aut] (), Mike Smith [ctb] (), Martin Morgan [ctb], Wolfgang Huber [ctb], Bioconductor Package [cre] Maintainer: Bioconductor Package URL: https://github.com/Bioconductor/BiocStyle VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocStyle/issues git_url: https://git.bioconductor.org/packages/BiocStyle git_branch: RELEASE_3_14 git_last_commit: 86250b6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocStyle_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocStyle_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocStyle_2.22.0.tgz vignettes: vignettes/BiocStyle/inst/doc/LatexStyle2.pdf, vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.html vignetteTitles: Bioconductor LaTeX Style 2.0, Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: 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DropletTestFiles, DuoClustering2018, easierData, ELMER.data, emtdata, ewceData, furrowSeg, GenomicDistributionsData, GeuvadisTranscriptExpr, GSE103322, GSE13015, GSE159526, GSE62944, HarmanData, HCAData, HD2013SGI, HDCytoData, HelloRangesData, HighlyReplicatedRNASeq, Hiiragi2013, HMP16SData, HMP2Data, HumanAffyData, IHWpaper, imcdatasets, LRcellTypeMarkers, mCSEAdata, MetaGxOvarian, MetaGxPancreas, MethylAidData, MethylSeqData, microbiomeDataSets, minionSummaryData, MMAPPR2data, MouseGastrulationData, MouseThymusAgeing, msigdb, MSMB, msqc1, muscData, nanotubes, NestLink, OnassisJavaLibs, optimalFlowData, parathyroidSE, pasilla, PasillaTranscriptExpr, PCHiCdata, PepsNMRData, ppiData, preciseTADhub, ptairData, rcellminerData, RforProteomics, RGMQLlib, RLHub, RNAmodR.Data, RnaSeqSampleSizeData, sampleClassifierData, scanMiRData, scATAC.Explorer, SCLCBam, scpdata, scRNAseq, SimBenchData, Single.mTEC.Transcriptomes, SingleCellMultiModal, spatialLIBD, STexampleData, systemPipeRdata, TabulaMurisData, TabulaMurisSenisData, tartare, TCGAbiolinksGUI.data, TENxBrainData, TENxBUSData, TENxPBMCData, TENxVisiumData, timecoursedata, TimerQuant, tissueTreg, TMExplorer, tuberculosis, VariantToolsData, zebrafishRNASeq, annotation, arrays, BiocMetaWorkflow, CAGEWorkflow, chipseqDB, csawUsersGuide, EGSEA123, ExpressionNormalizationWorkflow, generegulation, highthroughputassays, liftOver, maEndToEnd, proteomics, recountWorkflow, RNAseq123, sequencing, SingscoreAMLMutations, variants, SingleRBook, asteRisk, BigDataStatMeth, bmstdr, BOSO, cyjShiny, EHRtemporalVariability, ggBubbles, i2dash, magmaR, MetaIntegrator, multiclassPairs, MVN, net4pg, NutrienTrackeR, openSkies, PlackettLuce, Rediscover, SourceSet dependencyCount: 30 Package: biocthis Version: 1.4.0 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: 9fc9f6824f7417423f44063abd896ef2 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/t/biocthis git_url: https://git.bioconductor.org/packages/biocthis git_branch: RELEASE_3_14 git_last_commit: 05b696a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biocthis_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biocthis_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biocthis_1.4.0.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: 52 Package: BiocVersion Version: 3.14.0 Depends: R (>= 4.1.0) License: Artistic-2.0 MD5sum: 9c5269da216bf51acf9d7a0512916264 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: master git_last_commit: aa56d93 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 source.ver: src/contrib/BiocVersion_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocVersion_3.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocVersion_3.14.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub, pkgndep suggestsMe: BiocManager dependencyCount: 0 Package: biocViews Version: 1.62.1 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 License: Artistic-2.0 MD5sum: b335a13bdeee2cb3115af16c6bed1df1 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 BugReports: https://github.com/Bioconductor/BiocViews/issues git_url: https://git.bioconductor.org/packages/biocViews git_branch: RELEASE_3_14 git_last_commit: b4b6834 git_last_commit_date: 2021-11-01 Date/Publication: 2021-11-02 source.ver: src/contrib/biocViews_1.62.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/biocViews_1.62.1.zip mac.binary.ver: bin/macosx/contrib/4.1/biocViews_1.62.1.tgz vignettes: vignettes/biocViews/inst/doc/createReposHtml.pdf, vignettes/biocViews/inst/doc/HOWTO-BCV.pdf 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 dependsOnMe: Risa importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, monocle, sigFeature, RforProteomics suggestsMe: packFinder dependencyCount: 16 Package: BiocWorkflowTools Version: 1.20.0 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE MD5sum: 6d91cd30ba29d8f8911f587dd5ffd285 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: RELEASE_3_14 git_last_commit: b5eeaf0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiocWorkflowTools_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiocWorkflowTools_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiocWorkflowTools_1.20.0.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: 56 Package: biodb Version: 1.2.2 Depends: R (>= 4.1.0) Imports: BiocFileCache, R6, RCurl, RSQLite, Rcpp, XML, chk, 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, git2r License: AGPL-3 Archs: i386, x64 MD5sum: c212d44bfec6397e1ae857614408bbfd 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: RELEASE_3_14 git_last_commit: 79469df git_last_commit_date: 2021-12-10 Date/Publication: 2021-12-12 source.ver: src/contrib/biodb_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/biodb_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/biodb_1.2.2.tgz vignettes: vignettes/biodb/inst/doc/biodb.html, vignettes/biodb/inst/doc/details.html, vignettes/biodb/inst/doc/entries.html, vignettes/biodb/inst/doc/new_connector.html, vignettes/biodb/inst/doc/new_entry_field.html vignetteTitles: Introduction to the biodb package., Details on general *biodb* usage and principles, Manipulating entry objects, Creating a new connector class for accessing a database., Creating a new field for entries. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodb/inst/doc/biodb.R, vignettes/biodb/inst/doc/details.R, vignettes/biodb/inst/doc/entries.R, vignettes/biodb/inst/doc/new_connector.R, vignettes/biodb/inst/doc/new_entry_field.R importsMe: biodbChebi, biodbHmdb, biodbKegg, biodbLipidmaps, biodbUniprot dependencyCount: 74 Package: biodbChebi Version: 1.0.1 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: 44fba864be6c99e82b232c85714d0d46 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: RELEASE_3_14 git_last_commit: 35326c0 git_last_commit_date: 2021-10-27 Date/Publication: 2021-10-27 source.ver: src/contrib/biodbChebi_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/biodbChebi_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/biodbChebi_1.0.1.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 dependencyCount: 75 Package: biodbHmdb Version: 1.0.3 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.1.5), Rcpp LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr, lgr License: AGPL-3 Archs: i386, x64 MD5sum: 2a6ff3127e1806ecf4ee5e5f9a4d2a4d 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: RELEASE_3_14 git_last_commit: b0dabfe git_last_commit_date: 2021-11-24 Date/Publication: 2021-11-25 source.ver: src/contrib/biodbHmdb_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/biodbHmdb_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/biodbHmdb_1.0.3.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: 75 Package: biodbKegg Version: 1.0.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.1.9), chk, lifecycle Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, igraph, magick, lgr License: AGPL-3 MD5sum: 1138ef0962dd57e26c8bd37d977428e0 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: RELEASE_3_14 git_last_commit: 5bb8a8a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biodbKegg_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biodbKegg_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biodbKegg_1.0.0.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: 75 Package: biodbLipidmaps Version: 1.0.1 Depends: R (>= 4.1) Imports: biodb (>= 1.1.5), lifecycle, R6 Suggests: BiocStyle, lgr, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr License: AGPL-3 MD5sum: d7f74b12d1fabfcc7c5960e1b1541a06 NeedsCompilation: no Title: biodbLipidmaps, a library for connecting to the Lipidmaps Structure database Description: The biodbLipidmaps library provides access to the Lipidmaps Structure Database, using biodb package framework. It allows to retrieve entries by their accession number, and run web the services lmsdSearch and lmsdRecord. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbLipidmaps VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbLipidmaps/issues git_url: https://git.bioconductor.org/packages/biodbLipidmaps git_branch: RELEASE_3_14 git_last_commit: bda9e29 git_last_commit_date: 2021-10-27 Date/Publication: 2021-10-28 source.ver: src/contrib/biodbLipidmaps_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/biodbLipidmaps_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/biodbLipidmaps_1.0.1.tgz vignettes: vignettes/biodbLipidmaps/inst/doc/biodbLipidmaps.html vignetteTitles: An introduction to biodbLipidmaps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbLipidmaps/inst/doc/biodbLipidmaps.R dependencyCount: 75 Package: biodbUniprot Version: 1.0.0 Depends: R (>= 4.1.0) Imports: R6, biodb (>= 1.1.10) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr, covr License: AGPL-3 MD5sum: 8513f8eae308f040b244daeea7bb7095 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: RELEASE_3_14 git_last_commit: 3e54101 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biodbUniprot_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biodbUniprot_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biodbUniprot_1.0.0.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: 75 Package: bioDist Version: 1.66.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 17fd41c71d5b4e6627004eeb4ea31bed 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: RELEASE_3_14 git_last_commit: a81b002 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bioDist_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bioDist_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bioDist_1.66.0.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: PhyloProfile dependencyCount: 7 Package: biomaRt Version: 2.50.3 Depends: methods Imports: utils, XML, AnnotationDbi, progress, stringr, httr, digest, BiocFileCache, rappdirs, xml2 Suggests: BiocStyle, knitr, rmarkdown, testthat, mockery License: Artistic-2.0 MD5sum: c1d82b6a3f38ff64b7ddd2998c427f2e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biomaRt git_branch: RELEASE_3_14 git_last_commit: 83a519a git_last_commit_date: 2022-02-02 Date/Publication: 2022-02-03 source.ver: src/contrib/biomaRt_2.50.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/biomaRt_2.50.3.zip mac.binary.ver: bin/macosx/contrib/4.1/biomaRt_2.50.3.tgz vignettes: vignettes/biomaRt/inst/doc/accessing_ensembl.html, vignettes/biomaRt/inst/doc/accessing_other_marts.html vignetteTitles: Accessing Ensembl annotation with biomaRt, Using a BioMart other than Ensembl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/accessing_ensembl.R, vignettes/biomaRt/inst/doc/accessing_other_marts.R dependsOnMe: BrainSABER, chromPlot, coMET, customProDB, DrugVsDisease, genefu, GenomicOZone, MineICA, NetSAM, PPInfer, PSICQUIC, RepViz, VegaMC, annotation importsMe: ArrayExpressHTS, ASpediaFI, BadRegionFinder, BgeeCall, branchpointer, BUSpaRse, ChIPpeakAnno, CHRONOS, conclus, cosmosR, dagLogo, DEXSeq, diffloop, DominoEffect, easyRNASeq, EDASeq, ELMER, EWCE, FRASER, GDCRNATools, GeneAccord, GenomicFeatures, GenVisR, gespeR, glmSparseNet, GOexpress, goSTAG, gpart, Gviz, InterCellar, isobar, mCSEA, MEDIPS, MetaboSignal, metaseqR2, MGFR, MouseFM, OncoScore, oposSOM, pcaExplorer, phenoTest, PrecisionTrialDrawer, pRoloc, ProteoMM, psygenet2r, pwOmics, R453Plus1Toolbox, ramwas, recoup, rgsepd, RIPAT, scPipe, seq2pathway, SeqGSEA, sitadela, SPLINTER, surfaltr, SWATH2stats, TCGAbiolinks, TFEA.ChIP, TimiRGeN, transcriptogramer, trena, ViSEAGO, yarn, ExpHunterSuite, TCGAWorkflow, biomartr, BioVenn, convertid, DiNAMIC.Duo, GOxploreR, intePareto, kangar00, liayson, snplist, utr.annotation suggestsMe: AnnotationForge, bioassayR, celda, cellTree, chromstaR, ClusterJudge, CNVgears, ctgGEM, cTRAP, epistack, fedup, FELLA, GeneAnswers, h5vc, MAGeCKFlute, martini, massiR, MethReg, MineICA, miQC, MiRaGE, MutationalPatterns, netSmooth, oligo, OrganismDbi, piano, Pigengene, progeny, PubScore, R3CPET, Rcade, RnBeads, rTRM, scater, ShortRead, SIM, sincell, SummarizedBenchmark, trackViewer, wiggleplotr, zinbwave, BloodCancerMultiOmics2017, ccTutorial, leeBamViews, RegParallel, RforProteomics, BED, BioInsight, cinaR, DGEobj, DGEobj.utils, dnapath, MoBPS, Patterns, R.SamBada, scDiffCom, SNPassoc dependencyCount: 70 Package: biomformat Version: 1.22.0 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: 3130da186e5fae23a1ba0d1bd612d00c 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: RELEASE_3_14 git_last_commit: ab7c641 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biomformat_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biomformat_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biomformat_1.22.0.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: animalcules, microbiomeExplorer, microbiomeMarker, phyloseq suggestsMe: metagenomeSeq, mia, MicrobiotaProcess, metacoder, PLNmodels dependencyCount: 14 Package: BioMM Version: 1.10.0 Depends: R (>= 3.6) Imports: stats, utils, grDevices, lattice, BiocParallel, glmnet, rms, precrec, nsprcomp, ranger, e1071, ggplot2, vioplot, CMplot, imager, topGO, xlsx Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: b1184731d04f5731c9bf1b92f59f725f NeedsCompilation: no Title: BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data Description: The identification of reproducible biological patterns from high-dimensional omics data is a key factor in understanding the biology of complex disease or traits. Incorporating prior biological knowledge into machine learning is an important step in advancing such research. We have proposed a biologically informed multi-stage machine learing framework termed BioMM specifically for phenotype prediction based on omics-scale data where we can evaluate different machine learning models with prior biological meta information. biocViews: Genetics, Classification, Regression, Pathways, GO, Software Author: Junfang Chen and Emanuel Schwarz Maintainer: Junfang Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioMM git_branch: RELEASE_3_14 git_last_commit: 861a791 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BioMM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioMM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioMM_1.10.0.tgz vignettes: vignettes/BioMM/inst/doc/BioMMtutorial.html vignetteTitles: BioMMtutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioMM/inst/doc/BioMMtutorial.R dependencyCount: 145 Package: BioMVCClass Version: 1.62.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: e553c303003e1649d939eed5c930b409 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: RELEASE_3_14 git_last_commit: 346bc9e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BioMVCClass_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioMVCClass_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioMVCClass_1.62.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: biomvRCNS Version: 1.34.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: c84dd927861d807b31a8c9c030d6c3f6 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: RELEASE_3_14 git_last_commit: ce77ae7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biomvRCNS_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biomvRCNS_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biomvRCNS_1.34.0.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: 143 Package: BioNERO Version: 1.2.0 Depends: R (>= 4.1) Imports: WGCNA, dynamicTreeCut, matrixStats, DESeq2, sva, RColorBrewer, ComplexHeatmap, ggplot2, reshape2, igraph, ggnetwork, intergraph, networkD3, ggnewscale, ggpubr, NetRep, stats, grDevices, graphics, utils, methods, BiocParallel, minet, GENIE3, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle, covr License: GPL-3 MD5sum: 7d76e8e665f55474094a2c403c4ef9d2 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 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: RELEASE_3_14 git_last_commit: c11998f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BioNERO_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioNERO_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioNERO_1.2.0.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: 197 Package: BioNet Version: 1.54.0 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: e561b8a7d92777807f5012440f00fb62 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: RELEASE_3_14 git_last_commit: 09b4d2f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BioNet_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioNet_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioNet_1.54.0.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: SMITE suggestsMe: SANTA, mwcsr dependencyCount: 53 Package: BioNetStat Version: 1.14.0 Depends: R (>= 4.0), shiny, igraph, shinyBS, pathview, DT Imports: BiocParallel, RJSONIO, whisker, yaml, pheatmap, ggplot2, plyr, utils, stats, RColorBrewer, Hmisc, psych, knitr, rmarkdown, markdown License: GPL (>= 3) MD5sum: a1e2d025f1cdcda8ea3be6e94f71afad NeedsCompilation: no Title: Biological Network Analysis Description: A package to perform differential network analysis, differential node analysis (differential coexpression analysis), network and metabolic pathways view. biocViews: Network, NetworkInference, Pathways, GraphAndNetwork, Sequencing, Microarray, Metabolomics, Proteomics, GeneExpression, RNASeq, SystemsBiology, DifferentialExpression, GeneSetEnrichment, ImmunoOncology Author: Vinícius Jardim, Suzana Santos, André Fujita, and Marcos Buckeridge Maintainer: Vinicius Jardim URL: http://github.com/jardimViniciusC/BioNetStat VignetteBuilder: knitr, rmarkdown BugReports: http://github.com/jardimViniciusC/BioNetStat/issues git_url: https://git.bioconductor.org/packages/BioNetStat git_branch: RELEASE_3_14 git_last_commit: 8ff3b2a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BioNetStat_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioNetStat_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioNetStat_1.14.0.tgz vignettes: vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line_pt.html, vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line_us.html, vignettes/BioNetStat/inst/doc/vignette.html vignetteTitles: 3. Tutorial para o console do R, 2. R console tutorial, 1. Interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 139 Package: BioPlex Version: 1.0.2 Depends: R (>= 4.1.0), SummarizedExperiment Imports: BiocFileCache, GEOquery, graph, methods, utils Suggests: AnnotationDbi, AnnotationHub, BiocStyle, ExperimentHub, depmap, knitr, rmarkdown License: Artistic-2.0 MD5sum: a8814b7f8334f9c91f89f498f5dcf3ab NeedsCompilation: no Title: R-side access to BioPlex protein-protein interaction data Description: The BioPlex package implements access to the BioPlex protein-protein interaction networks and related resources from within R. Besides protein-protein interaction networks for HEK293 and HCT116 cells, this includes access to CORUM protein complex data, and transcriptome and proteome data for the two cell lines. Functionality focuses on importing the various data resources and storing them in dedicated Bioconductor data structures, as a foundation for integrative downstream analysis of the data. biocViews: DataImport, DataRepresentation, GeneExpression, GraphAndNetwork, MassSpectrometry, Network, Transcriptomics, Proteomics Author: Ludwig Geistlinger [aut, cre], Robert Gentleman [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/ccb-hms/BioPlex VignetteBuilder: knitr BugReports: https://github.com/ccb-hms/BioPlex/issues git_url: https://git.bioconductor.org/packages/BioPlex git_branch: RELEASE_3_14 git_last_commit: 44f81d5 git_last_commit_date: 2022-03-02 Date/Publication: 2022-03-06 source.ver: src/contrib/BioPlex_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioPlex_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/BioPlex_1.0.2.tgz vignettes: vignettes/BioPlex/inst/doc/BasicChecks.html, vignettes/BioPlex/inst/doc/BioPlex.html vignetteTitles: 2. Data checks, 1. Data retrieval hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioPlex/inst/doc/BasicChecks.R, vignettes/BioPlex/inst/doc/BioPlex.R dependencyCount: 83 Package: BioQC Version: 1.22.0 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: i386, x64 MD5sum: c31f8ce470def433e764a482cfce44be 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: RELEASE_3_14 git_last_commit: ed54d1a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BioQC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioQC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioQC_1.22.0.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: 13 Package: biosigner Version: 1.22.0 Depends: Biobase, ropls Imports: methods, e1071, MultiDataSet, randomForest Suggests: BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 26ca570b4739f9b5346d9baa97395896 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 Author: Philippe Rinaudo , Etienne Thevenot Maintainer: Philippe Rinaudo , Etienne Thevenot VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biosigner git_branch: RELEASE_3_14 git_last_commit: 72d39bd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biosigner_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biosigner_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biosigner_1.22.0.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R importsMe: multiSight dependencyCount: 66 Package: Biostrings Version: 2.62.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2), GenomeInfoDb Imports: methods, utils, grDevices, graphics, stats, crayon LinkingTo: S4Vectors, IRanges, XVector Suggests: 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 Enhances: Rmpi License: Artistic-2.0 Archs: i386, x64 MD5sum: 4401cbd82e4292071ea3be55078cdc33 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: H. Pagès, P. Aboyoun, R. Gentleman, and S. DebRoy Maintainer: H. Pagès URL: https://bioconductor.org/packages/Biostrings BugReports: https://github.com/Bioconductor/Biostrings/issues git_url: https://git.bioconductor.org/packages/Biostrings git_branch: RELEASE_3_14 git_last_commit: 53ed287 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Biostrings_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Biostrings_2.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Biostrings_2.62.0.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/matchprobes.pdf, vignettes/Biostrings/inst/doc/MultipleAlignments.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Handling probe sequence information, Multiple Alignments, Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R, vignettes/Biostrings/inst/doc/PairwiseAlignments.R dependsOnMe: altcdfenvs, amplican, Basic4Cseq, BRAIN, BSgenome, chimeraviz, ChIPanalyser, ChIPsim, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, GOTHiC, HelloRanges, hiReadsProcessor, iPAC, kebabs, MethTargetedNGS, minfi, Modstrings, MotifDb, msa, muscle, oligo, ORFhunteR, periodicDNA, pqsfinder, PWMEnrich, qrqc, QSutils, R453Plus1Toolbox, R4RNA, REDseq, rGADEM, RiboProfiling, rRDP, Rsamtools, RSVSim, sangeranalyseR, sangerseqR, SCAN.UPC, SELEX, seqbias, ShortRead, SICtools, SimFFPE, Structstrings, systemPipeR, topdownr, TreeSummarizedExperiment, triplex, VarCon, FDb.FANTOM4.promoters.hg19, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, 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cliProfiler, CNEr, CNVfilteR, consensusDE, coRdon, CrispRVariants, customProDB, dada2, dagLogo, DAMEfinder, decompTumor2Sig, diffHic, DNAshapeR, DominoEffect, easyRNASeq, EDASeq, enhancerHomologSearch, ensembldb, ensemblVEP, EpiTxDb, esATAC, eudysbiome, EventPointer, FastqCleaner, GA4GHclient, gcapc, gcrma, genbankr, GeneRegionScan, genomation, GenomicAlignments, GenomicDistributions, GenomicFeatures, GenomicScores, genphen, GenVisR, ggbio, ggmsa, girafe, gmapR, gmoviz, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiLDA, HiTC, HTSeqGenie, icetea, idpr, IMMAN, InPAS, IntEREst, InterMineR, IONiseR, ipdDb, IsoformSwitchAnalyzeR, KEGGREST, LowMACA, LymphoSeq, m6Aboost, MACPET, MADSEQ, MatrixRider, MDTS, MEDIPS, MEDME, memes, MesKit, metaseqR2, methimpute, methylscaper, mia, microbiomeMarker, MicrobiotaProcess, microRNA, MMDiff2, monaLisa, motifbreakR, motifcounter, motifmatchr, motifStack, MSnID, MSstatsLiP, MSstatsPTM, multicrispr, MungeSumstats, musicatk, MutationalPatterns, NanoStringNCTools, ngsReports, nucleR, NxtIRFcore, oligoClasses, OmaDB, openPrimeR, ORFik, OTUbase, packFinder, pdInfoBuilder, PhyloProfile, phyloseq, pipeFrame, podkat, polyester, primirTSS, proBAMr, procoil, ProteoDisco, ProteomicsAnnotationHubData, PureCN, Pviz, qPLEXanalyzer, qrqc, qsea, QuasR, r3Cseq, ramwas, RCAS, Rcpi, recoup, regioneR, regutools, REMP, Repitools, rfaRm, rGADEM, RiboCrypt, ribosomeProfilingQC, RNAmodR, RNASeqR, Rqc, rtracklayer, sarks, scanMiR, scanMiRApp, scmeth, SCOPE, scoreInvHap, scruff, SeqArray, seqcombo, seqPattern, SGSeq, signeR, SigsPack, SingleMoleculeFootprinting, sitadela, SNPhood, soGGi, SomaticSignatures, SparseSignatures, spiky, SPLINTER, sscu, StructuralVariantAnnotation, supersigs, surfaltr, svaNUMT, svaRetro, SynExtend, SynMut, TAPseq, TarSeqQC, TFBSTools, transite, trena, tRNA, tRNAdbImport, tRNAscanImport, TVTB, txcutr, tximeta, Ularcirc, UMI4Cats, universalmotif, VariantAnnotation, VariantExperiment, VariantFiltering, VariantTools, wavClusteR, XNAString, YAPSA, EuPathDB, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1, microbiomeDataSets, pd.atdschip.tiling, PhyloProfileData, ActiveDriverWGS, alakazam, BALCONY, BASiNET, BASiNETEntropy, biomartr, crispRdesignR, CSESA, deepredeff, dowser, EncDNA, ensembleTax, ExomeDepth, genBaRcode, hoardeR, ICAMS, immuneSIM, kibior, metaCluster, MitoHEAR, PACVr, Platypus, PredCRG, ptm, RAPIDR, seqmagick, simMP, SMITIDstruct, SNPassoc, TrustVDJ, utr.annotation, vhcub suggestsMe: annotate, AnnotationForge, AnnotationHub, bambu, BANDITS, BiocGenerics, BRGenomics, CSAR, eisaR, exomeCopy, GenomicFiles, GenomicRanges, GWASTools, HPiP, maftools, methrix, methylumi, MiRaGE, mitoClone2, nuCpos, RNAmodR.AlkAnilineSeq, rpx, rSWeeP, rTRM, spatzie, splatter, systemPipeTools, treeio, tripr, XVector, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases, AhoCorasickTrie, bbl, bio3d, datelife, DDPNA, file2meco, gkmSVM, maGUI, minSNPs, msaR, NameNeedle, phangorn, polyRAD, protr, rDNAse, sigminer, Signac, tidysq linksToMe: DECIPHER, kebabs, MatrixRider, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 17 Package: BioTIP Version: 1.8.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges, Hmisc, MASS Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: 8b47c6e69c1abea31ec2349fd5ee3103 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: RELEASE_3_14 git_last_commit: b7c8488 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BioTIP_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BioTIP_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BioTIP_1.8.0.tgz vignettes: vignettes/BioTIP/inst/doc/BioTIP.html vignetteTitles: BioTIP- an R package for characterization of Biological Tipping-Point hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R dependencyCount: 84 Package: biotmle Version: 1.18.0 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: cbce248b2e52a9d3523431cadf9d0a16 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: RELEASE_3_14 git_last_commit: a95145b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biotmle_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biotmle_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biotmle_1.18.0.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: 75 Package: biovizBase Version: 1.42.0 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 Archs: i386, x64 MD5sum: 3b602322ba3da13d05dc4eab56bab065 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: RELEASE_3_14 git_last_commit: f1627b2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biovizBase_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biovizBase_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biovizBase_1.42.0.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, qrqc importsMe: BubbleTree, ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, qrqc, Rqc suggestsMe: CINdex, derfinderPlot, NanoStringNCTools, R3CPET, regionReport, StructuralVariantAnnotation, Signac dependencyCount: 140 Package: BiRewire Version: 3.26.5 Depends: igraph, slam, Rtsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: d677a3402e771f947a7abb67e43c7a2f 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: RELEASE_3_14 git_last_commit: c35677f git_last_commit_date: 2022-04-04 Date/Publication: 2022-04-05 source.ver: src/contrib/BiRewire_3.26.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiRewire_3.26.5.zip mac.binary.ver: bin/macosx/contrib/4.1/BiRewire_3.26.5.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 importsMe: NetSci dependencyCount: 14 Package: biscuiteer Version: 1.8.0 Depends: R (>= 3.6), biscuiteerData, bsseq Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools, data.table, Biobase, GenomicRanges, 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 License: GPL-3 MD5sum: 891a1d740d8b21729e9b9070a78247f7 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, Jr. [aut, cre], Wanding Zhou [aut], Ben Johnson [aut], Jacob Morrison [aut], Lyong Heo [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: RELEASE_3_14 git_last_commit: 2a5989a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/biscuiteer_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/biscuiteer_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/biscuiteer_1.8.0.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: 193 Package: BiSeq Version: 1.34.0 Depends: R (>= 2.15.2), 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: 19fa1c38738f95a94d76b26c192973e2 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: RELEASE_3_14 git_last_commit: 5d4449a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BiSeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BiSeq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BiSeq_1.34.0.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: 85 Package: BitSeq Version: 1.38.0 Depends: Rsamtools (>= 1.99.3) Imports: S4Vectors, IRanges, methods, utils LinkingTo: Rhtslib (>= 1.15.5) Suggests: BiocStyle License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 0988b1de7f1c3e7aae12518a0982ed63 NeedsCompilation: yes Title: Transcript expression inference and differential expression analysis for RNA-seq data Description: The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Sequencing, RNASeq, Bayesian, AlternativeSplicing, DifferentialSplicing, Transcription Author: Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Antti Honkela , Panagiotis Papastamoulis SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/BitSeq git_branch: RELEASE_3_14 git_last_commit: 7a8f12b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BitSeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BitSeq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BitSeq_1.38.0.tgz vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf vignetteTitles: BitSeq User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BitSeq/inst/doc/BitSeq.R dependencyCount: 29 Package: blacksheepr Version: 1.8.0 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: e3d1b6ea0628e33438b2dc2e315a5b99 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: RELEASE_3_14 git_last_commit: a785f7f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/blacksheepr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/blacksheepr_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/blacksheepr_1.8.0.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: 72 Package: blima Version: 1.28.0 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 Archs: i386, x64 MD5sum: 5c415a098ca631ca86a714f40e88c0ac 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: RELEASE_3_14 git_last_commit: 2a638a4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/blima_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/blima_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/blima_1.28.0.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: 82 Package: BLMA Version: 1.18.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase, metafor, methods Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: a133e4a44864d77fe07b38e99d065a8d 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: Hung Nguyen git_url: https://git.bioconductor.org/packages/BLMA git_branch: RELEASE_3_14 git_last_commit: f5e4a71 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BLMA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BLMA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BLMA_1.18.0.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: 72 Package: BloodGen3Module Version: 1.2.0 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: 551a0a8ed72355754b70d5f84e7e21d2 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: RELEASE_3_14 git_last_commit: 0a233b6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BloodGen3Module_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BloodGen3Module_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BloodGen3Module_1.2.0.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: 146 Package: bluster Version: 1.4.0 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 License: GPL-3 Archs: i386, x64 MD5sum: 76f2490a4b1c1d6180c7628da9ffcc21 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] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bluster git_branch: RELEASE_3_14 git_last_commit: 6f4b821 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bluster_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bluster_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bluster_1.4.0.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.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: scDblFinder, scran, Canek suggestsMe: batchelor, dittoSeq, mbkmeans, mumosa, SingleRBook dependencyCount: 26 Package: bnbc Version: 1.16.0 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 LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: ef9ff0d8954152b4126560cc424302c0 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: RELEASE_3_14 git_last_commit: 26377da git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bnbc_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bnbc_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bnbc_1.16.0.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: 88 Package: bnem Version: 1.2.0 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 License: GPL-3 MD5sum: 06d0cafb0eb8a93096d14ac5e762e8f8 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: RELEASE_3_14 git_last_commit: 7189a51 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bnem_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bnem_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bnem_1.2.0.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: 171 Package: BPRMeth Version: 1.20.0 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 Archs: i386, x64 MD5sum: 51b05fcf169f1bb82de9d07089af97d3 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: RELEASE_3_14 git_last_commit: 0135f88 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BPRMeth_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BPRMeth_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BPRMeth_1.20.0.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: 94 Package: BRAIN Version: 1.40.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: f2cba6b8747b47d19c83c217c296356e 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: RELEASE_3_14 git_last_commit: 1598666 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BRAIN_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BRAIN_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BRAIN_1.40.0.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, RforProteomics dependencyCount: 22 Package: brainflowprobes Version: 1.8.0 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 MD5sum: 759e5d626ece34c8c6fae1880dbd0b20 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/brainflowprobes git_branch: RELEASE_3_14 git_last_commit: eb34416 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/brainflowprobes_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/brainflowprobes_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/brainflowprobes_1.8.0.tgz vignettes: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.html vignetteTitles: brainflowprobes users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.R dependencyCount: 187 Package: BrainSABER Version: 1.4.0 Depends: R (>= 4.1.0), biomaRt, SummarizedExperiment Imports: data.table, lsa, methods, S4Vectors, utils, BiocFileCache, shiny Suggests: BiocStyle, ComplexHeatmap, fastcluster, heatmaply, knitr, plotly, rmarkdown License: Artistic-2.0 MD5sum: 3668b402ccccc868cd16007ff35b7420 NeedsCompilation: no Title: Brain Span Atlas in Biobase Expressionset R toolset Description: The Allen Institute for Brain Science provides an RNA sequencing (RNA-Seq) data resource for studying transcriptional mechanisms involved in human brain development known as BrainSpan. BrainSABER is an R package that facilitates comparison of user data with the various developmental stages and brain structures found in the BrainSpan atlas by generating dynamic similarity heatmaps for the two data sets. It also provides a self-validating container for user data. biocViews: GeneExpression, Visualization, Sequencing Author: Carrie Minette [aut], Evgeni Radichev [aut], USD Biomedical Engineering [aut, cre] Maintainer: USD Biomedical Engineering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BrainSABER git_branch: RELEASE_3_14 git_last_commit: 27ae2bc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-28 source.ver: src/contrib/BrainSABER_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BrainSABER_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BrainSABER_1.4.0.tgz vignettes: vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.html vignetteTitles: BrainSABER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.R dependencyCount: 96 Package: branchpointer Version: 1.20.0 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: 1ab1891e33e453e4acab8363a3cbee88 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: RELEASE_3_14 git_last_commit: 09d2e40 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/branchpointer_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/branchpointer_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/branchpointer_1.20.0.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: 148 Package: breakpointR Version: 1.12.0 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: 5fc2f4b77cbf1c24aec5a1ec8a15fd2d 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: RELEASE_3_14 git_last_commit: 1624453 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/breakpointR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/breakpointR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/breakpointR_1.12.0.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: 74 Package: brendaDb Version: 1.8.0 Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel, crayon, utils, tidyr, curl, xml2, grDevices, rlang, BiocFileCache, rappdirs LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools License: MIT + file LICENSE Archs: i386, x64 MD5sum: ad9e1775584e1724b71965e12a7fed81 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: RELEASE_3_14 git_last_commit: 61bce49 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/brendaDb_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/brendaDb_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/brendaDb_1.8.0.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: 60 Package: BRGenomics Version: 1.6.0 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 MD5sum: 8680c0ec234715aa737a4d0d23e88707 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/BRGenomics git_branch: RELEASE_3_14 git_last_commit: ff5ff54 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BRGenomics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BRGenomics_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BRGenomics_1.6.0.tgz vignettes: vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.html, vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.html, vignettes/BRGenomics/inst/doc/GettingStarted.html, vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.html, vignettes/BRGenomics/inst/doc/ImportingProcessingData.html, vignettes/BRGenomics/inst/doc/Overview.html, vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.html, vignettes/BRGenomics/inst/doc/SequenceExtraction.html, vignettes/BRGenomics/inst/doc/SignalCounting.html, vignettes/BRGenomics/inst/doc/SpikeInNormalization.html vignetteTitles: Analyzing Multiple Datasets, DESeq2 with Global Perturbations, Getting Started, Importing and Modifying Annotations, Importing and Processing Data, Overview, Profile Plots and Bootstrapping, Sequence Extraction, Signal Counting, Spike-in Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.R, vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.R, vignettes/BRGenomics/inst/doc/GettingStarted.R, vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.R, vignettes/BRGenomics/inst/doc/ImportingProcessingData.R, vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.R, vignettes/BRGenomics/inst/doc/SequenceExtraction.R, vignettes/BRGenomics/inst/doc/SignalCounting.R, vignettes/BRGenomics/inst/doc/SpikeInNormalization.R dependencyCount: 101 Package: bridge Version: 1.58.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: i386, x64 MD5sum: ec19c8484fd785a9fa257f341070fd79 NeedsCompilation: yes Title: Bayesian Robust Inference for Differential Gene Expression Description: Test for differentially expressed genes with microarray data. This package can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. biocViews: Microarray,OneChannel,TwoChannel,DifferentialExpression Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/bridge git_branch: RELEASE_3_14 git_last_commit: 4474bc7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bridge_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bridge_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bridge_1.58.0.tgz vignettes: vignettes/bridge/inst/doc/bridge.pdf vignetteTitles: bridge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bridge/inst/doc/bridge.R dependencyCount: 1 Package: BridgeDbR Version: 2.4.0 Depends: R (>= 3.3.0), rJava Imports: curl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 MD5sum: 11a948e409850d09f7821644a9f934d6 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 , Anwesha Bohler , Lars Eijssen 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: RELEASE_3_14 git_last_commit: 52a7cbe git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BridgeDbR_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BridgeDbR_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BridgeDbR_2.4.0.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: 2.16.0 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: e78370d16925fa8529f326d3eb639a62 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: Paul Shannon URL: https://paul-shannon.github.io/BrowserViz/ VignetteBuilder: knitr BugReports: https://github.com/paul-shannon/BrowserViz/issues git_url: https://git.bioconductor.org/packages/BrowserViz git_branch: RELEASE_3_14 git_last_commit: 0311b05 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BrowserViz_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BrowserViz_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BrowserViz_2.16.0.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: 13 Package: BSgenome Version: 1.62.0 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), rtracklayer (>= 1.39.7) Imports: methods, utils, stats, matrixStats, BiocGenerics, S4Vectors, IRanges, XVector (>= 0.29.3), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, rtracklayer Suggests: BiocManager, Biobase, 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 License: Artistic-2.0 MD5sum: a0991297226969308d5802c1b74f7ebb 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 Maintainer: H. Pagès URL: https://bioconductor.org/packages/BSgenome BugReports: https://github.com/Bioconductor/BSgenome/issues git_url: https://git.bioconductor.org/packages/BSgenome git_branch: RELEASE_3_14 git_last_commit: 9b1859e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BSgenome_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BSgenome_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BSgenome_1.62.0.tgz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/BSgenomeForge.R, vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: bambu, ChIPanalyser, GOTHiC, HelloRanges, MEDIPS, periodicDNA, REDseq, rGADEM, VarCon, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, leeBamViews, annotation importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq, BUSpaRse, CAGEr, chromVAR, cleanUpdTSeq, cliProfiler, CRISPRseek, crisprseekplus, diffHic, dpeak, enhancerHomologSearch, enrichTF, esATAC, EventPointer, FRASER, gcapc, genomation, GenVisR, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, InPAS, IsoformSwitchAnalyzeR, m6Aboost, MADSEQ, methrix, MethylSeekR, MMDiff2, monaLisa, motifbreakR, motifmatchr, msgbsR, multicrispr, MungeSumstats, musicatk, MutationalPatterns, NxtIRFcore, ORFik, PING, pipeFrame, podkat, qsea, QuasR, R453Plus1Toolbox, RareVariantVis, RCAS, regioneR, REMP, Repitools, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE, SigsPack, SingleMoleculeFootprinting, SparseSignatures, spatzie, spiky, TAPseq, TFBSTools, trena, tRNAscanImport, Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, XNAString, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, 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.v3.1.1.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, GenomicDistributionsData, ActiveDriverWGS, deconstructSigs, ICAMS, simMP suggestsMe: Biostrings, biovizBase, ChIPpeakAnno, chipseq, easyRNASeq, eisaR, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, maftools, metaseqR2, MiRaGE, plotgardener, ProteoDisco, PWMEnrich, QDNAseq, recoup, RiboCrypt, rtracklayer, sitadela, SNPlocs.Hsapiens.dbSNP.20101109, gkmSVM, sigminer, Signac, SNPassoc dependencyCount: 44 Package: bsseq Version: 1.30.0 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), BatchJobs License: Artistic-2.0 Archs: i386, x64 MD5sum: cf455a0cd18638d3ef9bf2589dbea44f 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 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues git_url: https://git.bioconductor.org/packages/bsseq git_branch: RELEASE_3_14 git_last_commit: 7eb5223 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/bsseq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bsseq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bsseq_1.30.0.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: DMRcate, methylCC, methylSig, MIRA, NanoMethViz, scmeth, tcgaWGBSData.hg19 suggestsMe: methrix, tissueTreg dependencyCount: 72 Package: BubbleTree Version: 2.24.0 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: ff9da2db959959bad4a988a680ac2a76 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: RELEASE_3_14 git_last_commit: c74ad0d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BubbleTree_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BubbleTree_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BubbleTree_2.24.0.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: 157 Package: BufferedMatrix Version: 1.58.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: 51e00e786e3ca4d5862092c5a1ee2a9a 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: RELEASE_3_14 git_last_commit: 2d3839c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BufferedMatrix_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BufferedMatrix_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BufferedMatrix_1.58.0.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: 1.58.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: d3c92af05afda5e27e03f5eaaf4025dd 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: RELEASE_3_14 git_last_commit: bf7041d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BufferedMatrixMethods_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BufferedMatrixMethods_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BufferedMatrixMethods_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: bugsigdbr Version: 1.0.1 Depends: R (>= 4.1) Imports: BiocFileCache, vroom, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: f94fa2eef2da8c5f593f1aec0d60e742 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: RELEASE_3_14 git_last_commit: 2f43bd1 git_last_commit_date: 2021-11-02 Date/Publication: 2021-11-02 source.ver: src/contrib/bugsigdbr_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/bugsigdbr_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/bugsigdbr_1.0.1.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: 53 Package: BUMHMM Version: 1.18.0 Depends: R (>= 3.4) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 MD5sum: af9226519d3753af415c392faafd3ef1 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: RELEASE_3_14 git_last_commit: b2c2258 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BUMHMM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BUMHMM_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BUMHMM_1.18.0.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: 98 Package: bumphunter Version: 1.36.0 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, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 6d934238a635ddefeb29a2a580a63260 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: RELEASE_3_14 git_last_commit: db50fcf git_last_commit_date: 2021-10-27 Date/Publication: 2021-10-27 source.ver: src/contrib/bumphunter_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/bumphunter_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/bumphunter_1.36.0.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: brainflowprobes, DAMEfinder, derfinder, dmrseq, epivizr, methylCC, rnaEditr, GenomicState, recountWorkflow suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport dependencyCount: 103 Package: BumpyMatrix Version: 1.2.0 Imports: utils, methods, Matrix, S4Vectors, IRanges Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: ce955c49321bca85c27120cca052486b 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: RELEASE_3_14 git_last_commit: 155531a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BumpyMatrix_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BumpyMatrix_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BumpyMatrix_1.2.0.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 importsMe: CoreGx, MouseGastrulationData suggestsMe: ggspavis, SpatialExperiment dependencyCount: 12 Package: BUS Version: 1.50.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: i386, x64 MD5sum: 01c12102267b23753a256e6ae3e84576 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: RELEASE_3_14 git_last_commit: 077804c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BUS_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BUS_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BUS_1.50.0.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: 1.12.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 88e826ca667652c85ab3ac54013c93f4 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: RELEASE_3_14 git_last_commit: 55c99c8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BUScorrect_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BUScorrect_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BUScorrect_1.12.0.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: 29 Package: BUSpaRse Version: 1.8.0 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, TENxBUSData, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: ff5f897eb9128553e3ad5144bf356def 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: RELEASE_3_14 git_last_commit: fa528cf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BUSpaRse_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BUSpaRse_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BUSpaRse_1.8.0.tgz vignettes: vignettes/BUSpaRse/inst/doc/sparse-matrix.html, vignettes/BUSpaRse/inst/doc/tr2g.html vignetteTitles: Converting BUS format into sparse matrix, Transcript to gene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUSpaRse/inst/doc/sparse-matrix.R, vignettes/BUSpaRse/inst/doc/tr2g.R dependencyCount: 121 Package: BUSseq Version: 1.0.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, gplots, grDevices, methods, stats, utils Suggests: BiocStyle, knitr, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: d9c65fd72a2baeeaeed563d3f654349e 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: RELEASE_3_14 git_last_commit: aae9920 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/BUSseq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/BUSseq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/BUSseq_1.0.0.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: 30 Package: CAEN Version: 1.2.0 Depends: R (>= 4.1) Imports: stats,PoiClaClu,SummarizedExperiment,methods Suggests: knitr,rmarkdown License: GPL-2 MD5sum: 0350702e028564caab1845b06d75153c 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: RELEASE_3_14 git_last_commit: a6caa7e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CAEN_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAEN_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAEN_1.2.0.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: 26 Package: CAFE Version: 1.30.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 127d9fb1c15f438eba154e00aa4310a8 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: RELEASE_3_14 git_last_commit: 95b0e67 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CAFE_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAFE_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAFE_1.30.0.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: 159 Package: CAGEfightR Version: 1.14.0 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: a6d6f0be628b2137e2a8a14e08825f68 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: RELEASE_3_14 git_last_commit: 6c27321 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CAGEfightR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAGEfightR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAGEfightR_1.14.0.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 suggestsMe: nanotubes dependencyCount: 149 Package: cageminer Version: 1.0.0 Depends: R (>= 4.1) Imports: ggplot2, ggbio, ggtext, GenomeInfoDb, GenomicRanges, IRanges, reshape2, methods, BioNERO Suggests: testthat (>= 3.0.0), SummarizedExperiment, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: cd4b017e16e66621c549a26cb545b894 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: RELEASE_3_14 git_last_commit: 32c8304 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cageminer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cageminer_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cageminer_1.0.0.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: 234 Package: CAGEr Version: 2.0.2 Depends: methods, MultiAssayExperiment, R (>= 4.1.0) Imports: BiocGenerics, BiocParallel, BSgenome, 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: 12c79802c2ae6ad10acb68ae5a478161 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: RELEASE_3_14 git_last_commit: 9218aa9 git_last_commit_date: 2021-11-16 Date/Publication: 2021-11-18 source.ver: src/contrib/CAGEr_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAGEr_2.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/CAGEr_2.0.2.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: seqPattern dependencyCount: 102 Package: calm Version: 1.8.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 27b19c46d7cb6783ab40ccd0094bd0f4 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: RELEASE_3_14 git_last_commit: 7fdb151 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/calm_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/calm_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/calm_1.8.0.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: 1.50.0 Depends: R (>= 2.1.0), methods, Biobase, xcms (>= 1.13.5) Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics Enhances: Rmpi, snow License: GPL (>= 2) Archs: i386, x64 MD5sum: 2bab737c033818909e9cb6f72d4d2337 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: RELEASE_3_14 git_last_commit: 1b7c510 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CAMERA_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAMERA_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAMERA_1.50.0.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf, vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf, vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf vignetteTitles: Molecule Identification with CAMERA, Atom count expectations with compoundQuantiles, Isotope pattern validation with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS, PtH2O2lipids suggestsMe: cliqueMS, msPurity, RMassBank, mtbls2 dependencyCount: 126 Package: canceR Version: 1.28.04 Depends: R (>= 4.1), tcltk, cgdsr Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, graphics, stats, utils, grDevices Suggests: testthat (>= 3.1), knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: f66345191c7f3c76066987ee7081162e 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: RELEASE_3_14 git_last_commit: cc36858 git_last_commit_date: 2022-03-12 Date/Publication: 2022-03-13 source.ver: src/contrib/canceR_1.28.04.tar.gz win.binary.ver: bin/windows/contrib/4.1/canceR_1.28.04.zip mac.binary.ver: bin/macosx/contrib/4.1/canceR_1.28.04.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: 154 Package: cancerclass Version: 1.38.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: i386, x64 MD5sum: ae759bf1ed490ac4087d25f1043ee997 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: RELEASE_3_14 git_last_commit: 21a3549 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cancerclass_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cancerclass_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cancerclass_1.38.0.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: CancerInSilico Version: 2.14.0 Depends: R (>= 3.4), Rcpp Imports: methods, utils, graphics, stats LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle, Rtsne, viridis, rgl, gplots License: GPL-2 Archs: i386, x64 MD5sum: c755effeb427d64aae23339c58609e6e NeedsCompilation: yes Title: An R interface for computational modeling of tumor progression Description: The CancerInSilico package provides an R interface for running mathematical models of tumor progresson and generating gene expression data from the results. This package has the underlying models implemented in C++ and the output and analysis features implemented in R. biocViews: ImmunoOncology, MathematicalBiology, SystemsBiology, CellBiology, BiomedicalInformatics, GeneExpression, RNASeq, SingleCell Author: Thomas D. Sherman, Raymond Cheng, Elana J. Fertig Maintainer: Thomas D. Sherman , Elana J. Fertig VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CancerInSilico git_branch: RELEASE_3_14 git_last_commit: 3ebbb60 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CancerInSilico_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CancerInSilico_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CancerInSilico_2.14.0.tgz vignettes: vignettes/CancerInSilico/inst/doc/CancerInSilico.html vignetteTitles: The CancerInSilico Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerInSilico/inst/doc/CancerInSilico.R dependencyCount: 6 Package: CancerSubtypes Version: 1.20.0 Depends: R (>= 4.0), sigclust, NMF Imports: iCluster, cluster, impute, limma, ConsensusClusterPlus, grDevices, survival Suggests: BiocGenerics, knitr, RTCGA.mRNA, rmarkdown License: GPL (>= 2) MD5sum: d3b815c69cda7b0af2c9fa786ff61673 NeedsCompilation: no Title: Cancer subtypes identification, validation and visualization based on multiple genomic data sets Description: CancerSubtypes integrates the current common computational biology methods for cancer subtypes identification and provides a standardized framework for cancer subtype analysis based multi-omics data, such as gene expression, miRNA expression, DNA methylation and others. biocViews: Clustering, Software, Visualization, GeneExpression Author: Taosheng Xu [aut, cre] Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/CancerSubtypes VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/CancerSubtypes/issues git_url: https://git.bioconductor.org/packages/CancerSubtypes git_branch: RELEASE_3_14 git_last_commit: 1980bae git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CancerSubtypes_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CancerSubtypes_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CancerSubtypes_1.20.0.tgz vignettes: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.html vignetteTitles: CancerSubtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.R dependencyCount: 73 Package: CAnD Version: 1.26.0 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 67e81bc784efe5ec69345035b3b0ea5a NeedsCompilation: no Title: Perform Chromosomal Ancestry Differences (CAnD) Analyses Description: Functions to perform the CAnD test on a set of ancestry proportions. For a particular ancestral subpopulation, a user will supply the estimated ancestry proportion for each sample, and each chromosome or chromosomal segment of interest. A p-value for each chromosome as well as an overall CAnD p-value will be returned for each test. Plotting functions are also available. biocViews: Genetics, StatisticalMethod, GeneticVariability, SNP Author: Caitlin McHugh, Timothy Thornton Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/CAnD git_branch: RELEASE_3_14 git_last_commit: b8a49be git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CAnD_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CAnD_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CAnD_1.26.0.tgz vignettes: vignettes/CAnD/inst/doc/CAnD.pdf vignetteTitles: Detecting heterogenity in population structure across chromosomes with the "CAnD" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAnD/inst/doc/CAnD.R dependencyCount: 41 Package: caOmicsV Version: 1.24.0 Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2) License: GPL (>=2.0) MD5sum: c425d3d60548cc868602c1b47ed7e6af NeedsCompilation: no Title: Visualization of multi-dimentional cancer genomics data Description: caOmicsV package provides methods to visualize multi-dimentional cancer genomics data including of patient information, gene expressions, DNA methylations, DNA copy number variations, and SNP/mutations in matrix layout or network layout. biocViews: ImmunoOncology, Visualization, Network, RNASeq Author: Henry Zhang Maintainer: Henry Zhang git_url: https://git.bioconductor.org/packages/caOmicsV git_branch: RELEASE_3_14 git_last_commit: 8813875 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/caOmicsV_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/caOmicsV_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/caOmicsV_1.24.0.tgz vignettes: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.pdf vignetteTitles: Intrudoction_to_caOmicsV hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.R dependencyCount: 14 Package: Cardinal Version: 2.12.0 Depends: BiocGenerics, BiocParallel, EBImage, graphics, methods, S4Vectors (>= 0.27.3), stats, ProtGenerics Imports: Biobase, dplyr, irlba, lattice, Matrix, matter, magrittr, mclust, nlme, parallel, signal, sp, stats4, utils, viridisLite Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: a8cc9f97974ba645f7b72324f96dfc38 NeedsCompilation: yes 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 A. Bemis Maintainer: Kylie A. Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cardinal git_branch: RELEASE_3_14 git_last_commit: a9cad46 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Cardinal_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Cardinal_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Cardinal_2.12.0.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal-2-guide.html, vignettes/Cardinal/inst/doc/Cardinal-2-stats.html vignetteTitles: 1. Cardinal 2: User guide for mass spectrometry imaging analysis, 2. Cardinal 2: Statistical methods for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cardinal/inst/doc/Cardinal-2-guide.R, vignettes/Cardinal/inst/doc/Cardinal-2-stats.R dependsOnMe: CardinalWorkflows dependencyCount: 65 Package: CARNIVAL Version: 2.4.0 Depends: R (>= 4.0) Imports: readr, stringr, lpSolve, igraph, dplyr, rjson, rmarkdown, methods Suggests: knitr, testthat (>= 2.1.0) License: GPL-3 MD5sum: 12c9be16392216f2e85b39283f8416b9 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], Olga Ivanova [cre] Maintainer: Olga Ivanova 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: RELEASE_3_14 git_last_commit: c22da3e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CARNIVAL_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CARNIVAL_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CARNIVAL_2.4.0.tgz vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html vignetteTitles: narray Usage Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R importsMe: cosmosR dependencyCount: 60 Package: casper Version: 2.28.0 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) Archs: i386, x64 MD5sum: 030a39739e0b826356cb7a879d47fc51 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: RELEASE_3_14 git_last_commit: 479cb8d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/casper_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/casper_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/casper_2.28.0.tgz 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: 111 Package: CATALYST Version: 1.18.1 Depends: R (>= 4.0), SingleCellExperiment Imports: circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot, data.table, dplyr, drc, flowCore, FlowSOM, ggplot2, ggrepel, ggridges, graphics, grDevices, grid, gridExtra, magrittr, 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: 6f42fd0972ae33e304e1931a8e7a133e NeedsCompilation: no Title: Cytometry dATa anALYSis Tools Description: Mass cytometry (CyTOF) uses heavy metal isotopes rather than fluorescent tags as reporters to label antibodies, thereby substantially decreasing spectral overlap and allowing for examination of over 50 parameters at the single cell level. While spectral overlap is significantly less pronounced in CyTOF than flow cytometry, spillover due to detection sensitivity, isotopic impurities, and oxide formation can impede data interpretability. We designed CATALYST (Cytometry dATa anALYSis Tools) to provide a pipeline for preprocessing of cytometry data, including i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. biocViews: Clustering, 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: RELEASE_3_14 git_last_commit: efeedac git_last_commit_date: 2022-01-14 Date/Publication: 2022-01-16 source.ver: src/contrib/CATALYST_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/CATALYST_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/CATALYST_1.18.1.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: cytofWorkflow suggestsMe: diffcyt, imcRtools, treekoR dependencyCount: 236 Package: Category Version: 2.60.0 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: 056a39817aec1c5affa4548c9c011e89 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: RELEASE_3_14 git_last_commit: 55210d8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Category_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Category_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Category_2.60.0.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, ppiStats, scTensor suggestsMe: qpgraph, RnBeads, maGUI dependencyCount: 58 Package: categoryCompare Version: 1.38.0 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: b76e25e8043fd698566d357f73ea394d 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: RELEASE_3_14 git_last_commit: 13d544a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/categoryCompare_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/categoryCompare_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/categoryCompare_1.38.0.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: 86 Package: CausalR Version: 1.26.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 3a27215a84189f0d3ea57d0aeaea7be3 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: RELEASE_3_14 git_last_commit: 565fa99 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CausalR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CausalR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CausalR_1.26.0.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: 11 Package: cbaf Version: 1.16.0 Depends: R (>= 3.5.0) Imports: BiocFileCache, RColorBrewer, cgdsr, genefilter, gplots, grDevices, stats, utils, openxlsx Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: f5e1d3daac0c23b31fc8a3773b1a62b1 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, 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: RELEASE_3_14 git_last_commit: 34f89cc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cbaf_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cbaf_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cbaf_1.16.0.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: 80 Package: cBioPortalData Version: 2.6.1 Depends: R (>= 4.0.0), AnVIL, 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: 24908e1d5e85bbc6b2ce37d8b2de40e3 NeedsCompilation: no Title: Exposes and makes available data from the cBioPortal web resources Description: The cBioPortalData package takes compressed resources from repositories such as cBioPortal and assembles a MultiAssayExperiment object with Bioconductor classes. biocViews: Software, Infrastructure, ThirdPartyClient Author: Levi Waldron [aut], Marcel Ramos [aut, cre] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/cBioPortalData/issues git_url: https://git.bioconductor.org/packages/cBioPortalData git_branch: RELEASE_3_14 git_last_commit: d97dfe4 git_last_commit_date: 2022-01-27 Date/Publication: 2022-01-30 source.ver: src/contrib/cBioPortalData_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/cBioPortalData_2.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/cBioPortalData_2.6.1.tgz vignettes: vignettes/cBioPortalData/inst/doc/cBioPortalData.html, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.html, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.html vignetteTitles: cBioPortal User Guide, cBioPortal Data Build Errors, cBioPortal Quick-start Guide 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 dependencyCount: 118 Package: cbpManager Version: 1.2.2 Depends: shiny, shinydashboard Imports: utils, DT, htmltools, vroom, plyr, dplyr, magrittr, jsonlite, rapportools, basilisk, reticulate, shinyBS, shinycssloaders, rintrojs, markdown Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE MD5sum: 0914b18e54a48564b2402018bdd0aec6 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: RELEASE_3_14 git_last_commit: fd7d9e9 git_last_commit_date: 2021-12-02 Date/Publication: 2021-12-07 source.ver: src/contrib/cbpManager_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/cbpManager_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/cbpManager_1.2.2.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: 85 Package: ccfindR Version: 1.14.0 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: i386, x64 MD5sum: e65c8857f94648cfbfd70b7a0cb517bf 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: RELEASE_3_14 git_last_commit: 316c420 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ccfindR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ccfindR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ccfindR_1.14.0.tgz 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: 38 Package: ccmap Version: 1.20.0 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: 66edb0f59935462c416e27742723da1b 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: RELEASE_3_14 git_last_commit: d35437b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/ccmap_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ccmap_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ccmap_1.20.0.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: CCPROMISE Version: 1.20.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: dc11ca9885669dd35b3b88f247e04bc0 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: RELEASE_3_14 git_last_commit: 2271cab git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CCPROMISE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CCPROMISE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CCPROMISE_1.20.0.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: 1.30.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: 043591e4acd9f19d793bb0963a429f59 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: RELEASE_3_14 git_last_commit: 39d80d1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ccrepe_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ccrepe_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ccrepe_1.30.0.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: celaref Version: 1.12.0 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: 557a49da40f70cfa4fa338fe964b01de 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: RELEASE_3_14 git_last_commit: 45b360b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/celaref_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/celaref_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/celaref_1.12.0.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: 79 Package: celda Version: 1.10.0 Depends: R (>= 4.0) 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, SingleCellExperiment, dbscan, DelayedArray, stringr, Matrix, ComplexHeatmap, multipanelfigure, circlize LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr, BiocManager, BiocStyle, TENxPBMCData, singleCellTK, M3DExampleData License: MIT + file LICENSE Archs: i386, x64 MD5sum: a52300341b9c3b96f7e2bd86a3e64636 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 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: RELEASE_3_14 git_last_commit: fb720ad git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/celda_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/celda_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/celda_1.10.0.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: singleCellTK dependencyCount: 137 Package: CellaRepertorium Version: 1.4.0 Depends: R (>= 4.0) Imports: dplyr, tibble, stringr, Biostrings, Rcpp, reshape2, methods, rlang (>= 0.3), purrr, Matrix, S4Vectors, BiocGenerics, tidyr, forcats, progress, stats, utils LinkingTo: Rcpp Suggests: testthat, readr, knitr, rmarkdown, ggplot2, BiocStyle, ggdendro, broom, lme4, RColorBrewer, SingleCellExperiment, scater, broom.mixed, cowplot, igraph, ggraph License: GPL-3 Archs: i386, x64 MD5sum: c1a30c1ccd633056b39341a429119afb 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 git_url: https://git.bioconductor.org/packages/CellaRepertorium git_branch: RELEASE_3_14 git_last_commit: ee7f173 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CellaRepertorium_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellaRepertorium_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellaRepertorium_1.4.0.tgz vignettes: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.html, vignettes/CellaRepertorium/inst/doc/cr-overview.html, vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.html, vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.html vignetteTitles: Clustering and differential usage of repertoire CDR3 sequences, An Introduction to CellaRepertorium, Quality control and Exploration of UMI-based repertoire data, Combining Repertoire with Expression with SingleCellExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.R, vignettes/CellaRepertorium/inst/doc/cr-overview.R, vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.R, vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.R dependencyCount: 49 Package: CellBarcode Version: 1.0.0 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 LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: ddb59a66629290d7298a9bda1c35cb98 NeedsCompilation: yes Title: Cellular DNA Barcode Analysis toolkit Description: This package performs Cellular DNA Barcode (genetic lineage tracing) analysis. The package can handle all kinds of DNA barcodes, as long as the barcode within a single sequencing read and has a pattern which can be matched by a regular expression. This package can handle barcode with flexible length, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing of some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing and meta genome data. biocViews: Preprocessing, QualityControl, Sequencing, CRISPR Author: Wenjie Sun [cre], Anne-Marie Lyne [aut], Leila Perie [aut] Maintainer: Wenjie Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellBarcode git_branch: RELEASE_3_14 git_last_commit: 62e29f1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CellBarcode_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellBarcode_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellBarcode_1.0.0.tgz vignettes: vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.html vignetteTitles: UMI_Barcode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.R dependencyCount: 82 Package: cellbaseR Version: 1.18.0 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: 9c19a981d6df57500982783ed360b9c6 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: RELEASE_3_14 git_last_commit: 5598bf6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cellbaseR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellbaseR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellbaseR_1.18.0.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: 64 Package: CellBench Version: 1.10.0 Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats, tibble, utils Imports: 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: 66bec3a1aed57744e5b8bff4bbacaa57 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 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: RELEASE_3_14 git_last_commit: c1d1e03 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CellBench_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellBench_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellBench_1.10.0.tgz vignettes: vignettes/CellBench/inst/doc/DataManipulation.pdf, vignettes/CellBench/inst/doc/TidyversePatterns.pdf, vignettes/CellBench/inst/doc/CellBenchCaseStudy.html, vignettes/CellBench/inst/doc/Introduction.html, vignettes/CellBench/inst/doc/Timing.html, vignettes/CellBench/inst/doc/WritingWrappers.html vignetteTitles: Data Manipulation, Tidyverse Patterns, CellBenchCaseStudy.html, Introduction, 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 dependencyCount: 78 Package: cellHTS2 Version: 2.58.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: GSEABase, Category, stats4, BiocGenerics Suggests: ggplot2 License: Artistic-2.0 MD5sum: 6bab8de97f2aafbafcd22d1541060815 NeedsCompilation: no Title: Analysis of cell-based screens - revised version of cellHTS Description: This package provides tools for the analysis of high-throughput assays that were performed in microtitre plate formats (including but not limited to 384-well plates). The functionality includes data import and management, normalisation, quality assessment, replicate summarisation and statistical scoring. A webpage that provides a detailed graphical overview over the data and analysis results is produced. In our work, we have applied the package to RNAi screens on fly and human cells, and for screens of yeast libraries. See ?cellHTS2 for a brief introduction. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Visualization Author: Ligia Bras, Wolfgang Huber , Michael Boutros , Gregoire Pau , Florian Hahne Maintainer: Joseph Barry URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber git_url: https://git.bioconductor.org/packages/cellHTS2 git_branch: RELEASE_3_14 git_last_commit: 5e1ff80 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cellHTS2_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellHTS2_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellHTS2_2.58.0.tgz vignettes: vignettes/cellHTS2/inst/doc/cellhts2.pdf, vignettes/cellHTS2/inst/doc/cellhts2Complete.pdf, vignettes/cellHTS2/inst/doc/twoChannels.pdf, vignettes/cellHTS2/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Main vignette (complete version): End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: enhancer-suppressor screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS2/inst/doc/cellhts2.R, vignettes/cellHTS2/inst/doc/cellhts2Complete.R, vignettes/cellHTS2/inst/doc/twoChannels.R, vignettes/cellHTS2/inst/doc/twoWay.R dependsOnMe: imageHTS, staRank importsMe: gespeR, RNAinteract suggestsMe: bioassayR dependencyCount: 90 Package: CelliD Version: 1.2.1 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: i386, x64 MD5sum: 8b544347eb1b686fed841ff68a23d8eb 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: RELEASE_3_14 git_last_commit: 477d6e9 git_last_commit_date: 2022-01-08 Date/Publication: 2022-01-09 source.ver: src/contrib/CelliD_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/CelliD_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/CelliD_1.2.1.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: 182 Package: cellity Version: 1.22.0 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: 3e36370cb4e36c017b2b307cee7c2ec2 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: RELEASE_3_14 git_last_commit: f0807a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cellity_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellity_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellity_1.22.0.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: 85 Package: CellMapper Version: 1.20.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 8567aa6122c0e1f8f89f0342c6c92365 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: RELEASE_3_14 git_last_commit: d49bd75 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CellMapper_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellMapper_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellMapper_1.20.0.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: 1.2.0 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: b841e4fa1960c4b8ac78284b91860d03 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: RELEASE_3_14 git_last_commit: 18f748f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cellmigRation_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellmigRation_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellmigRation_1.2.0.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: 136 Package: CellMixS Version: 1.10.0 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: 6e8df9a53695517654a96e605e3c750b 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: RELEASE_3_14 git_last_commit: 8690b35 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CellMixS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellMixS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellMixS_1.10.0.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: 94 Package: CellNOptR Version: 1.40.0 Depends: R (>= 4.0.0), RBGL, graph, methods, hash, RCurl, Rgraphviz, XML, ggplot2 Imports: igraph, stringi, stringr, Suggests: data.table, dplyr, tidyr, readr, RUnit, BiocGenerics, Enhances: doParallel, plyr, foreach License: GPL-3 Archs: i386, x64 MD5sum: 4d632bea136334508a3e8d15d35153f5 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: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve, E.Gjerga, A.Gabor Maintainer: A.Gabor SystemRequirements: Graphviz version >= 2.2 git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: RELEASE_3_14 git_last_commit: 5643aae git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CellNOptR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellNOptR_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellNOptR_1.40.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf vignetteTitles: Main vignette:Playing with networks using 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: 52 Package: cellscape Version: 1.18.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0), plyr (>= 1.8.3), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 5894dc5f2511cccc7c5507012b462a0d 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: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellscape git_branch: RELEASE_3_14 git_last_commit: 6d75da9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cellscape_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellscape_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellscape_1.18.0.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: 36 Package: CellScore Version: 1.14.0 Depends: R (>= 3.5.0) 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) Suggests: hgu133plus2CellScore, knitr License: GPL-3 MD5sum: 8885737af75fae13255b9495a5b2ba24 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, Katerina Taskova Maintainer: Nancy Mah VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellScore git_branch: RELEASE_3_14 git_last_commit: f1b13c0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CellScore_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellScore_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellScore_1.14.0.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 dependencyCount: 16 Package: CellTrails Version: 1.12.0 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: be83adfa623c800d6eb950dc3e69094a 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: RELEASE_3_14 git_last_commit: c5f714a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CellTrails_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CellTrails_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CellTrails_1.12.0.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: 76 Package: cellTree Version: 1.24.0 Depends: R (>= 3.3), topGO Imports: topicmodels, slam, maptpx, igraph, xtable, gplots Suggests: BiocStyle, knitr, HSMMSingleCell, biomaRt, org.Hs.eg.db, Biobase, tools License: Artistic-2.0 MD5sum: 393535f0a9b4c0f9b388dfffae6d38ac NeedsCompilation: no Title: Inference and visualisation of Single-Cell RNA-seq data as a hierarchical tree structure Description: This packages computes a Latent Dirichlet Allocation (LDA) model of single-cell RNA-seq data and builds a compact tree modelling the relationship between individual cells over time or space. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology, GO, TimeCourse, Microarray Author: David duVerle [aut, cre], Koji Tsuda [aut] Maintainer: David duVerle URL: http://tsudalab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellTree git_branch: RELEASE_3_14 git_last_commit: 2a079ea git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cellTree_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cellTree_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cellTree_1.24.0.tgz vignettes: vignettes/cellTree/inst/doc/cellTree-vignette.pdf vignetteTitles: Inference and visualisation of Single-Cell RNA-seq Data data as a hierarchical tree structure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellTree/inst/doc/cellTree-vignette.R dependencyCount: 69 Package: CEMiTool Version: 1.18.1 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: f2a4b7bbddd706f5fb240940194179a1 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: RELEASE_3_14 git_last_commit: d0af180 git_last_commit_date: 2021-10-28 Date/Publication: 2021-10-28 source.ver: src/contrib/CEMiTool_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/CEMiTool_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/CEMiTool_1.18.1.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: 186 Package: censcyt Version: 1.2.1 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: 27b3d98a0c3c0c72f951d6b84bf80edb 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: RELEASE_3_14 git_last_commit: f940257 git_last_commit_date: 2022-03-28 Date/Publication: 2022-03-29 source.ver: src/contrib/censcyt_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/censcyt_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/censcyt_1.2.1.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: 230 Package: Cepo Version: 1.0.0 Depends: GSEABase, R (>= 4.1) Imports: DelayedMatrixStats, DelayedArray, HDF5Array, S4Vectors, methods, SingleCellExperiment, SummarizedExperiment, ggplot2, rlang, grDevices, patchwork, reshape2, BiocParallel, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, covr, UpSetR, scater, scMerge, fgsea, escape, pheatmap, patchwork License: MIT + file LICENSE MD5sum: 3b5b0478b4baf5ac2a2ca07bb9eebcb8 NeedsCompilation: no Title: Cepo for the identification of differentially stable genes Description: Defining the identity of a cell is fundamental to understand the heterogeneity of cells to various environmental signals and perturbations. We present Cepo, a new method to explore cell identities from single-cell RNA-sequencing data using differential stability as a new metric to define cell identity genes. Cepo computes cell-type specific gene statistics pertaining to differential stable gene expression. biocViews: Classification, GeneExpression, SingleCell, Software, Sequencing, DifferentialExpression Author: Hani Jieun Kim [aut, cre] (), Kevin Wang [aut] () Maintainer: Hani Jieun Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cepo git_branch: RELEASE_3_14 git_last_commit: c43e0f5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Cepo_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Cepo_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Cepo_1.0.0.tgz vignettes: vignettes/Cepo/inst/doc/cepo.html vignetteTitles: Cepo method for differential stability analysis of scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Cepo/inst/doc/cepo.R importsMe: scClassify dependencyCount: 101 Package: ceRNAnetsim Version: 1.6.99 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: c53f163530812bab7496f0939f78f7af 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: RELEASE_3_14 git_last_commit: 0d0d7ac git_last_commit_date: 2022-03-03 Date/Publication: 2022-03-06 source.ver: src/contrib/ceRNAnetsim_1.6.99.tar.gz win.binary.ver: bin/windows/contrib/4.1/ceRNAnetsim_1.6.99.zip mac.binary.ver: bin/macosx/contrib/4.1/ceRNAnetsim_1.6.99.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: 65 Package: CeTF Version: 1.6.0 Depends: R (>= 4.0), methods Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, dplyr, GenomicTools, GenomicTools.fileHandler, GGally, ggnetwork, ggplot2, ggpubr, ggrepel, graphics, grid, igraph, Matrix, network, Rcpp, RCy3, stats, SummarizedExperiment, S4Vectors, utils LinkingTo: Rcpp, RcppArmadillo Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 52a43118e46ce805703a2524d49987ac 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: RELEASE_3_14 git_last_commit: 65513aa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CeTF_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CeTF_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CeTF_1.6.0.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: 223 Package: CexoR Version: 1.32.0 Depends: R (>= 4.0.0), 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: fdb3b3db4759aefc721fa89dc2dfe17c 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: RELEASE_3_14 git_last_commit: 422af9e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CexoR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CexoR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CexoR_1.32.0.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: 99 Package: CFAssay Version: 1.28.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: 122625747bad213e93cb04303ad505bd 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: RELEASE_3_14 git_last_commit: 608088f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CFAssay_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CFAssay_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CFAssay_1.28.0.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: cfDNAPro Version: 1.0.0 Depends: R (>= 4.0), magrittr (>= 1.5.0), Imports: 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) Suggests: scales, ggpubr, knitr (>= 1.23), rmarkdown (>= 1.14), devtools (>= 2.3.0), BiocStyle, testthat License: GPL-3 MD5sum: a2dbfbd9244025c725d21482fd92fb02 NeedsCompilation: no Title: cfDNAPro Helps Characterise and Visualise Whole Genome Sequencing Data from Liquid Biopsy Description: cfDNA fragment size metrics are important features for utilizing liquid biopsy in tumor early detection, diagnosis, therapy personlization and monitoring. Analyzing and visualizing insert size metrics could be time intensive. This package intends to simplify this exploration process, and it offers two sets of functions for data characterization and data visualization. biocViews: Visualization, Sequencing, WholeGenome Author: Haichao Wang [aut, cre], Nitzan Rosenfeld [ctb], Hui Zhao [ctb], Christopher Smith [ctb] Maintainer: Haichao Wang URL: https://github.com/hw538/cfDNAPro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cfDNAPro git_branch: RELEASE_3_14 git_last_commit: 26d33b2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cfDNAPro_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cfDNAPro_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cfDNAPro_1.0.0.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: 72 Package: CGEN Version: 3.30.0 Depends: R (>= 4.0), survival, mvtnorm Imports: stats, graphics, utils, grDevices Suggests: cluster License: GPL-2 + file LICENSE Archs: i386, x64 MD5sum: 95a370acee1c4c18d1659380932a6f5e 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: RELEASE_3_14 git_last_commit: 15192e1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CGEN_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGEN_3.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGEN_3.30.0.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: 1.54.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: dbd7d35e770852e0205bc3d9f170cbf4 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: RELEASE_3_14 git_last_commit: 88afa50 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CGHbase_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGHbase_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGHbase_1.54.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak importsMe: CGHnormaliter, QDNAseq, ragt2ridges dependencyCount: 9 Package: CGHcall Version: 2.56.0 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: 65c32d5f742cab3e13b9223349df18d5 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: RELEASE_3_14 git_last_commit: 9d6ed61 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CGHcall_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGHcall_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGHcall_2.56.0.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: 14 Package: cghMCR Version: 1.52.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 044bab4c35c79fe590e35f962d59a049 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: RELEASE_3_14 git_last_commit: 669771b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cghMCR_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cghMCR_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cghMCR_1.52.0.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: 57 Package: CGHnormaliter Version: 1.48.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 4f371a23f518cfe50810761d12c9a1b9 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: RELEASE_3_14 git_last_commit: 1d2812a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CGHnormaliter_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGHnormaliter_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGHnormaliter_1.48.0.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: 15 Package: CGHregions Version: 1.52.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 7ae1a558a44f38ed67a2836f5c0767cc 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: RELEASE_3_14 git_last_commit: 1582418 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CGHregions_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CGHregions_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CGHregions_1.52.0.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: 10 Package: ChAMP Version: 2.24.0 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest, DT, RPMM Imports: prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b4.hg19, limma, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq,missMethyl,kpmt,ggplot2, GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat Suggests: knitr,rmarkdown License: GPL-3 MD5sum: 689e066cc9fdec4aa2b968b260e3435f 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 git_url: https://git.bioconductor.org/packages/ChAMP git_branch: RELEASE_3_14 git_last_commit: 7ba19da git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChAMP_2.24.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ChAMP_2.24.0.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.html vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R suggestsMe: GeoTcgaData dependencyCount: 253 Package: ChemmineOB Version: 1.32.0 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager, rmarkdown Enhances: ChemmineR (>= 2.13.0) License: file LICENSE Archs: i386, x64 MD5sum: b04e68388aa6c7103040dc50a1b23949 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: RELEASE_3_14 git_last_commit: 01c4d21 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChemmineOB_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChemmineOB_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChemmineOB_1.32.0.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: 3.46.0 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 Enhances: ChemmineOB License: Artistic-2.0 Archs: i386, x64 MD5sum: 446b83c0452bef89f10af8f74643d8ad 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: RELEASE_3_14 git_last_commit: a998b14 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChemmineR_3.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChemmineR_3.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChemmineR_3.46.0.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, customCMPdb, eiR, fmcsR, MetID, Rcpi, DRviaSPCN, MetaDBparse, uCAREChemSuiteCLI suggestsMe: ChemmineOB, xnet dependencyCount: 62 Package: ChIC Version: 1.14.0 Depends: spp, R (>= 3.6) Imports: ChIC.data (>= 1.11.1), caTools, methods, GenomicRanges, IRanges, parallel, progress, randomForest, caret, grDevices, stats, utils, graphics, S4Vectors, BiocGenerics, genomeIntervals, Rsamtools License: GPL-2 MD5sum: 8619f7967122d995390cf501b89d8f18 NeedsCompilation: no Title: Quality Control Pipeline for ChIP-Seq Data Description: Quality control (QC) pipeline for ChIP-seq data using a comprehensive set of QC metrics, including previously proposed metrics as well as novel ones, based on local characteristics of the enrichment profile. The package provides functions to calculate a set of QC metrics, a compendium with reference values and machine learning models to score sample quality. biocViews: ChIPSeq, QualityControl Author: Carmen Maria Livi Maintainer: Carmen Maria Livi git_url: https://git.bioconductor.org/packages/ChIC git_branch: RELEASE_3_14 git_last_commit: 14ce4c1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChIC_1.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ChIC_1.14.0.tgz vignettes: vignettes/ChIC/inst/doc/ChIC-Vignette.pdf vignetteTitles: ChIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIC/inst/doc/ChIC-Vignette.R dependencyCount: 111 Package: Chicago Version: 1.22.0 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: a459b158b25c6e5943c6a0ece2c4ec4c 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: RELEASE_3_14 git_last_commit: c377e14 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Chicago_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Chicago_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Chicago_1.22.0.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: 70 Package: chimeraviz Version: 1.20.0 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: c626cc86f9ffba337bf2fa3ab573ea43 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: RELEASE_3_14 git_last_commit: 2e8573d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/chimeraviz_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chimeraviz_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chimeraviz_1.20.0.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: 163 Package: ChIPanalyser Version: 1.16.0 Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll, parallel Imports: methods, IRanges, S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR, BiocManager,GenomeInfoDb Suggests: BSgenome.Dmelanogaster.UCSC.dm3,knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 3accd5257e1b7657ab639ddcafdc292e NeedsCompilation: no Title: ChIPanalyser: Predicting Transcription Factor Binding Sites Description: Based on a statistical thermodynamic framework, ChIPanalyser tries to produce ChIP-seq like profile. The model relies on four consideration: TF binding sites can be scored using a Position weight Matrix, DNA accessibility plays a role in Transcription Factor binding, binding profiles are dependant on the number of transcription factors bound to DNA and finally binding energy (another way of describing PWM's) or binding specificity should be modulated (hence the introduction of a binding specificity modulator). The end result of ChIPanalyser is to produce profiles simulating real ChIP-seq profile and provide accuracy measurements of these predicted profiles after being compared to real ChIP-seq data. The ultimate goal is to produce ChIP-seq like profiles predicting ChIP-seq like profile to circumvent the need to produce costly ChIP-seq experiments. 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: RELEASE_3_14 git_last_commit: b95956a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChIPanalyser_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPanalyser_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPanalyser_1.16.0.tgz vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf vignetteTitles: ChIPanalyser User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R dependencyCount: 53 Package: ChIPComp Version: 1.24.0 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 Archs: i386, x64 MD5sum: b005abf06492fbde95c3169f31739b19 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: RELEASE_3_14 git_last_commit: 854d1b5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChIPComp_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPComp_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPComp_1.24.0.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: 48 Package: chipenrich Version: 2.18.0 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: 8eec4b3bf0eee22c8d4c95248dbfe5fa 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: Raymond G. Cavalcante VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipenrich git_branch: RELEASE_3_14 git_last_commit: 3af75df git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/chipenrich_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chipenrich_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chipenrich_2.18.0.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: 146 Package: ChIPexoQual Version: 1.18.0 Depends: R (>= 3.4.0), 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: f7b00fe3e4f3447d1ce345e580de66e0 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: RELEASE_3_14 git_last_commit: 3746d89 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChIPexoQual_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPexoQual_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPexoQual_1.18.0.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: 151 Package: ChIPpeakAnno Version: 3.28.1 Depends: R (>= 3.5), methods, IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), S4Vectors (>= 0.17.25) Imports: AnnotationDbi, BiocGenerics (>= 0.1.0), Biostrings (>= 2.47.6), DBI, dplyr, ensembldb, GenomeInfoDb, GenomicAlignments, GenomicFeatures, RBGL, Rsamtools, SummarizedExperiment, VennDiagram, biomaRt, ggplot2, grDevices, graph, graphics, grid, InteractionSet, KEGGREST, matrixStats, multtest, regioneR, rtracklayer, stats, utils 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, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, GO.db, gplots, UpSetR, knitr, rmarkdown, testthat, trackViewer, motifStack, OrganismDbi License: GPL (>= 2) MD5sum: 985d8b0fe2a85723d014186805cd9793 NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges Description: The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs). This package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPpeakAnno git_branch: RELEASE_3_14 git_last_commit: b25b79c git_last_commit_date: 2022-02-04 Date/Publication: 2022-02-06 source.ver: src/contrib/ChIPpeakAnno_3.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPpeakAnno_3.28.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPpeakAnno_3.28.1.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html, vignettes/ChIPpeakAnno/inst/doc/FAQs.html, vignettes/ChIPpeakAnno/inst/doc/pipeline.html, vignettes/ChIPpeakAnno/inst/doc/quickStart.html vignetteTitles: ChIPpeakAnno Vignette, ChIPpeakAnno FAQs, ChIPpeakAnno Annotation Pipeline, ChIPpeakAnno Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R, vignettes/ChIPpeakAnno/inst/doc/FAQs.R, vignettes/ChIPpeakAnno/inst/doc/pipeline.R, vignettes/ChIPpeakAnno/inst/doc/quickStart.R dependsOnMe: REDseq, csawBook importsMe: ATACseqQC, DEScan2, GUIDEseq suggestsMe: R3CPET, seqsetvis, chipseqDB dependencyCount: 122 Package: ChIPQC Version: 1.30.0 Depends: R (>= 3.0.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19) 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, BiocParallel, 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: 6b9bb577a22d1089a62166162c9bca2f 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: RELEASE_3_14 git_last_commit: 566bb1a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChIPQC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPQC_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPQC_1.30.0.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: 163 Package: ChIPseeker Version: 1.30.3 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocGenerics, boot, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, gtools, methods, plotrix, dplyr, parallel, magrittr, RColorBrewer, rtracklayer, S4Vectors, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ReactomePA, org.Hs.eg.db, knitr, rmarkdown, testthat, tibble License: Artistic-2.0 MD5sum: 648c94b53750f6266289d85bf59b2374 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], Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/ChIPseeker VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: RELEASE_3_14 git_last_commit: cce74f0 git_last_commit_date: 2021-12-15 Date/Publication: 2021-12-16 source.ver: src/contrib/ChIPseeker_1.30.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPseeker_1.30.3.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPseeker_1.30.3.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, TCGAWorkflow, cinaR suggestsMe: curatedAdipoChIP dependencyCount: 155 Package: chipseq Version: 1.44.0 Depends: R (>= 2.10), 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 License: Artistic-2.0 Archs: i386, x64 MD5sum: 6cc9ca1ff1cb3320f8f0b006a90b9482 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, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/chipseq git_branch: RELEASE_3_14 git_last_commit: b64d0d2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/chipseq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chipseq_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chipseq_1.44.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.pdf vignetteTitles: A Sample ChIP-Seq analysis workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi, transcriptR dependencyCount: 44 Package: ChIPseqR Version: 1.48.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25) Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 22181ab59721799a6cb9fcd7af093c1d 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: RELEASE_3_14 git_last_commit: c68564e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChIPseqR_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPseqR_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPseqR_1.48.0.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: 53 Package: ChIPsim Version: 1.48.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: 921e96a43476450aebac227485e1e92c 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: RELEASE_3_14 git_last_commit: 5b9f34f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChIPsim_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChIPsim_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChIPsim_1.48.0.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: 44 Package: ChIPXpress Version: 1.38.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 1a93707e44117e5c31f92052aa6caccb 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: RELEASE_3_14 git_last_commit: 22f647d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChIPXpress_1.38.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ChIPXpress_1.38.0.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: 98 Package: chopsticks Version: 1.60.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: dc99952f129b77e3113b7e2f35f1dfca 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: RELEASE_3_14 git_last_commit: d3ad9ab git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/chopsticks_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chopsticks_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chopsticks_1.60.0.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 importsMe: CrypticIBDcheck, rJPSGCS dependencyCount: 10 Package: chromDraw Version: 2.24.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 Archs: i386, x64 MD5sum: 4061239725226412f20e1e4f837e20bb 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: RELEASE_3_14 git_last_commit: 48ae15b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/chromDraw_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromDraw_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chromDraw_2.24.0.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: 17 Package: ChromHeatMap Version: 1.48.0 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: dc62f572346853e45141935113ce361b 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: RELEASE_3_14 git_last_commit: 488d509 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChromHeatMap_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChromHeatMap_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChromHeatMap_1.48.0.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: 72 Package: chromPlot Version: 1.22.0 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: 3f972e1282c9b9c7e34ba0991de2b159 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: RELEASE_3_14 git_last_commit: 3a9a688 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/chromPlot_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromPlot_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chromPlot_1.22.0.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: 74 Package: ChromSCape Version: 1.4.0 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, qualV, stringdist, 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, Sushi, forcats, Rcpp, coop, matrixTests, DelayedArray LinkingTo: Rcpp Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, Signac, future License: GPL-3 Archs: i386, x64 MD5sum: ff44a274dc74192d6f77f875a3f27843 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: 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: RELEASE_3_14 git_last_commit: df9f2c5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ChromSCape_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ChromSCape_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ChromSCape_1.4.0.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: 216 Package: chromstaR Version: 1.20.2 Depends: R (>= 3.3), GenomicRanges, ggplot2, chromstaRData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals, mvtnorm Suggests: knitr, BiocStyle, testthat, biomaRt License: Artistic-2.0 Archs: i386, x64 MD5sum: c202b8ed87459a1865abb1288729400c 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: RELEASE_3_14 git_last_commit: b8ef93b git_last_commit_date: 2021-11-03 Date/Publication: 2021-11-07 source.ver: src/contrib/chromstaR_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromstaR_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.1/chromstaR_1.20.2.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: 79 Package: chromswitch Version: 1.16.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.26.4) Imports: cluster (>= 2.0.6), Biobase (>= 2.36.2), BiocParallel (>= 1.8.2), dplyr (>= 0.5.0), gplots(>= 3.0.1), graphics, grDevices, IRanges (>= 2.4.8), lazyeval (>= 0.2.0), matrixStats (>= 0.52), magrittr (>= 1.5), methods, NMF (>= 0.20.6), rtracklayer (>= 1.36.4), S4Vectors (>= 0.23.19), stats, tidyr (>= 0.6.3) Suggests: BiocStyle, DescTools (>= 0.99.19), devtools (>= 1.13.3), GenomeInfoDb (>= 1.16.0), knitr, rmarkdown, mclust (>= 5.3), testthat License: MIT + file LICENSE MD5sum: a444a864bee5f102dd6ecf1adbfab72e NeedsCompilation: no Title: An R package to detect chromatin state switches from epigenomic data Description: Chromswitch implements a flexible method to detect chromatin state switches between samples in two biological conditions in a specific genomic region of interest given peaks or chromatin state calls from ChIP-seq data. biocViews: ImmunoOncology, MultipleComparison, Transcription, GeneExpression, DifferentialPeakCalling, HistoneModification, Epigenetics, FunctionalGenomics, Clustering Author: Selin Jessa [aut, cre], Claudia L. Kleinman [aut] Maintainer: Selin Jessa URL: https://github.com/sjessa/chromswitch VignetteBuilder: knitr BugReports: https://github.com/sjessa/chromswitch/issues git_url: https://git.bioconductor.org/packages/chromswitch git_branch: RELEASE_3_14 git_last_commit: 0d65abe git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/chromswitch_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromswitch_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chromswitch_1.16.0.tgz vignettes: vignettes/chromswitch/inst/doc/chromswitch_intro.html vignetteTitles: An introduction to `chromswitch` for detecting chromatin state switches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromswitch/inst/doc/chromswitch_intro.R dependencyCount: 102 Package: chromVAR Version: 1.16.0 Depends: R (>= 3.4) 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 Archs: i386, x64 MD5sum: 1558db63b13d795474466ec89912d1c1 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: RELEASE_3_14 git_last_commit: f4109be git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/chromVAR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/chromVAR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/chromVAR_1.16.0.tgz vignettes: vignettes/chromVAR/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromVAR/inst/doc/Introduction.R suggestsMe: Signac dependencyCount: 151 Package: CHRONOS Version: 1.22.0 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: 6599193cd703b4bd3f9055e794b7c16c 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: RELEASE_3_14 git_last_commit: 94f869e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CHRONOS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CHRONOS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CHRONOS_1.22.0.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: 90 Package: cicero Version: 1.12.0 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, rmarkdown, rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr License: MIT + file LICENSE MD5sum: 6784bd8051bd38246da76f61365c0378 NeedsCompilation: no Title: Precict 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: RELEASE_3_14 git_last_commit: 1991cbd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cicero_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cicero_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cicero_1.12.0.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: 173 Package: CIMICE Version: 1.2.1 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: 897511962ff33400bfe44f843bb68eed 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: RELEASE_3_14 git_last_commit: 46eaa9d git_last_commit_date: 2022-03-12 Date/Publication: 2022-03-13 source.ver: src/contrib/CIMICE_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/CIMICE_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/CIMICE_1.2.1.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: 78 Package: CINdex Version: 1.22.0 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: a83c9a204cbac96397391f9502921e77 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: RELEASE_3_14 git_last_commit: c6af6ad git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CINdex_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CINdex_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CINdex_1.22.0.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: 46 Package: circRNAprofiler Version: 1.8.0 Depends: R(>= 4.1.0) 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: 9171dcef2b12f9eeb5373c8bad2fb68e 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: RELEASE_3_14 git_last_commit: 92fcc06 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/circRNAprofiler_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/circRNAprofiler_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/circRNAprofiler_1.8.0.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: 163 Package: cisPath Version: 1.34.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 88d0c6202ba52a512253853ede0e3273 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: RELEASE_3_14 git_last_commit: c20d643 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cisPath_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cisPath_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cisPath_1.34.0.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: 1.6.0 Depends: R (>= 4.0) Imports: SingleCellExperiment (>= 1.8.0), SummarizedExperiment (>= 1.16.0), Matrix, mixtools, cowplot, ggplot2, gridExtra, grid, dbscan, propr, uwot, Rtsne, S4Vectors (>= 0.24.0), igraph, scales, scran (>= 1.14.6), graphics, methods, stats, utils, reshape2, ggridges, randomForest, pheatmap, ggraph, grDevices, rhdf5, rlang Suggests: knitr, rmarkdown, DT, mclust, scater, ExPosition, BiocStyle, pkgdown License: GPL-3 MD5sum: c63795a674f02fc0f3853a9ebdbcceba NeedsCompilation: no 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: RELEASE_3_14 git_last_commit: b774212 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CiteFuse_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CiteFuse_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CiteFuse_1.6.0.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 dependencyCount: 127 Package: ClassifyR Version: 2.14.0 Depends: R (>= 3.5.0), methods, S4Vectors (>= 0.18.0), MultiAssayExperiment (>= 1.6.0), BiocParallel Imports: locfit, grid, utils, plyr, MultiAssayExperiment (>= 1.6.0) Suggests: limma, genefilter, edgeR, car, Rmixmod, ggplot2 (>= 3.0.0), gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu, parathyroidSE, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, randomForest, robustbase, glmnet, class License: GPL-3 MD5sum: 5b2a610795ce44ba71d54a8ad7ddaa8e NeedsCompilation: no 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 in R. There are four stages; Data transformation, feature selection, classifier training, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly carries out a number of cross-validation schemes. Functions for differential expression, 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, John Ormerod, Graham Mann, Jean Yang Maintainer: Dario Strbenac VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: RELEASE_3_14 git_last_commit: 133d20a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ClassifyR_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ClassifyR_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ClassifyR_2.14.0.tgz vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html, vignettes/ClassifyR/inst/doc/wrapper.html vignetteTitles: An Introduction to the ClassifyR Package, Example: Creating a Wrapper Function for the k-NN Classifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R, vignettes/ClassifyR/inst/doc/wrapper.R dependencyCount: 57 Package: cleanUpdTSeq Version: 1.32.0 Depends: R (>= 3.5.0), BSgenome.Drerio.UCSC.danRer7, methods Imports: BSgenome, GenomicRanges, seqinr, e1071, Biostrings, GenomeInfoDb, IRanges, utils, stringr, stats Suggests: BiocStyle, rmarkdown, knitr, RUnit, BiocGenerics (>= 0.1.0) License: GPL-2 MD5sum: d77f7554efbc4d95bd685cacf64a4400 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: RELEASE_3_14 git_last_commit: 4298e78 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cleanUpdTSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cleanUpdTSeq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cleanUpdTSeq_1.32.0.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 importsMe: InPAS dependencyCount: 59 Package: cleaver Version: 1.32.0 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 (>= 2.1.4) License: GPL (>= 3) MD5sum: 61ed89b689e7829d9ac97d1c3c63a8fc 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: RELEASE_3_14 git_last_commit: 375589c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cleaver_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cleaver_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cleaver_1.32.0.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 suggestsMe: RforProteomics dependencyCount: 18 Package: clippda Version: 1.44.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: c85b9e73a296d515e0bf448e3e01c881 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: RELEASE_3_14 git_last_commit: 2d11172 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/clippda_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clippda_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clippda_1.44.0.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: 31 Package: clipper Version: 1.34.0 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS, BiocStyle Enhances: RCy3 License: AGPL-3 MD5sum: f809bd2a83eca24368aa22f1ff876ac4 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: RELEASE_3_14 git_last_commit: 02cc60b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/clipper_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clipper_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clipper_1.34.0.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 suggestsMe: graphite, simPATHy dependencyCount: 111 Package: cliProfiler Version: 1.0.0 Depends: S4Vectors, methods, R (>= 4.1) Imports: dplyr, rtracklayer, GenomicRanges, ggplot2, BSgenome, Biostrings, utils Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: 88d1845ea30e74651286710eb0f04175 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: RELEASE_3_14 git_last_commit: f3e8322 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cliProfiler_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cliProfiler_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cliProfiler_1.0.0.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: 78 Package: cliqueMS Version: 1.8.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, qlcMatrix, matrixStats, methods LinkingTo: Rcpp, BH, RcppArmadillo Suggests: knitr, rmarkdown, testthat, CAMERA License: GPL (>= 2) Archs: i386, x64 MD5sum: 14b0c461bfb3d1a54510d291a01d786a NeedsCompilation: yes Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data Description: Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20), 2019), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite. biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut], Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes [aut], Roger Guimera [aut], Marta Sales-Pardo [aut] Maintainer: Oriol Senan Campos URL: http://cliquems.seeslab.net SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/osenan/cliqueMS/issues git_url: https://git.bioconductor.org/packages/cliqueMS git_branch: RELEASE_3_14 git_last_commit: d840675 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cliqueMS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cliqueMS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cliqueMS_1.8.0.tgz vignettes: vignettes/cliqueMS/inst/doc/annotate_features.html vignetteTitles: Annotating LC/MS data with cliqueMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliqueMS/inst/doc/annotate_features.R dependencyCount: 101 Package: Clomial Version: 1.30.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: bd99847eccc6c4aad7e257b053badfd1 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: RELEASE_3_14 git_last_commit: 1687bb1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Clomial_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Clomial_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Clomial_1.30.0.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: Clonality Version: 1.42.0 Depends: R (>= 2.12.2), DNAcopy Imports: grDevices, graphics, stats, utils Suggests: gdata License: GPL-3 MD5sum: eb9b5b2db711d3c6c66f9ab7826a8ee2 NeedsCompilation: no Title: Clonality testing Description: Statistical tests for clonality versus independence of tumors from the same patient based on their LOH or genomewide copy number profiles biocViews: CopyNumber, Classification, aCGH, Mutations, Diagnosis, metastasis Author: Irina Ostrovnaya Maintainer: Irina Ostrovnaya git_url: https://git.bioconductor.org/packages/Clonality git_branch: RELEASE_3_14 git_last_commit: 3792eeb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Clonality_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Clonality_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Clonality_1.42.0.tgz vignettes: vignettes/Clonality/inst/doc/Clonality.pdf vignetteTitles: Clonality hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clonality/inst/doc/Clonality.R dependencyCount: 5 Package: clonotypeR Version: 1.32.1 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, rmarkdown, RUnit, vegan License: file LICENSE MD5sum: dbb5a4ddfc6b2007259449035fb3d54e NeedsCompilation: no Title: High throughput analysis of T cell antigen receptor sequences Description: High throughput analysis of T cell antigen receptor sequences The genes encoding T cell receptors are created by somatic recombination, generating an immense combination of V, (D) and J segments. Additional processes during the recombination create extra sequence diversity between the V an J segments. Collectively, this hyper-variable region is called the CDR3 loop. The purpose of this package is to process and quantitatively analyse millions of V-CDR3-J combination, called clonotypes, from multiple sequence libraries. biocViews: Sequencing Author: Charles Plessy Maintainer: Charles Plessy URL: http://clonotyper.branchable.com/ VignetteBuilder: knitr BugReports: http://clonotyper.branchable.com/Bugs/ git_url: https://git.bioconductor.org/packages/clonotypeR git_branch: RELEASE_3_14 git_last_commit: 3aedf6d git_last_commit_date: 2021-10-29 Date/Publication: 2021-10-29 source.ver: src/contrib/clonotypeR_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/clonotypeR_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.1/clonotypeR_1.32.1.tgz vignettes: vignettes/clonotypeR/inst/doc/clonotypeR.html vignetteTitles: clonotypeR User's Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R dependencyCount: 1 Package: clst Version: 1.42.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 31bc13f58b0376e175e2b0e3324be51c 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: RELEASE_3_14 git_last_commit: 3c4dc9e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/clst_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clst_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clst_1.42.0.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: 18 Package: clstutils Version: 1.42.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit License: GPL-3 MD5sum: 13105dd95495de6e3fec5d76b0e03320 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: RELEASE_3_14 git_last_commit: d7008cc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/clstutils_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clstutils_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clstutils_1.42.0.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: 1.10.0 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: 454e57ff0be72dc78ee220b9563dfb08 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: RELEASE_3_14 git_last_commit: 75609ff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CluMSID_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CluMSID_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CluMSID_1.10.0.tgz 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: 119 Package: clustComp Version: 1.22.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 67db30e1b782b8e3f508a8d26e825976 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: RELEASE_3_14 git_last_commit: 51d95a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/clustComp_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clustComp_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clustComp_1.22.0.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: 2.14.0 Depends: R (>= 3.6.0), SingleCellExperiment, SummarizedExperiment (>= 1.15.4), BiocGenerics Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats, limma, howmany, 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: i386, x64 MD5sum: ca3dbe8f054b2f891ec46b4f09b66af4 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: RELEASE_3_14 git_last_commit: c963b95 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/clusterExperiment_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clusterExperiment_2.14.0.zip 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: 151 Package: ClusterJudge Version: 1.16.0 Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: 1690870b368d1a55220176130339af3c 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: RELEASE_3_14 git_last_commit: d9af46d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ClusterJudge_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ClusterJudge_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ClusterJudge_1.16.0.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: 21 Package: clusterProfiler Version: 4.2.2 Depends: R (>= 3.5.0) Imports: AnnotationDbi, downloader, DOSE (>= 3.13.1), dplyr, enrichplot (>= 1.9.3), GO.db, GOSemSim, magrittr, methods, plyr, qvalue, rlang, stats, tidyr, utils, yulab.utils Suggests: AnnotationHub, knitr, rmarkdown, org.Hs.eg.db, prettydoc, ReactomePA, testthat License: Artistic-2.0 MD5sum: 83757143e80884e8050cca4eeafb9fc3 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], Meijun Chen [ctb], Giovanni Dall'Olio [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: RELEASE_3_14 git_last_commit: 4ebb9de git_last_commit_date: 2022-01-12 Date/Publication: 2022-01-13 source.ver: src/contrib/clusterProfiler_4.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/clusterProfiler_4.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/clusterProfiler_4.2.2.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, CEMiTool, CeTF, conclus, DAPAR, debrowser, eegc, enrichTF, esATAC, famat, fcoex, GDCRNATools, IRISFGM, MAGeCKFlute, methylGSA, MicrobiomeProfiler, miRspongeR, MoonlightR, multiSight, netboxr, PFP, Pigengene, RNASeqR, signatureSearch, TCGAbiolinksGUI, TimiRGeN, ExpHunterSuite, recountWorkflow, TCGAWorkflow, DRviaSPCN, genekitr, immcp, pathwayTMB, PMAPscore, RVA, tinyarray suggestsMe: ChIPseeker, cola, DOSE, enrichplot, epihet, GeneTonic, GenomicSuperSignature, GOSemSim, GSEAmining, MesKit, paxtoolsr, ReactomePA, rrvgo, scGPS, simplifyEnrichment, TCGAbiolinks, tidybulk, org.Mxanthus.db, cRegulome, GeoTcgaData dependencyCount: 124 Package: clusterSeq Version: 1.18.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: 95e7a9f2728d3099d63720411731d419 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 & Irene Papatheodorou Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/clusterSeq git_branch: RELEASE_3_14 git_last_commit: 4bf21d4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/clusterSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clusterSeq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clusterSeq_1.18.0.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: 33 Package: ClusterSignificance Version: 1.22.0 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: 8117a3c4274babdb0f518290ffe7bb49 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: RELEASE_3_14 git_last_commit: 3fb82d0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ClusterSignificance_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ClusterSignificance_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ClusterSignificance_1.22.0.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: 1.66.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: f7b1aa31565d5dc8178e170b93f36dd3 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: RELEASE_3_14 git_last_commit: 85aab97 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/clusterStab_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clusterStab_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clusterStab_1.66.0.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: 1.6.0 Depends: R (>= 4.0) Imports: cowplot, dplyr, entropy, fgsea, ggplot2, Matrix, readr, rlang, scales, stringr, tibble, tidyr, stats, methods, SingleCellExperiment, SummarizedExperiment, matrixStats, S4Vectors, proxy, httr, utils Suggests: ComplexHeatmap, covr, knitr, rmarkdown, testthat, ggrepel, BiocStyle, BiocManager, remotes, shiny, gprofiler2, purrr License: MIT + file LICENSE MD5sum: 924341d3517141873632e4a4b68f7182 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, cre], Kent Riemondy [aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb], RNA Bioscience Initiative [fnd] Maintainer: Rui Fu URL: http://github.com/rnabioco/clustifyr#readme, https://rnabioco.github.io/clustifyr/ VignetteBuilder: knitr BugReports: https://github.com/rnabioco/clustifyr/issues git_url: https://git.bioconductor.org/packages/clustifyr git_branch: RELEASE_3_14 git_last_commit: e07d1df git_last_commit_date: 2021-10-26 Date/Publication: 2021-11-07 source.ver: src/contrib/clustifyr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/clustifyr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/clustifyr_1.6.0.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: 96 Package: CMA Version: 1.52.0 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: 2e336eee69e4a41c7d3c33e67353d78d 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: RELEASE_3_14 git_last_commit: db591c3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CMA_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CMA_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CMA_1.52.0.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: 1.6.0 Depends: R (>= 4.0) Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment, matrixStats Suggests: knitr, testthat, BiocStyle, rmarkdown License: file LICENSE MD5sum: cf6fb9645883179c4a0620ba748b5e59 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: RELEASE_3_14 git_last_commit: a9d9f50 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cmapR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cmapR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cmapR_1.6.0.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: 36 Package: cn.farms Version: 1.42.0 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: i386, x64 MD5sum: 6649aa92f2342f8f71ec101ac9d1c0a1 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: RELEASE_3_14 git_last_commit: e1a3008 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cn.farms_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cn.farms_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cn.farms_1.42.0.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: 55 Package: cn.mops Version: 1.40.0 Depends: R (>= 2.12), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb, S4Vectors, exomeCopy Suggests: DNAcopy License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 3eef667b6bf4d559445df0645b032c83 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: RELEASE_3_14 git_last_commit: 498dbf7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cn.mops_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cn.mops_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cn.mops_1.40.0.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 suggestsMe: CNVgears dependencyCount: 31 Package: CNAnorm Version: 1.40.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: b650af2acc21af2a58f5d9f938490a71 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: RELEASE_3_14 git_last_commit: fe146be git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNAnorm_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNAnorm_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNAnorm_1.40.0.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: 1.30.0 Depends: R (>= 3.4) Imports: Biostrings (>= 2.33.4), 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 Archs: i386, x64 MD5sum: 5df113572db0d7a3203b144965b4fadf 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: RELEASE_3_14 git_last_commit: e682f2a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNEr_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNEr_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNEr_1.30.0.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: 115 Package: CNORdt Version: 1.36.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 1fade329c09575abf85c1530f9020ac1 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: RELEASE_3_14 git_last_commit: 0a09111 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNORdt_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNORdt_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNORdt_1.36.0.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: 54 Package: CNORfeeder Version: 1.34.0 Depends: R (>= 3.6.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph Enhances: MEIGOR License: GPL-3 MD5sum: 44bc45458c098767492760cfaf8766c4 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: F.Eduati, E. Gjerga Maintainer: E.Gjerga git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: RELEASE_3_14 git_last_commit: 39356db git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNORfeeder_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNORfeeder_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNORfeeder_1.34.0.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: 53 Package: CNORfuzzy Version: 1.36.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: 20cfa239ff0f4606c9070c8641f6a3d7 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: RELEASE_3_14 git_last_commit: d6315a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNORfuzzy_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNORfuzzy_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNORfuzzy_1.36.0.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: 69 Package: CNORode Version: 1.36.0 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR, doParallel, foreach License: GPL-2 Archs: i386, x64 MD5sum: 5ade534102ee3d6b891c5a933608f723 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 git_url: https://git.bioconductor.org/packages/CNORode git_branch: RELEASE_3_14 git_last_commit: e8a9fdc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNORode_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNORode_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNORode_1.36.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR dependencyCount: 54 Package: CNTools Version: 1.50.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: 57f37c6ab376ce40062bbcfd000eb334 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: RELEASE_3_14 git_last_commit: 2fa2457 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNTools_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNTools_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNTools_1.50.0.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: 54 Package: CNVfilteR Version: 1.8.0 Depends: R (>= 4.1) Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma, regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics, utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings, methods Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, rmarkdown License: Artistic-2.0 MD5sum: 6963c81cdd6a1c2c56cbe2000beee6c8 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: RELEASE_3_14 git_last_commit: f09ee78 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNVfilteR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVfilteR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVfilteR_1.8.0.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: 152 Package: CNVgears Version: 1.2.0 Depends: R (>= 4.1), data.table Imports: ggplot2 Suggests: VariantAnnotation, DelayedArray, knitr, biomaRt, evobiR, rmarkdown, devtools, cowplot, usethis, scales, testthat, GenomicRanges, cn.mops, R.utils License: GPL-3 MD5sum: 212a20371d10a353b07b1553da8cfb62 NeedsCompilation: no Title: A Framework of Functions to Combine, Analize and Interpret CNVs Calling Results Description: This package contains a set of functions to perform several type of processing and analysis on CNVs calling pipelines/algorithms results in an integrated manner and regardless of the raw data type (SNPs array or NGS). It provides functions to combine multiple CNV calling results into a single object, filter them, compute CNVRs (CNV Regions) and inheritance patterns, detect genic load, and more. The package is best suited for studies in human family-based cohorts. biocViews: Software, WorkflowStep, Preprocessing Author: Simone Montalbano [cre, aut] Maintainer: Simone Montalbano VignetteBuilder: knitr BugReports: https://github.com/SinomeM/CNVgears/issues git_url: https://git.bioconductor.org/packages/CNVgears git_branch: RELEASE_3_14 git_last_commit: 705feb3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNVgears_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVgears_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVgears_1.2.0.tgz vignettes: vignettes/CNVgears/inst/doc/CNVgears.html vignetteTitles: CNVgears package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVgears/inst/doc/CNVgears.R dependencyCount: 39 Package: cnvGSA Version: 1.38.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: 8da0bd4baeb68518088fcd9b356ad7db 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: RELEASE_3_14 git_last_commit: 3678199 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cnvGSA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cnvGSA_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cnvGSA_1.38.0.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: 25 Package: CNViz Version: 1.2.0 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: 61d3adcd7ba3f34c823717967eb5cde7 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: RELEASE_3_14 git_last_commit: e13c459 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNViz_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNViz_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNViz_1.2.0.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: 168 Package: CNVPanelizer Version: 1.26.0 Depends: R (>= 3.2.0), GenomicRanges Imports: BiocGenerics, S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics, methods, shiny, shinyFiles, shinyjs, grid, openxlsx Suggests: knitr, RUnit License: GPL-3 MD5sum: 4836c93e23d8712f47be0409aa9aeff0 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: RELEASE_3_14 git_last_commit: 29442f8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNVPanelizer_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVPanelizer_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVPanelizer_1.26.0.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: 113 Package: CNVRanger Version: 1.10.3 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, curatedTCGAData, ensembldb, grid, knitr, regioneR, rmarkdown License: Artistic-2.0 MD5sum: d4109b25786b65222b8762294d933123 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: RELEASE_3_14 git_last_commit: 36b5477 git_last_commit_date: 2022-02-17 Date/Publication: 2022-02-20 source.ver: src/contrib/CNVRanger_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVRanger_1.10.3.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVRanger_1.10.3.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: 55 Package: CNVrd2 Version: 1.32.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 9bbe50ce9b8fa1e3ae880a6c90550e7e 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: RELEASE_3_14 git_last_commit: e00be65 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CNVrd2_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CNVrd2_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CNVrd2_1.32.0.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: 116 Package: CoCiteStats Version: 1.66.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 82d4d8f6a3081b89bf1945a1d7987f0c 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: RELEASE_3_14 git_last_commit: f43f5f0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CoCiteStats_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CoCiteStats_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CoCiteStats_1.66.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 46 Package: COCOA Version: 2.8.0 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: 617f28c7512c3e7d62194a426097a03d 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: RELEASE_3_14 git_last_commit: 30a31c3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/COCOA_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/COCOA_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/COCOA_2.8.0.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: 113 Package: codelink Version: 1.62.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: f7abc364eb54d9aac53c6dce72070c43 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: RELEASE_3_14 git_last_commit: c6116c2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/codelink_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/codelink_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/codelink_1.62.0.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: 49 Package: CODEX Version: 1.26.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: cc0a8a98411dfea3f416123a415e7c23 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: RELEASE_3_14 git_last_commit: 729fd10 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CODEX_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CODEX_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CODEX_1.26.0.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: 46 Package: CoGAPS Version: 3.14.0 Depends: R (>= 3.5.0) Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5 LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: 143957699fdd615f151e24f2fba49d11 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: 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 , Melanie L. Loth VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: RELEASE_3_14 git_last_commit: f937a7c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CoGAPS_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CoGAPS_3.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CoGAPS_3.14.0.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 importsMe: projectR dependencyCount: 44 Package: cogena Version: 1.28.0 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: f40b8921ed8e00a73a108e203dfe1d28 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: RELEASE_3_14 git_last_commit: 639d8fb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cogena_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cogena_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cogena_1.28.0.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: 128 Package: Cogito Version: 1.0.0 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: 887679d1b44ed1a9c4fe3523bfc493b8 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: RELEASE_3_14 git_last_commit: 3eb15c7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Cogito_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Cogito_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Cogito_1.0.0.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: 125 Package: coGPS Version: 1.38.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: 65b2b8eed606c6f9fca9e3c124acf653 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: RELEASE_3_14 git_last_commit: 0699d23 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/coGPS_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/coGPS_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/coGPS_1.38.0.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: COHCAP Version: 1.40.0 Depends: WriteXLS, COHCAPanno, RColorBrewer, gplots Imports: Rcpp, RcppArmadillo, BH LinkingTo: Rcpp, BH License: GPL-3 Archs: i386, x64 MD5sum: a7ea76b99aa7e26f878daad7d5a74213 NeedsCompilation: yes Title: CpG Island Analysis Pipeline for Illumina Methylation Array and Targeted BS-Seq Data Description: COHCAP (pronounced "co-cap") provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k/EPIC methylation array, targeted BS-Seq, etc.). It provides differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, with visualizaton options. Discussion Group: https://sourceforge.net/p/cohcap/discussion/bioconductor/ biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics, DifferentialMethylation Author: Charles Warden , Yate-Ching Yuan , Xiwei Wu Maintainer: Charles Warden SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/COHCAP git_branch: RELEASE_3_14 git_last_commit: 32cf684 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/COHCAP_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/COHCAP_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/COHCAP_1.40.0.tgz vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf vignetteTitles: COHCAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COHCAP/inst/doc/COHCAP.R dependencyCount: 14 Package: cola Version: 2.0.0 Depends: R (>= 3.6.0) 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, markdown, digest, impute, brew, Rcpp (>= 0.11.0), BiocGenerics, eulerr, foreach, doParallel, 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 License: MIT + file LICENSE Archs: i386, x64 MD5sum: 568d61b58cb07abb1bc72fb4e86a6455 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 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: RELEASE_3_14 git_last_commit: 236148e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cola_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cola_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cola_2.0.0.tgz vignettes: vignettes/cola/inst/doc/cola.html vignetteTitles: Use of cola hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment dependencyCount: 64 Package: combi Version: 1.6.0 Depends: R (>= 3.5.0) Imports: ggplot2, nleqslv, phyloseq, tensor, stats, limma, Matrix, BB, reshape2, alabama, cobs, Biobase, vegan, grDevices, graphics, methods, SummarizedExperiment Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 409d93ac86fac0f42011aa7c69ff2f26 NeedsCompilation: no Title: Compositional omics model based visual integration Description: Combine quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration is available, the results are shown as interpretable multiplots. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization, Metabolomics Author: Stijn Hawinkel Maintainer: Joris Meys VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/combi/issues git_url: https://git.bioconductor.org/packages/combi git_branch: RELEASE_3_14 git_last_commit: dcf1756 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/combi_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/combi_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/combi_1.6.0.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: 93 Package: coMET Version: 1.26.0 Depends: R (>= 3.7.0), grid, utils, biomaRt, Gviz, psych Imports: colortools, hash,grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, stats, corrplot Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: a469fc9f2142aadca92468a75fafdc7b 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 long 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: RELEASE_3_14 git_last_commit: ed4f33f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/coMET_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/coMET_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/coMET_1.26.0.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 dependencyCount: 148 Package: compartmap Version: 1.12.0 Depends: R (>= 4.1.0), SummarizedExperiment, RaggedExperiment, BiocSingular, HDF5Array Imports: GenomicRanges, parallel, grid, ggplot2, reshape2, scales, DelayedArray, rtracklayer, DelayedMatrixStats, Matrix, RMTstat Suggests: covr, testthat, knitr, Rcpp, rmarkdown, markdown License: GPL-3 + file LICENSE MD5sum: 871e89ff4d972b4d44444f5e52b3d2fd NeedsCompilation: no Title: Higher-order chromatin domain inference in single cells from scRNA-seq and scATAC-seq Description: Compartmap performs direct inference of higher-order chromatin from scRNA-seq and scATAC-seq. This package implements a James-Stein estimator for computing single-cell level higher-order chromatin domains. Further, we utilize random matrix theory as a method to de-noise correlation matrices to achieve a similar "plaid-like" patterning as observed in Hi-C and scHi-C data. biocViews: Genetics, Epigenetics, ATACSeq, RNASeq, SingleCell Author: Benjamin Johnson [aut, cre], Tim Triche [aut], Hui Shen [aut], Kasper Hansen [aut], Jean-Philippe Fortin [aut] Maintainer: Benjamin Johnson URL: https://github.com/biobenkj/compartmap VignetteBuilder: knitr BugReports: https://github.com/biobenkj/compartmap/issues git_url: https://git.bioconductor.org/packages/compartmap git_branch: RELEASE_3_14 git_last_commit: 3fce1b6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/compartmap_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/compartmap_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/compartmap_1.12.0.tgz vignettes: vignettes/compartmap/inst/doc/compartmap_vignette.html vignetteTitles: Higher-order chromatin inference with compartmap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/compartmap/inst/doc/compartmap_vignette.R dependencyCount: 91 Package: COMPASS Version: 1.32.0 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: i386, x64 MD5sum: b0ad4c5b6c2778d7369043312e247389 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: RELEASE_3_14 git_last_commit: ca59e56 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/COMPASS_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/COMPASS_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/COMPASS_1.32.0.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: 69 Package: compcodeR Version: 1.30.0 Depends: sm Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods, utils, stats, grDevices, graphics Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), baySeq (>= 2.2.0), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0), rmarkdown, testthat Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: e7e086c55e2b4b4191bcdb2ef758b35f 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] () 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: RELEASE_3_14 git_last_commit: bd52d89 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/compcodeR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/compcodeR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/compcodeR_1.30.0.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.html vignetteTitles: compcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R dependencyCount: 75 Package: ComplexHeatmap Version: 2.10.0 Depends: R (>= 3.5.0), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.5), GetoptLong, colorspace, clue, RColorBrewer, GlobalOptions (>= 0.1.0), png, digest, IRanges, matrixStats, foreach, doParallel 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 License: MIT + file LICENSE MD5sum: aca713951141252d511784838c5f917f 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 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: RELEASE_3_14 git_last_commit: 170df82 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ComplexHeatmap_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ComplexHeatmap_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ComplexHeatmap_2.10.0.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, recoup, countToFPKM importsMe: airpart, BiocOncoTK, BioNERO, blacksheepr, BloodGen3Module, CATALYST, celda, CeTF, COCOA, cola, cytoKernel, DEComplexDisease, DEGreport, DEP, diffcyt, diffUTR, ELMER, fCCAC, gCrisprTools, GeneTonic, GenomicSuperSignature, gmoviz, InteractiveComplexHeatmap, InterCellar, iSEE, LineagePulse, MatrixQCvis, MesKit, microbiomeMarker, MOMA, monaLisa, muscat, musicatk, MWASTools, PathoStat, PeacoQC, pipeComp, POMA, profileplyr, RLSeq, sechm, segmenter, SEtools, simplifyEnrichment, singleCellTK, sparrow, TBSignatureProfiler, Xeva, YAPSA, TCGAWorkflow, armada, bulkAnalyseR, conos, MAFDash, MitoHEAR, MKomics, pkgndep, rKOMICS, RNAseqQC, RVA, scITD, tidyHeatmap, visxhclust, wilson suggestsMe: artMS, bambu, BindingSiteFinder, BrainSABER, clustifyr, CNVRanger, dittoSeq, EnrichmentBrowser, gtrellis, HilbertCurve, msImpute, plotgardener, projectR, QFeatures, scDblFinder, spiky, TCGAbiolinks, TCGAutils, TimeSeriesExperiment, weitrix, NanoporeRNASeq, CIARA, circlize, eclust, i2dash, IOHanalyzer, MOSS, multipanelfigure, spiralize, tinyarray dependencyCount: 28 Package: ComPrAn Version: 1.2.0 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: 235b2df8fcc8b0edf09201c7ec7fb3ae 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: RELEASE_3_14 git_last_commit: 6f13eb8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ComPrAn_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ComPrAn_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ComPrAn_1.2.0.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: 99 Package: conclus Version: 1.2.4 Depends: R (>= 4.1) Imports: org.Hs.eg.db, org.Mm.eg.db, dbscan, fpc, factoextra, Biobase, BiocFileCache, parallel, doParallel, foreach, SummarizedExperiment, biomaRt, AnnotationDbi, methods, dplyr, scran, scater, pheatmap, ggplot2, gridExtra, SingleCellExperiment, stats, utils, scales, grDevices, graphics, Rtsne, GEOquery, clusterProfiler, stringr, tools, rlang, DT Suggests: knitr, rmarkdown, BiocStyle, S4Vectors, matrixStats, dynamicTreeCut, testthat License: GPL-3 MD5sum: d3128b9900d7bb552b57c638819d6e7c NeedsCompilation: no Title: ScRNA-seq Workflow CONCLUS - From CONsensus CLUSters To A Meaningful CONCLUSion Description: CONCLUS is a tool for robust clustering and positive marker features selection of single-cell RNA-seq (sc-RNA-seq) datasets. It takes advantage of a consensus clustering approach that greatly simplify sc-RNA-seq data analysis for the user. Of note, CONCLUS does not cover the preprocessing steps of sequencing files obtained following next-generation sequencing. CONCLUS is organized into the following steps: Generation of multiple t-SNE plots with a range of parameters including different selection of genes extracted from PCA. Use the Density-based spatial clustering of applications with noise (DBSCAN) algorithm for idenfication of clusters in each generated t-SNE plot. All DBSCAN results are combined into a cell similarity matrix. The cell similarity matrix is used to define "CONSENSUS" clusters conserved accross the previously defined clustering solutions. Identify marker genes for each concensus cluster. biocViews: Software, Technology, SingleCell, Sequencing, Clustering, ATACSeq, Classification Author: Ilyess Rachedi [cre], Nicolas Descostes [aut], Polina Pavlovich [aut], Christophe Lancrin [aut] Maintainer: Ilyess Rachedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conclus git_branch: RELEASE_3_14 git_last_commit: 4f3d751 git_last_commit_date: 2022-04-08 Date/Publication: 2022-04-10 source.ver: src/contrib/conclus_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/conclus_1.2.4.zip mac.binary.ver: bin/macosx/contrib/4.1/conclus_1.2.4.tgz vignettes: vignettes/conclus/inst/doc/conclus_vignette.pdf vignetteTitles: conclus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conclus/inst/doc/conclus_vignette.R dependencyCount: 252 Package: condiments Version: 1.2.0 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 Suggests: knitr, testthat, rmarkdown, covr, viridis, ggplot2, RColorBrewer, randomForest, tidyr, TSCAN License: MIT + file LICENSE MD5sum: 869eacb287f09579e8219cd7c997da50 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: RELEASE_3_14 git_last_commit: 2e393ee git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/condiments_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/condiments_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/condiments_1.2.0.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: 128 Package: CONFESS Version: 1.22.0 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: 3b3d3791a60bccf5baa72ff9b6e8ee0b 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: RELEASE_3_14 git_last_commit: f55571a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CONFESS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CONFESS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CONFESS_1.22.0.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: 1.12.0 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: 68829491d6685304ba90a2ab311e4ea8 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: RELEASE_3_14 git_last_commit: 7df4146 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/consensus_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/consensus_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/consensus_1.12.0.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: 1.58.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: c9cf3e6deb88d6533f02363eb06ad358 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: RELEASE_3_14 git_last_commit: d8131dd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ConsensusClusterPlus_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ConsensusClusterPlus_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ConsensusClusterPlus_1.58.0.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: CancerSubtypes, CATALYST, ChromSCape, DEGreport, FlowSOM, PDATK, DeSousa2013, iSubGen, longmixr, neatmaps, scRNAtools suggestsMe: TCGAbiolinks dependencyCount: 9 Package: consensusDE Version: 1.12.0 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: 0a3249d1e8b7130872b876a9ea81cc69 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: RELEASE_3_14 git_last_commit: 935d9e9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/consensusDE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/consensusDE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/consensusDE_1.12.0.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: 145 Package: consensusOV Version: 1.16.0 Depends: R (>= 3.6) Imports: Biobase, GSVA, gdata, genefu, limma, matrixStats, randomForest, stats, utils, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown License: Artistic-2.0 MD5sum: d342a5037b7d63acc89c726c4655f291 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, Lavanya Kannan, Ludwig Geistlinger, Victor Kofia, Levi Waldron, Benjamin Haibe-Kains 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: RELEASE_3_14 git_last_commit: 23c1ec0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/consensusOV_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/consensusOV_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/consensusOV_1.16.0.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 dependencyCount: 143 Package: consensusSeekeR Version: 1.22.0 Depends: R (>= 2.10), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 84a325c831287d68bde069ec63b37af1 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. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, Transcription, PeakDetection, Sequencing, Coverage Author: Astrid Deschenes [cre, aut], Fabien Claude Lamaze [ctb], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/ArnaudDroitLab/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues git_url: https://git.bioconductor.org/packages/consensusSeekeR git_branch: RELEASE_3_14 git_last_commit: d88164a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/consensusSeekeR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/consensusSeekeR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/consensusSeekeR_1.22.0.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: 48 Package: CONSTANd Version: 1.2.0 Depends: R (>= 4.1) Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra, magick, Cairo, limma License: file LICENSE MD5sum: 3a3e8eca04d2c0d5e8e34f5ca7cc7a20 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: RELEASE_3_14 git_last_commit: 9719758 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CONSTANd_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CONSTANd_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CONSTANd_1.2.0.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: contiBAIT Version: 1.22.0 Depends: BH (>= 1.51.0-3), Rsamtools (>= 1.21) Imports: data.table, grDevices, clue, cluster, gplots, BiocGenerics (>= 0.31.6), S4Vectors, IRanges, GenomicRanges, Rcpp, TSP, GenomicFiles, gtools, rtracklayer, BiocParallel, DNAcopy, colorspace, reshape2, ggplot2, methods, exomeCopy, GenomicAlignments, diagram LinkingTo: Rcpp, BH Suggests: BiocStyle License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 7e563813173803e652d06ee4921d7625 NeedsCompilation: yes Title: Improves Early Build Genome Assemblies using Strand-Seq Data Description: Using strand inheritance data from multiple single cells from the organism whose genome is to be assembled, contiBAIT can cluster unbridged contigs together into putative chromosomes, and order the contigs within those chromosomes. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, WholeGenome, Genetics, GenomeAssembly Author: Kieran O'Neill, Mark Hills, Mike Gottlieb Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/contiBAIT git_branch: RELEASE_3_14 git_last_commit: 7642d19 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/contiBAIT_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/contiBAIT_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/contiBAIT_1.22.0.tgz vignettes: vignettes/contiBAIT/inst/doc/contiBAIT.pdf vignetteTitles: flowBi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/contiBAIT/inst/doc/contiBAIT.R dependencyCount: 130 Package: conumee Version: 1.28.0 Depends: R (>= 3.0), 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: 8ab16302c635faed5fa94a1820508e2b 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: RELEASE_3_14 git_last_commit: 49326cb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/conumee_1.28.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/conumee_1.28.0.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: 147 Package: convert Version: 1.70.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 0f859893d5809bc46d00c3b9e1d8fe13 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: RELEASE_3_14 git_last_commit: 42fef35 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/convert_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/convert_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/convert_1.70.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maigesPack, TurboNorm suggestsMe: dyebias, OLIN, dyebiasexamples dependencyCount: 9 Package: copa Version: 1.62.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: i386, x64 MD5sum: 8bc13f18967ae41d16bda2095249d177 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: RELEASE_3_14 git_last_commit: f6926db git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/copa_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/copa_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/copa_1.62.0.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: copynumber Version: 1.34.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: 9d3a6d05221532aafd12d28e50cb53e9 NeedsCompilation: no Title: Segmentation of single- and multi-track copy number data by penalized least squares regression. Description: Penalized least squares regression is applied to fit piecewise constant curves to copy number data to locate genomic regions of constant copy number. Procedures are available for individual segmentation of each sample, joint segmentation of several samples and joint segmentation of the two data tracks from SNP-arrays. Several plotting functions are available for visualization of the data and the segmentation results. biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde. Maintainer: Gro Nilsen git_url: https://git.bioconductor.org/packages/copynumber git_branch: RELEASE_3_14 git_last_commit: 958e926 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/copynumber_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/copynumber_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/copynumber_1.34.0.tgz vignettes: vignettes/copynumber/inst/doc/copynumber.pdf vignetteTitles: copynumber.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copynumber/inst/doc/copynumber.R importsMe: sequenza suggestsMe: PureCN, sigminer dependencyCount: 16 Package: CopyNumberPlots Version: 1.10.0 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: e4da14e9e52388328d0d0fec353bf7b7 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: RELEASE_3_14 git_last_commit: 4ab61ef git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CopyNumberPlots_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CopyNumberPlots_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CopyNumberPlots_1.10.0.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: 150 Package: CopywriteR Version: 2.26.0 Depends: R(>= 3.2), BiocParallel Imports: matrixStats, gtools, data.table, S4Vectors, chipseq, IRanges, Rsamtools, DNAcopy, GenomicAlignments, GenomicRanges, CopyhelpeR, GenomeInfoDb, futile.logger Suggests: BiocStyle, SCLCBam, snow License: GPL-2 MD5sum: 8dd76032413d3d4f9a951b773b138c71 NeedsCompilation: no Title: Copy number information from targeted sequencing using off-target reads Description: CopywriteR extracts DNA copy number information from targeted sequencing by utiizing off-target reads. It allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, CopywriteR constitutes a widely applicable alternative to available copy number detection tools. biocViews: ImmunoOncology, TargetedResequencing, ExomeSeq, CopyNumberVariation, Preprocessing, Visualization, Coverage Author: Thomas Kuilman Maintainer: Oscar Krijgsman URL: https://github.com/PeeperLab/CopywriteR git_url: https://git.bioconductor.org/packages/CopywriteR git_branch: RELEASE_3_14 git_last_commit: 84f6999 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CopywriteR_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CopywriteR_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CopywriteR_2.26.0.tgz vignettes: vignettes/CopywriteR/inst/doc/CopywriteR.pdf vignetteTitles: CopywriteR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopywriteR/inst/doc/CopywriteR.R dependencyCount: 49 Package: coRdon Version: 1.12.0 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: e4dcd8f394554a795a4ae13814dd6ca1 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: RELEASE_3_14 git_last_commit: ba08f6b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/coRdon_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/coRdon_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/coRdon_1.12.0.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: 58 Package: CoRegNet Version: 1.32.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: e4f702f0317b857f8d1eb8c56b1fa8a9 NeedsCompilation: yes Title: CoRegNet : reconstruction and integrated analysis of co-regulatory networks Description: This package provides methods to identify active transcriptional programs. Methods and classes are provided to import or infer large scale co-regulatory network from transcriptomic data. The specificity of the encoded networks is to model Transcription Factor cooperation. External regulation evidences (TFBS, ChIP,...) can be integrated to assess the inferred network and refine it if necessary. Transcriptional activity of the regulators in the network can be estimated using an measure of their influence in a given sample. Finally, an interactive UI can be used to navigate through the network of cooperative regulators and to visualize their activity in a specific sample or subgroup sample. The proposed visualization tool can be used to integrate gene expression, transcriptional activity, copy number status, sample classification and a transcriptional network including co-regulation information. biocViews: NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork,SystemsBiology, Network, Visualization, Transcription Author: Remy Nicolle, Thibault Venzac and Mohamed Elati Maintainer: Remy Nicolle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoRegNet git_branch: RELEASE_3_14 git_last_commit: a0822a8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CoRegNet_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CoRegNet_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CoRegNet_1.32.0.tgz vignettes: vignettes/CoRegNet/inst/doc/CoRegNet.html vignetteTitles: Custom Print Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R dependencyCount: 42 Package: CoreGx Version: 1.6.0 Depends: R (>= 4.1), BiocGenerics, SummarizedExperiment Imports: Biobase, S4Vectors, MultiAssayExperiment, MatrixGenerics, piano, BiocParallel, BumpyMatrix, checkmate, methods, stats, utils, graphics, grDevices, lsa, data.table, crayon, glue, rlang Suggests: pander, markdown, BiocStyle, rmarkdown, knitr, formatR, testthat License: GPL-3 MD5sum: 87a38fed896a580126bd298f98d4686a 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: Petr Smirnov [aut], Ian Smith [aut], Christopher Eeles [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoreGx git_branch: RELEASE_3_14 git_last_commit: 33bef81 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CoreGx_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CoreGx_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CoreGx_1.6.0.tgz vignettes: vignettes/CoreGx/inst/doc/coreGx.html, vignettes/CoreGx/inst/doc/LongTable.html vignetteTitles: CoreGx: Class and Function Abstractions, The LongTable Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoreGx/inst/doc/coreGx.R, vignettes/CoreGx/inst/doc/LongTable.R dependsOnMe: PharmacoGx, RadioGx, ToxicoGx importsMe: PDATK dependencyCount: 119 Package: Cormotif Version: 1.40.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: c302cbc680edce19196bbb5dd0ca1208 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: RELEASE_3_14 git_last_commit: 82e920f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Cormotif_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Cormotif_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Cormotif_1.40.0.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: 13 Package: corral Version: 1.4.0 Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix, methods, MultiAssayExperiment, pals, SingleCellExperiment, SummarizedExperiment, transport Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 3a9af1fa5cfbbe8146e698a881caf663 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 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, 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: RELEASE_3_14 git_last_commit: fcef936 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/corral_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/corral_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/corral_1.4.0.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: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/corral/inst/doc/corral_dimred.R, vignettes/corral/inst/doc/corralm_alignment.R dependsOnMe: OSCA.advanced dependencyCount: 76 Package: CORREP Version: 1.60.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 8e4f4badd0d6d09a68bdc80eda129072 NeedsCompilation: no Title: Multivariate Correlation Estimator and Statistical Inference Procedures. Description: Multivariate correlation estimation and statistical inference. See package vignette. biocViews: Microarray, Clustering, GraphAndNetwork Author: Dongxiao Zhu and Youjuan Li Maintainer: Dongxiao Zhu git_url: https://git.bioconductor.org/packages/CORREP git_branch: RELEASE_3_14 git_last_commit: 7eb266e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CORREP_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CORREP_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CORREP_1.60.0.tgz vignettes: vignettes/CORREP/inst/doc/CORREP.pdf vignetteTitles: Multivariate Correlation Estimator hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CORREP/inst/doc/CORREP.R dependencyCount: 9 Package: coseq Version: 1.18.0 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: e53e15b6e0c5dbd09c174b1814e22f48 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: RELEASE_3_14 git_last_commit: 6db558b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/coseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/coseq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/coseq_1.18.0.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: 111 Package: cosmiq Version: 1.28.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 8b723454397ba44d248c6cb4126d63aa 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: RELEASE_3_14 git_last_commit: 6587876 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cosmiq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cosmiq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cosmiq_1.28.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R dependencyCount: 97 Package: cosmosR Version: 1.2.0 Depends: R (>= 4.1) Imports: AnnotationDbi, biomaRt, CARNIVAL, dorothea, dplyr, ggplot2, GSEABase, igraph, magrittr, org.Hs.eg.db, plyr, purrr, readr, rlang, scales, stringr, tibble, utils, visNetwork Suggests: testthat, knitr, rmarkdown, piano License: GPL-3 MD5sum: 02eafa13becb8acbb3d99c5f41837fd5 NeedsCompilation: no Title: COSMOS (Causal Oriented Search of Multi-Omic Space) Description: COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets. biocViews: CellBiology, Pathways, Network, Proteomics, Metabolomics, Transcriptomics, GeneSignaling Author: Aurélien Dugourd [aut] (), Attila Gabor [aut] (), Katharina Zirngibl [cre, aut] () Maintainer: Katharina Zirngibl URL: https://github.com/saezlab/COSMOSR VignetteBuilder: knitr BugReports: https://github.com/saezlab/COSMOSR/issues git_url: https://git.bioconductor.org/packages/cosmosR git_branch: RELEASE_3_14 git_last_commit: dcb4b9c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/cosmosR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cosmosR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cosmosR_1.2.0.tgz vignettes: vignettes/cosmosR/inst/doc/tutorial.html vignetteTitles: cosmosR tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmosR/inst/doc/tutorial.R dependencyCount: 130 Package: COSNet Version: 1.28.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 7b3c80918ae3d08cdeaade5411474f75 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: RELEASE_3_14 git_last_commit: 1ff4a74 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/COSNet_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/COSNet_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/COSNet_1.28.0.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: countsimQC Version: 1.12.1 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 Suggests: knitr, testthat License: GPL (>=2) MD5sum: afc8c4f51c962bc23971fd650826f648 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: RELEASE_3_14 git_last_commit: 8eada5b git_last_commit_date: 2022-02-01 Date/Publication: 2022-02-03 source.ver: src/contrib/countsimQC_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/countsimQC_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/countsimQC_1.12.1.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: 125 Package: covEB Version: 1.20.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: 0fb69ef374b5c48ff71d9822f1d2677d 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: RELEASE_3_14 git_last_commit: 9e6d604 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/covEB_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/covEB_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/covEB_1.20.0.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: 17 Package: CoverageView Version: 1.32.0 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: 60adc4fac1a18bd324f2c67da9512cdc 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: RELEASE_3_14 git_last_commit: fd9a94e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CoverageView_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/CoverageView_1.32.0.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: 44 Package: covRNA Version: 1.20.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 0e2ac2234745bd945359fce806299112 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: RELEASE_3_14 git_last_commit: 6e2cebd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/covRNA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/covRNA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/covRNA_1.20.0.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: 59 Package: cpvSNP Version: 1.26.0 Depends: R (>= 2.10), 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: 17556f9718088d0ca98f47878e00bd98 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: RELEASE_3_14 git_last_commit: 1f6411f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cpvSNP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cpvSNP_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cpvSNP_1.26.0.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: 116 Package: cqn Version: 1.40.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: f4c57b4860ced9da836d42c2633ae6ea 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: RELEASE_3_14 git_last_commit: 4762342 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cqn_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cqn_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cqn_1.40.0.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: exomePeak2, KnowSeq importsMe: tweeDEseq, GeoTcgaData dependencyCount: 15 Package: CRImage Version: 1.42.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: e6407c6b6acb3b68a60497dc72e363ae 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: RELEASE_3_14 git_last_commit: 7a30984 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CRImage_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CRImage_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CRImage_1.42.0.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: 42 Package: CRISPRseek Version: 1.34.2 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 License: GPL (>= 2) MD5sum: 2844bb1a2b97e5b3640373122a2a8eb8 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, 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: RELEASE_3_14 git_last_commit: c5ca587 git_last_commit_date: 2022-04-11 Date/Publication: 2022-04-12 source.ver: src/contrib/CRISPRseek_1.34.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/CRISPRseek_1.33.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CRISPRseek_1.34.2.tgz 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 dependsOnMe: crisprseekplus importsMe: GUIDEseq, multicrispr dependencyCount: 95 Package: crisprseekplus Version: 1.20.0 Depends: R (>= 3.3.0), shiny, shinyjs, CRISPRseek Imports: DT, utils, GUIDEseq, GenomicRanges, GenomicFeatures, BiocManager, BSgenome, AnnotationDbi, hash Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: 2d9bd433ca349eddef4340239f2b2bef NeedsCompilation: no Title: crisprseekplus Description: Bioinformatics platform containing interface to work with offTargetAnalysis and compare2Sequences in the CRISPRseek package, and GUIDEseqAnalysis. biocViews: GeneRegulation, SequenceMatching, Software Author: Sophie Wigmore , Alper Kucukural , Lihua Julie Zhu , Michael Brodsky , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/crisprseekplus VignetteBuilder: knitr, R.rsp BugReports: https://github.com/UMMS-Biocore/crisprseekplus/issues/new git_url: https://git.bioconductor.org/packages/crisprseekplus git_branch: RELEASE_3_14 git_last_commit: 25d40a3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/crisprseekplus_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/crisprseekplus_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/crisprseekplus_1.20.0.tgz vignettes: vignettes/crisprseekplus/inst/doc/crisprseekplus.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprseekplus/inst/doc/crisprseekplus.R dependencyCount: 171 Package: CrispRVariants Version: 1.22.0 Depends: R (>= 3.5), 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, gdata, GenomicFeatures, knitr, rmarkdown, rtracklayer, sangerseqR, testthat, VariantAnnotation License: GPL-2 MD5sum: 6fe049e089e33857f9b4b7859c0af277 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 Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrispRVariants git_branch: RELEASE_3_14 git_last_commit: 74a5dc2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CrispRVariants_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CrispRVariants_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CrispRVariants_1.22.0.tgz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf vignetteTitles: CrispRVariants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R dependencyCount: 92 Package: crlmm Version: 1.52.0 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: i386, x64 MD5sum: 1aceff82c8ae9b92ce58bb201e253e37 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: RELEASE_3_14 git_last_commit: 9ecc85f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/crlmm_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/crlmm_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/crlmm_1.52.0.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: 63 Package: crossmeta Version: 1.20.0 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), DESeq2, 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), RColorBrewer (>= 1.1.2), 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), statmod (>= 1.4.34), SummarizedExperiment, tibble, XML (>= 3.98.1.17), readxl (>= 1.3.1) Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat, tximportData License: MIT + file LICENSE MD5sum: 1d3111ee94cd862a078b86c5c6b6f5e6 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 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 git_url: https://git.bioconductor.org/packages/crossmeta git_branch: RELEASE_3_14 git_last_commit: 4ebad78 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/crossmeta_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/crossmeta_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/crossmeta_1.20.0.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: 162 Package: CSAR Version: 1.46.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: 0fecd768e9a6b04ef6ad36ccc3f512f6 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: RELEASE_3_14 git_last_commit: 6b0de68 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CSAR_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CSAR_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CSAR_1.46.0.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: 16 Package: csaw Version: 1.28.0 Depends: 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 Archs: i386, x64 MD5sum: 3b91ebfea55ac6c60a28811c7b1361a3 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: RELEASE_3_14 git_last_commit: b68e21d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/csaw_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/csaw_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/csaw_1.28.0.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, icetea, NADfinder, vulcan, BinQuasi suggestsMe: chipseqDB dependencyCount: 42 Package: csdR Version: 1.0.0 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 Archs: i386, x64 MD5sum: 0cacae64f16c7c7fc66f5fdb39095e27 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: RELEASE_3_14 git_last_commit: 4d1e1ad git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/csdR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/csdR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/csdR_1.0.0.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: 110 Package: CSSP Version: 1.32.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: adfbc7ea118975fb92d425b93ba07bc9 NeedsCompilation: yes Title: ChIP-Seq Statistical Power Description: Power computation for ChIP-Seq data based on Bayesian estimation for local poisson counting process. biocViews: ChIPSeq, Sequencing, QualityControl, Bayesian Author: Chandler Zuo, Sunduz Keles Maintainer: Chandler Zuo git_url: https://git.bioconductor.org/packages/CSSP git_branch: RELEASE_3_14 git_last_commit: 0f0928b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CSSP_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CSSP_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CSSP_1.32.0.tgz vignettes: vignettes/CSSP/inst/doc/cssp.pdf vignetteTitles: cssp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSP/inst/doc/cssp.R dependencyCount: 4 Package: CSSQ Version: 1.6.0 Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, rtracklayer Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, markdown License: Artistic-2.0 MD5sum: f16e813ad6c9ef1aa741c5c1c4dfa634 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: RELEASE_3_14 git_last_commit: a7b96ca git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CSSQ_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CSSQ_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CSSQ_1.6.0.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: 110 Package: ctc Version: 1.68.0 Depends: amap License: GPL-2 MD5sum: c75cb25a828dd5d1c30052a82d15717e 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: RELEASE_3_14 git_last_commit: c273353 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ctc_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ctc_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ctc_1.68.0.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: CTDquerier Version: 2.2.0 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: c3598a95fac2e5ae11c676f74e24c237 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: RELEASE_3_14 git_last_commit: a121b76 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CTDquerier_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CTDquerier_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CTDquerier_2.2.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 76 Package: ctgGEM Version: 1.6.0 Depends: monocle, SummarizedExperiment, Imports: Biobase, BiocGenerics, graphics, grDevices, igraph, Matrix, methods, utils, sincell, TSCAN Suggests: BiocStyle, biomaRt, HSMMSingleCell, irlba, knitr, rmarkdown, VGAM License: GPL(>=2) MD5sum: 2cabd2ed5e09438f4681f8f090a66b13 NeedsCompilation: no Title: Generating Tree Hierarchy Visualizations from Gene Expression Data Description: Cell Tree Generator for Gene Expression Matrices (ctgGEM) streamlines the building of cell-state hierarchies from single-cell gene expression data across multiple existing tools for improved comparability and reproducibility. It supports pseudotemporal ordering algorithms and visualization tools from monocle, cellTree, TSCAN, sincell, and destiny, and provides a unified output format for integration with downstream data analysis workflows and Cytoscape. biocViews: GeneExpression, Visualization, Sequencing, SingleCell, Clustering, RNASeq, ImmunoOncology, DifferentialExpression, MultipleComparison, QualityControl, DataImport Author: Mark Block [aut], Carrie Minette [aut], Evgeni Radichev [aut], Etienne Gnimpieba [aut], Mariah Hoffman [aut], USD Biomedical Engineering [aut, cre] Maintainer: USD Biomedical Engineering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ctgGEM git_branch: RELEASE_3_14 git_last_commit: 619fedc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ctgGEM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ctgGEM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ctgGEM_1.6.0.tgz vignettes: vignettes/ctgGEM/inst/doc/ctgGEM-Vignette.html vignetteTitles: ctgGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctgGEM/inst/doc/ctgGEM-Vignette.R dependencyCount: 135 Package: cTRAP Version: 1.12.0 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: 7f0865d76c20f2990de4d03aec64321d 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: RELEASE_3_14 git_last_commit: 8282ad2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cTRAP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cTRAP_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cTRAP_1.12.0.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: 155 Package: ctsGE Version: 1.20.0 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: a491a237c857401a3916bdc817e08c0e 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: RELEASE_3_14 git_last_commit: cd43af8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ctsGE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ctsGE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ctsGE_1.20.0.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: 71 Package: cummeRbund Version: 2.36.0 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: d9e67cd419b92b52b8d470ed012dbcf2 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: RELEASE_3_14 git_last_commit: 36d62b1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cummeRbund_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cummeRbund_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cummeRbund_2.36.0.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: 145 Package: customCMPdb Version: 1.4.0 Depends: R (>= 4.0) Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats, rappdirs, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: e222af6e0ed1645d689eaa971ad3c6f4 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: RELEASE_3_14 git_last_commit: a069660 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/customCMPdb_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/customCMPdb_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/customCMPdb_1.4.0.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: 110 Package: customProDB Version: 1.34.0 Depends: R (>= 3.0.1), 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, AhoCorasickTrie, methods Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 3b1ba76ae2e85d89bd643ae02e13244e 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: RELEASE_3_14 git_last_commit: b7cfa9d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/customProDB_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/customProDB_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/customProDB_1.34.0.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: 100 Package: cyanoFilter Version: 1.2.0 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: 1a059c59997fcd7443c1a8214c999210 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: RELEASE_3_14 git_last_commit: f126547 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cyanoFilter_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cyanoFilter_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cyanoFilter_1.2.0.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: 172 Package: cycle Version: 1.48.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 57fd7f246d005e30ccdb1705f19c4b50 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: RELEASE_3_14 git_last_commit: 4d654cf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cycle_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cycle_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cycle_1.48.0.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: 1.18.0 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: i386, x64 MD5sum: 51bd45a7afc71fca72ebfb0a8695f7bb 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: RELEASE_3_14 git_last_commit: 8dd093c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cydar_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cydar_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cydar_1.18.0.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: 94 Package: CytoDx Version: 1.14.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: aef71fe5f2ab4a6ed5d5facdca159075 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: RELEASE_3_14 git_last_commit: a0336e4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CytoDx_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CytoDx_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CytoDx_1.14.0.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: 51 Package: CyTOFpower Version: 1.0.0 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: 6643fbde0559d6ae1f9f6d20f755920a 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: RELEASE_3_14 git_last_commit: 72c95c5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CyTOFpower_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CyTOFpower_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CyTOFpower_1.0.0.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: 281 Package: CytoGLMM Version: 1.2.0 Imports: stats, methods, BiocParallel, RColorBrewer, cowplot, doParallel, dplyr, factoextra, flexmix, ggplot2, magrittr, mbest, pheatmap, speedglm, stringr, strucchange, tibble, ggrepel, MASS, logging, Matrix, tidyr, caret, rlang, grDevices Suggests: knitr, rmarkdown, testthat, BiocStyle License: LGPL-3 MD5sum: bbf10148f1b314c63dc4e1e7c925412e 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: RELEASE_3_14 git_last_commit: 13036ea git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CytoGLMM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CytoGLMM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CytoGLMM_1.2.0.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: 166 Package: cytoKernel Version: 1.0.0 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: i386, x64 MD5sum: 88a75705d1983ace7b9f6e219d2bf23a 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: RELEASE_3_14 git_last_commit: 44dfcc3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cytoKernel_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cytoKernel_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cytoKernel_1.0.0.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: 77 Package: cytolib Version: 2.6.2 Depends: R (>= 3.4) Imports: RcppParallel, RProtoBufLib LinkingTo: Rcpp, BH(>= 1.75.0.0), RProtoBufLib(>= 2.3.5),Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1) Suggests: knitr, rmarkdown License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 801be8e95f1eb970ace403d099c08a4b 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: RELEASE_3_14 git_last_commit: b6c4227 git_last_commit_date: 2022-02-07 Date/Publication: 2022-02-08 source.ver: src/contrib/cytolib_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/cytolib_2.6.2.zip mac.binary.ver: bin/macosx/contrib/4.1/cytolib_2.6.2.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: 9 Package: cytomapper Version: 1.6.0 Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods Imports: S4Vectors, BiocParallel, HDF5Array, DelayedArray, RColorBrewer, viridis, utils, SummarizedExperiment, tools, graphics, raster, grDevices, stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny, shinydashboard, matrixStats, rhdf5 Suggests: BiocStyle, knitr, rmarkdown, markdown, cowplot, testthat, shinytest License: GPL (>= 2) MD5sum: 19818e4f5a480112bc9a9116e6136ed7 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, cre] (), Nicolas Damond [aut] (), Tobias Hoch [ctb] Maintainer: Nils Eling 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: RELEASE_3_14 git_last_commit: 72e006f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/cytomapper_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/cytomapper_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/cytomapper_1.6.0.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: imcRtools dependencyCount: 110 Package: CytoML Version: 2.6.0 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, base64enc, plyr, dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml, lattice, stats, corpcor, RUnit, tibble, RcppParallel, xml2 LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib, cytolib, Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1), flowWorkspace Suggests: testthat, flowWorkspaceData , knitr, rmarkdown, parallel License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 267c8c27024bc2458aeb97c52cf576a4 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: RELEASE_3_14 git_last_commit: a3a698e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/CytoML_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CytoML_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CytoML_2.6.0.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 importsMe: FlowSOM suggestsMe: flowWorkspace, openCyto dependencyCount: 127 Package: CytoTree Version: 1.4.0 Depends: R (>= 4.0), igraph Imports: FlowSOM, Rtsne, ggplot2, destiny, gmodels, flowUtils, Biobase, Matrix, flowCore, sva, matrixStats, methods, mclust, prettydoc, RANN(>= 2.5), Rcpp (>= 0.12.0), BiocNeighbors, cluster, pheatmap, scatterpie, umap, scatterplot3d, limma, stringr, grDevices, grid, stats LinkingTo: Rcpp Suggests: BiocGenerics, knitr, RColorBrewer, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 37d27f2fec3d3c8c67fa5a30551cef7f NeedsCompilation: yes Title: A Toolkit for Flow And Mass Cytometry Data Description: A trajectory inference toolkit for flow and mass cytometry data. CytoTree is a valuable tool to build a tree-shaped trajectory using flow and mass cytometry data. The application of CytoTree ranges from clustering and dimensionality reduction to trajectory reconstruction and pseudotime estimation. It offers complete analyzing workflow for flow and mass cytometry data. biocViews: CellBiology, Clustering, Visualization, Software, CellBasedAssays, FlowCytometry, NetworkInference, Network Author: Yuting Dai [aut, cre] Maintainer: Yuting Dai URL: http://www.r-project.org, https://github.com/JhuangLab/CytoTree VignetteBuilder: knitr BugReports: https://github.com/JhuangLab/CytoTree/issues git_url: https://git.bioconductor.org/packages/CytoTree git_branch: RELEASE_3_14 git_last_commit: c190c92 git_last_commit_date: 2021-10-26 Date/Publication: 2021-12-14 source.ver: src/contrib/CytoTree_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/CytoTree_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/CytoTree_1.4.0.tgz vignettes: vignettes/CytoTree/inst/doc/Tutorial.html vignetteTitles: Quick_start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoTree/inst/doc/Tutorial.R dependencyCount: 261 Package: dada2 Version: 1.22.0 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 Archs: i386, x64 MD5sum: 10a54cf646550ccc540cb9cc75b759aa 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: RELEASE_3_14 git_last_commit: 3abe06c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dada2_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dada2_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dada2_1.22.0.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 importsMe: Rbec suggestsMe: mia dependencyCount: 78 Package: dagLogo Version: 1.32.0 Depends: R (>= 3.0.1), methods, grid Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils, biomaRt, motifStack Suggests: XML, grImport, grImport2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: a2f1ba6a145f8b0d6a8ee8cfcda1f3e2 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: RELEASE_3_14 git_last_commit: 5a04a5f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dagLogo_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/dagLogo_1.32.0.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: 98 Package: daMA Version: 1.66.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: 0b989aab5b1192a4d5d8937c344757a1 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: RELEASE_3_14 git_last_commit: 8ea2454 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/daMA_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/daMA_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/daMA_1.66.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DAMEfinder Version: 1.6.0 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: 4e2678149c3a93c676bc0590d25a4adb 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: RELEASE_3_14 git_last_commit: a15961a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DAMEfinder_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DAMEfinder_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DAMEfinder_1.6.0.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: 128 Package: DaMiRseq Version: 2.6.0 Depends: R (>= 3.4), 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: 67fb209153e399cb013318ef4c031c82 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: RELEASE_3_14 git_last_commit: 64d7151 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DaMiRseq_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DaMiRseq_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DaMiRseq_2.6.0.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: 242 Package: DAPAR Version: 1.26.1 Depends: R (>= 4.1.0) Imports: Biobase, MSnbase, tibble, RColorBrewer, stats, preprocessCore, Cairo, png, lattice, reshape2, gplots, pcaMethods, ggplot2, limma, knitr, tmvtnorm, norm, impute, stringr, grDevices, graphics, openxlsx, utils, cp4p (>= 0.3.5), scales, Matrix, vioplot, imp4p (>= 1.1), forcats, methods, DAPARdata (>= 1.24.0), siggenes, graph, lme4, readxl, highcharter, clusterProfiler, dplyr, tidyr, AnnotationDbi, tidyverse, vsn, FactoMineR, factoextra, multcomp, purrr, visNetwork, foreach, parallel, doParallel, igraph, dendextend, Mfuzz, apcluster, diptest, cluster Suggests: BiocGenerics, testthat, BiocStyle License: Artistic-2.0 MD5sum: 70b7f95161a03febdeff4292930aefd7 NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: This package contains a collection of functions for the visualisation and the statistical analysis of proteomic data. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: Samuel Wieczorek [aut, cre], Florence Combes [aut], Thomas Burger [aut], Vasile-Cosmin Lazar [ctb], Enora Fremy [ctb], Helene Borges [ctb] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/samWieczorek/DAPAR/issues git_url: https://git.bioconductor.org/packages/DAPAR git_branch: RELEASE_3_14 git_last_commit: 9232810 git_last_commit_date: 2021-11-22 Date/Publication: 2021-11-23 source.ver: src/contrib/DAPAR_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/DAPAR_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.1/DAPAR_1.26.1.tgz vignettes: vignettes/DAPAR/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar user manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAPAR/inst/doc/Prostar_UserManual.R importsMe: Prostar suggestsMe: DAPARdata dependencyCount: 301 Package: DART Version: 1.42.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: b55bf2e1f4e7666ade631220b6f331ce 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: RELEASE_3_14 git_last_commit: ef2d964 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DART_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DART_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DART_1.42.0.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: 11 Package: dasper Version: 1.4.3 Depends: R (>= 4.0) Imports: basilisk, BiocFileCache, BiocParallel, data.table, dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, ggpubr, ggrepel, grid, IRanges, magrittr, megadepth, methods, plyranges, readr, reticulate, rtracklayer, S4Vectors, stringr, SummarizedExperiment, tidyr Suggests: AnnotationFilter, BiocStyle, covr, ensembldb, GenomicState, knitr, lifecycle, markdown, recount, RefManageR, rmarkdown, sessioninfo, testthat, tibble License: Artistic-2.0 MD5sum: 9e094e10887623d1e0c42f492e76da97 NeedsCompilation: no Title: Detecting abberant splicing events from RNA-sequencing data Description: The aim of dasper is to detect aberrant splicing events from RNA-seq data. dasper will use as input both junction and coverage data from RNA-seq to calculate the deviation of each splicing event in a patient from a set of user-defined controls. dasper uses an unsupervised outlier detection algorithm to score each splicing event in the patient with an outlier score representing the degree to which that splicing event looks abnormal. biocViews: Software, RNASeq, Transcriptomics, AlternativeSplicing, Coverage, Sequencing Author: David Zhang [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: David Zhang URL: https://github.com/dzhang32/dasper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/dasper git_url: https://git.bioconductor.org/packages/dasper git_branch: RELEASE_3_14 git_last_commit: 031d42e git_last_commit_date: 2022-03-26 Date/Publication: 2022-03-27 source.ver: src/contrib/dasper_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/dasper_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dasper_1.4.3.tgz vignettes: vignettes/dasper/inst/doc/dasper.html vignetteTitles: Introduction to dasper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dasper/inst/doc/dasper.R importsMe: ODER dependencyCount: 183 Package: dcanr Version: 1.10.0 Depends: R (>= 3.6.0) Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix, graphics, stats, RColorBrewer, circlize, doRNG Suggests: EBcoexpress, testthat, EBarrays, GeneNet, COSINE, mclust, minqa, SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle, edgeR Enhances: parallel, doSNOW, doParallel License: GPL-3 MD5sum: e558f772350e75bbec98562763dc08ec NeedsCompilation: no Title: Differential co-expression/association network analysis Description: Methods and an evaluation framework for the inference of differential co-expression/association networks. 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: RELEASE_3_14 git_last_commit: 57551c6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dcanr_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dcanr_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dcanr_1.10.0.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: SingscoreAMLMutations dependencyCount: 30 Package: dce Version: 1.2.0 Depends: R (>= 4.1) Imports: stats, methods, assertthat, graph, pcalg, purrr, tidyverse, Matrix, ggraph, tidygraph, ggplot2, rlang, expm, MASS, CombinePValue, edgeR, epiNEM, igraph, metap, mnem, naturalsort, ppcor, glm2, graphite, reshape2, dplyr, glue, Rgraphviz, harmonicmeanp, org.Hs.eg.db, logger Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR, cowplot, dagitty, lmtest, sandwich, devtools, curatedTCGAData, TCGAutils, SummarizedExperiment License: GPL-3 MD5sum: c72b92163ee0cef50d78d0a60092d4e0 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: RELEASE_3_14 git_last_commit: 633dfc7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dce_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dce_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dce_1.2.0.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: 236 Package: dcGSA Version: 1.22.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: 40ff83404ec9af5096b85883823e6476 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: RELEASE_3_14 git_last_commit: 8a04caf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dcGSA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dcGSA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dcGSA_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 16 Package: ddCt Version: 1.50.0 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: 0e4ea1b3a99a4c9bd727aedb415ece12 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: RELEASE_3_14 git_last_commit: 5bd9712 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ddCt_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ddCt_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ddCt_1.50.0.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: 1.14.0 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: 47fa8f0f0aeada09dc8d5aad6ed08d94 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: RELEASE_3_14 git_last_commit: ca0d23f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ddPCRclust_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ddPCRclust_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ddPCRclust_1.14.0.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: 156 Package: dearseq Version: 1.6.0 Depends: R (>= 3.6.0) Imports: ggplot2, KernSmooth, matrixStats, methods, patchwork, parallel, pbapply, stats, statmod, survey, viridisLite Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA, knitr, limma, readxl, rmarkdown, S4Vectors, SummarizedExperiment, testthat, covr License: GPL-2 | file LICENSE MD5sum: 8d6404df4da64272fc9a3d24ee2f1472 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] Maintainer: Boris P. Hejblum VignetteBuilder: knitr BugReports: https://github.com/borishejblum/dearseq/issues git_url: https://git.bioconductor.org/packages/dearseq git_branch: RELEASE_3_14 git_last_commit: 40a9767 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dearseq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dearseq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dearseq_1.6.0.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 suggestsMe: TcGSA dependencyCount: 51 Package: debCAM Version: 1.12.0 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: abd7cdfb7c7993886e3b3d2fc0aea746 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: RELEASE_3_14 git_last_commit: 66667aa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/debCAM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/debCAM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/debCAM_1.12.0.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: 116 Package: debrowser Version: 1.22.5 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: 9e868a7ed1728bcd1b561c5f7814d908 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: RELEASE_3_14 git_last_commit: 5920c6b git_last_commit_date: 2022-03-15 Date/Publication: 2022-03-17 source.ver: src/contrib/debrowser_1.22.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/debrowser_1.22.5.zip mac.binary.ver: bin/macosx/contrib/4.1/debrowser_1.22.5.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: 209 Package: DECIPHER Version: 2.22.0 Depends: R (>= 3.5.0), Biostrings (>= 2.59.1), RSQLite (>= 1.1), stats, parallel Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector License: GPL-3 Archs: i386, x64 MD5sum: 85714cb598e7e8d4c20b0144394cd830 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: RELEASE_3_14 git_last_commit: 45da5ca git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DECIPHER_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DECIPHER_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DECIPHER_2.22.0.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/ClassifySequences.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 vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify Sequences, 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 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/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 dependsOnMe: AssessORF, sangeranalyseR, SynExtend importsMe: mia, openPrimeR, AssessORFData, ensembleTax suggestsMe: MicrobiotaProcess, microbial, pagoo dependencyCount: 34 Package: deco Version: 1.10.0 Depends: R (>= 3.5.0), AnnotationDbi, BiocParallel, SummarizedExperiment, limma Imports: stats, methods, ggplot2, foreign, graphics, BiocStyle, Biobase, cluster, gplots, RColorBrewer, locfit, made4, ade4, sfsmisc, scatterplot3d, gdata, grDevices, utils, reshape2, gridExtra Suggests: knitr, curatedTCGAData, MultiAssayExperiment, Homo.sapiens, rmarkdown License: GPL (>=3) MD5sum: ee013d79d821abb0d43707c71e835e77 NeedsCompilation: no Title: Decomposing Heterogeneous Cohorts using Omic Data Profiling Description: This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels. biocViews: Software, FeatureExtraction, Clustering, MultipleComparison, DifferentialExpression, Transcriptomics, BiomedicalInformatics, Proteomics, Bayesian, GeneExpression, Transcription, Sequencing, Microarray, ExonArray, RNASeq, MicroRNAArray, mRNAMicroarray Author: Francisco Jose Campos-Laborie, Jose Manuel Sanchez-Santos and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Francisco Jose Campos Laborie URL: https://github.com/fjcamlab/deco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deco git_branch: RELEASE_3_14 git_last_commit: d18f24e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/deco_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/deco_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/deco_1.10.0.tgz vignettes: vignettes/deco/inst/doc/DECO.html vignetteTitles: deco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deco/inst/doc/DECO.R dependencyCount: 121 Package: DEComplexDisease Version: 1.14.0 Depends: R (>= 3.3.3) Imports: Rcpp (>= 0.12.7), DESeq2, edgeR, SummarizedExperiment, ComplexHeatmap, grid, parallel, BiocParallel, grDevices, graphics, stats, methods, utils LinkingTo: Rcpp Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: f66ebeac43adfe51cbbd15260cebcf79 NeedsCompilation: yes Title: A tool for differential expression analysis and DEGs based investigation to complex diseases by bi-clustering analysis Description: It is designed to find the differential expressed genes (DEGs) for complex disease, which is characterized by the heterogeneous genomic expression profiles. Different from the established DEG analysis tools, it does not assume the patients of complex diseases to share the common DEGs. By applying a bi-clustering algorithm, DECD finds the DEGs shared by as many patients. In this way, DECD describes the DEGs of complex disease in a novel syntax, e.g. a gene list composed of 200 genes are differentially expressed in 30% percent of studied complex disease. Applying the DECD analysis results, users are possible to find the patients affected by the same mechanism based on the shared signatures. biocViews: DNASeq, WholeGenome, FunctionalGenomics, DifferentialExpression,GeneExpression, Clustering Author: Guofeng Meng Maintainer: Guofeng Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEComplexDisease git_branch: RELEASE_3_14 git_last_commit: 67836f3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DEComplexDisease_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEComplexDisease_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEComplexDisease_1.14.0.tgz vignettes: vignettes/DEComplexDisease/inst/doc/vignettes.pdf, vignettes/DEComplexDisease/inst/doc/decd.html vignetteTitles: DEComplexDisease: a R package for DE analysis, DEComplexDisease: a R package for DE analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEComplexDisease/inst/doc/decd.R dependencyCount: 107 Package: decompTumor2Sig Version: 2.10.0 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: 5e854f609e91719985592947238b29b5 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: RELEASE_3_14 git_last_commit: e07e999 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/decompTumor2Sig_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/decompTumor2Sig_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/decompTumor2Sig_2.10.0.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: 123 Package: DeconRNASeq Version: 1.36.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: c70d0f4f6145083827931b36b93563da 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: RELEASE_3_14 git_last_commit: ca5cb97 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DeconRNASeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeconRNASeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DeconRNASeq_1.36.0.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: 45 Package: decontam Version: 1.14.0 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: 38383c63eba17d6065abf775967870a4 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: RELEASE_3_14 git_last_commit: b710769 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/decontam_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/decontam_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/decontam_1.14.0.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: 44 Package: deconvR Version: 1.0.1 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 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: d509a34047612841d739135da2914409 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: İrem B. Gündüz [aut, cre] (), Veronika Ebenal [aut] (), Altuna Akalin [aut] () Maintainer: İrem 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: RELEASE_3_14 git_last_commit: cda4d8b git_last_commit_date: 2021-10-28 Date/Publication: 2021-10-28 source.ver: src/contrib/deconvR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/deconvR_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/deconvR_1.0.1.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: 132 Package: decoupleR Version: 2.0.1 Depends: R (>= 4.0) Imports: broom, dplyr, magrittr, Matrix, purrr, rlang, stats, stringr, tibble, tidyr, tidyselect, withr Suggests: glmnet (>= 4.1.0), GSVA, viper, fgsea (>= 1.15.4), AUCell, SummarizedExperiment, rpart, ranger, BiocStyle, covr, knitr, pkgdown, RefManageR, rmarkdown, roxygen2, sessioninfo, pheatmap, testthat, OmnipathR, tidyverse, Seurat License: GPL-3 + file LICENSE MD5sum: a6275533a5f9f1bbb4cffe01cbf1e809 NeedsCompilation: no Title: decoupleR: Ensemble of computational methods to infer biological activities from omics data Description: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. biocViews: DifferentialExpression, FunctionalGenomics, GeneExpression, GeneRegulation, Network, Software, StatisticalMethod, Transcription, Author: Pau Badia-i-Mompel [aut, cre] (), 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 git_url: https://git.bioconductor.org/packages/decoupleR git_branch: RELEASE_3_14 git_last_commit: 4b4c2e8 git_last_commit_date: 2022-04-01 Date/Publication: 2022-04-03 source.ver: src/contrib/decoupleR_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/decoupleR_2.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/decoupleR_2.0.1.tgz vignettes: vignettes/decoupleR/inst/doc/decoupleR.html, vignettes/decoupleR/inst/doc/pw_bk.html, vignettes/decoupleR/inst/doc/pw_sc.html, vignettes/decoupleR/inst/doc/tf_bk.html, vignettes/decoupleR/inst/doc/tf_sc.html vignetteTitles: Introduction, Pathway activity inference in bulk RNA-seq, Pathway activity activity inference from scRNA-seq, Transcription factor activity inference in bulk RNA-seq, Transcription factor activity inference from scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/decoupleR/inst/doc/decoupleR.R, vignettes/decoupleR/inst/doc/pw_bk.R, vignettes/decoupleR/inst/doc/pw_sc.R, vignettes/decoupleR/inst/doc/tf_bk.R, vignettes/decoupleR/inst/doc/tf_sc.R dependencyCount: 49 Package: DeepBlueR Version: 1.20.0 Depends: R (>= 3.3), XML, RCurl Imports: GenomicRanges, data.table, stringr, diffr, dplyr, methods, rjson, utils, R.utils, foreach, withr, rtracklayer, GenomeInfoDb, settings, filehash Suggests: knitr, rmarkdown, LOLA, Gviz, gplots, ggplot2, tidyr, RColorBrewer, matrixStats License: GPL (>=2.0) MD5sum: 547e3c34b5f71ba7ec7cfb42dffa1e9b NeedsCompilation: no Title: DeepBlueR Description: Accessing the DeepBlue Epigenetics Data Server through R. biocViews: DataImport, DataRepresentation, ThirdPartyClient, GeneRegulation, GenomeAnnotation, CpGIsland, DNAMethylation, Epigenetics, Annotation, Preprocessing, ImmunoOncology Author: Felipe Albrecht, Markus List Maintainer: Felipe Albrecht , Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepBlueR git_branch: RELEASE_3_14 git_last_commit: cbb9c75 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DeepBlueR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeepBlueR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DeepBlueR_1.20.0.tgz vignettes: vignettes/DeepBlueR/inst/doc/DeepBlueR.html vignetteTitles: The DeepBlue epigenomic data server - R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepBlueR/inst/doc/DeepBlueR.R dependencyCount: 80 Package: DeepPINCS Version: 1.2.2 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: 916346241ce4db5a6e8d4116fe2937b9 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: RELEASE_3_14 git_last_commit: 9443813 git_last_commit_date: 2022-04-03 Date/Publication: 2022-04-05 source.ver: src/contrib/DeepPINCS_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeepPINCS_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/DeepPINCS_1.2.2.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: VAExprs dependencyCount: 145 Package: deepSNV Version: 1.40.0 Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.13.44), Imports: Rhtslib LinkingTo: Rhtslib (>= 1.13.1) Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 5f9fb8bb99fe87d8013925e2b4fd0615 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], 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: RELEASE_3_14 git_last_commit: 875148c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/deepSNV_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/deepSNV_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/deepSNV_1.40.0.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: 100 Package: DEFormats Version: 1.22.0 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: cfb4cb546e7b4f2da0835870e761004c 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: RELEASE_3_14 git_last_commit: ff4d252 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DEFormats_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEFormats_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEFormats_1.22.0.tgz vignettes: vignettes/DEFormats/inst/doc/DEFormats.html vignetteTitles: Differential gene expression data formats converter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R importsMe: regionReport suggestsMe: ideal dependencyCount: 98 Package: DegNorm Version: 1.4.0 Depends: R (>= 4.0.0), methods Imports: Rcpp (>= 1.0.2),GenomicFeatures, 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) Archs: i386, x64 MD5sum: 0dcd2d35e3394542790f1a373cb5eee8 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: RELEASE_3_14 git_last_commit: a1776e6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DegNorm_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DegNorm_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DegNorm_1.4.0.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: 144 Package: DEGraph Version: 1.46.0 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: 94c8ba349e5ee8d0084dabae88bb5c95 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: RELEASE_3_14 git_last_commit: c3c8b03 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DEGraph_1.46.0.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: 62 Package: DEGreport Version: 1.30.3 Depends: R (>= 3.6.0) Imports: utils, methods, Biobase, BiocGenerics, broom, circlize, ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, DESeq2, dplyr, edgeR, ggplot2, ggdendro, grid, ggrepel, grDevices, knitr, logging, lasso2, magrittr, Nozzle.R1, psych, RColorBrewer, reshape, rlang, scales, stats, stringr, S4Vectors, SummarizedExperiment, tidyr, tibble Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown, statmod, testthat License: MIT + file LICENSE MD5sum: e42e96283f295262a5025100752f16f5 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] 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: RELEASE_3_14 git_last_commit: 3554bce git_last_commit_date: 2022-03-26 Date/Publication: 2022-03-27 source.ver: src/contrib/DEGreport_1.30.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEGreport_1.30.3.zip mac.binary.ver: bin/macosx/contrib/4.1/DEGreport_1.30.3.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: 135 Package: DEGseq Version: 1.48.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: i386, x64 MD5sum: ec2f963e8284ffe9d230e9b67d67f394 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 and Xi Wang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: RELEASE_3_14 git_last_commit: 667bae7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DEGseq_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEGseq_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEGseq_1.48.0.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: 45 Package: DelayedArray Version: 0.20.0 Depends: R (>= 4.0.0), methods, stats4, Matrix, BiocGenerics (>= 0.37.0), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.27.2), IRanges (>= 2.17.3) Imports: stats LinkingTo: S4Vectors Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter, SummarizedExperiment, airway, lobstr, DelayedMatrixStats, knitr, rmarkdown, BiocStyle, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 5616e0fe535e5690c1666cb831aaedfd 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 , with contributions from Peter Hickey and Aaron Lun 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: RELEASE_3_14 git_last_commit: 829b529 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DelayedArray_0.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DelayedArray_0.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DelayedArray_0.20.0.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: DelayedDataFrame, DelayedMatrixStats, DelayedRandomArray, GDSArray, HDF5Array, rhdf5client, SCArray, singleCellTK, TileDBArray, VCFArray importsMe: batchelor, beachmat, bigPint, BiocSingular, bsseq, CAGEr, celaref, celda, Cepo, ChromSCape, clusterExperiment, compartmap, CRISPRseek, cytomapper, DelayedTensor, DEScan2, DropletUtils, DSS, ELMER, flowWorkspace, FRASER, GenomicScores, glmGamPoi, GSVA, hipathia, LoomExperiment, mbkmeans, MethReg, methrix, methylSig, mia, miaViz, minfi, MOFA2, mumosa, netSmooth, NewWave, NxtIRFcore, orthogene, PCAtools, ResidualMatrix, RTCGAToolbox, ScaledMatrix, scater, scDblFinder, scMerge, scmeth, scPCA, scran, scry, scuttle, signatureSearch, SingleCellExperiment, SingleR, SummarizedExperiment, transformGamPoi, TSCAN, VariantExperiment, velociraptor, weitrix, zellkonverter, celldex, imcdatasets, scDiffCom suggestsMe: BiocGenerics, ChIPpeakAnno, CNVgears, gwascat, iSEE, MAST, ProteoDisco, S4Vectors, satuRn, SQLDataFrame, TrajectoryUtils, BigDataStatMeth, digitalDLSorteR dependencyCount: 14 Package: DelayedDataFrame Version: 1.10.0 Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5) Imports: methods, stats, BiocGenerics Suggests: testthat, knitr, rmarkdown, SeqArray, GDSArray License: GPL-3 MD5sum: e0bafcdb4eda502dfec750404c830275 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: RELEASE_3_14 git_last_commit: f7c2365 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DelayedDataFrame_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DelayedDataFrame_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DelayedDataFrame_1.10.0.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: 15 Package: DelayedMatrixStats Version: 1.16.0 Depends: MatrixGenerics (>= 1.5.3), DelayedArray (>= 0.17.6) Imports: methods, matrixStats (>= 0.60.0), sparseMatrixStats, Matrix, S4Vectors (>= 0.17.5), IRanges (>= 2.25.10) Suggests: testthat, knitr, rmarkdown, covr, BiocStyle, microbenchmark, profmem, HDF5Array License: MIT + file LICENSE MD5sum: 1823df7e90bfd0bf93157d53a0db95a5 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: RELEASE_3_14 git_last_commit: d44a3d7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DelayedMatrixStats_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DelayedMatrixStats_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DelayedMatrixStats_1.16.0.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: batchelor, biscuiteer, bsseq, CAGEr, Cepo, compartmap, dmrseq, DropletUtils, FRASER, glmGamPoi, GSVA, methrix, methylSig, mia, minfi, mumosa, NxtIRFcore, orthogene, PCAtools, SCArray, scater, scMerge, scran, scuttle, singleCellTK, SingleR, sparrow, weitrix, celldex suggestsMe: DelayedArray, MatrixGenerics, mbkmeans, scPCA, slingshot, TrajectoryUtils, digitalDLSorteR dependencyCount: 17 Package: DelayedRandomArray Version: 1.2.0 Depends: DelayedArray Imports: methods, dqrng, Rcpp LinkingTo: dqrng, BH, Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix License: GPL-3 Archs: i386, x64 MD5sum: 925dad60cdf727bddf0fc24b1415eddf 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: RELEASE_3_14 git_last_commit: 486e509 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DelayedRandomArray_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DelayedRandomArray_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DelayedRandomArray_1.2.0.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: 19 Package: DelayedTensor Version: 1.0.0 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: 7fd44fa4c392576920d886f087acc685 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: RELEASE_3_14 git_last_commit: feff2b4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DelayedTensor_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DelayedTensor_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DelayedTensor_1.0.0.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: 41 Package: deltaCaptureC Version: 1.8.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2, tictoc Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: fc3e1420031e30bc53ac472d09b8f795 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: RELEASE_3_14 git_last_commit: b2be724 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/deltaCaptureC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/deltaCaptureC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/deltaCaptureC_1.8.0.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: 94 Package: deltaGseg Version: 1.34.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: c8be48078d38ce52a11d7c3497397e04 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: RELEASE_3_14 git_last_commit: 821e790 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/deltaGseg_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/deltaGseg_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/deltaGseg_1.34.0.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: 57 Package: DeMAND Version: 1.24.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: 66d59b6b3ce05608ba7a5aade1334693 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: RELEASE_3_14 git_last_commit: ecd6008 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DeMAND_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeMAND_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DeMAND_1.24.0.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: 1.10.0 Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment, knitr, KernSmooth, matrixcalc, rmarkdown Imports: matrixStats, stats, truncdist, base64enc, ggplot2 LinkingTo: Rcpp License: GPL-3 Archs: i386, x64 MD5sum: 16479ef3890fde71d6dfad7d7e2a22e2 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: Shaolong Cao, Peng Yang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: RELEASE_3_14 git_last_commit: c4b178c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DeMixT_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DeMixT_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DeMixT_1.10.0.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: 79 Package: densvis Version: 1.4.0 Imports: Rcpp, basilisk, assertthat, reticulate LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, ggplot2, Rtsne, uwot, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 1d9cdd21e5314b2cf63b1ce600ee4c73 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 VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/densvis/issues git_url: https://git.bioconductor.org/packages/densvis git_branch: RELEASE_3_14 git_last_commit: 75f5aa0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/densvis_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/densvis_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/densvis_1.4.0.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 dependsOnMe: OSCA.advanced dependencyCount: 24 Package: DEP Version: 1.16.0 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: a572285835dc947719da2673ed0bace6 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: RELEASE_3_14 git_last_commit: ce28ade git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DEP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEP_1.16.0.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: 154 Package: DepecheR Version: 1.10.0 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) LinkingTo: Rcpp, RcppEigen Suggests: uwot, reshape2, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: i386, x64 MD5sum: 9f165935b7b1eaa4b795385a575d814b 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: RELEASE_3_14 git_last_commit: d9ec88d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DepecheR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DepecheR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DepecheR_1.10.0.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: 85 Package: DEqMS Version: 1.12.1 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 MD5sum: cbbe2e9b2575facc4d4f69d10fafd017 NeedsCompilation: no Title: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data. Description: DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing, DifferentialExpression, MultipleComparison,Normalization,Bayesian Author: Yafeng Zhu Maintainer: Yafeng Zhu VignetteBuilder: knitr BugReports: https://github.com/yafeng/DEqMS/issues git_url: https://git.bioconductor.org/packages/DEqMS git_branch: RELEASE_3_14 git_last_commit: fa586ec git_last_commit_date: 2022-01-18 Date/Publication: 2022-01-20 source.ver: src/contrib/DEqMS_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEqMS_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/DEqMS_1.12.1.tgz vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html vignetteTitles: DEqMS R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R dependencyCount: 40 Package: derfinder Version: 1.28.0 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: 836f1dee45118329a9fed7017bd2dfc7 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: RELEASE_3_14 git_last_commit: 9f12ac9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/derfinder_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/derfinder_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/derfinder_1.28.0.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: brainflowprobes, derfinderPlot, ODER, recount, regionReport, GenomicState, recountWorkflow suggestsMe: megadepth dependencyCount: 148 Package: derfinderHelper Version: 1.28.0 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: 6d19f4e0fdaa2dddaf24cb0d7a3d3444 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: RELEASE_3_14 git_last_commit: 9047b48 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/derfinderHelper_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/derfinderHelper_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/derfinderHelper_1.28.0.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: 1.28.1 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: 76b4fb52aca62b321d4bb69a5442e712 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: RELEASE_3_14 git_last_commit: 62c9d98 git_last_commit_date: 2021-11-22 Date/Publication: 2021-11-23 source.ver: src/contrib/derfinderPlot_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/derfinderPlot_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.1/derfinderPlot_1.28.1.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: brainflowprobes, recountWorkflow suggestsMe: derfinder, regionReport, GenomicState dependencyCount: 165 Package: DEScan2 Version: 1.14.1 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 Archs: i386, x64 MD5sum: cacb2211382774935ade540b7df3f9bb 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: RELEASE_3_14 git_last_commit: 750fd91 git_last_commit_date: 2021-11-18 Date/Publication: 2021-11-21 source.ver: src/contrib/DEScan2_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEScan2_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/DEScan2_1.14.1.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: 126 Package: DESeq2 Version: 1.34.0 Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter, methods, stats4, locfit, geneplotter, ggplot2, Rcpp (>= 0.11.0) 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) Archs: i386, x64 MD5sum: 155d927d4d9154be0b08f393d705fb32 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/mikelove/DESeq2 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/DESeq2 git_branch: RELEASE_3_14 git_last_commit: 25d4f74 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DESeq2_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DESeq2_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DESeq2_1.34.0.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, rgsepd, SeqGSEA, TCC, tRanslatome, rnaseqDTU, rnaseqGene, Brundle, DRomics, ordinalbayes importsMe: Anaquin, animalcules, APAlyzer, benchdamic, BioNERO, BRGenomics, CeTF, circRNAprofiler, consensusDE, coseq, countsimQC, crossmeta, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, deltaCaptureC, DEsubs, DiffBind, easier, EBSEA, eegc, ERSSA, GDCRNATools, GeneTonic, Glimma, HTSFilter, icetea, ideal, INSPEcT, IntEREst, isomiRs, kissDE, microbiomeExplorer, microbiomeMarker, MLSeq, multiSight, muscat, NBAMSeq, ORFik, OUTRIDER, PathoStat, pcaExplorer, phantasus, proActiv, RegEnrich, regionReport, ReportingTools, RiboDiPA, Rmmquant, RNASeqR, scBFA, scGPS, SEtools, singleCellTK, SNPhood, spatialHeatmap, srnadiff, systemPipeTools, TBSignatureProfiler, TimeSeriesExperiment, UMI4Cats, vidger, vulcan, BloodCancerMultiOmics2017, FieldEffectCrc, IHWpaper, ExpHunterSuite, recountWorkflow, bulkAnalyseR, cinaR, HeritSeq, HTSSIP, intePareto, limorhyde2, MetaLonDA, microbial, RNAseqQC, sRNAGenetic, wilson suggestsMe: aggregateBioVar, apeglm, bambu, biobroom, BiocGenerics, BioCor, BiocSet, CAGEr, compcodeR, dearseq, derfinder, diffloop, dittoSeq, EDASeq, EnhancedVolcano, EnrichmentBrowser, fishpond, gage, GenomicAlignments, GenomicRanges, glmGamPoi, HiCDCPlus, IHW, InteractiveComplexHeatmap, miRmine, NxtIRFcore, OPWeight, PCAtools, phyloseq, progeny, recount, RUVSeq, scran, sparrow, subSeq, SummarizedBenchmark, systemPipeR, systemPipeShiny, TFEA.ChIP, tidybulk, topconfects, tximeta, tximport, variancePartition, Wrench, zinbwave, curatedAdipoChIP, curatedAdipoRNA, RegParallel, Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics, conos, FateID, GeoTcgaData, metaRNASeq, RaceID, seqgendiff, Seurat dependencyCount: 92 Package: DEsingle Version: 1.14.0 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: 64b079edd686eb391425e6fc5ea2ec6a 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: RELEASE_3_14 git_last_commit: 40a3c5e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DEsingle_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEsingle_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEsingle_1.14.0.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: 37 Package: destiny Version: 3.8.1 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, 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: i386, x64 MD5sum: ebaa2ba687763360af153569c4467e42 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: RELEASE_3_14 git_last_commit: 02eeb9c git_last_commit_date: 2022-01-29 Date/Publication: 2022-01-30 source.ver: src/contrib/destiny_3.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/destiny_3.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/destiny_3.8.1.tgz vignettes: vignettes/destiny/inst/doc/Diffusion-Map-recap.html, vignettes/destiny/inst/doc/Diffusion-Maps.html, vignettes/destiny/inst/doc/DPT.html, vignettes/destiny/inst/doc/Gene-Relevance.html, vignettes/destiny/inst/doc/Global-Sigma.html, vignettes/destiny/inst/doc/tidyverse.html vignetteTitles: Reproduce the Diffusion Map vignette with the supplied data(), destiny main vignette: Start here!, destiny 2.0 brought the Diffusion Pseudo Time (DPT) class, detecting relevant genes with destiny 3, The effects of a global vs. local kernel, tidyverse and ggplot integration with destiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/destiny/inst/doc/Diffusion-Map-recap.R, vignettes/destiny/inst/doc/Diffusion-Maps.R, vignettes/destiny/inst/doc/DPT.R, vignettes/destiny/inst/doc/Gene-Relevance.R, vignettes/destiny/inst/doc/Global-Sigma.R, vignettes/destiny/inst/doc/tidyverse.R importsMe: CytoTree, phemd suggestsMe: CelliD, CellTrails, monocle, scater dependencyCount: 129 Package: DEsubs Version: 1.20.0 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: 30f9fe574090fb0f8d66f52a2ded9748 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: RELEASE_3_14 git_last_commit: cc9d333 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DEsubs_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEsubs_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEsubs_1.20.0.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: 128 Package: DEWSeq Version: 1.8.0 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, rmarkdown, testthat, BiocStyle, IHW License: LGPL (>= 3) MD5sum: 23135b9a31b7dd1f70535da2a641cb40 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: RELEASE_3_14 git_last_commit: 7686f86 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DEWSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEWSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEWSeq_1.8.0.tgz vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R dependencyCount: 97 Package: DExMA Version: 1.2.1 Depends: R (>= 4.1), DExMAdata Imports: Biobase, GEOquery, impute, limma, pheatmap, plyr, scales, snpStats, sva, swamp, stats, methods, utils, bnstruct Suggests: BiocStyle, qpdf, BiocGenerics, RUnit License: GPL-2 MD5sum: 95e604cf40a303341e3b8ec46011aa25 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: RELEASE_3_14 git_last_commit: 275215f git_last_commit_date: 2021-11-30 Date/Publication: 2021-12-02 source.ver: src/contrib/DExMA_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/DExMA_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/DExMA_1.2.1.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: 118 Package: DEXSeq Version: 1.40.0 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.9.11), AnnotationDbi, RColorBrewer, S4Vectors (>= 0.23.18) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 3) MD5sum: 27a783be319be424b1c380ea294c1327 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: RELEASE_3_14 git_last_commit: 7d2d639 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DEXSeq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DEXSeq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DEXSeq_1.40.0.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, rnaseqDTU importsMe: diffUTR, IntEREst suggestsMe: bambu, GenomicRanges, satuRn, stageR, subSeq, pasilla dependencyCount: 113 Package: DFP Version: 1.52.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: fa07f37d26cd31fec8bbcb6f5a481bb0 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: RELEASE_3_14 git_last_commit: 7cc849a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DFP_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DFP_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DFP_1.52.0.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: 2.2.0 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: i386, x64 MD5sum: 0e62069bbc3d9cf72e68774501c25d62 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: RELEASE_3_14 git_last_commit: c3e29e4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DIAlignR_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DIAlignR_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DIAlignR_2.2.0.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: 81 Package: DiffBind Version: 3.4.11 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 (>= 2.0), DESeq2, methods, graphics, ggrepel, apeglm, ashr, GreyListChIP LinkingTo: Rhtslib (>= 1.15.3), Rcpp Suggests: BiocStyle, testthat, xtable Enhances: rgl, XLConnect, edgeR, csaw, BSgenome, GenomeInfoDb, profileplyr, rtracklayer, grid License: Artistic-2.0 Archs: i386, x64 MD5sum: 9abba38d6ed1dfeaa99fd7c7705e01ea 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: RELEASE_3_14 git_last_commit: 7e24840 git_last_commit_date: 2022-02-22 Date/Publication: 2022-02-24 source.ver: src/contrib/DiffBind_3.4.11.tar.gz win.binary.ver: bin/windows/contrib/4.1/DiffBind_3.4.11.zip mac.binary.ver: bin/macosx/contrib/4.1/DiffBind_3.4.11.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, Brundle dependencyCount: 141 Package: diffcoexp Version: 1.14.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery License: GPL (>2) MD5sum: 54d275e388d46e37a22b87a9c012f497 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: RELEASE_3_14 git_last_commit: 05e288d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/diffcoexp_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffcoexp_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffcoexp_1.14.0.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: 121 Package: diffcyt Version: 1.14.0 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: 6cf4ad55a27b2d70fc4c8271890c031d 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: RELEASE_3_14 git_last_commit: 18cad99 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/diffcyt_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffcyt_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffcyt_1.14.0.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, treekoR suggestsMe: CATALYST dependencyCount: 212 Package: diffGeneAnalysis Version: 1.76.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 6ef6b60998fc87858fc1dc7d581f9eb5 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: RELEASE_3_14 git_last_commit: 48b5e2f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/diffGeneAnalysis_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffGeneAnalysis_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffGeneAnalysis_1.76.0.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: 1.26.0 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 Archs: i386, x64 MD5sum: 29df2eb190ba8ead3e854a48a029570e 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: RELEASE_3_14 git_last_commit: 7a9008e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/diffHic_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffHic_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffHic_1.26.0.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 dependencyCount: 55 Package: DiffLogo Version: 2.18.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: fd351dc66ce82e40e42bee87b4bdbfb3 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: RELEASE_3_14 git_last_commit: 8ac0955 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DiffLogo_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DiffLogo_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DiffLogo_2.18.0.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: diffloop Version: 1.22.0 Imports: methods, GenomicRanges, foreach, plyr, dplyr, reshape2, ggplot2, matrixStats, Sushi, edgeR, locfit, statmod, biomaRt, GenomeInfoDb, S4Vectors, IRanges, grDevices, graphics, stats, utils, Biobase, readr, data.table, rtracklayer, pbapply, limma Suggests: DESeq2, diffloopdata, ggrepel, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 2aed9fa7ec1ace70a68f93a6e44096e4 NeedsCompilation: no Title: Identifying differential DNA loops from chromatin topology data Description: A suite of tools for subsetting, visualizing, annotating, and statistically analyzing the results of one or more ChIA-PET experiments or other assays that infer chromatin loops. biocViews: Preprocessing, QualityControl, Visualization, DataImport, DataRepresentation, GO Author: Caleb Lareau [aut, cre], Martin Aryee [aut] Maintainer: Caleb Lareau URL: https://github.com/aryeelab/diffloop VignetteBuilder: knitr BugReports: https://github.com/aryeelab/diffloop/issues git_url: https://git.bioconductor.org/packages/diffloop git_branch: RELEASE_3_14 git_last_commit: 684b6b0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/diffloop_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffloop_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffloop_1.22.0.tgz vignettes: vignettes/diffloop/inst/doc/diffloop.html vignetteTitles: diffloop: Identifying differential DNA loops from chromatin topology data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffloop/inst/doc/diffloop.R dependencyCount: 127 Package: diffuStats Version: 1.14.0 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: i386, x64 MD5sum: bf378ab6c1098b490b62cc50151808b9 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: RELEASE_3_14 git_last_commit: f349e21 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/diffuStats_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffuStats_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffuStats_1.14.0.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: 51 Package: diffUTR Version: 1.2.0 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: 48cc6a56b1a6d2008472e13b14cb3834 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: RELEASE_3_14 git_last_commit: feef7a8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/diffUTR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diffUTR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diffUTR_1.2.0.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: 140 Package: diggit Version: 1.26.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: dad238e5ed0e2b6d08290603087376c6 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: RELEASE_3_14 git_last_commit: 73cd356 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/diggit_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/diggit_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/diggit_1.26.0.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: 34 Package: Dino Version: 1.0.0 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 License: GPL-3 MD5sum: d62fe57595dc42a82af57f89a5f50a12 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: RELEASE_3_14 git_last_commit: 2809674 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Dino_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Dino_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Dino_1.0.0.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: 180 Package: dir.expiry Version: 1.2.0 Imports: utils, filelock Suggests: rmarkdown, knitr, testthat, BiocStyle License: GPL-3 MD5sum: 3099413d8be5010c6733d667ffb55865 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: RELEASE_3_14 git_last_commit: 3ee6a95 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dir.expiry_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dir.expiry_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dir.expiry_1.2.0.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: 1.20.0 Depends: R (>= 4.0) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE MD5sum: 31db758c4cc12e72838fbd6e679ea8f4 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: RELEASE_3_14 git_last_commit: 9ab6a22 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Director_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Director_1.19.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Director_1.20.0.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: 1.36.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: i386, x64 MD5sum: 2d5423cb5c9facf69c8158095b037c7c 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: RELEASE_3_14 git_last_commit: 926baff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DirichletMultinomial_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DirichletMultinomial_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DirichletMultinomial_1.36.0.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 dependencyCount: 8 Package: discordant Version: 1.18.0 Depends: R (>= 3.4) Imports: Biobase, stats, biwt, gtools, MASS, tools Suggests: BiocStyle, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 6439217bb394fadd8bc94619d29e8ed2 NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is a method to determine differential correlation of molecular feature pairs from -omics data using mixture models. Algorithm is explained further in Siska et al. biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [cre,aut], Katerina Kechris [aut] Maintainer: Charlotte Siska URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: RELEASE_3_14 git_last_commit: 7872d84 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/discordant_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/discordant_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/discordant_1.18.0.tgz vignettes: vignettes/discordant/inst/doc/Discordant_vignette.pdf vignetteTitles: Discordant hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Discordant_vignette.R dependencyCount: 19 Package: DiscoRhythm Version: 1.10.1 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: 5b0a44bd4fbc0e1cc08e4e66ad70d345 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: RELEASE_3_14 git_last_commit: b272edf git_last_commit_date: 2022-03-07 Date/Publication: 2022-03-08 source.ver: src/contrib/DiscoRhythm_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/DiscoRhythm_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/DiscoRhythm_1.10.1.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: 156 Package: distinct Version: 1.6.0 Depends: R (>= 4.0) Imports: Rcpp, stats, SummarizedExperiment, SingleCellExperiment, methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2, limma, scater LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, UpSetR License: GPL (>= 3) Archs: i386, x64 MD5sum: dc7b01095d00253588c885b3846cd7c0 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], Mark D. Robinson [aut]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/distinct SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/distinct/issues git_url: https://git.bioconductor.org/packages/distinct git_branch: RELEASE_3_14 git_last_commit: 52f192e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/distinct_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/distinct_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/distinct_1.6.0.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: spatialHeatmap dependencyCount: 91 Package: dittoSeq Version: 1.6.0 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: c0f8999e1fe8bb285d09448783b3d539 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: RELEASE_3_14 git_last_commit: 55ba253 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dittoSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dittoSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dittoSeq_1.6.0.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 suggestsMe: escape, tidySingleCellExperiment, magmaR dependencyCount: 66 Package: divergence Version: 1.10.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 9935765eb7e8ae3104a0a234098c693f 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: RELEASE_3_14 git_last_commit: 3431649 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/divergence_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/divergence_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/divergence_1.10.0.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: 25 Package: dks Version: 1.40.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 665b8e2a019ff0262b07a6aadffd122f 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: RELEASE_3_14 git_last_commit: 95dc7dc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dks_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dks_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dks_1.40.0.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: 1.8.0 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 License: GPL-3 MD5sum: debe7078cf69e550134aa42f6f0850b3 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: RELEASE_3_14 git_last_commit: eab8fbe git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DMCFB_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMCFB_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMCFB_1.8.0.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: 108 Package: DMCHMM Version: 1.16.0 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: c17ed1a9d1f494703f22e815d9f42e4a 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: RELEASE_3_14 git_last_commit: efaf39a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DMCHMM_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMCHMM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMCHMM_1.16.0.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: 55 Package: DMRcaller Version: 1.26.0 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: 653d9ffa681a173fadb52a8cf3807ee8 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: RELEASE_3_14 git_last_commit: 164d55c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DMRcaller_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMRcaller_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMRcaller_1.26.0.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: 30 Package: DMRcate Version: 2.8.5 Depends: R (>= 4.0.0) Imports: ExperimentHub, bsseq, GenomeInfoDb, limma, edgeR, DSS, minfi, missMethyl, GenomicRanges, plyr, Gviz, IRanges, stats, utils, S4Vectors, methods, graphics, SummarizedExperiment Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, FlowSorted.Blood.EPIC, tissueTreg, DMRcatedata License: file LICENSE MD5sum: a7de993329687f4659890db3eaf6be8a 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: RELEASE_3_14 git_last_commit: c65dc79 git_last_commit_date: 2022-02-07 Date/Publication: 2022-02-08 source.ver: src/contrib/DMRcate_2.8.5.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/DMRcate_2.8.5.tgz vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf vignetteTitles: The DMRcate package user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/DMRcate.R dependsOnMe: methylationArrayAnalysis suggestsMe: missMethyl dependencyCount: 220 Package: DMRforPairs Version: 1.30.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: 955cb9ac55516adc5fc8fcbbf8bd29ac NeedsCompilation: no Title: DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles Description: DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance. biocViews: Microarray, DNAMethylation, DifferentialMethylation, ReportWriting, Visualization, Annotation Author: Martin Rijlaarsdam [aut, cre], Yvonne vd Zwan [aut], Lambert Dorssers [aut], Leendert Looijenga [aut] Maintainer: Martin Rijlaarsdam URL: http://www.martinrijlaarsdam.nl, http://www.erasmusmc.nl/pathologie/research/lepo/3898639/ git_url: https://git.bioconductor.org/packages/DMRforPairs git_branch: RELEASE_3_14 git_last_commit: 93fa270 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DMRforPairs_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMRforPairs_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMRforPairs_1.30.0.tgz vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf vignetteTitles: DMRforPairs_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.R dependencyCount: 143 Package: DMRScan Version: 1.16.0 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: 7884812f64d2c3607ca6cfc9a1936cf7 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: RELEASE_3_14 git_last_commit: 347754a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DMRScan_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DMRScan_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DMRScan_1.16.0.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: 25 Package: dmrseq Version: 1.14.0 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 License: MIT + file LICENSE MD5sum: 1e756e8d07df901dd1214471611c0a08 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: RELEASE_3_14 git_last_commit: ae24c3a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dmrseq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dmrseq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dmrseq_1.14.0.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: 165 Package: DNABarcodeCompatibility Version: 1.10.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, DNABarcodes, stats, utils, methods Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE MD5sum: 0156ffacda75e81d608a4555221ecd59 NeedsCompilation: no 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: Céline Trébeau VignetteBuilder: knitr BugReports: https://github.com/comoto-pasteur-fr/DNABarcodeCompatibility/issues git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility git_branch: RELEASE_3_14 git_last_commit: 1d9de52 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DNABarcodeCompatibility_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DNABarcodeCompatibility_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DNABarcodeCompatibility_1.10.0.tgz vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html vignetteTitles: Introduction to DNABarcodeCompatibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R dependencyCount: 36 Package: DNABarcodes Version: 1.24.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 6b26540e86205a6803c98a8c36d0970f NeedsCompilation: yes Title: A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments Description: The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode. biocViews: Preprocessing, Sequencing Author: Tilo Buschmann Maintainer: Tilo Buschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNABarcodes git_branch: RELEASE_3_14 git_last_commit: 4fee550 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DNABarcodes_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DNABarcodes_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DNABarcodes_1.24.0.tgz vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html vignetteTitles: DNABarcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R importsMe: DNABarcodeCompatibility dependencyCount: 11 Package: DNAcopy Version: 1.68.0 License: GPL (>= 2) Archs: i386, x64 MD5sum: d1443e082303ecb3c38754e86eb8bc6c 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: RELEASE_3_14 git_last_commit: 08f039f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DNAcopy_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DNAcopy_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DNAcopy_1.68.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, Clonality, CRImage, PureCN, CSclone, ParDNAcopy, saasCNV importsMe: ADaCGH2, AneuFinder, ChAMP, cn.farms, CNAnorm, CNVrd2, contiBAIT, conumee, CopywriteR, GWASTools, MDTS, MEDIPS, MinimumDistance, QDNAseq, Repitools, SCOPE, sesame, snapCGH, cghRA, jointseg, PSCBS suggestsMe: beadarraySNP, cn.mops, CopyNumberPlots, fastseg, nullranges, ACNE, aroma.cn, aroma.core, bcp, calmate dependencyCount: 0 Package: DNAshapeR Version: 1.22.0 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 Archs: i386, x64 MD5sum: b53458021b410c40e3c73f521ea1144a 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: RELEASE_3_14 git_last_commit: 851b8af git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DNAshapeR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DNAshapeR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DNAshapeR_1.22.0.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: 58 Package: DominoEffect Version: 1.14.0 Depends: R(>= 3.5) Imports: biomaRt, data.table, utils, stats, Biostrings, SummarizedExperiment, VariantAnnotation, AnnotationDbi, GenomeInfoDb, IRanges, GenomicRanges, methods Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 5635cae7243b26aa8c8eab8f8464f7e6 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: RELEASE_3_14 git_last_commit: edcdc89 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DominoEffect_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DominoEffect_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DominoEffect_1.14.0.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: 99 Package: doppelgangR Version: 1.22.0 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: a5e95f67b0ec8c66f283ced137c5d9b1 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: RELEASE_3_14 git_last_commit: 595a642 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/doppelgangR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/doppelgangR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/doppelgangR_1.22.0.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: 77 Package: Doscheda Version: 1.16.0 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: c6e81112cfe37c45ccf3cd123732d168 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: RELEASE_3_14 git_last_commit: cb8611a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Doscheda_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Doscheda_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Doscheda_1.16.0.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: 156 Package: DOSE Version: 3.20.1 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocParallel, DO.db, fgsea, ggplot2, GOSemSim (>= 2.0.0), methods, qvalue, reshape2, stats, utils Suggests: prettydoc, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, testthat License: Artistic-2.0 MD5sum: 29105f38e9b3dfdb0260642c49c456cb 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: RELEASE_3_14 git_last_commit: bf434f2 git_last_commit_date: 2021-11-17 Date/Publication: 2021-11-18 source.ver: src/contrib/DOSE_3.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/DOSE_3.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/DOSE_3.20.1.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, eegc, enrichplot, GDCRNATools, meshes, miRspongeR, MoonlightR, ReactomePA, RegEnrich, RNASeqR, scTensor, signatureSearch suggestsMe: cola, GOSemSim, MAGeCKFlute, Pigengene, rrvgo, scGPS, simplifyEnrichment, genekitr dependencyCount: 91 Package: doseR Version: 1.10.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: 2fe5442aec13a9cfc4ea65f94cc23d72 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: RELEASE_3_14 git_last_commit: 48979e0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/doseR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/doseR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/doseR_1.10.0.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: 71 Package: dpeak Version: 1.6.0 Depends: R (>= 4.0.0), methods, stats, utils, graphics, Rcpp Imports: MASS, IRanges, BSgenome, grDevices, parallel LinkingTo: Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805 License: GPL (>= 2) Archs: i386, x64 MD5sum: fdbad6a10be52190f097450164103f58 NeedsCompilation: yes Title: dPeak (Deconvolution of Peaks in ChIP-seq Analysis) Description: dPeak is a statistical framework for the high resolution identification of protein-DNA interaction sites using PET and SET ChIP-Seq and ChIP-exo data. It provides computationally efficient and user friendly interface to process ChIP-seq and ChIP-exo data, implement exploratory analysis, fit dPeak model, and export list of predicted binding sites for downstream analysis. biocViews: ChIPSeq, Genetics, Sequencing, Software, Transcription Author: Dongjun Chung, Carter Allen Maintainer: Dongjun Chung SystemRequirements: GNU make, meme, fimo BugReports: https://github.com/dongjunchung/dpeak/issues git_url: https://git.bioconductor.org/packages/dpeak git_branch: RELEASE_3_14 git_last_commit: 0981797 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dpeak_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dpeak_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dpeak_1.6.0.tgz vignettes: vignettes/dpeak/inst/doc/dpeak-example.pdf vignetteTitles: dPeak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dpeak/inst/doc/dpeak-example.R dependencyCount: 47 Package: drawProteins Version: 1.14.0 Depends: R (>= 4.0) Imports: ggplot2, httr, dplyr, readr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 2f5af194a3c1833984207cb696f08546 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: RELEASE_3_14 git_last_commit: cf4314f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/drawProteins_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/drawProteins_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/drawProteins_1.14.0.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 dependencyCount: 61 Package: DRIMSeq Version: 1.22.0 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: 7509e71d266e9803e28805eab35b5577 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: RELEASE_3_14 git_last_commit: a22c59b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DRIMSeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DRIMSeq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DRIMSeq_1.22.0.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, IsoformSwitchAnalyzeR dependencyCount: 66 Package: DriverNet Version: 1.34.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 5fab0618280c6f366f9086ef690782bf 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: RELEASE_3_14 git_last_commit: a6f3e1e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DriverNet_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DriverNet_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DriverNet_1.34.0.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: 1.14.2 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 Archs: i386, x64 MD5sum: c602e5989814f36543f3a5c8d957dad0 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: RELEASE_3_14 git_last_commit: 945504d git_last_commit_date: 2022-01-08 Date/Publication: 2022-01-09 source.ver: src/contrib/DropletUtils_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/DropletUtils_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.1/DropletUtils_1.14.2.tgz vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html vignetteTitles: Utilities for handling droplet-based single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R dependsOnMe: OSCA.advanced, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: scCB2, singleCellTK, Spaniel, SpatialExperiment suggestsMe: mumosa, Nebulosa, DropletTestFiles, muscData, SoupX dependencyCount: 51 Package: drugTargetInteractions Version: 1.2.0 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: 518a9731e109440efd575daf13a87621 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: RELEASE_3_14 git_last_commit: 0c4904f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/drugTargetInteractions_1.2.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/drugTargetInteractions_1.2.0.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: 101 Package: DrugVsDisease Version: 2.36.0 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: 83e75e86754a9b8f4bfd918976057806 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: RELEASE_3_14 git_last_commit: 06798d2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DrugVsDisease_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DrugVsDisease_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DrugVsDisease_2.36.0.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: 129 Package: DSS Version: 2.42.0 Depends: R (>= 3.3), methods, Biobase, BiocParallel, bsseq Imports: utils, graphics, stats, splines, DelayedArray Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: i386, x64 MD5sum: 022156cee2c24354bfb496c004941748 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: RELEASE_3_14 git_last_commit: 33e8745 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DSS_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DSS_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DSS_2.42.0.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 importsMe: DMRcate, kissDE, metaseqR2, methylSig suggestsMe: biscuiteer, methrix, NanoMethViz dependencyCount: 74 Package: dStruct Version: 1.0.0 Depends: R (>= 4.1) Imports: zoo, ggplot2, purrr, reshape2, parallel, IRanges, S4Vectors, rlang, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 4ee5b72750144dd69d5c045fe5261d18 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: RELEASE_3_14 git_last_commit: 55b954a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dStruct_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dStruct_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dStruct_1.0.0.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: 51 Package: DTA Version: 2.40.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 9b258dac5490c4bf9406a5e39f70edf4 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: RELEASE_3_14 git_last_commit: f1e9cda git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DTA_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DTA_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DTA_2.40.0.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 dependencyCount: 5 Package: Dune Version: 1.6.0 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: 5f91745f3fac215654c96c5c21c50de4 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: RELEASE_3_14 git_last_commit: e79ce4b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Dune_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Dune_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Dune_1.6.0.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: 78 Package: dupRadar Version: 1.24.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1) Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: ce448594bfe36f7d2f96e35be3b56da8 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: RELEASE_3_14 git_last_commit: 4d892ac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dupRadar_1.24.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/dupRadar_1.24.0.tgz vignettes: vignettes/dupRadar/inst/doc/dupRadar.html vignetteTitles: Using dupRadar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R dependencyCount: 9 Package: dyebias Version: 1.54.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: a01a2556c017eb40a9132e91be3277e7 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: RELEASE_3_14 git_last_commit: 4b9e833 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/dyebias_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/dyebias_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/dyebias_1.54.0.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: 9 Package: DynDoc Version: 1.72.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: e5e9914a93ae4231e96cb2a65ceb0da1 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: RELEASE_3_14 git_last_commit: d123fc1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/DynDoc_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/DynDoc_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/DynDoc_1.72.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: easier Version: 1.0.0 Depends: R (>= 4.1.0) Imports: progeny, easierData, dorothea (>= 1.0.0), quantiseqr, ROCR, grDevices, stats, graphics, ggplot2, grid, DESeq2, utils, dplyr, matrixStats, rlang, arules, BiocParallel, reshape2, rstatix, ggrepel, coin Suggests: knitr, rmarkdown, BiocStyle, testthat, SummarizedExperiment License: MIT + file LICENSE MD5sum: 37733b34f5c21b342f25fa61f43de9ac NeedsCompilation: no Title: Estimate Systems Immune Response from RNA-seq data Description: This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy. biocViews: GeneExpression, Software, Transcription, SystemsBiology, Pathways, GeneSetEnrichment, ImmunoOncology, Epigenetics, Classification, BiomedicalInformatics, Regression, ExperimentHubSoftware Author: Oscar Lapuente-Santana [aut, cre] (), Federico Marini [aut] (), Arsenij Ustjanzew [aut] (), Francesca Finotello [aut] (), Federica Eduati [aut] () Maintainer: Oscar Lapuente-Santana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easier git_branch: RELEASE_3_14 git_last_commit: efb058c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/easier_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/easier_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/easier_1.0.0.tgz vignettes: vignettes/easier/inst/doc/easier_user_manual.html vignetteTitles: easier User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/easier/inst/doc/easier_user_manual.R dependencyCount: 202 Package: easyreporting Version: 1.6.0 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: 2bb276653b7a8a82e99c80cc4448de10 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: RELEASE_3_14 git_last_commit: 149f550 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/easyreporting_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/easyreporting_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/easyreporting_1.6.0.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: 44 Package: easyRNASeq Version: 2.30.0 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 MD5sum: d2c05648f015e58968a135e232e95ab6 NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing, ImmunoOncology Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easyRNASeq git_branch: RELEASE_3_14 git_last_commit: deeb59e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/easyRNASeq_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/easyRNASeq_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/easyRNASeq_2.30.0.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf, vignettes/easyRNASeq/inst/doc/simpleRNASeq.html vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis, geneNetworkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R importsMe: msgbsR dependencyCount: 101 Package: EBarrays Version: 2.58.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: bdb6f0eafba9c6a48504cdab9366d81b 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: RELEASE_3_14 git_last_commit: d574475 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EBarrays_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBarrays_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBarrays_2.58.0.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: 1.38.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: i386, x64 MD5sum: 484e2dd3e6cb1e97ec2e31100f87638a 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: RELEASE_3_14 git_last_commit: b5bb5f1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EBcoexpress_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBcoexpress_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBcoexpress_1.38.0.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: 4.36.0 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: i386, x64 MD5sum: e14b9307d45442d763207fa2c291b951 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: RELEASE_3_14 git_last_commit: 7919aaf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EBImage_4.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBImage_4.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBImage_4.36.0.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: Cardinal, CRImage, cytomapper, flowcatchR, imageHTS, DonaPLLP2013, furrowSeg, GiNA, nucim, ShinyImage importsMe: bnbc, flowCHIC, heatmaps, imcRtools, synapsis, yamss, BioImageDbs, bioimagetools, CropDetectR, GoogleImage2Array, LFApp, LOMAR, RockFab, SAFARI, trackter suggestsMe: HilbertVis, tofsims, DmelSGI, aroma.core, cooltools, ExpImage, graphx, ijtiff, juicr, lidR, metagear, pliman, ProFound dependencyCount: 24 Package: EBSEA Version: 1.22.0 Depends: R (>= 4.0.0) Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod Suggests: knitr, rmarkdown License: GPL-2 MD5sum: c6a5f608ac70236aaf571106a1c689b1 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: RELEASE_3_14 git_last_commit: 2d148e5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EBSEA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBSEA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBSEA_1.22.0.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: 94 Package: EBSeq Version: 1.34.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 MD5sum: 6e52442223e1ba0c89c64044d6f5d242 NeedsCompilation: no 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: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeq git_branch: RELEASE_3_14 git_last_commit: 3398c86 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EBSeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBSeq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBSeq_1.34.0.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: EBSeqHMM, Oscope importsMe: DEsubs, scDD suggestsMe: compcodeR dependencyCount: 47 Package: EBSeqHMM Version: 1.28.0 Depends: EBSeq License: Artistic-2.0 MD5sum: d79166c2bac15c3e733cadc58f1c60f7 NeedsCompilation: no Title: Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments Description: The EBSeqHMM package implements an auto-regressive hidden Markov model for statistical analysis in ordered RNA-seq experiments (e.g. time course or spatial course data). The EBSeqHMM package provides functions to identify genes and isoforms that have non-constant expression profile over the time points/positions, and cluster them into expression paths. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing, GeneExpression, Bayesian, HiddenMarkovModel, TimeCourse Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeqHMM git_branch: RELEASE_3_14 git_last_commit: a980624 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EBSeqHMM_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EBSeqHMM_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EBSeqHMM_1.28.0.tgz vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf vignetteTitles: HMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.R dependencyCount: 48 Package: ecolitk Version: 1.66.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: 2819ce01e4a9eca79f89990ee16fb3ff 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: RELEASE_3_14 git_last_commit: 7494e6f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ecolitk_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ecolitk_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ecolitk_1.66.0.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: 2.28.0 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: 7dbb0e09a328b4179a229f6b59b9df77 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: RELEASE_3_14 git_last_commit: 358891b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EDASeq_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EDASeq_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EDASeq_2.28.0.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, ribosomeProfilingQC suggestsMe: awst, bigPint, DEScan2, easyreporting, HTSFilter, TCGAbiolinks dependencyCount: 106 Package: edge Version: 2.26.0 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, snm, jackstraw, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE Archs: i386, x64 MD5sum: b1300abe5402c726c37277de1998f6b7 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 snm, 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: RELEASE_3_14 git_last_commit: 6eaa176 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/edge_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/edge_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/edge_2.26.0.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: 124 Package: edgeR Version: 3.36.0 Depends: R (>= 3.6.0), limma (>= 3.41.5) Imports: methods, graphics, stats, utils, locfit, Rcpp LinkingTo: Rcpp Suggests: jsonlite, readr, rhdf5, splines, Biobase, AnnotationDbi, SummarizedExperiment, org.Hs.eg.db License: GPL (>=2) Archs: i386, x64 MD5sum: 701baffaf8e985b0affc2d016f66acbb 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 Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, 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: http://bioinf.wehi.edu.au/edgeR, https://bioconductor.org/packages/edgeR SystemRequirements: C++11 git_url: https://git.bioconductor.org/packages/edgeR git_branch: RELEASE_3_14 git_last_commit: c7db03a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/edgeR_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/edgeR_3.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/edgeR_3.36.0.tgz vignettes: vignettes/edgeR/inst/doc/edgeR.pdf, vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ASpli, IntEREst, methylMnM, miloR, RNASeqR, RUVSeq, TCC, tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU, RnaSeqGeneEdgeRQL, csawBook, OSCA.advanced, OSCA.multisample, OSCA.workflows, babel, BALLI, BioInsight, edgeRun, GSAgm importsMe: affycoretools, ArrayExpressHTS, ATACseqQC, autonomics, AWFisher, baySeq, benchdamic, BioQC, censcyt, ChromSCape, circRNAprofiler, clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq, countsimQC, crossmeta, csaw, DaMiRseq, dce, debrowser, DEComplexDisease, DEFormats, DEGreport, DEsubs, diffcyt, diffHic, diffloop, diffUTR, DMRcate, doseR, DRIMSeq, DropletUtils, easyRNASeq, eegc, EGSEA, eisaR, EnrichmentBrowser, erccdashboard, ERSSA, GDCRNATools, Glimma, GSEABenchmarkeR, HTSFilter, icetea, infercnv, IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, MEDIPS, metaseqR2, microbiomeMarker, MIGSA, MLSeq, moanin, msgbsR, msmsTests, multiHiCcompare, muscat, NBSplice, PathoStat, PhIPData, ppcseq, PROPER, psichomics, RCM, regsplice, Repitools, ROSeq, scCB2, scde, scone, scran, SEtools, SIMD, SingleCellSignalR, singscore, sparrow, spatialHeatmap, splatter, SPsimSeq, srnadiff, STATegRa, sva, TBSignatureProfiler, TCseq, TimeSeriesExperiment, tradeSeq, treekoR, tweeDEseq, vidger, yarn, zinbwave, emtdata, ExpHunterSuite, recountWorkflow, SingscoreAMLMutations, BinQuasi, bulkAnalyseR, CAMML, CIDER, cinaR, DGEobj.utils, digitalDLSorteR, HTSCluster, MetaLonDA, microbial, myTAI, QuasiSeq, RVA, scITD, SCRIP, scRNAtools, SPUTNIK, ssizeRNA, TSGS suggestsMe: ABSSeq, bigPint, biobroom, ClassifyR, clonotypeR, cqn, cydar, dcanr, dearseq, DEScan2, dittoSeq, easyreporting, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, glmGamPoi, goseq, groHMM, GSAR, GSVA, ideal, iSEEu, missMethyl, multiMiR, recount, regionReport, ribosomeProfilingQC, satuRn, SeqGate, stageR, subSeq, SummarizedBenchmark, systemPipeR, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, variancePartition, weitrix, Wrench, zFPKM, leeBamViews, CAGEWorkflow, chipseqDB, DGEobj, DiPALM, GeoTcgaData, glmmSeq, langevitour, seqgendiff, SIBERG dependencyCount: 10 Package: eegc Version: 1.20.0 Depends: R (>= 3.4.0) Imports: R.utils, gplots, sna, wordcloud, igraph, pheatmap, edgeR, DESeq2, clusterProfiler, S4Vectors, ggplot2, org.Hs.eg.db, org.Mm.eg.db, limma, DOSE, AnnotationDbi Suggests: knitr License: GPL-2 MD5sum: f99f3ddb25e5f1a9018ba764db4d8468 NeedsCompilation: no Title: Engineering Evaluation by Gene Categorization (eegc) Description: This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic cells to primary cells based on high throughput gene expression data screened either by DNA microarray or RNA sequencing. The package takes gene expression profiles as inputs from three types of samples: (i) somatic or stem cells to be (trans)differentiated (input of the engineering process), (ii) induced cells to be evaluated (output of the engineering process) and (iii) target primary cells (reference for the output). The package performs differential gene expression analysis for each pair-wise sample comparison to identify and evaluate the transcriptional differences among the 3 types of samples (input, output, reference). The ideal goal is to have induced and primary reference cell showing overlapping profiles, both very different from the original cells. biocViews: ImmunoOncology, Microarray, Sequencing, RNASeq, DifferentialExpression, GeneRegulation, GeneSetEnrichment, GeneExpression, GeneTarget Author: Xiaoyuan Zhou, Guofeng Meng, Christine Nardini, Hongkang Mei Maintainer: Xiaoyuan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eegc git_branch: RELEASE_3_14 git_last_commit: fdab2cb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/eegc_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/eegc_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/eegc_1.20.0.tgz vignettes: vignettes/eegc/inst/doc/eegc.pdf vignetteTitles: Engineering Evaluation by Gene Categorization (eegc) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eegc/inst/doc/eegc.R dependencyCount: 155 Package: EGAD Version: 1.22.0 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: d69b8ede16e057fb316313f00118b012 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: RELEASE_3_14 git_last_commit: dc2b9cc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EGAD_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EGAD_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EGAD_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 68 Package: EGSEA Version: 1.22.0 Depends: R (>= 3.5), 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: 6a09150a64cde7fe8297bf0940da6dcc 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. 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, Luyi Tian, Milica Ng and Matthew Ritchie Maintainer: Monther Alhamdoosh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA git_branch: RELEASE_3_14 git_last_commit: 5a14266 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EGSEA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EGSEA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EGSEA_1.22.0.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: EGSEAdata dependencyCount: 177 Package: eiR Version: 1.34.0 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 License: Artistic-2.0 Archs: x64 MD5sum: 7644a4e70f7a7a12ccfde9406de9655b 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: RELEASE_3_14 git_last_commit: 2f67dc0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/eiR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/eiR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/eiR_1.34.0.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: 67 Package: eisaR Version: 1.6.0 Depends: R (>= 4.1) Imports: graphics, stats, GenomicRanges, S4Vectors, IRanges, limma, edgeR, methods, SummarizedExperiment, BiocGenerics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie, Rhisat2, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38, ensembldb, AnnotationDbi, GenomicFeatures, rtracklayer License: GPL-3 MD5sum: 9b4bf1ef195fd0530348c2760493cd71 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: RELEASE_3_14 git_last_commit: e42ce13 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/eisaR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/eisaR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/eisaR_1.6.0.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: 29 Package: ELMER Version: 2.18.0 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 (>= 2.9.2), 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, DelayedArray Suggests: BiocStyle, knitr, testthat, data.table, DT, GenomicInteractions, webshot, R.utils, covr, sesameData License: GPL-3 MD5sum: e75fd606037ca382a552f981e2a6b50a 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: RELEASE_3_14 git_last_commit: b116994 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ELMER_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ELMER_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ELMER_2.18.0.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: TCGAbiolinksGUI, TCGAWorkflow dependencyCount: 217 Package: EMDomics Version: 2.24.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: 524734f0cda42e2260fe73d16fe78e7d 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: RELEASE_3_14 git_last_commit: e13cad0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EMDomics_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EMDomics_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EMDomics_2.24.0.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: 50 Package: EmpiricalBrownsMethod Version: 1.22.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: d653f5357f8ca79caefe4e7328a078fd 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: RELEASE_3_14 git_last_commit: 6cf0f02 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EmpiricalBrownsMethod_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EmpiricalBrownsMethod_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EmpiricalBrownsMethod_1.22.0.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: 1.12.0 Depends: ggplot2, ggrepel Imports: ggalt, ggrastr Suggests: RUnit, BiocGenerics, knitr, DESeq2, pasilla, airway, org.Hs.eg.db, gridExtra, magrittr, rmarkdown License: GPL-3 MD5sum: f8e8a65740c52f7f5c6a99c5e04bb142 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: RELEASE_3_14 git_last_commit: d991f38 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EnhancedVolcano_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EnhancedVolcano_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EnhancedVolcano_1.12.0.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 dependencyCount: 82 Package: enhancerHomologSearch Version: 1.0.1 Depends: R (>= 4.1.0), methods Imports: BiocGenerics, Biostrings, BSgenome, BiocParallel, BiocFileCache, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, motifmatchr, Matrix, 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) Archs: i386, x64 MD5sum: ff4f4b536586fe4ca9525b7f4f13331f 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: RELEASE_3_14 git_last_commit: 33957ae git_last_commit_date: 2022-03-26 Date/Publication: 2022-03-27 source.ver: src/contrib/enhancerHomologSearch_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/enhancerHomologSearch_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/enhancerHomologSearch_1.0.1.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: 132 Package: EnMCB Version: 1.6.0 Depends: R (>= 4.0) Imports: foreach, doParallel, parallel, stats, survivalROC, glmnet, rms, mboost, survivalsvm, ggplot2, IlluminaHumanMethylation450kanno.ilmn12.hg19, minfi, boot, survival, utils Suggests: SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, prognosticROC, rmarkdown License: GPL-2 MD5sum: a895ef22f7d5b3ebc64db9a0c64e6eb8 NeedsCompilation: no Title: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models Description: Creation of the correlated blocks using DNA methylation profiles. A stacked ensemble of machine learning models, which combined the cox, support vector machine and elastic-net regression model, can be constructed to predict 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: RELEASE_3_14 git_last_commit: 54fb4f0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EnMCB_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EnMCB_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EnMCB_1.6.0.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: 199 Package: ENmix Version: 1.30.03 Depends: R (>= 3.5.0), parallel, doParallel, foreach, SummarizedExperiment, stats Imports: grDevices,graphics,preprocessCore,matrixStats,methods,utils, geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools, Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors Suggests: minfiData, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 15d58409214a72a1d7d6749b258cdb53 NeedsCompilation: no Title: Quality control and analysis tools for Illumina DNA methylation BeadChip Description: Tool kits for quanlity control, analysis and visulization of Illumina DNA methylation arrays. 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 git_url: https://git.bioconductor.org/packages/ENmix git_branch: RELEASE_3_14 git_last_commit: 605d40e git_last_commit_date: 2022-04-01 Date/Publication: 2022-04-03 source.ver: src/contrib/ENmix_1.30.03.tar.gz win.binary.ver: bin/windows/contrib/4.1/ENmix_1.30.03.zip mac.binary.ver: bin/macosx/contrib/4.1/ENmix_1.30.03.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.pdf vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENmix/inst/doc/ENmix.R dependencyCount: 174 Package: EnrichedHeatmap Version: 1.24.0 Depends: R (>= 3.1.2), methods, grid, ComplexHeatmap (>= 2.5.1), 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 Archs: i386, x64 MD5sum: b972774d5bf640b5ffafe7de62700be7 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: RELEASE_3_14 git_last_commit: 40ef524 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EnrichedHeatmap_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EnrichedHeatmap_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EnrichedHeatmap_1.24.0.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, InteractiveComplexHeatmap dependencyCount: 40 Package: EnrichmentBrowser Version: 2.24.2 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 License: Artistic-2.0 MD5sum: 31bd3929a8de6e0449b78541d1b811f2 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], 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: RELEASE_3_14 git_last_commit: a31c968 git_last_commit_date: 2022-02-11 Date/Publication: 2022-02-13 source.ver: src/contrib/EnrichmentBrowser_2.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/EnrichmentBrowser_2.24.2.zip mac.binary.ver: bin/macosx/contrib/4.1/EnrichmentBrowser_2.24.2.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf vignetteTitles: EnrichmentBrowser Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R importsMe: GSEABenchmarkeR suggestsMe: GenomicSuperSignature dependencyCount: 91 Package: enrichplot Version: 1.14.2 Depends: R (>= 3.5.0) Imports: aplot, DOSE (>= 3.16.0), ggplot2, ggraph, graphics, grid, igraph, methods, plyr, purrr, RColorBrewer, reshape2, stats, utils, scatterpie, shadowtext, GOSemSim, magrittr, ggtree, yulab.utils (>= 0.0.4) Suggests: clusterProfiler, dplyr, europepmc, ggupset, knitr, rmarkdown, org.Hs.eg.db, prettydoc, tibble, tidyr, ggforce, AnnotationDbi, ggplotify, ggridges, grDevices, gridExtra, ggnewscale, ggrepel (>= 0.9.0), ggstar, treeio, scales, tidytree, ggtreeExtra License: Artistic-2.0 MD5sum: b2071b6146d677c1814484071fd47d8a 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] 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: RELEASE_3_14 git_last_commit: 7ffc704 git_last_commit_date: 2022-02-23 Date/Publication: 2022-02-24 source.ver: src/contrib/enrichplot_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/enrichplot_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.1/enrichplot_1.14.2.tgz vignettes: vignettes/enrichplot/inst/doc/enrichplot.html vignetteTitles: enrichplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maEndToEnd importsMe: ChIPseeker, clusterProfiler, debrowser, MAGeCKFlute, meshes, MicrobiomeProfiler, multiSight, ReactomePA, ExpHunterSuite suggestsMe: methylGSA dependencyCount: 122 Package: enrichTF Version: 1.10.0 Depends: pipeFrame Imports: BSgenome, rtracklayer, motifmatchr, TFBSTools, R.utils, methods, JASPAR2018, GenomeInfoDb, GenomicRanges, IRanges, BiocGenerics, S4Vectors, utils, parallel, stats, ggpubr, heatmap3, ggplot2, clusterProfiler, rmarkdown, grDevices, magrittr Suggests: knitr, testthat, webshot License: GPL-3 MD5sum: c279944c0d0e821fbb4163d06aa2138f NeedsCompilation: no Title: Transcription Factors Enrichment Analysis Description: As transcription factors (TFs) play a crucial role in regulating the transcription process through binding on the genome alone or in a combinatorial manner, TF enrichment analysis is an efficient and important procedure to locate the candidate functional TFs from a set of experimentally defined regulatory regions. While it is commonly accepted that structurally related TFs may have similar binding preference to sequences (i.e. motifs) and one TF may have multiple motifs, TF enrichment analysis is much more challenging than motif enrichment analysis. Here we present a R package for TF enrichment analysis which combine motif enrichment with the PECA model. biocViews: Software, GeneTarget, MotifAnnotation, GraphAndNetwork, Transcription Author: Zheng Wei, Zhana Duren, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/enrichTF VignetteBuilder: knitr BugReports: https://github.com/wzthu/enrichTF/issues git_url: https://git.bioconductor.org/packages/enrichTF git_branch: RELEASE_3_14 git_last_commit: bc4115f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/enrichTF_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/enrichTF_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/enrichTF_1.10.0.tgz vignettes: vignettes/enrichTF/inst/doc/enrichTF.html vignetteTitles: An Introduction to enrichTF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enrichTF/inst/doc/enrichTF.R dependencyCount: 219 Package: ensembldb Version: 2.18.4 Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.31.18), GenomicFeatures (>= 1.29.10), 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), magrittr, rmarkdown, AnnotationHub Enhances: RMariaDB, shiny License: LGPL MD5sum: 63a4974ec0cdf9d6301d26d20fec1a05 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 and Christian Weichenberger. 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: RELEASE_3_14 git_last_commit: 0c7105f git_last_commit_date: 2022-03-22 Date/Publication: 2022-03-24 source.ver: src/contrib/ensembldb_2.18.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/ensembldb_2.18.4.zip mac.binary.ver: bin/macosx/contrib/4.1/ensembldb_2.18.4.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, AHEnsDbs, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v75, EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v75, EnsDb.Rnorvegicus.v79 importsMe: APAlyzer, biovizBase, BUSpaRse, ChIPpeakAnno, consensusDE, diffUTR, epivizrData, ggbio, Gviz, ldblock, metagene, scanMiRApp, TVTB, tximeta, GenomicDistributionsData, scRNAseq, RNAseqQC, utr.annotation suggestsMe: alpine, CNVRanger, dasper, eisaR, EpiTxDb, GenomicFeatures, multicrispr, satuRn, wiggleplotr dependencyCount: 99 Package: ensemblVEP Version: 1.36.1 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment, GenomeInfoDb, stats Suggests: RUnit License: Artistic-2.0 MD5sum: 8334f186ea61f0adb503dd20648bd3e9 NeedsCompilation: no Title: R Interface to Ensembl Variant Effect Predictor Description: Query the Ensembl Variant Effect Predictor via the perl API. biocViews: Annotation, VariantAnnotation, SNP Author: Valerie Obenchain and Lori Shepherd Maintainer: Bioconductor Package Maintainer SystemRequirements: Ensembl VEP (API version 105) and the Perl modules DBI and DBD::mysql must be installed. See the package README and Ensembl installation instructions: http://www.ensembl.org/info/docs/tools/vep/script/vep_download.html#installer git_url: https://git.bioconductor.org/packages/ensemblVEP git_branch: RELEASE_3_14 git_last_commit: e425fbd git_last_commit_date: 2022-03-29 Date/Publication: 2022-03-31 source.ver: src/contrib/ensemblVEP_1.36.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ensemblVEP_1.36.1.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.pdf vignetteTitles: ensemblVEP, PreV90EnsemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.R importsMe: MMAPPR2, TVTB dependencyCount: 98 Package: epialleleR Version: 1.2.0 Depends: R (>= 4.1) Imports: stats, methods, utils, GenomicRanges, BiocGenerics, GenomeInfoDb, SummarizedExperiment, VariantAnnotation, stringi, data.table LinkingTo: Rcpp, BH, Rhtslib, zlibbioc Suggests: RUnit, knitr, rmarkdown License: Artistic-2.0 MD5sum: 08f6206c96c6ab6a189da9f192f99b44 NeedsCompilation: yes Title: Fast, Epiallele-Aware Methylation Reporter Description: Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls hypermethylated epiallele frequencies at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Other functionality includes computing the empirical cumulative distribution function for per-read beta values, and testing the significance of the association between epiallele methylation status and base frequencies at particular genomic positions (SNPs). 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: RELEASE_3_14 git_last_commit: 625bf03 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epialleleR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epialleleR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epialleleR_1.2.0.tgz vignettes: vignettes/epialleleR/inst/doc/epialleleR.html vignetteTitles: epialleleR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epialleleR/inst/doc/epialleleR.R dependencyCount: 99 Package: epidecodeR Version: 1.2.0 Depends: R (>= 3.1.0) Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges, rstatix, ggpubr, methods, stats, utils, dplyr Suggests: knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 7d866bfba57675a46228ff577707b52f 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: RELEASE_3_14 git_last_commit: 34677d2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epidecodeR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epidecodeR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epidecodeR_1.2.0.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: 130 Package: EpiDISH Version: 2.10.0 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: c11ec92ca9abc29638f012acc83ce1fc 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: RELEASE_3_14 git_last_commit: 187ec2a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EpiDISH_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EpiDISH_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EpiDISH_2.10.0.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, FlowSorted.Blood.EPIC dependencyCount: 22 Package: epigenomix Version: 1.34.0 Depends: R (>= 3.2.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: 2b0bb472ecda2968be4507a9e5f5ed24 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: RELEASE_3_14 git_last_commit: 11b683e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epigenomix_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epigenomix_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epigenomix_1.34.0.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: 102 Package: epigraHMM Version: 1.2.2 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: c256ab7add6322c7f5dad27934183b61 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: RELEASE_3_14 git_last_commit: 67481cd git_last_commit_date: 2022-01-13 Date/Publication: 2022-01-16 source.ver: src/contrib/epigraHMM_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/epigraHMM_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/epigraHMM_1.2.2.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: 142 Package: epihet Version: 1.10.0 Depends: R(>= 3.6), GenomicRanges, IRanges, S4Vectors, ggplot2, foreach, Rtsne, igraph Imports: data.table, doParallel, EntropyExplorer, graphics, stats, grDevices, pheatmap, utils, qvalue, WGCNA, ReactomePA Suggests: knitr, clusterProfiler, ggfortify, org.Hs.eg.db, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: c493f4c7a42c31240dc0eed9c4e93d9f NeedsCompilation: no Title: Determining Epigenetic Heterogeneity from Bisulfite Sequencing Data Description: epihet is an R-package that calculates the epigenetic heterogeneity between cancer cells and/or normal cells. The functions establish a pipeline that take in bisulfite sequencing data from multiple samples and use the data to track similarities and differences in epipolymorphism,proportion of discordantly methylated sequencing reads (PDR),and Shannon entropy values at epialleles that are shared between the samples.epihet can be used to perform analysis on the data by creating pheatmaps, box plots, PCA plots, and t-SNE plots. MA plots can also be created by calculating the differential heterogeneity of the samples. And we construct co-epihet network and perform network analysis. biocViews: DNAMethylation, Epigenetics, MethylSeq, Sequencing, Software Author: Xiaowen Chen [aut, cre], Haitham Ashoor [aut], Ryan Musich [aut], Mingsheng Zhang [aut], Jiahui Wang [aut], Sheng Li [aut] Maintainer: Xiaowen Chen URL: https://github.com/TheJacksonLaboratory/epihet VignetteBuilder: knitr BugReports: https://github.com/TheJacksonLaboratory/epihet/issues git_url: https://git.bioconductor.org/packages/epihet git_branch: RELEASE_3_14 git_last_commit: 59ace58 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epihet_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epihet_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epihet_1.10.0.tgz vignettes: vignettes/epihet/inst/doc/epihet.pdf vignetteTitles: epihet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epihet/inst/doc/epihet.R dependencyCount: 163 Package: epiNEM Version: 1.18.0 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 License: GPL-3 MD5sum: f985ccd7cde6701dcd2758fdef18e247 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: RELEASE_3_14 git_last_commit: 1a58b62 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epiNEM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epiNEM_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epiNEM_1.18.0.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: 108 Package: epistack Version: 1.0.0 Depends: R (>= 4.1) Imports: GenomicRanges, BiocGenerics, S4Vectors, IRanges, viridisLite, graphics, plotrix, grDevices, stats Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, EnrichedHeatmap, biomaRt, rtracklayer, covr, vdiffr, magick License: MIT + file LICENSE MD5sum: 9c909a7af1b66401d7a8b60771ba43e6 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. biocViews: RNASeq, Preprocessing, ChIPSeq, GeneExpression Author: SACI Safia [aut], DEVAILLY Guillaume [cre] Maintainer: DEVAILLY Guillaume VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epistack git_branch: RELEASE_3_14 git_last_commit: fe0a920 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epistack_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epistack_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epistack_1.0.0.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: 19 Package: EpiTxDb Version: 1.6.0 Depends: R (>= 4.0), AnnotationDbi, Modstrings Imports: methods, utils, httr, xml2, curl, GenomicFeatures, 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: f0457772502e95c551f274a16f62753a 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: RELEASE_3_14 git_last_commit: 18f60f3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EpiTxDb_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EpiTxDb_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EpiTxDb_1.6.0.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: 114 Package: epivizr Version: 2.24.0 Depends: R (>= 3.0), 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: da1e6456d68532232f29617b2eae786c 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: RELEASE_3_14 git_last_commit: 3199fac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epivizr_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizr_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizr_2.24.0.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 importsMe: metavizr dependencyCount: 117 Package: epivizrChart Version: 1.16.0 Depends: R (>= 3.4.0) 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: 4c6c640f6d8fd92ea0ff1007f17f48e0 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: RELEASE_3_14 git_last_commit: 8d712a2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epivizrChart_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizrChart_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizrChart_1.16.0.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: 111 Package: epivizrData Version: 1.22.0 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: 4a4c6c7cb1e023c18fee82600456318c 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: RELEASE_3_14 git_last_commit: 2f00af5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epivizrData_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizrData_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizrData_1.22.0.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, metavizr, scTreeViz dependencyCount: 108 Package: epivizrServer Version: 1.22.0 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: 3adbbe8110ac8a2ebbae0b4ede0cb52a 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: RELEASE_3_14 git_last_commit: ae9284c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epivizrServer_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizrServer_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizrServer_1.22.0.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, metavizr, scTreeViz dependencyCount: 13 Package: epivizrStandalone Version: 1.22.0 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: fd7dabba8d91a46721f498967e91af06 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: RELEASE_3_14 git_last_commit: f6cc682 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/epivizrStandalone_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/epivizrStandalone_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/epivizrStandalone_1.22.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: metavizr suggestsMe: scTreeViz dependencyCount: 119 Package: erccdashboard Version: 1.28.0 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: ea4aa4aff2893048ca938dc675222558 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: RELEASE_3_14 git_last_commit: 7191330 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/erccdashboard_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/erccdashboard_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/erccdashboard_1.28.0.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: 55 Package: erma Version: 1.10.0 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: f7fff602e0cc50e3586f4e60ebd30662 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: RELEASE_3_14 git_last_commit: 8ff4f3c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/erma_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/erma_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/erma_1.10.0.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: 135 Package: ERSSA Version: 1.12.0 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), grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: c1233d98290cebd752a274bc844d6d12 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: RELEASE_3_14 git_last_commit: d644e22 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ERSSA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ERSSA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ERSSA_1.12.0.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: 96 Package: esATAC Version: 1.16.0 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, 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 License: GPL-3 | file LICENSE MD5sum: 8cb25d71fa98ea32fec47131ddf9a229 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: RELEASE_3_14 git_last_commit: 094b015 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/esATAC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/esATAC_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/esATAC_1.16.0.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: 201 Package: escape Version: 1.4.0 Depends: R (>= 4.0) Imports: grDevices, dplyr, ggplot2, GSEABase, GSVA, SingleCellExperiment, limma, ggridges, msigdbr, stats, BiocParallel, Matrix Suggests: Seurat, SeuratObject, knitr, rmarkdown, BiocStyle, testthat, dittoSeq (>= 1.1.2) License: Apache License 2.0 Archs: x64 MD5sum: 7d57e2e0349393996d4aca3735e3e015 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 GSEA 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: RELEASE_3_14 git_last_commit: b3d13db git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/escape_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/escape_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/escape_1.4.0.tgz vignettes: vignettes/escape/inst/doc/vignette.html vignetteTitles: Escape-ingToWork hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/escape/inst/doc/vignette.R suggestsMe: Cepo dependencyCount: 111 Package: esetVis Version: 1.20.0 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices, methods Suggests: ggplot2, ggvis, rbokeh, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment License: GPL-3 MD5sum: f18d2cbe95ac615fc4ef76c4348a1324 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: RELEASE_3_14 git_last_commit: 25993b9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/esetVis_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/esetVis_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/esetVis_1.20.0.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: 56 Package: eudysbiome Version: 1.24.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: a6ab5de51509956c4b2da1709c5ebaf9 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: RELEASE_3_14 git_last_commit: 6ba3494 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/eudysbiome_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/eudysbiome_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/eudysbiome_1.24.0.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: 34 Package: evaluomeR Version: 1.10.0 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: 073904f9a2e9a69f43d2b9d892e7aca7 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: RELEASE_3_14 git_last_commit: 1989f6a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/evaluomeR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/evaluomeR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/evaluomeR_1.10.0.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: 119 Package: EventPointer Version: 3.2.0 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 Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr, kableExtra License: Artistic-2.0 MD5sum: c8683aac2d62a88d62c134a19c3eeed7 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] Maintainer: Juan A. Ferrer-Bonsoms VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues git_url: https://git.bioconductor.org/packages/EventPointer git_branch: RELEASE_3_14 git_last_commit: a608b07 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EventPointer_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EventPointer_3.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EventPointer_3.2.0.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: 153 Package: EWCE Version: 1.2.0 Depends: R(>= 4.1), RNOmni (>= 1.0) Imports: AnnotationHub, ewceData, ExperimentHub, ggplot2, grDevices, grid, reshape2, biomaRt, limma, stringr, cowplot, HGNChelper, ggdendro, gridExtra, Matrix, methods, parallel, future, scales, SummarizedExperiment, stats, utils Suggests: devtools, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), data.table, sctransform, readxl, SingleCellExperiment, memoise, markdown License: Artistic-2.0 Archs: i386, x64 MD5sum: b2f80f9c2b4336346755008331cfc1c4 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] (), Nathan Skene [aut] () Maintainer: Alan Murphy URL: https://github.com/NathanSkene/EWCE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EWCE git_branch: RELEASE_3_14 git_last_commit: 1ec3e6d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/EWCE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/EWCE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/EWCE_1.2.0.tgz vignettes: vignettes/EWCE/inst/doc/EWCE.html vignetteTitles: Expression Weighted Celltype Enrichment with EWCE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EWCE/inst/doc/EWCE.R dependencyCount: 133 Package: ExCluster Version: 1.12.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: 812d1049ea4fe53eaca4b066f788a7cb 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: RELEASE_3_14 git_last_commit: 033bb91 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ExCluster_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ExCluster_1.12.0.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: 45 Package: ExiMiR Version: 2.36.0 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: 5367fe5b38d93667bbd13c31ced0dc9f 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: RELEASE_3_14 git_last_commit: 0b40a1f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ExiMiR_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExiMiR_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ExiMiR_2.36.0.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: 13 Package: exomeCopy Version: 1.40.0 Depends: IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) MD5sum: f70b455255e33cf8f5620ff4674669f4 NeedsCompilation: yes Title: Copy number variant detection from exome sequencing read depth Description: Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. biocViews: CopyNumberVariation, Sequencing, Genetics Author: Michael Love Maintainer: Michael Love git_url: https://git.bioconductor.org/packages/exomeCopy git_branch: RELEASE_3_14 git_last_commit: ebde39b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/exomeCopy_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/exomeCopy_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/exomeCopy_1.40.0.tgz vignettes: vignettes/exomeCopy/inst/doc/exomeCopy.pdf vignetteTitles: Copy number variant detection in exome sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomeCopy/inst/doc/exomeCopy.R importsMe: cn.mops, CNVPanelizer, contiBAIT dependencyCount: 29 Package: exomePeak2 Version: 1.6.1 Depends: R (>= 3.5.0), SummarizedExperiment, cqn Imports: Rsamtools,GenomicAlignments,GenomicRanges,GenomicFeatures,DESeq2,ggplot2,mclust,genefilter,Biostrings,BSgenome,BiocParallel,IRanges,S4Vectors,reshape2,rtracklayer,apeglm,methods,stats,utils,Biobase,GenomeInfoDb,BiocGenerics Suggests: knitr, rmarkdown, RMariaDB License: GPL (>= 2) Archs: i386, x64 MD5sum: 201e4c2e21f00756030b8da321a0c0bf NeedsCompilation: no Title: Bias-aware Peak Calling and Quantification for MeRIP-Seq Description: exomePeak2 provides bias-aware quantification and peak detection for Methylated RNA immunoprecipitation sequencing data (MeRIP-Seq). MeRIP-Seq is a commonly applied sequencing technology that can measure the location and abundance of RNA modification sites under given cell line conditions. However, quantification and peak calling in MeRIP-Seq are sensitive to PCR amplification biases, which generally present in next-generation sequencing (NGS) technologies. In addition, the count data generated by RNA-Seq exhibits significant biological variations between biological replicates. exomePeak2 collectively address the challenges by introducing a series of robust data science tools tailored for MeRIP-Seq. Using exomePeak2, users can perform peak calling, modification site quantification and differential analysis through a straightforward single-step function. Alternatively, multi-step functions can be used to generate diagnostic plots and perform customized analyses. biocViews: Sequencing, MethylSeq, RNASeq, ExomeSeq, Coverage, Normalization, Preprocessing, DifferentialExpression Author: Zhen Wei [aut, cre] Maintainer: Zhen Wei VignetteBuilder: knitr BugReports: https://github.com/ZW-xjtlu/exomePeak2/issues git_url: https://git.bioconductor.org/packages/exomePeak2 git_branch: RELEASE_3_14 git_last_commit: 6269c12 git_last_commit_date: 2021-12-10 Date/Publication: 2021-12-12 source.ver: src/contrib/exomePeak2_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/exomePeak2_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/exomePeak2_1.6.1.tgz vignettes: vignettes/exomePeak2/inst/doc/Vignette_V_1.00.html vignetteTitles: The exomePeak2 user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomePeak2/inst/doc/Vignette_V_1.00.R dependencyCount: 137 Package: ExperimentHub Version: 2.2.1 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 2.19.3), BiocFileCache (>= 1.5.1) Imports: utils, S4Vectors, BiocManager, curl, rappdirs Suggests: knitr, BiocStyle, rmarkdown, HubPub Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: e002551a9eefa041c8bea3c8068edc16 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: RELEASE_3_14 git_last_commit: 4e10686 git_last_commit_date: 2022-01-20 Date/Publication: 2022-01-23 source.ver: src/contrib/ExperimentHub_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExperimentHub_2.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ExperimentHub_2.2.1.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, LRcell, SeqSQC, alpineData, BeadSorted.Saliva.EPIC, benchmarkfdrData2019, biscuiteerData, bodymapRat, brainImageRdata, CellMapperData, clustifyrdatahub, curatedAdipoChIP, DMRcatedata, ewceData, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, muscData, NanoporeRNASeq, NestLink, nullrangesData, ObMiTi, restfulSEData, RNAmodR.Data, SCATEData, scpdata, sesameData, SimBenchData, spatialDmelxsim, STexampleData, tartare, tcgaWGBSData.hg19, TENxVisiumData importsMe: BloodGen3Module, DMRcate, EWCE, ExperimentHubData, GSEABenchmarkeR, m6Aboost, MACSr, methylclock, PhyloProfile, restfulSE, signatureSearch, singleCellTK, adductData, BioImageDbs, celldex, chipseqDBData, CLLmethylation, curatedMetagenomicData, curatedTBData, curatedTCGAData, depmap, DropletTestFiles, DuoClustering2018, easierData, emtdata, FieldEffectCrc, GenomicDistributionsData, HarmonizedTCGAData, HCAData, HMP16SData, HMP2Data, imcdatasets, LRcellTypeMarkers, methylclockData, MethylSeqData, microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing, msigdb, NxtIRFdata, PhyloProfileData, preciseTADhub, pwrEWAS.data, RLHub, scRNAseq, signatureSearchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis suggestsMe: ANF, AnnotationHub, bambu, BioPlex, celaref, CellMapper, genomicInstability, HDF5Array, metavizr, missMethyl, MsBackendRawFileReader, muscat, quantiseqr, rawrr, recountmethylation, SingleMoleculeFootprinting, celarefData, curatedAdipoArray, GSE103322, GSE13015, GSE159526, GSE62944, tissueTreg dependencyCount: 86 Package: ExperimentHubData Version: 1.20.1 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: cb1f113b4ae0ea2f64e979083c0d5fd6 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: RELEASE_3_14 git_last_commit: 6c862d8 git_last_commit_date: 2021-11-24 Date/Publication: 2021-11-25 source.ver: src/contrib/ExperimentHubData_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExperimentHubData_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ExperimentHubData_1.20.1.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 dependencyCount: 135 Package: ExperimentSubset Version: 1.4.0 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: d9b8b0f83e8fb3da22260a3c3c83394b 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: RELEASE_3_14 git_last_commit: 1c9d547 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ExperimentSubset_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExperimentSubset_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ExperimentSubset_1.4.0.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: 105 Package: ExploreModelMatrix Version: 1.6.0 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: 3e8a14213a33abec20301710518f6b54 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 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: RELEASE_3_14 git_last_commit: 0b09f48 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ExploreModelMatrix_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExploreModelMatrix_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ExploreModelMatrix_1.6.0.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: 78 Package: ExpressionAtlas Version: 1.22.1 Depends: R (>= 4.1.1), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2 Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 0244b744d45d256e517fd9866a9a455d 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 Maintainer: Pedro Madrigal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: RELEASE_3_14 git_last_commit: 24f6ab5 git_last_commit_date: 2022-04-06 Date/Publication: 2022-04-07 source.ver: src/contrib/ExpressionAtlas_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ExpressionAtlas_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ExpressionAtlas_1.22.1.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 Package: fabia Version: 2.40.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 4401fcebd478d021d6a5311a7ef28661 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: RELEASE_3_14 git_last_commit: e5cd3a4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fabia_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fabia_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fabia_2.40.0.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, CSFA suggestsMe: fabiaData dependencyCount: 7 Package: factDesign Version: 1.70.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL Archs: i386, x64 MD5sum: 63c676a2b48cc6cf8457e6179b577ec0 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: RELEASE_3_14 git_last_commit: 66faf14 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/factDesign_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/factDesign_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/factDesign_1.70.0.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: FamAgg Version: 1.22.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: 34554e137f9b3d004f925c6c8b28c32f 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: RELEASE_3_14 git_last_commit: 293594d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FamAgg_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FamAgg_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FamAgg_1.22.0.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: 23 Package: famat Version: 1.4.0 Depends: R (>= 4.0) Imports: KEGGREST, MPINet, 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: a22340427c377f4b93e19853a1af280b 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: RELEASE_3_14 git_last_commit: 0c4e508 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/famat_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/famat_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/famat_1.4.0.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: 156 Package: farms Version: 1.46.0 Depends: R (>= 2.8), affy (>= 1.20.0), MASS, methods Imports: affy, MASS, Biobase (>= 1.13.41), methods, graphics Suggests: affydata, Biobase, utils License: LGPL (>= 2.1) MD5sum: 9a8fed9ab2fa49c5cac9d5f0434f6005 NeedsCompilation: no Title: FARMS - Factor Analysis for Robust Microarray Summarization Description: The package provides the summarization algorithm called Factor Analysis for Robust Microarray Summarization (FARMS) and a novel unsupervised feature selection criterion called "I/NI-calls" biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Djork-Arne Clevert Maintainer: Djork-Arne Clevert URL: http://www.bioinf.jku.at/software/farms/farms.html git_url: https://git.bioconductor.org/packages/farms git_branch: RELEASE_3_14 git_last_commit: a3cde8d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/farms_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/farms_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/farms_1.46.0.tgz vignettes: vignettes/farms/inst/doc/farms.pdf vignetteTitles: Using farms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/farms/inst/doc/farms.R dependencyCount: 13 Package: fastLiquidAssociation Version: 1.30.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: d89f96a52f05d3eac49129c31fc539d2 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: RELEASE_3_14 git_last_commit: 59a3557 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fastLiquidAssociation_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fastLiquidAssociation_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fastLiquidAssociation_1.30.0.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: 120 Package: FastqCleaner Version: 1.12.0 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: 877e946d937b105dfd9433493593afe8 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: RELEASE_3_14 git_last_commit: 8368d47 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FastqCleaner_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FastqCleaner_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FastqCleaner_1.12.0.tgz vignettes: vignettes/FastqCleaner/inst/doc/Overview.pdf vignetteTitles: An Introduction to FastqCleaner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R dependencyCount: 78 Package: fastseg Version: 1.40.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 4fc0b887f25f610b2c526a7a3caec5a5 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 Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/fastseg/fastseg.html git_url: https://git.bioconductor.org/packages/fastseg git_branch: RELEASE_3_14 git_last_commit: e78ee7a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fastseg_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fastseg_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fastseg_1.40.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.pdf vignetteTitles: fastseg: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R importsMe: methylKit dependencyCount: 17 Package: FCBF Version: 2.2.0 Depends: R (>= 4.1) Imports: ggplot2, gridExtra, pbapply, parallel, SummarizedExperiment, stats, mclust Suggests: caret, mlbench, SingleCellExperiment, knitr, rmarkdown, testthat, BiocManager License: MIT + file LICENSE Archs: i386, x64 MD5sum: bad50a37d20c3b201848385549821e11 NeedsCompilation: no Title: Fast Correlation Based Filter for Feature Selection Description: This package provides a simple R implementation for the Fast Correlation Based Filter described in Yu, L. and Liu, H.; Feature Selection for High-Dimensional Data: A Fast Correlation Based Filter Solution,Proc. 20th Intl. Conf. Mach. Learn. (ICML-2003), Washington DC, 2003 The current package is an intent to make easier for bioinformaticians to use FCBF for feature selection, especially regarding transcriptomic data.This implies discretizing expression (function discretize_exprs) before calculating the features that explain the class, but are not predictable by other features. The functions are implemented based on the algorithm of Yu and Liu, 2003 and Rajarshi Guha's implementation from 13/05/2005 available (as of 26/08/2018) at http://www.rguha.net/code/R/fcbf.R . biocViews: GeneTarget, FeatureExtraction, Classification, GeneExpression, SingleCell, ImmunoOncology Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FCBF git_branch: RELEASE_3_14 git_last_commit: d4708d5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FCBF_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FCBF_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FCBF_2.2.0.tgz vignettes: vignettes/FCBF/inst/doc/FCBF-Vignette.html vignetteTitles: FCBF : Fast Correlation Based Filter for Feature Selection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FCBF/inst/doc/FCBF-Vignette.R importsMe: fcoex suggestsMe: PubScore dependencyCount: 59 Package: fCCAC Version: 1.20.0 Depends: R (>= 3.3.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 663e8755aab8598e369bcb21d4102c71 NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: An application of 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. biocViews: Epigenetics, Transcription, Sequencing, Coverage, ChIPSeq, FunctionalGenomics Author: Pedro Madrigal [aut, cre] () Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/fCCAC git_branch: RELEASE_3_14 git_last_commit: bb89ba7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fCCAC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fCCAC_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fCCAC_1.20.0.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: 128 Package: fCI Version: 1.24.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 6135507a2a51c35aa4d4aaf9225aaa58 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: RELEASE_3_14 git_last_commit: 1dbe6dd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fCI_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fCI_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fCI_1.24.0.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: 39 Package: fcoex Version: 1.8.0 Depends: R (>= 4.1) Imports: FCBF, parallel, progress, dplyr, ggplot2, ggrepel, igraph, grid, intergraph, stringr, clusterProfiler, data.table, grDevices, methods, network, scales, sna, utils, stats, SingleCellExperiment, pathwayPCA, Matrix Suggests: testthat (>= 2.1.0), devtools, BiocManager, TENxPBMCData, scater, schex, gridExtra, scran, Seurat, knitr, rmarkdown License: GPL-3 MD5sum: 9e92dd6b9aa1a89f1eab8c23a7c74bd2 NeedsCompilation: no Title: FCBF-based Co-Expression Networks for Single Cells Description: The fcoex package implements an easy-to use interface to co-expression analysis based on the FCBF (Fast Correlation-Based Filter) algorithm. it was implemented especifically to deal with single-cell data. The modules found can be used to redefine cell populations, unrevel novel gene associations and predict gene function by guilt-by-association. The package structure is adapted from the CEMiTool package, relying on visualizations and code designed and written by CEMiTool's authors. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology, SingleCell Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcoex git_branch: RELEASE_3_14 git_last_commit: 6ba7fe5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fcoex_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fcoex_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fcoex_1.8.0.tgz vignettes: vignettes/fcoex/inst/doc/fcoex_and_seurat.html, vignettes/fcoex/inst/doc/fcoex.html vignetteTitles: fcoex: co-expression for single-cell data integrated with Seurat, fcoex: co-expression for single-cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcoex/inst/doc/fcoex_and_seurat.R, vignettes/fcoex/inst/doc/fcoex.R dependencyCount: 145 Package: fcScan Version: 1.8.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges, foreach, doParallel, parallel Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 82c9e935216fc0f10b2d7f43ccfcae96 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: RELEASE_3_14 git_last_commit: d81e849 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fcScan_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fcScan_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fcScan_1.8.0.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: 103 Package: fdrame Version: 1.66.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) MD5sum: 742ffc384656f93708eb66a996adea7a 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: RELEASE_3_14 git_last_commit: cc7e6e2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fdrame_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fdrame_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fdrame_1.66.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FEAST Version: 1.2.0 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: i386, x64 MD5sum: 3af523e79c129dc013654aa5a4e6a7d7 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: RELEASE_3_14 git_last_commit: cf2e5e8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FEAST_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FEAST_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FEAST_1.2.0.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: 116 Package: fedup Version: 1.2.0 Depends: R (>= 4.1) Imports: openxlsx, tibble, dplyr, data.table, ggplot2, ggthemes, forcats, RColorBrewer, RCy3, utils, stats Suggests: biomaRt, tidyr, testthat, knitr, rmarkdown, devtools, covr License: MIT + file LICENSE Archs: i386, x64 MD5sum: 651d47c72d08677aad299153de06a594 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: RELEASE_3_14 git_last_commit: c13bb2e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fedup_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fedup_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fedup_1.2.0.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: 79 Package: FELLA Version: 1.14.0 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: 9517ee6d8aa7a429bb58604d8ea0549b 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: RELEASE_3_14 git_last_commit: 89f6b9d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FELLA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FELLA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FELLA_1.14.0.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: 36 Package: ffpe Version: 1.38.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: d0058e7d1ca2337efc32e0e5949d59a6 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: RELEASE_3_14 git_last_commit: 8c9afae git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ffpe_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ffpe_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ffpe_1.38.0.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 importsMe: benchdamic dependencyCount: 167 Package: fgga Version: 1.2.0 Depends: R (>= 4.1), RBGL Imports: graph, stats, e1071, methods, gRbase, jsonlite, BiocFileCache, curl Suggests: knitr, rmarkdown, GOstats, PerfMeas, GO.db, BiocGenerics License: GPL-3 MD5sum: 19e4e2509f07fcc4981903850e7e891a 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 GO 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: Spetale Flavio [aut, cre], Elizabeth Tapia [aut, ctb] Maintainer: Spetale Flavio URL: https://github.com/fspetale/fgga VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fgga git_branch: RELEASE_3_14 git_last_commit: f575f34 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fgga_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fgga_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fgga_1.2.0.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: 65 Package: FGNet Version: 3.28.0 Depends: R (>= 2.15) 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, RGtk2, BiocManager License: GPL (>= 2) MD5sum: 2747229d7fd8eafff2f2a8ab98be4ed2 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: RELEASE_3_14 git_last_commit: 7938907 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FGNet_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FGNet_3.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FGNet_3.28.0.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: 26 Package: fgsea Version: 1.20.0 Depends: R (>= 3.3) Imports: Rcpp, data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), gridExtra, grid, fastmatch, Matrix, utils LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel, org.Mm.eg.db, limma, GEOquery License: MIT + file LICENCE MD5sum: ca596a521eaa6e035da5e521e28172d6 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: RELEASE_3_14 git_last_commit: b704f81 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fgsea_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fgsea_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fgsea_1.20.0.tgz vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html vignetteTitles: Using fgsea package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R dependsOnMe: gsean, PPInfer importsMe: ASpediaFI, CelliD, CEMiTool, clustifyr, cTRAP, DOSE, fobitools, lipidr, mCSEA, multiGSEA, NanoTube, phantasus, piano, RegEnrich, sesame, signatureSearch, ViSEAGO, cinaR, scITD suggestsMe: Cepo, decoupleR, gCrisprTools, mdp, Pi, sparrow, ttgsea, genekitr, Platypus, rliger dependencyCount: 50 Package: FilterFFPE Version: 1.4.0 Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools, parallel, S4Vectors Suggests: BiocStyle License: LGPL-3 Archs: i386, x64 MD5sum: 99d05a86e0ced2227435320a6e79f74e 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: RELEASE_3_14 git_last_commit: 6dffc7d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FilterFFPE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FilterFFPE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FilterFFPE_1.4.0.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: 33 Package: FindIT2 Version: 1.0.3 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: 9bbd6c66cf56bfa0c21d9e403f3d217f 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: RELEASE_3_14 git_last_commit: e395076 git_last_commit_date: 2021-12-28 Date/Publication: 2021-12-30 source.ver: src/contrib/FindIT2_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/FindIT2_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/FindIT2_1.0.3.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: 125 Package: FISHalyseR Version: 1.28.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: 215978e73859951be8b21b161f64c886 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: RELEASE_3_14 git_last_commit: f2ce1a2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FISHalyseR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FISHalyseR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FISHalyseR_1.28.0.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: 25 Package: fishpond Version: 2.0.1 Imports: graphics, stats, utils, methods, abind, gtools, qvalue, S4Vectors, SummarizedExperiment, matrixStats, svMisc, Rcpp, Matrix, SingleCellExperiment, jsonlite LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, macrophage, tximeta, org.Hs.eg.db, samr, DESeq2, apeglm, tximportData, limma License: GPL-2 MD5sum: afcb6eb1f7d723e25585ff4883459dd4 NeedsCompilation: yes Title: Fishpond: differential transcript and gene expression with inferential replicates 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 utilities for working with Salmon and Alevin quantification files. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Normalization, Regression, MultipleComparison, BatchEffect, Visualization, DifferentialExpression, DifferentialSplicing, AlternativeSplicing, SingleCell Author: Anzi 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], Scott Van Buren [ctb], Dongze He [ctb], Steve Lianoglou [ctb], Wes Wilson [ctb] Maintainer: Michael Love URL: https://github.com/mikelove/fishpond SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fishpond git_branch: RELEASE_3_14 git_last_commit: 64edeb7 git_last_commit_date: 2021-11-30 Date/Publication: 2021-12-02 source.ver: src/contrib/fishpond_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/fishpond_2.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/fishpond_2.0.1.tgz vignettes: vignettes/fishpond/inst/doc/swish.html vignetteTitles: DTE and DGE with inferential replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fishpond/inst/doc/swish.R importsMe: singleCellTK suggestsMe: tximport dependencyCount: 66 Package: FitHiC Version: 1.20.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 7fc6c2d59d1bafe03c127e5386f2b67f 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: RELEASE_3_14 git_last_commit: fbe358a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FitHiC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FitHiC_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FitHiC_1.20.0.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: 1.50.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: i386, x64 MD5sum: 0e532562e187ec913e105e3087d2dadb NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography - mass spectrometry metabolomics data biocViews: ImmunoOncology, DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli git_url: https://git.bioconductor.org/packages/flagme git_branch: RELEASE_3_14 git_last_commit: 4dd88f1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flagme_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flagme_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flagme_1.50.0.tgz vignettes: vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme.R dependencyCount: 133 Package: FLAMES Version: 1.0.2 Imports: basilisk, reticulate, SingleCellExperiment, SummarizedExperiment, Rsamtools, utils, zlibbioc, scater, dplyr, tidyr, magrittr, S4Vectors, scuttle, stats LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, knitr, rmarkdown, markdown, BiocFileCache License: GPL (>= 2) Archs: i386, x64 MD5sum: fb8efbb6d8bd531df94bcf65d2f677ae 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 Author: Tian Luyi [aut], Voogd Oliver [aut, cre], Schuster Jakob [aut], Wang Changqing [aut], Su Shian [aut], Ritchie Matthew [ctb] Maintainer: Voogd Oliver URL: https://github.com/OliverVoogd/FLAMES SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FLAMES git_branch: RELEASE_3_14 git_last_commit: e96c59c git_last_commit_date: 2021-11-03 Date/Publication: 2021-11-04 source.ver: src/contrib/FLAMES_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/FLAMES_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/FLAMES_1.0.2.tgz vignettes: vignettes/FLAMES/inst/doc/FLAMES_vignette.html, vignettes/FLAMES/inst/doc/windows_FLAMES_pipeline.html vignetteTitles: FLAMES, Vignette for FLAMES on Windows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FLAMES/inst/doc/FLAMES_vignette.R, vignettes/FLAMES/inst/doc/windows_FLAMES_pipeline.R dependencyCount: 103 Package: flowAI Version: 1.24.0 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) Archs: i386, x64 MD5sum: 774567e3394674654fc81bceabead4e4 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: RELEASE_3_14 git_last_commit: 7805860 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowAI_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowAI_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowAI_1.24.0.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 dependencyCount: 74 Package: flowBeads Version: 1.32.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 6741056a7eb85c8a47b26af48dbe6398 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: RELEASE_3_14 git_last_commit: bfcebe6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowBeads_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowBeads_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowBeads_1.32.0.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: 36 Package: flowBin Version: 1.30.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: f730c4027b992a6692cc781e7e8402d2 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: RELEASE_3_14 git_last_commit: 0aebfe5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowBin_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowBin_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowBin_1.30.0.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: 33 Package: flowcatchR Version: 1.28.0 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: 15ebc49ab2171d774de0e1204490e05a 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 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: RELEASE_3_14 git_last_commit: 4d658d4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowcatchR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowcatchR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowcatchR_1.28.0.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: 95 Package: flowCHIC Version: 1.28.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: e499717d3bc0031a83de8d37af143a7d 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: RELEASE_3_14 git_last_commit: 03c4f45 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowCHIC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCHIC_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCHIC_1.28.0.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: 70 Package: flowCL Version: 1.32.0 Depends: R (>= 3.4), Rgraphviz, SPARQL Imports: methods, grDevices, utils, graph Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 6f9e8bd50e802045dc114d47b1ef9040 NeedsCompilation: no Title: Semantic labelling of flow cytometric cell populations Description: Semantic labelling of flow cytometric cell populations. biocViews: FlowCytometry, ImmunoOncology Author: Justin Meskas, Radina Droumeva Maintainer: Justin Meskas git_url: https://git.bioconductor.org/packages/flowCL git_branch: RELEASE_3_14 git_last_commit: 29ae7ec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowCL_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCL_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCL_1.32.0.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 14 Package: flowClean Version: 1.32.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 0011c00ec15a128e0a61aec8452a0609 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: RELEASE_3_14 git_last_commit: e9d397d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowClean_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowClean_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowClean_1.32.0.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R dependencyCount: 25 Package: flowClust Version: 3.32.0 Depends: R(>= 2.5.0) Imports: BiocGenerics, methods, Biobase, graph, ellipse, flowViz, flowCore, clue, corpcor, mnormt, parallel Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown, openCyto License: Artistic-2.0 MD5sum: 065ab45e07e1f545da36ba7242ae7ae3 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: RELEASE_3_14 git_last_commit: 2978117 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowClust_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowClust_3.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowClust_3.32.0.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: 37 Package: flowCore Version: 2.6.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics, methods, stats, utils, stats4, Rcpp, matrixStats, cytolib (>= 2.3.4), S4Vectors LinkingTo: Rcpp, RcppArmadillo, BH(>= 1.65.0.1), cytolib, RProtoBufLib Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 Archs: i386, x64 MD5sum: 957a7e5618e0e948f633727d8e7bfed0 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: RELEASE_3_14 git_last_commit: 947b975 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowCore_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCore_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCore_2.6.0.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, CytoML, CytoTree, ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowMeans, flowPloidy, FlowSOM, flowSpecs, flowStats, flowTrans, flowUtils, flowViz, flowWorkspace, GateFinder, ImmuneSpaceR, MetaCyto, oneSENSE, PeacoQC, scDataviz, Sconify suggestsMe: COMPASS, flowPloidyData, beadplexr, hypergate, segmenTier dependencyCount: 17 Package: flowCut Version: 1.4.0 Depends: R (>= 3.4), flowCore Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics, stats,methods Suggests: RUnit, BiocGenerics, knitr, markdown, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 0275a4cc1497af5bd28aefacaa0646d1 NeedsCompilation: no Title: Precise and Accurate 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: RELEASE_3_14 git_last_commit: c75895b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowCut_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCut_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCut_1.4.0.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: 150 Package: flowCyBar Version: 1.30.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 04010bb8ac0bcfa3556b948b7b3c513d 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: RELEASE_3_14 git_last_commit: a3b5b76 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowCyBar_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowCyBar_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowCyBar_1.30.0.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: 1.28.0 Imports: flowCore, graphics, flowViz (>= 1.46.1), car, sp, rgeos, gplots, RFOC, flowWorkspace (>= 3.33.1), methods, stats, grDevices Suggests: knitr,rmarkdown License: Artistic-2.0 MD5sum: 8bb7dea19e056c3d503eaf661541ec2c 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: RELEASE_3_14 git_last_commit: 26bbabf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowDensity_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowDensity_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowDensity_1.28.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensity.html vignetteTitles: Introduction to automated gating hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R importsMe: cyanoFilter, ddPCRclust, flowCut dependencyCount: 145 Package: flowFP Version: 1.52.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 MD5sum: 53086c5fc3ac023b9012e07e8478e9c4 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 git_url: https://git.bioconductor.org/packages/flowFP git_branch: RELEASE_3_14 git_last_commit: 5e0174a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowFP_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowFP_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowFP_1.52.0.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: 29 Package: flowGraph Version: 1.2.0 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, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: 532f3265f0552acd90d5b757bc381eb6 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: RELEASE_3_14 git_last_commit: 992e839 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowGraph_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowGraph_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowGraph_1.2.0.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: 69 Package: flowMap Version: 1.32.0 Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>= 1.4.0), reshape2(>= 1.2.2), scales(>= 0.2.3), Matrix(>= 1.1-4), methods (>= 2.14) Suggests: BiocStyle, knitr License: GPL (>=2) MD5sum: 6fea6e191f0389e1bfb07357b51d4b6c NeedsCompilation: no Title: Mapping cell populations in flow cytometry data for cross-sample comparisons using the Friedman-Rafsky Test Description: flowMap quantifies the similarity of cell populations across multiple flow cytometry samples using a nonparametric multivariate statistical test. The method is able to map cell populations of different size, shape, and proportion across multiple flow cytometry samples. The algorithm can be incorporate in any flow cytometry work flow that requires accurat quantification of similarity between cell populations. biocViews: ImmunoOncology, MultipleComparison, FlowCytometry Author: Chiaowen Joyce Hsiao, Yu Qian, and Richard H. Scheuermann Maintainer: Chiaowen Joyce Hsiao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMap git_branch: RELEASE_3_14 git_last_commit: 9b3876f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowMap_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowMap_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowMap_1.32.0.tgz vignettes: vignettes/flowMap/inst/doc/flowMap.pdf vignetteTitles: Mapping cell populations in flow cytometry data flowMap-FR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMap/inst/doc/flowMap.R dependencyCount: 36 Package: flowMatch Version: 1.30.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 MD5sum: 85d72593ce3f3c76b954c0c3235c0b4b 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: RELEASE_3_14 git_last_commit: 87a457c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowMatch_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowMatch_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowMatch_1.30.0.tgz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R dependencyCount: 18 Package: flowMeans Version: 1.54.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 Archs: i386, x64 MD5sum: f666984b76c1f3f2a3d35644be93c49e 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: RELEASE_3_14 git_last_commit: 7cdf615 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowMeans_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowMeans_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowMeans_1.54.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: optimalFlow dependencyCount: 40 Package: flowMerge Version: 2.42.0 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: 2060d7e5bf538ee3eff41d8ff85157c4 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: RELEASE_3_14 git_last_commit: 9eaf3cf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowMerge_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowMerge_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowMerge_2.42.0.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: 62 Package: flowPeaks Version: 1.40.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 MD5sum: 28758fde3786590deb4667911fbd948f 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: RELEASE_3_14 git_last_commit: b787a41 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowPeaks_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowPeaks_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowPeaks_1.40.0.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: 1.20.0 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 Archs: i386, x64 MD5sum: fd3b9e55385d4ba659c9f5ec6a3b1c02 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: RELEASE_3_14 git_last_commit: 74d74f3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowPloidy_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowPloidy_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowPloidy_1.20.0.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: 122 Package: flowPlots Version: 1.42.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: 328abadb1359c5471fc6a9a9978e0c5e 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: RELEASE_3_14 git_last_commit: efef1da git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowPlots_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowPlots_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowPlots_1.42.0.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: 2.2.0 Depends: R (>= 4.0), igraph Imports: stats, utils, BiocGenerics, colorRamps, ConsensusClusterPlus, CytoML, dplyr, flowCore, flowWorkspace, ggforce, ggnewscale, ggplot2, ggpointdensity, ggpubr, ggrepel, grDevices, magrittr, methods, pheatmap, RColorBrewer, rlang, Rtsne, tidyr, XML, scattermore Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: a401f4bc506471ce8ef2d2c88a4405f9 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: RELEASE_3_14 git_last_commit: 44e9c9b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FlowSOM_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FlowSOM_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FlowSOM_2.2.0.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, CytoTree, diffcyt suggestsMe: HDCytoData dependencyCount: 188 Package: flowSpecs Version: 1.8.0 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 Archs: i386, x64 MD5sum: d2aaa25dda1655ef3049880c5e381a77 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: RELEASE_3_14 git_last_commit: bdf4b32 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowSpecs_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowSpecs_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowSpecs_1.8.0.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: 64 Package: flowStats Version: 4.6.0 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 Suggests: xtable, testthat, openCyto, ggcyto, ggridges Enhances: RBGL,graph License: Artistic-2.0 MD5sum: 5b565f2ec0cfd607b1ff8a72c4578f7c 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 , Jake Wagner 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: RELEASE_3_14 git_last_commit: afc7826 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowStats_4.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowStats_4.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowStats_4.6.0.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, flowCore, flowTime, flowViz, ggcyto dependencyCount: 109 Package: flowTime Version: 1.18.0 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: 81c83238b55e0f51611fb02212eb60bc 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: RELEASE_3_14 git_last_commit: ea5611b git_last_commit_date: 2021-11-04 Date/Publication: 2021-11-04 source.ver: src/contrib/flowTime_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowTime_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowTime_1.18.0.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: 37 Package: flowTrans Version: 1.46.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 27673b64cd88210d54cc2ec4d31fb91b 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: RELEASE_3_14 git_last_commit: 34530f9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowTrans_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowTrans_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowTrans_1.46.0.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: 38 Package: flowUtils Version: 1.58.0 Depends: R (>= 2.2.0) Imports: Biobase, graph, methods, stats, utils, corpcor, RUnit, XML, flowCore (>= 1.32.0) Suggests: gatingMLData License: Artistic-2.0 MD5sum: 3f66c7a7f9bfc8e846b5c4b317cecef8 NeedsCompilation: no Title: Utilities for flow cytometry Description: Provides utilities for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, DecisionTree Author: J. Spidlen., N. Gopalakrishnan, F. Hahne, B. Ellis, R. Gentleman, M. Dalphin, N. Le Meur, B. Purcell, W. Jiang Maintainer: Josef Spidlen URL: https://github.com/jspidlen/flowUtils BugReports: https://github.com/jspidlen/flowUtils/issues git_url: https://git.bioconductor.org/packages/flowUtils git_branch: RELEASE_3_14 git_last_commit: 87aa66e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowUtils_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowUtils_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowUtils_1.58.0.tgz vignettes: vignettes/flowUtils/inst/doc/HowTo-flowUtils.pdf vignetteTitles: Gating-ML support in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowUtils/inst/doc/HowTo-flowUtils.R importsMe: CytoTree suggestsMe: gatingMLData dependencyCount: 22 Package: flowViz Version: 1.58.0 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: 497f201adc40549fed692c33d6d54a3e 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 , Jake Wagner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: RELEASE_3_14 git_last_commit: c9fba2a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowViz_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowViz_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowViz_1.58.0.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: flowClust, flowDensity, flowStats, flowTrans suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto dependencyCount: 28 Package: flowVS Version: 1.26.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: 343818f3ac5a7a37e71cce04bf4b0672 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: RELEASE_3_14 git_last_commit: 3e78d21 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowVS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowVS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowVS_1.26.0.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: 110 Package: flowWorkspace Version: 4.6.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics, cytolib (>= 2.3.9), lattice, latticeExtra, XML, ggplot2, graph, graphics, grDevices, methods, stats, stats4, utils, RBGL, tools, Rgraphviz, data.table, dplyr, Rcpp, scales, matrixStats, RcppParallel, RProtoBufLib, digest, aws.s3, aws.signature, flowCore(>= 2.1.1), ncdfFlow(>= 2.25.4), DelayedArray, S4Vectors LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib(>= 1.99.4), cytolib (>= 2.3.7),Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1) Suggests: testthat, flowWorkspaceData (>= 2.23.2), knitr, rmarkdown, ggcyto, parallel, CytoML, openCyto License: file LICENSE License_restricts_use: yes MD5sum: 66150421e69941e2730b8a2c997b46a0 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: RELEASE_3_14 git_last_commit: 60bc06d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/flowWorkspace_4.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/flowWorkspace_4.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/flowWorkspace_4.6.0.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: ggcyto, highthroughputassays importsMe: CytoML, flowDensity, FlowSOM, flowStats, ImmuneSpaceR, PeacoQC suggestsMe: CATALYST, COMPASS, flowClust, flowCore linksToMe: CytoML dependencyCount: 80 Package: fmcsR Version: 1.36.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 949901c79aae013a6ff19d5fa9825918 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: RELEASE_3_14 git_last_commit: 4552dbc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fmcsR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fmcsR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fmcsR_1.36.0.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: Rcpi suggestsMe: ChemmineR, xnet dependencyCount: 65 Package: fmrs Version: 1.4.0 Depends: R (>= 4.1.0) Imports: methods, survival, stats Suggests: BiocGenerics, testthat, knitr, utils, rmarkdown License: GPL (>= 3) Archs: i386, x64 MD5sum: bcfab35578e0d1cd70b11467b3fc196d NeedsCompilation: yes Title: Variable Selection in Finite Mixture of AFT Regression and FMR Description: Provides parameter estimation as well as variable selection in Finite Mixture of Accelerated Failure Time Regression and Finite Mixture of Regression Models. 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: RELEASE_3_14 git_last_commit: c105d70 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fmrs_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fmrs_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fmrs_1.4.0.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 dependencyCount: 10 Package: fobitools Version: 1.2.0 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 Archs: i386, x64 MD5sum: 02a3fcd00e34b865e491a638b6a74dde NeedsCompilation: no Title: Tools For Manipulating FOBI Ontology Description: A set of tools for interacting with 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] (), Cristina Andrés-Lacueva [aut] (), 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: RELEASE_3_14 git_last_commit: 1a3c4a4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/fobitools_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/fobitools_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/fobitools_1.2.0.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: 123 Package: FoldGO Version: 1.12.0 Depends: R (>= 4.0) Imports: topGO (>= 2.30.1), ggplot2 (>= 2.2.1), tidyr (>= 0.8.0), stats, methods Suggests: knitr, rmarkdown, devtools, kableExtra License: GPL-3 MD5sum: b77de668875285173745a96dfe9be8af NeedsCompilation: no Title: Package for Fold-specific GO Terms Recognition Description: FoldGO is a package designed to annotate gene sets derived from expression experiments and identify fold-change-specific GO terms. biocViews: DifferentialExpression, GeneExpression, GO, Software Author: Daniil Wiebe [aut, cre] Maintainer: Daniil Wiebe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FoldGO git_branch: RELEASE_3_14 git_last_commit: 7f8ce76 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FoldGO_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FoldGO_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FoldGO_1.12.0.tgz vignettes: vignettes/FoldGO/inst/doc/vignette.html vignetteTitles: FoldGO: a tool for fold-change-specific functional enrichment analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FoldGO/inst/doc/vignette.R dependencyCount: 82 Package: FRASER Version: 1.6.1 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: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, License: MIT + file LICENSE MD5sum: cd7bcfcef5e8e541556de70af1747cd4 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], Vicente Yepez [ctb], Julien Gagneur [aut] Maintainer: Christian Mertes URL: https://github.com/gagneurlab/FRASER VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FRASER git_branch: RELEASE_3_14 git_last_commit: b5d66f9 git_last_commit_date: 2022-04-08 Date/Publication: 2022-04-10 source.ver: src/contrib/FRASER_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/FRASER_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/FRASER_1.6.1.tgz vignettes: vignettes/FRASER/inst/doc/FRASER.pdf vignetteTitles: FRASER: Find RAre Splicing Evens in RNA-seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FRASER/inst/doc/FRASER.R dependencyCount: 172 Package: frenchFISH Version: 1.6.0 Imports: utils, MCMCpack, NHPoisson Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 27d0f5065525f269da08a82d7f4a377c 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: RELEASE_3_14 git_last_commit: f10ea30 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/frenchFISH_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/frenchFISH_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/frenchFISH_1.6.0.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: 92 Package: FRGEpistasis Version: 1.30.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: 0fb17bc0ab81a5fbafc4b55676198ddd 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: RELEASE_3_14 git_last_commit: 6132aad git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FRGEpistasis_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FRGEpistasis_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FRGEpistasis_1.30.0.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: 61 Package: frma Version: 1.46.0 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: f2fa0ee7dc92494c21f5f34effec3f5b 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: RELEASE_3_14 git_last_commit: c00c4df git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/frma_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/frma_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/frma_1.46.0.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, antiProfilesData dependencyCount: 55 Package: frmaTools Version: 1.46.0 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: 561911f9dc6f660d76a108f566d6f283 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: RELEASE_3_14 git_last_commit: a4a868c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/frmaTools_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/frmaTools_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/frmaTools_1.46.0.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: FScanR Version: 1.4.0 Depends: R (>= 4.0) Imports: stats Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: b775be0f00b60855c00e7dc4ec8cd59e NeedsCompilation: no Title: Detect Programmed Ribosomal Frameshifting Events from mRNA/cDNA BLASTX Output Description: 'FScanR' identifies Programmed Ribosomal Frameshifting (PRF) events from BLASTX homolog sequence alignment between targeted genomic/cDNA/mRNA sequences against the peptide library of the same species or a close relative. The output by BLASTX or diamond BLASTX will be used as input of 'FScanR' and should be in a tabular format with 14 columns. For BLASTX, the output parameter should be: -outfmt '6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qframe sframe'. For diamond BLASTX, the output parameter should be: -outfmt 6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qframe qframe. biocViews: Alignment, Annotation, Software Author: Xiao Chen [aut, cre] () Maintainer: Xiao Chen VignetteBuilder: knitr BugReports: https://github.com/seanchen607/FScanR/issues git_url: https://git.bioconductor.org/packages/FScanR git_branch: RELEASE_3_14 git_last_commit: d5cb37a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FScanR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FScanR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FScanR_1.4.0.tgz vignettes: vignettes/FScanR/inst/doc/FScanR.html vignetteTitles: FScanR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FScanR/inst/doc/FScanR.R dependencyCount: 1 Package: FunChIP Version: 1.20.0 Depends: R (>= 3.2), GenomicRanges Imports: shiny, fda, doParallel, GenomicAlignments, Rcpp, methods, foreach, parallel, GenomeInfoDb, Rsamtools, grDevices, graphics, stats, RColorBrewer LinkingTo: Rcpp License: Artistic-2.0 MD5sum: 3f00b25c496b04af2f2cdf1011a6763f NeedsCompilation: yes Title: Clustering and Alignment of ChIP-Seq peaks based on their shapes Description: Preprocessing and smoothing of ChIP-Seq peaks and efficient implementation of the k-mean alignment algorithm to classify them. biocViews: StatisticalMethod, Clustering, ChIPSeq Author: Alice Parodi [aut, cre], Marco Morelli [aut, cre], Laura M. Sangalli [aut], Piercesare Secchi [aut], Simone Vantini [aut] Maintainer: Alice Parodi git_url: https://git.bioconductor.org/packages/FunChIP git_branch: RELEASE_3_14 git_last_commit: 91d2320 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/FunChIP_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/FunChIP_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/FunChIP_1.20.0.tgz vignettes: vignettes/FunChIP/inst/doc/FunChIP.pdf vignetteTitles: An introduction to FunChIP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunChIP/inst/doc/FunChIP.R dependencyCount: 111 Package: funtooNorm Version: 1.18.0 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 Archs: i386, x64 MD5sum: af8a576ba0c6d896136ba1912bfa3c86 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: RELEASE_3_14 git_last_commit: 60b7864 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/funtooNorm_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/funtooNorm_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/funtooNorm_1.18.0.tgz vignettes: vignettes/funtooNorm/inst/doc/funtooNorm.pdf vignetteTitles: Normalizing Illumina Infinium Human Methylation 450k for multiple cell types with funtooNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/funtooNorm/inst/doc/funtooNorm.R dependencyCount: 145 Package: GA4GHclient Version: 1.18.0 Depends: 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: ea1dd0a6750fd92f9b68552ba3b80ad8 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: RELEASE_3_14 git_last_commit: c427dfd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GA4GHclient_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GA4GHclient_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GA4GHclient_1.18.0.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: 98 Package: GA4GHshiny Version: 1.16.0 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: 0d17079b1776b16b29261cd08d1ee5be 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: RELEASE_3_14 git_last_commit: 648de55 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GA4GHshiny_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GA4GHshiny_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GA4GHshiny_1.16.0.tgz vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html vignetteTitles: GA4GHshiny hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R dependencyCount: 123 Package: gaga Version: 2.40.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) MD5sum: ef771d98c880f69b174a05f2915f2bd8 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: RELEASE_3_14 git_last_commit: 8821347 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gaga_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gaga_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gaga_2.40.0.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: 2.44.0 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) Archs: i386, x64 MD5sum: 5301a405f1f3af85a158b1686d302501 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: RELEASE_3_14 git_last_commit: c1af55a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gage_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gage_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gage_2.44.0.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 importsMe: exp2flux suggestsMe: FGNet, pathview, SBGNview, gageData dependencyCount: 47 Package: gaggle Version: 1.62.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: 80ada9f6e5b68e78167e216581b93232 NeedsCompilation: no Title: Broadcast data between R and Gaggle Description: This package contains functions enabling data exchange between R and Gaggle enabled bioinformatics software, including Cytoscape, Firegoose and Gaggle Genome Browser. biocViews: ThirdPartyClient, Visualization, Annotation, GraphAndNetwork, DataImport Author: Paul Shannon Maintainer: Christopher Bare URL: http://gaggle.systemsbiology.net/docs/geese/r/ git_url: https://git.bioconductor.org/packages/gaggle git_branch: RELEASE_3_14 git_last_commit: a13232a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gaggle_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gaggle_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gaggle_1.62.0.tgz vignettes: vignettes/gaggle/inst/doc/gaggle.pdf vignetteTitles: Gaggle Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaggle/inst/doc/gaggle.R dependencyCount: 9 Package: gaia Version: 2.38.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 5fb33f905628e4b2a217b86cec53680b NeedsCompilation: no Title: GAIA: An R package for genomic analysis of significant chromosomal aberrations. Description: This package allows to assess the statistical significance of chromosomal aberrations. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella et al. Maintainer: S. Morganella git_url: https://git.bioconductor.org/packages/gaia git_branch: RELEASE_3_14 git_last_commit: 39b70a4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gaia_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gaia_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gaia_2.38.0.tgz vignettes: vignettes/gaia/inst/doc/gaia.pdf vignetteTitles: gaia hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaia/inst/doc/gaia.R dependencyCount: 0 Package: GAPGOM Version: 1.10.0 Depends: R (>= 4.0) Imports: stats, utils, methods, Matrix, fastmatch, plyr, dplyr, magrittr, data.table, igraph, graph, RBGL, GO.db, org.Hs.eg.db, org.Mm.eg.db, GOSemSim, GEOquery, AnnotationDbi, Biobase, BiocFileCache, matrixStats Suggests: org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Dr.eg.db, org.Ce.eg.db, org.At.tair.db, org.EcK12.eg.db, org.Bt.eg.db, org.Cf.eg.db, org.Ag.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Pt.eg.db, org.Pf.plasmo.db, org.Mmu.eg.db, org.Ss.eg.db, org.Xl.eg.db, testthat, pryr, knitr, rmarkdown, prettydoc, ggplot2, kableExtra, profvis, reshape2 License: MIT + file LICENSE MD5sum: ff14b83caf5a03ab26467f66f58c9bf4 NeedsCompilation: no Title: GAPGOM (novel Gene Annotation Prediction and other GO Metrics) Description: Collection of various measures and tools for lncRNA annotation prediction put inside a redistributable R package. The package contains two main algorithms; lncRNA2GOA and TopoICSim. lncRNA2GOA tries to annotate novel genes (in this specific case lncRNAs) by using various correlation/geometric scoring methods on correlated expression data. After correlating/scoring, the results are annotated and enriched. TopoICSim is a topologically based method, that compares gene similarity based on the topology of the GO DAG by information content (IC) between GO terms. biocViews: GO, GeneExpression, GenePrediction Author: Rezvan Ehsani [aut, cre], Casper van Mourik [aut], Finn Drabløs [aut] Maintainer: Rezvan Ehsani URL: https://github.com/Berghopper/GAPGOM/ VignetteBuilder: knitr BugReports: https://github.com/Berghopper/GAPGOM/issues/ git_url: https://git.bioconductor.org/packages/GAPGOM git_branch: RELEASE_3_14 git_last_commit: f096758 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GAPGOM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GAPGOM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GAPGOM_1.10.0.tgz vignettes: vignettes/GAPGOM/inst/doc/benchmarks.html, vignettes/GAPGOM/inst/doc/GAPGOM.html vignetteTitles: Benchmarks and other GO similarity methods, An Introduction to GAPGOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GAPGOM/inst/doc/benchmarks.R, vignettes/GAPGOM/inst/doc/GAPGOM.R dependencyCount: 92 Package: GAprediction Version: 1.20.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 1d115b20094b53afcbd904e564900a5f 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: RELEASE_3_14 git_last_commit: b73578b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GAprediction_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GAprediction_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GAprediction_1.20.0.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: 1.22.0 Suggests: knitr License: GPL-3 MD5sum: f21f5251c88717e279581f838e0414b7 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: RELEASE_3_14 git_last_commit: f0a2702 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/garfield_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/garfield_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/garfield_1.22.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GARS Version: 1.14.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 87f052b1eaa48ade7a97270f1f753150 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: RELEASE_3_14 git_last_commit: e3de20c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GARS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GARS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GARS_1.14.0.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: 248 Package: GateFinder Version: 1.14.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 8e476b9d227b7b1a2d037e85d4b71fab 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: RELEASE_3_14 git_last_commit: 829cba8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GateFinder_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GateFinder_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GateFinder_1.14.0.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: 37 Package: gcapc Version: 1.18.0 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: eab63d032c1b9810036aa479ae81fafa 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: RELEASE_3_14 git_last_commit: 98b82d1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gcapc_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gcapc_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gcapc_1.18.0.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: 47 Package: gcatest Version: 1.24.0 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 MD5sum: 3cd7c59a7bf757737a29bc5b280bd4b6 NeedsCompilation: yes 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. biocViews: SNP, DimensionReduction, PrincipalComponent, GenomeWideAssociation Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey 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: RELEASE_3_14 git_last_commit: d2a815c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gcatest_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gcatest_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gcatest_1.24.0.tgz vignettes: vignettes/gcatest/inst/doc/gcatest.pdf vignetteTitles: gcat Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcatest/inst/doc/gcatest.R dependencyCount: 3 Package: gCrisprTools Version: 2.0.0 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 Archs: i386, x64 MD5sum: 39a77cdbe61b217fe45354df176d881e 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: RELEASE_3_14 git_last_commit: 4783d8f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gCrisprTools_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gCrisprTools_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gCrisprTools_2.0.0.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: 89 Package: gcrma Version: 2.66.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocManager Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL MD5sum: 64e4510d3e24882e7c97b2bb6839e992 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: RELEASE_3_14 git_last_commit: ba134b3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gcrma_2.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gcrma_2.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gcrma_2.66.0.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: 24 Package: GCSConnection Version: 1.6.0 Depends: R (>= 4.0.0) Imports: Rcpp (>= 1.0.2), httr, googleAuthR, googleCloudStorageR, methods, jsonlite, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: a18bc0103d5e191cccb305a6a33a7544 NeedsCompilation: yes Title: Creating R Connection with Google Cloud Storage Description: Create R 'connection' objects to google cloud storage buckets using the Google REST interface. Both read and write connections are supported. The package also provides functions to view and manage files on Google Cloud. biocViews: Infrastructure Author: Jiefei Wang [cre] Maintainer: Jiefei Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GCSConnection git_branch: RELEASE_3_14 git_last_commit: 21952ee git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GCSConnection_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GCSConnection_1.5.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GCSConnection_1.6.0.tgz vignettes: vignettes/GCSConnection/inst/doc/Introduction.html vignetteTitles: quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCSConnection/inst/doc/Introduction.R suggestsMe: GCSFilesystem dependencyCount: 32 Package: GCSFilesystem Version: 1.4.0 Depends: R (>= 4.0.0) Imports: stats Suggests: testthat, knitr, rmarkdown, BiocStyle, GCSConnection License: GPL (>= 2) Archs: i386, x64 MD5sum: 0dc767afb8679cb1292a38b391da0ce3 NeedsCompilation: no Title: Mounting a Google Cloud bucket to a local directory Description: Mounting a Google Cloud bucket to a local directory. The files in the bucket can be viewed and read as if they are locally stored. For using the package, you need to install GCSDokan on Windows or gcsfuse on Linux and MacOs. biocViews: Infrastructure Author: Jiefei Wang [aut, cre] Maintainer: Jiefei Wang SystemRequirements: GCSDokan for Windows, gcsfuse for Linux and macOs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GCSFilesystem git_branch: RELEASE_3_14 git_last_commit: 0b3a288 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GCSFilesystem_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GCSFilesystem_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GCSFilesystem_1.4.0.tgz vignettes: vignettes/GCSFilesystem/inst/doc/Quick-Start-Guide.html vignetteTitles: Quick-Start-Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 1 Package: GCSscore Version: 1.8.0 Depends: R (>= 3.6) Imports: BiocManager, Biobase, utils, methods, RSQLite, devtools, dplR, stringr, graphics, stats, affxparser, data.table Suggests: siggenes, GEOquery, R.utils License: GPL (>=3) MD5sum: e87d4db33dc521da16d98730c2dacead NeedsCompilation: no Title: GCSscore: an R package for microarray analysis for Affymetrix/Thermo Fisher arrays Description: For differential expression analysis of 3'IVT and WT-style microarrays from Affymetrix/Thermo-Fisher. Based on S-score algorithm originally described by Zhang et al 2002. biocViews: DifferentialExpression, Microarray, OneChannel, ProprietaryPlatforms, DataImport Author: Guy M. Harris & Shahroze Abbas & Michael F. Miles Maintainer: Guy M. Harris git_url: https://git.bioconductor.org/packages/GCSscore git_branch: RELEASE_3_14 git_last_commit: d7f279c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GCSscore_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GCSscore_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GCSscore_1.8.0.tgz vignettes: vignettes/GCSscore/inst/doc/GCSscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCSscore/inst/doc/GCSscore.R dependencyCount: 102 Package: GDCRNATools Version: 1.14.0 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, rmarkdown License: Artistic-2.0 MD5sum: 489b72f5e592bcbae5a307a8792ce32c 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: RELEASE_3_14 git_last_commit: 9b69c00 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GDCRNATools_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GDCRNATools_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GDCRNATools_1.14.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GDCRNATools/inst/doc/GDCRNATools.R dependencyCount: 236 Package: GDSArray Version: 1.14.1 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: 3213989c564c4adf78a243cb50aa799e 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: RELEASE_3_14 git_last_commit: 5b75cd5 git_last_commit_date: 2022-01-06 Date/Publication: 2022-01-09 source.ver: src/contrib/GDSArray_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GDSArray_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GDSArray_1.14.1.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: 29 Package: gdsfmt Version: 1.30.0 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics License: LGPL-3 MD5sum: f7eaeefab308e90d349da01c444d2cf9 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: http://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/gdsfmt/issues git_url: https://git.bioconductor.org/packages/gdsfmt git_branch: RELEASE_3_14 git_last_commit: d27dde6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gdsfmt_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gdsfmt_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gdsfmt_1.30.0.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, SAIGEgds, SCArray, SeqArray, SNPRelate, Mega2R importsMe: CNVRanger, GENESIS, GWASTools, SeqSQC, SeqVarTools, VariantExperiment, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: AnnotationHub, HIBAG linksToMe: SeqArray, SNPRelate dependencyCount: 1 Package: GEM Version: 1.20.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics, rmarkdown, markdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 07a6a0ac41850a483b458516b60e904b 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: RELEASE_3_14 git_last_commit: 8abf722 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GEM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEM_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEM_1.20.0.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: 39 Package: gemini Version: 1.8.0 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: 4f1baad660f02b55a1897f12ec1ed920 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: RELEASE_3_14 git_last_commit: fb7a7eb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gemini_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gemini_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gemini_1.8.0.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: 48 Package: genArise Version: 1.70.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 26a8ad19007263a533512f9c6c2fcf79 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: RELEASE_3_14 git_last_commit: c1ee2fa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genArise_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genArise_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genArise_1.70.0.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: genbankr Version: 1.22.0 Depends: methods Imports: BiocGenerics, IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), GenomicFeatures (>= 1.31.5), Biostrings, VariantAnnotation, rtracklayer, S4Vectors (>= 0.17.28), GenomeInfoDb, Biobase Suggests: RUnit, rentrez, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: e01037e1aa68e5abc04ae1b23ed06f8c NeedsCompilation: no Title: Parsing GenBank files into semantically useful objects Description: Reads Genbank files. biocViews: Infrastructure, DataImport Author: Gabriel Becker [aut, cre], Michael Lawrence [aut] Maintainer: Gabriel Becker VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genbankr git_branch: RELEASE_3_14 git_last_commit: be3c635 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genbankr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genbankr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genbankr_1.22.0.tgz vignettes: vignettes/genbankr/inst/doc/genbankr.html vignetteTitles: An introduction to genbankr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genbankr/inst/doc/genbankr.R importsMe: PACVr dependencyCount: 98 Package: GeneAccord Version: 1.12.0 Depends: R (>= 3.5) Imports: biomaRt, caTools, dplyr, ggplot2, graphics, grDevices, gtools, ggpubr, magrittr, maxLik, RColorBrewer, reshape2, stats, tibble, utils Suggests: assertthat, BiocStyle, devtools, knitr, rmarkdown, testthat License: file LICENSE MD5sum: 968d79f30b0026c8bb2ee8c4af13283e NeedsCompilation: no Title: Detection of clonally exclusive gene or pathway pairs in a cohort of cancer patients Description: A statistical framework to examine the combinations of clones that co-exist in tumors. More precisely, the algorithm finds pairs of genes that are mutated in the same tumor but in different clones, i.e. their subclonal mutation profiles are mutually exclusive. We refer to this as clonally exclusive. It means that the mutations occurred in different branches of the tumor phylogeny, indicating parallel evolution of the clones. Our statistical framework assesses whether a pattern of clonal exclusivity occurs more often than expected by chance alone across a cohort of patients. The required input data are the mutated gene-to-clone assignments from a cohort of cancer patients, which were obtained by running phylogenetic tree inference methods. Reconstructing the evolutionary history of a tumor and detecting the clones is challenging. For nondeterministic algorithms, repeated tree inference runs may lead to slightly different mutation-to-clone assignments. Therefore, our algorithm was designed to allow the input of multiple gene-to-clone assignments per patient. They may have been generated by repeatedly performing the tree inference, or by sampling from the posterior distribution of trees. The tree inference methods designate the mutations to individual clones. The mutations can then be mapped to genes or pathways. Hence our statistical framework can be applied on the gene level, or on the pathway level to detect clonally exclusive pairs of pathways. If a pair is significantly clonally exclusive, it points towards the fact that this specific clone configuration confers a selective advantage, possibly through synergies between the clones with these mutations. biocViews: BiomedicalInformatics, GeneticVariability, GenomicVariation, SomaticMutation, FunctionalGenomics, Genetics, MathematicalBiology, SystemsBiology, FeatureExtraction, PatternLogic, Pathways Author: Ariane L. Moore, Jack Kuipers and Niko Beerenwinkel Maintainer: Ariane L. Moore URL: https://github.com/cbg-ethz/GeneAccord VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneAccord git_branch: RELEASE_3_14 git_last_commit: 0f91325 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneAccord_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneAccord_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneAccord_1.12.0.tgz vignettes: vignettes/GeneAccord/inst/doc/GeneAccord.html vignetteTitles: GeneAccord hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneAccord/inst/doc/GeneAccord.R dependencyCount: 145 Package: GeneAnswers Version: 2.36.0 Depends: R (>= 3.0.0), igraph, KEGGREST, RCurl, annotate, Biobase (>= 1.12.0), methods, XML, RSQLite, MASS, Heatplus, RColorBrewer Imports: RBGL, annotate, downloader Suggests: GO.db, reactome.db, biomaRt, AnnotationDbi, org.Hs.eg.db, org.Rn.eg.db, org.Mm.eg.db, org.Dm.eg.db, graph License: LGPL (>= 2) MD5sum: 13f5c5631bfa5261624d6763168ef751 NeedsCompilation: no Title: Integrated Interpretation of Genes Description: GeneAnswers provides an integrated tool for biological or medical interpretation of the given one or more groups of genes by means of statistical test. biocViews: Infrastructure, DataRepresentation, Visualization, GraphsAndNetworks Author: Lei Huang, Gang Feng, Pan Du, Tian Xia, Xishu Wang, Jing, Wen, Warren Kibbe and Simon Lin Maintainer: Lei Huang and Gang Feng PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/GeneAnswers git_branch: RELEASE_3_14 git_last_commit: ada47cb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-28 source.ver: src/contrib/GeneAnswers_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneAnswers_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneAnswers_2.36.0.tgz vignettes: vignettes/GeneAnswers/inst/doc/geneAnswers.pdf, vignettes/GeneAnswers/inst/doc/getListGIF.pdf vignetteTitles: GeneAnswers, GeneAnswers web-based visualization module hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneAnswers/inst/doc/getListGIF.R suggestsMe: InterMineR dependencyCount: 61 Package: geneAttribution Version: 1.20.0 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: df497c0f2068f221550ed6485661be03 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: RELEASE_3_14 git_last_commit: 57613fe git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/geneAttribution_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneAttribution_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneAttribution_1.20.0.tgz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 97 Package: GeneBreak Version: 1.24.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: f7e0f345954e379ad411c73947d91408 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: RELEASE_3_14 git_last_commit: 99d931d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneBreak_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneBreak_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneBreak_1.24.0.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: 49 Package: geneClassifiers Version: 1.18.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: a247ad42fb485b4ca6f45e73e26e6e9b 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: RELEASE_3_14 git_last_commit: 72211ac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/geneClassifiers_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneClassifiers_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneClassifiers_1.18.0.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: 1.40.0 Depends: R (>= 4.0) Imports: Biobase, stats, methods Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: f0cf453c741b8808d4a048dc257db036 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: RELEASE_3_14 git_last_commit: bde0ce1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneExpressionSignature_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneExpressionSignature_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneExpressionSignature_1.40.0.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: 1.76.0 Imports: BiocGenerics, AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival, grDevices Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, RColorBrewer, BiocStyle, knitr License: Artistic-2.0 MD5sum: 9409aea143843999bbd12e699c5de1c9 NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes. biocViews: Microarray Author: Robert Gentleman, Vincent J. Carey, Wolfgang Huber, Florian Hahne Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefilter git_branch: RELEASE_3_14 git_last_commit: 8d630fd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genefilter_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genefilter_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genefilter_1.76.0.tgz vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf, vignettes/genefilter/inst/doc/howtogenefinder.pdf, vignettes/genefilter/inst/doc/independent_filtering_plots.pdf vignetteTitles: 01 - Using the genefilter function to filter genes from a microarray dataset, 02 - How to find genes whose expression profile is similar to that of specified genes, 03 - Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010) 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, FlowSorted.Blood.EPIC, Hiiragi2013, maEndToEnd, rnaseqGene, lmQCM importsMe: a4Base, annmap, arrayQualityMetrics, Category, cbaf, countsimQC, covRNA, DESeq2, DEXSeq, GISPA, GSRI, metaseqR2, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, NxtIRFcore, pcaExplorer, PECA, phenoTest, pwrEWAS, Ringo, spatialHeatmap, tilingArray, XDE, zinbwave, IHWpaper, RNAinteractMAPK, CoNI, dGAselID, INCATome, MiDA, netgsa, oncoPredict suggestsMe: annotate, BioNet, categoryCompare, ClassifyR, clusterStab, codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MMUPHin, npGSEA, oligo, phyloseq, pvac, qpgraph, rtracklayer, siggenes, TCGAbiolinks, topGO, BloodCancerMultiOmics2017, curatedBladderData, curatedCRCData, curatedOvarianData, ffpeExampleData, gageData, MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD, Single.mTEC.Transcriptomes, maGUI, rknn, SuperLearner dependencyCount: 53 Package: genefu Version: 2.26.0 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 Archs: i386, x64 MD5sum: 959254a6ac7233eee5c8024700f81518 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: RELEASE_3_14 git_last_commit: a884b26 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genefu_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genefu_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genefu_2.26.0.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: 110 Package: GeneGA Version: 1.44.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: 3a842186e12943b6cbafc1dec14eb033 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: RELEASE_3_14 git_last_commit: fc99115 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneGA_1.44.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/GeneGA_1.44.0.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: 14 Package: GeneGeneInteR Version: 1.20.0 Depends: R (>= 4.0) Imports: snpStats, mvtnorm, Rsamtools, igraph, kernlab, FactoMineR, IRanges, GenomicRanges, data.table,grDevices, graphics,stats, utils, methods License: GPL (>= 2) MD5sum: 8506eb632e917f1d6336c4210434687a 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: RELEASE_3_14 git_last_commit: ddbc7c1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneGeneInteR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneGeneInteR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneGeneInteR_1.20.0.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: 131 Package: GeneMeta Version: 1.66.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 Archs: i386, x64 MD5sum: 1a2fd3ff8d2a6e93fa130765ff21f084 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: RELEASE_3_14 git_last_commit: c16eb09 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneMeta_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneMeta_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneMeta_1.66.0.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: 54 Package: GeneNetworkBuilder Version: 1.36.1 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 License: GPL (>= 2) MD5sum: ebb1cd3861cc47006a0162e8fd293ee3 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: RELEASE_3_14 git_last_commit: 88425e8 git_last_commit_date: 2021-12-02 Date/Publication: 2021-12-05 source.ver: src/contrib/GeneNetworkBuilder_1.36.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneNetworkBuilder_1.36.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneNetworkBuilder_1.36.1.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: 22 Package: GeneOverlap Version: 1.30.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 11dda898cb6231c0cc2172089ef43c1e 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: RELEASE_3_14 git_last_commit: 80762a8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneOverlap_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneOverlap_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneOverlap_1.30.0.tgz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R dependencyCount: 9 Package: geneplast Version: 1.20.1 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.string.v91, ggplot2, ggpubr, plyr License: GPL (>= 2) MD5sum: dd0a8f94d5eac5d381de0feb3e6e6786 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: RELEASE_3_14 git_last_commit: 8aff315 git_last_commit_date: 2022-03-23 Date/Publication: 2022-03-24 source.ver: src/contrib/geneplast_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneplast_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/geneplast_1.20.1.tgz vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html, vignettes/geneplast/inst/doc/geneplast.html vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast: evolutionary rooting and plasticity analysis." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R, vignettes/geneplast/inst/doc/geneplast.R suggestsMe: TreeAndLeaf, geneplast.data dependencyCount: 18 Package: geneplotter Version: 1.72.0 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 License: Artistic-2.0 MD5sum: e48b766452fcf4796fcd6c12d664a31a NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: R. Gentleman, Biocore Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/geneplotter git_branch: RELEASE_3_14 git_last_commit: 57a1d83 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/geneplotter_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneplotter_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneplotter_1.72.0.tgz vignettes: vignettes/geneplotter/inst/doc/byChroms.pdf, vignettes/geneplotter/inst/doc/visualize.pdf vignetteTitles: How to assemble a chromLocation object, Visualization of Microarray Data 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, DESeq2, DEXSeq, IsoGeneGUI, MethylSeekR, RNAinteract suggestsMe: biocGraph, Category, EnrichmentBrowser, GOstats, Single.mTEC.Transcriptomes dependencyCount: 51 Package: geneRecommender Version: 1.66.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: 5862b32fc969f04a1693b32ddf523a7f 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: RELEASE_3_14 git_last_commit: 9547de6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/geneRecommender_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneRecommender_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneRecommender_1.66.0.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: 1.50.0 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: 11201d1f7d50997bea6d9f3daf75392c 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: RELEASE_3_14 git_last_commit: 15c71ee git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneRegionScan_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneRegionScan_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneRegionScan_1.50.0.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: 21 Package: geneRxCluster Version: 1.30.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: c94ed18125954cb030928dd13db42f16 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: RELEASE_3_14 git_last_commit: 3680d0c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/geneRxCluster_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneRxCluster_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneRxCluster_1.30.0.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: 16 Package: GeneSelectMMD Version: 2.38.0 Depends: R (>= 2.13.2), Biobase Imports: MASS, graphics, stats, limma Suggests: ALL License: GPL (>= 2) Archs: i386, x64 MD5sum: 6d0cb12b555390592e6453311673001b 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: RELEASE_3_14 git_last_commit: d43e828 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneSelectMMD_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneSelectMMD_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneSelectMMD_2.38.0.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: 9 Package: GENESIS Version: 2.24.2 Imports: Biobase, BiocGenerics, BiocParallel, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate, data.table, graphics, grDevices, igraph, Matrix, methods, reshape2, stats, utils Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey, testthat, BiocStyle, knitr, rmarkdown, GWASdata, dplyr, ggplot2, GGally, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 2cb711a1eb1e0add42b22b6a50b31514 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: RELEASE_3_14 git_last_commit: 1654c45 git_last_commit_date: 2022-03-14 Date/Publication: 2022-03-15 source.ver: src/contrib/GENESIS_2.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/GENESIS_2.24.2.zip mac.binary.ver: bin/macosx/contrib/4.1/GENESIS_2.24.2.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 dependencyCount: 103 Package: GeneStructureTools Version: 1.14.0 Imports: Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: e3580192af58c1d9cdc1bc77ad0c8c56 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: RELEASE_3_14 git_last_commit: 2d82a8c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneStructureTools_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneStructureTools_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneStructureTools_1.14.0.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: 145 Package: geNetClassifier Version: 1.34.0 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: 9aa475e00ea0ad86cd73b6622d4f70f1 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: RELEASE_3_14 git_last_commit: 2aec9d0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/geNetClassifier_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geNetClassifier_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geNetClassifier_1.34.0.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: 1.56.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE MD5sum: 0db70cc5e43e4c4da752aab195dac293 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: RELEASE_3_14 git_last_commit: b9f252a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeneticsPed_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneticsPed_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneticsPed_1.56.0.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 importsMe: LRQMM dependencyCount: 11 Package: GeneTonic Version: 1.6.4 Depends: R (>= 4.0.0) Imports: AnnotationDbi, backbone, bs4Dash (>= 2.0.0), circlize, colorspace, colourpicker, ComplexHeatmap, dendextend, DESeq2, dplyr, DT, dynamicTreeCut, expm, ggforce, ggplot2, ggrepel, 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 Archs: i386, x64 MD5sum: 565c4c016cb6cfc6302ad638ca3e2b46 NeedsCompilation: no Title: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis Description: This package provides a Shiny application that aims to combine at different levels the existing pieces of the transcriptome data and results, in a way that makes it easier to generate insightful observations and hypothesis - 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. biocViews: GUI, GeneExpression, Software, Transcription, Transcriptomics, Visualization, DifferentialExpression, Pathways, ReportWriting, GeneSetEnrichment, Annotation, GO 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: RELEASE_3_14 git_last_commit: c9ee95b git_last_commit_date: 2022-03-24 Date/Publication: 2022-03-27 source.ver: src/contrib/GeneTonic_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeneTonic_1.6.4.zip mac.binary.ver: bin/macosx/contrib/4.1/GeneTonic_1.6.4.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 dependencyCount: 171 Package: geneXtendeR Version: 1.20.0 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: 78769aca91fea8251c8ad3d6c9330c81 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: RELEASE_3_14 git_last_commit: 37a9c1c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/geneXtendeR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geneXtendeR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geneXtendeR_1.20.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 113 Package: GENIE3 Version: 1.16.0 Imports: stats, reshape2, dplyr Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 50340d2921a17fce52cf018b2981a164 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: RELEASE_3_14 git_last_commit: 5543b1b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GENIE3_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GENIE3_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GENIE3_1.16.0.tgz vignettes: vignettes/GENIE3/inst/doc/GENIE3.html vignetteTitles: GENIE3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R importsMe: BioNERO, MetNet, bulkAnalyseR suggestsMe: dnapath dependencyCount: 28 Package: genoCN Version: 1.46.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: f7bf735a9491da36982f69a109bddf51 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: RELEASE_3_14 git_last_commit: f11b85a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genoCN_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genoCN_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genoCN_1.46.0.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: 1.26.0 Depends: R (>= 3.0.0),grid Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table, GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute, IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern, rtracklayer (>= 1.39.7), Rcpp (>= 0.12.14) LinkingTo: Rcpp Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 926daff81fff26923f75cdd7e5aeab3f 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: RELEASE_3_14 git_last_commit: b2f8d07 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genomation_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genomation_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genomation_1.26.0.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, RCAS suggestsMe: methylKit dependencyCount: 97 Package: GenomeInfoDb Version: 1.30.1 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.25.12), IRanges (>= 2.13.12) Imports: stats, stats4, utils, RCurl, GenomeInfoDbData Suggests: GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 2c4dec9207de2d72b99337ceb16ca5e7 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, Martin Morgan, Marc Carlson, H. Pagès Maintainer: Bioconductor Package Maintainer 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: RELEASE_3_14 git_last_commit: bf8b385 git_last_commit_date: 2022-01-27 Date/Publication: 2022-01-30 source.ver: src/contrib/GenomeInfoDb_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomeInfoDb_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomeInfoDb_1.30.1.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf vignetteTitles: GenomeInfoDb: Submitting your organism to GenomeInfoDb, GenomeInfoDb: Introduction 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, BRGenomics, BSgenome, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, HelloRanges, Rsamtools, SCOPE, VariantAnnotation, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, RTIGER importsMe: AllelicImbalance, alpine, amplican, AneuFinder, AnnotationHubData, annotatr, ASpediaFI, ATACseqQC, atena, BaalChIP, ballgown, bambu, biovizBase, biscuiteer, BiSeq, bnbc, branchpointer, breakpointR, BSgenome, bsseq, BUSpaRse, CAGEfightR, cageminer, CAGEr, casper, cBioPortalData, CexoR, chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromstaR, chromVAR, circRNAprofiler, cleanUpdTSeq, cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, Cogito, consensusSeekeR, conumee, CopyNumberPlots, CopywriteR, CRISPRseek, CrispRVariants, csaw, customProDB, DAMEfinder, dasper, decompTumor2Sig, DeepBlueR, derfinder, derfinderPlot, DEScan2, DEWSeq, diffHic, diffloop, diffUTR, DMRcate, DMRScan, dmrseq, DominoEffect, easyRNASeq, ELMER, enhancerHomologSearch, enrichTF, ensembldb, ensemblVEP, epialleleR, epigenomix, epigraHMM, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, exomeCopy, FindIT2, FRASER, FunChIP, funtooNorm, GA4GHclient, GA4GHshiny, gcapc, genbankr, geneAttribution, genomation, genomeIntervals, GenomicDistributions, GenomicFiles, GenomicInteractions, GenomicOZone, GenomicScores, genotypeeval, GenVisR, ggbio, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiTC, HTSeqGenie, idr2d, IMAS, InPAS, INSPEcT, InteractionSet, IsoformSwitchAnalyzeR, IVAS, karyoploteR, ldblock, MACPET, MADSEQ, maser, metagene, metagene2, metaseqR2, metavizr, methimpute, methInheritSim, methylKit, methylSig, methylumi, minfi, MinimumDistance, MMAPPR2, monaLisa, mosaics, motifbreakR, motifmatchr, MouseFM, msgbsR, multicrispr, multiHiCcompare, MungeSumstats, musicatk, MutationalPatterns, myvariant, NADfinder, nearBynding, normr, nucleR, nullranges, NxtIRFcore, ODER, OMICsPCA, ORFik, Organism.dplyr, panelcn.mops, periodicDNA, Pi, pipeFrame, plyranges, podkat, pram, prebs, proActiv, profileplyr, ProteoDisco, ProteomicsAnnotationHubData, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, RaggedExperiment, RareVariantVis, Rcade, RCAS, RcisTarget, recount, recoup, regioneR, regionReport, REMP, Repitools, RiboCrypt, RiboProfiling, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, RLSeq, rnaEditr, RNAmodR, roar, RTCGAToolbox, rtracklayer, scanMiR, scanMiRApp, scmeth, scruff, segmentSeq, SeqArray, seqCAT, seqsetvis, sevenC, SGSeq, ShortRead, signeR, SigsPack, SingleMoleculeFootprinting, sitadela, SNPhood, soGGi, SomaticSignatures, SparseSignatures, spatzie, spiky, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, SummarizedExperiment, svaNUMT, svaRetro, TAPseq, TarSeqQC, TCGAutils, TFBSTools, TitanCNA, TnT, trackViewer, transcriptR, tRNAscanImport, TSRchitect, 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.v3.1.1.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, GenomicDistributionsData, MethylSeqData, TCGAWorkflow, ActiveDriverWGS, crispRdesignR, deconstructSigs, driveR, HiCfeat, ICAMS, intePareto, MAAPER, Mega2R, Signac, simMP suggestsMe: AnnotationForge, AnnotationHub, BindingSiteFinder, BiocOncoTK, chromswitch, ExperimentHubData, megadepth, methrix, parglms, plotgardener, QDNAseq, scTreeViz, splatter, systemPipeR, TFutils, gkmSVM, LDheatmap, polyRAD, Seurat dependencyCount: 11 Package: genomeIntervals Version: 1.50.0 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: c2e11d2c6086edf83400dfc6af9e7614 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: RELEASE_3_14 git_last_commit: d91ab47 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genomeIntervals_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genomeIntervals_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genomeIntervals_1.50.0.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, ChIC.data importsMe: ChIC, easyRNASeq dependencyCount: 17 Package: genomes Version: 3.24.0 Depends: readr, curl License: GPL-3 MD5sum: ee85663a7e81d5526b8fb3441410f1d6 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: RELEASE_3_14 git_last_commit: ffd9acf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genomes_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genomes_3.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genomes_3.24.0.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: 33 Package: GenomicAlignments Version: 1.30.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), GenomicRanges (>= 1.41.5), 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, BiocStyle License: Artistic-2.0 MD5sum: 15ba87ddfb386e7a96fb81781ceaec1c 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, Valerie Obenchain, Martin Morgan Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/GenomicAlignments 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: RELEASE_3_14 git_last_commit: 9046119 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GenomicAlignments_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicAlignments_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicAlignments_1.30.0.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.pdf vignetteTitles: An Introduction to the GenomicAlignments Package, Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides 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, alpineData, SCATEData, sequencing importsMe: alpine, AneuFinder, APAlyzer, ASpediaFI, ASpli, ATACseqQC, atena, BaalChIP, bambu, biovizBase, breakpointR, BRGenomics, CAGEfightR, CAGEr, chimeraviz, ChIPpeakAnno, ChIPQC, chromstaR, CNEr, consensusDE, contiBAIT, CopywriteR, CoverageView, CrispRVariants, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEScan2, DiffBind, easyRNASeq, FRASER, FunChIP, gcapc, genomation, GenomicFiles, ggbio, gmapR, gmoviz, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, icetea, IMAS, INSPEcT, IntEREst, MACPET, MADSEQ, MDTS, metagene, metagene2, metaseqR2, mosaics, msgbsR, NADfinder, PICS, plyranges, pram, proActiv, ramwas, Rcade, Repitools, RiboProfiling, ribosomeProfilingQC, RNAmodR, roar, Rqc, rtracklayer, SCATE, scruff, seqsetvis, SGSeq, soGGi, spiky, SplicingGraphs, SPLINTER, srnadiff, strandCheckR, TAPseq, TarSeqQC, TCseq, trackViewer, transcriptR, TSRchitect, Ularcirc, UMI4Cats, VaSP, VplotR, leeBamViews, alakazam, BinQuasi, ExomeDepth, intePareto, MAAPER, PACVr, pulseTD, RAPIDR, VALERIE suggestsMe: amplican, BindingSiteFinder, BiocParallel, csaw, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, IRanges, QuasR, Rsamtools, similaRpeak, Streamer, systemPipeR, alpineData, NanoporeRNASeq, parathyroidSE, RNAseqData.HNRNPC.bam.chr14, seqmagick dependencyCount: 37 Package: GenomicDataCommons Version: 1.18.0 Depends: R (>= 3.4.0), magrittr Imports: stats, httr, xml2, jsonlite, utils, rlang, readr, GenomicRanges, IRanges, dplyr, rappdirs, SummarizedExperiment, S4Vectors, tibble Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools, BiocParallel, TxDb.Hsapiens.UCSC.hg38.knownGene, VariantAnnotation License: Artistic-2.0 Archs: i386, x64 MD5sum: 9597584e4f2595b3f04280a4dd9f9554 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] Maintainer: Sean Davis URL: https://bioconductor.org/packages/GenomicDataCommons, http://github.com/Bioconductor/GenomicDataCommons VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicDataCommons/issues/new git_url: https://git.bioconductor.org/packages/GenomicDataCommons git_branch: RELEASE_3_14 git_last_commit: 48c12fe git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GenomicDataCommons_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicDataCommons_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicDataCommons_1.18.0.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 dependencyCount: 63 Package: GenomicDistributions Version: 1.2.0 Depends: R (>= 4.0), IRanges, GenomicRanges Imports: data.table, ggplot2, reshape2, methods, utils, Biostrings, plyr, dplyr, GenomeInfoDb Suggests: AnnotationFilter, rtracklayer, testthat, knitr, BiocStyle, rmarkdown Enhances: BSgenome, extrafont, ensembldb, GenomicFeatures License: BSD_2_clause + file LICENSE MD5sum: 5e9bd4465924277e16060b1507187e62 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: RELEASE_3_14 git_last_commit: 2a030e7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GenomicDistributions_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicDistributions_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicDistributions_1.2.0.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: 61 Package: GenomicFeatures Version: 1.46.5 Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.29), IRanges (>= 2.13.23), GenomeInfoDb (>= 1.25.7), GenomicRanges (>= 1.31.17), AnnotationDbi (>= 1.41.4) Imports: methods, utils, stats, tools, DBI, RSQLite (>= 2.0), RCurl, XVector (>= 0.19.7), Biostrings (>= 2.47.6), BiocIO, rtracklayer (>= 1.51.5), biomaRt (>= 2.17.1), Biobase (>= 2.15.1) Suggests: RMariaDB, 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, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 93ac2566651bbf1339c3d7b0562e5047 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, H. Pagès, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence, V. Obenchain Maintainer: Bioconductor Package Maintainer 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: RELEASE_3_14 git_last_commit: 72f352e git_last_commit_date: 2022-02-24 Date/Publication: 2022-02-27 source.ver: src/contrib/GenomicFeatures_1.46.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicFeatures_1.46.5.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicFeatures_1.46.5.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.pdf vignetteTitles: Making 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, OrganismDbi, OUTRIDER, RareVariantVis, RiboDiPA, SplicingGraphs, 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.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.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, alpine, AnnotationHubData, annotatr, APAlyzer, appreci8R, ASpediaFI, ASpli, bambu, BgeeCall, BiocOncoTK, biovizBase, bumphunter, BUSpaRse, CAGEfightR, casper, ChIPpeakAnno, ChIPQC, ChIPseeker, consensusDE, crisprseekplus, CSSQ, customProDB, dasper, decompTumor2Sig, derfinder, derfinderPlot, EDASeq, ELMER, EpiTxDb, epivizrData, epivizrStandalone, esATAC, EventPointer, FindIT2, FRASER, GA4GHshiny, genbankr, geneAttribution, GenVisR, ggbio, gmapR, gmoviz, Gviz, gwascat, HiLDA, HTSeqGenie, icetea, InPAS, INSPEcT, IntEREst, karyoploteR, lumi, mCSEA, metagene, metaseqR2, methylumi, msgbsR, multicrispr, musicatk, ORFik, Organism.dplyr, proActiv, proBAMr, ProteoDisco, PureCN, qpgraph, QuasR, RCAS, recoup, Rhisat2, RiboCrypt, RiboProfiling, ribosomeProfilingQC, RLSeq, RNAmodR, scanMiRApp, scruff, SGSeq, sitadela, spatzie, SplicingGraphs, SPLINTER, srnadiff, StructuralVariantAnnotation, svaNUMT, svaRetro, TAPseq, TCGAutils, TFEA.ChIP, trackViewer, transcriptR, 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, systemPipeRdata, driveR, MAAPER, oncoPredict, pathwayTMB, pulseTD, utr.annotation suggestsMe: AnnotationHub, BANDITS, biomvRCNS, Biostrings, chipseq, chromPlot, CrispRVariants, csaw, cummeRbund, DEXSeq, eisaR, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, HDF5Array, InteractiveComplexHeatmap, IRanges, MiRaGE, MutationalPatterns, ODER, pageRank, plotgardener, recount, RNAmodR.ML, Rsamtools, rtracklayer, ShortRead, SummarizedExperiment, systemPipeR, TFutils, TnT, VplotR, wiggleplotr, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer7, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Rnorvegicus.UCSC.rn6, curatedAdipoChIP, ObMiTi, parathyroidSE, Single.mTEC.Transcriptomes, CAGEWorkflow, polyRAD dependencyCount: 95 Package: GenomicFiles Version: 1.30.0 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, RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens License: Artistic-2.0 MD5sum: 0644aa09ff9a36d0f3db652153925fb3 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] Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=3PK_jx44QTs git_url: https://git.bioconductor.org/packages/GenomicFiles git_branch: RELEASE_3_14 git_last_commit: 6cde8a0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GenomicFiles_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicFiles_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicFiles_1.30.0.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R dependsOnMe: erma importsMe: CAGEfightR, contiBAIT, derfinder, ldblock, QuasR, Rqc, VCFArray suggestsMe: TFutils dependencyCount: 98 Package: genomicInstability Version: 1.0.0 Depends: R (>= 4.1.0), checkmate Imports: mixtools, SummarizedExperiment Suggests: SingleCellExperiment, ExperimentHub, pROC License: file LICENSE MD5sum: 3156add4fe79d3e5eb8dfef48256c135 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: RELEASE_3_14 git_last_commit: 523d6a1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genomicInstability_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/genomicInstability_1.0.0.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: 33 Package: GenomicInteractions Version: 1.28.0 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: 869a5ebfc7239569ccb0f232ee0fe496 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: RELEASE_3_14 git_last_commit: a4e55c9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GenomicInteractions_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicInteractions_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicInteractions_1.28.0.tgz vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R importsMe: CAGEfightR, spatzie suggestsMe: Chicago, ELMER, sevenC, chicane dependencyCount: 144 Package: GenomicOZone Version: 1.8.0 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: 34d1df6ced52c7ebf98a2971d3c17f2a 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: RELEASE_3_14 git_last_commit: e05e948 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GenomicOZone_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicOZone_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicOZone_1.8.0.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: 157 Package: GenomicRanges Version: 1.46.1 Depends: R (>= 4.0.0), methods, stats4, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), 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, 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: 090f3814a1f629dc3876a45f9001a9f1 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: P. Aboyoun, H. Pagès, and M. Lawrence Maintainer: Bioconductor Package Maintainer 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: RELEASE_3_14 git_last_commit: e422642 git_last_commit_date: 2021-11-16 Date/Publication: 2021-11-18 source.ver: src/contrib/GenomicRanges_1.46.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicRanges_1.46.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicRanges_1.46.1.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf, vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3. A quick introduction to GRanges and GRangesList objects (slides), 4. Ten Things You Didn't Know (slides from BioC 2016), 1. An Introduction to the GenomicRanges Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R, vignettes/GenomicRanges/inst/doc/Ten_things_slides.R dependsOnMe: AllelicImbalance, AneuFinder, annmap, AnnotationHubData, BaalChIP, Basic4Cseq, baySeq, BindingSiteFinder, biomvRCNS, BiSeq, bnbc, BPRMeth, breakpointR, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, CAGEfightR, casper, chimeraviz, ChIPpeakAnno, ChIPQC, chipseq, chromPlot, chromstaR, chromswitch, CINdex, cn.mops, cnvGSA, CNVPanelizer, CNVRanger, COCOA, Cogito, consensusSeekeR, CSAR, csaw, CSSQ, deepSNV, DEScan2, DESeq2, DEXSeq, DiffBind, diffHic, DMCFB, DMCHMM, DMRcaller, DMRforPairs, DNAshapeR, EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, epihet, esATAC, ExCluster, exomeCopy, fastseg, fCCAC, FindIT2, FunChIP, GeneBreak, GenomicAlignments, GenomicDistributions, GenomicFeatures, GenomicFiles, GenomicOZone, GenomicScores, GenomicTuples, gmapR, gmoviz, GOTHiC, GreyListChIP, groHMM, gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, hiAnnotator, HiTC, IdeoViz, igvR, InPAS, InTAD, intansv, InteractionSet, IntEREst, IWTomics, karyoploteR, m6Aboost, maser, MBASED, Melissa, metagene, metagene2, methimpute, methylKit, minfi, MotifDb, msgbsR, MutationalPatterns, NADfinder, ORFik, periodicDNA, plyranges, podkat, QuasR, r3Cseq, RaggedExperiment, ramr, Rcade, recoup, regioneR, RepViz, rfPred, rGREAT, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, RNAmodR, RnBeads, Rsamtools, RSVSim, rtracklayer, Scale4C, SCOPE, segmentSeq, seqbias, seqCAT, SeqGate, SGSeq, SICtools, SigFuge, SMITE, SNPhood, SomaticSignatures, spiky, StructuralVariantAnnotation, SummarizedExperiment, svaNUMT, svaRetro, TarSeqQC, TnT, trackViewer, TransView, tRNA, tRNAdbImport, tRNAscanImport, VanillaICE, VarCon, VariantAnnotation, VariantTools, VplotR, vtpnet, vulcan, wavClusteR, YAPSA, EuPathDB, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, ChAMPdata, EatonEtAlChIPseq, nullrangesData, RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5, SCATEData, WGSmapp, liftOver, sequencing, HiCfeat, PlasmaMutationDetector, RTIGER importsMe: ACE, ALDEx2, alpine, amplican, AnnotationFilter, annotatr, APAlyzer, apeglm, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, AssessORF, ATACseqQC, atena, BadRegionFinder, ballgown, bambu, bamsignals, BBCAnalyzer, beadarray, BEAT, BiFET, BiocOncoTK, BioTIP, biovizBase, biscuiteer, BiSeq, brainflowprobes, branchpointer, BRGenomics, BSgenome, BUSpaRse, cageminer, CAGEr, cBioPortalData, CexoR, ChIC, chipenrich, ChIPexoQual, ChIPseeker, chipseq, ChIPseqR, chromDraw, ChromHeatMap, ChromSCape, chromVAR, cicero, circRNAprofiler, cleanUpdTSeq, cliProfiler, CNEr, CNVfilteR, CNViz, coMET, compartmap, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, CRISPRseek, crisprseekplus, CrispRVariants, customProDB, DAMEfinder, dasper, debrowser, decompTumor2Sig, deconvR, DeepBlueR, DEFormats, DegNorm, deltaCaptureC, derfinder, derfinderPlot, DEWSeq, diffloop, diffUTR, DMRcate, dmrseq, DominoEffect, DRIMSeq, DropletUtils, easyRNASeq, EDASeq, eisaR, ELMER, enhancerHomologSearch, enrichTF, epialleleR, epidecodeR, epigraHMM, epistack, EpiTxDb, epivizr, epivizrData, erma, EventPointer, fcScan, FilterFFPE, FRASER, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicInteractions, genotypeeval, GenVisR, ggbio, GOfuncR, gpart, gwascat, h5vc, heatmaps, HiCcompare, HilbertCurve, HiLDA, hiReadsProcessor, HTSeqGenie, hummingbird, icetea, ideal, idr2d, IMAS, INSPEcT, InterMineR, ipdDb, IsoformSwitchAnalyzeR, isomiRs, iteremoval, IVAS, karyoploteR, loci2path, LOLA, LoomExperiment, lumi, MACPET, MADSEQ, mCSEA, MDTS, MEAL, MEDIPS, megadepth, memes, metaseqR2, methInheritSim, MethReg, methrix, methylCC, methylInheritance, MethylSeekR, methylSig, methylumi, MinimumDistance, MIRA, missMethyl, mitoClone2, MMAPPR2, MMDiff2, Modstrings, monaLisa, mosaics, motifbreakR, motifmatchr, MouseFM, MultiAssayExperiment, multicrispr, MultiDataSet, multiHiCcompare, MungeSumstats, musicatk, NanoMethViz, nanotatoR, ncRNAtools, nearBynding, normr, nucleR, nullranges, NxtIRFcore, ODER, oligoClasses, OmaDB, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder, pageRank, panelcn.mops, PAST, pcaExplorer, pepStat, PhIPData, Pi, PICS, PING, pqsfinder, pram, prebs, preciseTAD, PrecisionTrialDrawer, primirTSS, proActiv, proBAMr, profileplyr, ProteoDisco, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qsea, Qtlizer, R3CPET, R453Plus1Toolbox, RareVariantVis, RCAS, RcisTarget, recount, recount3, regioneR, regionReport, regutools, REMP, Repitools, rGADEM, RGMQL, Rhisat2, RiboCrypt, RiboDiPA, RiboProfiling, RIPAT, RLSeq, Rmmquant, rmspc, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, RTCGAToolbox, scanMiR, scanMiRApp, SCATE, scmeth, scoreInvHap, scPipe, scruff, scuttle, segmenter, seq2pathway, SeqArray, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, shinyepico, ShortRead, signeR, SigsPack, SimFFPE, SingleCellExperiment, SingleMoleculeFootprinting, sitadela, snapcount, soGGi, SparseSignatures, spatzie, SpectralTAD, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, systemPipeR, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, TFARM, TFBSTools, TFEA.ChIP, TFHAZ, tidybulk, TitanCNA, tLOH, tracktables, transcriptR, transite, trena, TRESS, tricycle, triplex, tscR, TSRchitect, TVTB, txcutr, tximeta, Ularcirc, UMI4Cats, uncoverappLib, Uniquorn, VariantFiltering, VaSP, VCFArray, wiggleplotr, XNAString, 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.v3.1.1.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, COSMIC.67, ELMER.data, GenomicDistributionsData, leeBamViews, MethylSeqData, pepDat, scRNAseq, SomaticCancerAlterations, spatialLIBD, systemPipeRdata, VariantToolsData, recountWorkflow, TCGAWorkflow, ActiveDriverWGS, BinQuasi, cinaR, crispRdesignR, driveR, ExomeDepth, geno2proteo, hoardeR, ICAMS, intePareto, LoopRig, MAAPER, MAFDash, MitoHEAR, noisyr, oncoPredict, PACVr, pagoo, RapidoPGS, RAPIDR, Signac, simMP, SNPassoc, utr.annotation, VALERIE suggestsMe: AnnotationHub, autonomics, biobroom, BiocGenerics, BiocParallel, Chicago, CNVgears, ComplexHeatmap, cummeRbund, epivizrChart, GenomeInfoDb, Glimma, GSReg, GWASTools, HDF5Array, InteractiveComplexHeatmap, interactiveDisplay, IRanges, maftools, MiRaGE, omicsPrint, parglms, plotgardener, recountmethylation, RTCGA, S4Vectors, SeqGSEA, splatter, TFutils, universalmotif, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, GenomicState, BeadArrayUseCases, GeuvadisTranscriptExpr, nanotubes, RNAmodR.Data, sesameData, Single.mTEC.Transcriptomes, CAGEWorkflow, cancerTiming, chicane, DGEobj, gkmSVM, LDheatmap, polyRAD, rliger, seqmagick, Seurat, sigminer, updog, valr dependencyCount: 15 Package: GenomicScores Version: 2.6.1 Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: stats, utils, XML, Biobase, BiocManager, BiocFileCache, IRanges (>= 2.3.23), Biostrings, GenomeInfoDb, AnnotationHub, rhdf5, DelayedArray, HDF5Array Suggests: RUnit, BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, phastCons100way.UCSC.hg19, MafDb.1Kgenomes.phase1.hs37d5, SNPlocs.Hsapiens.dbSNP144.GRCh37, VariantAnnotation, TxDb.Hsapiens.UCSC.hg19.knownGene, gwascat, RColorBrewer, shiny, shinyjs, shinycustomloader, data.table, DT, magrittr, shinydashboard License: Artistic-2.0 Archs: i386, x64 MD5sum: e2b4795c6e6eab5237c528ec5028aef0 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: RELEASE_3_14 git_last_commit: f7372dc git_last_commit_date: 2022-03-23 Date/Publication: 2022-03-24 source.ver: src/contrib/GenomicScores_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicScores_2.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicScores_2.6.1.tgz vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html vignetteTitles: An introduction to the GenomicScores package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R dependsOnMe: 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.v3.1.1.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons30way.UCSC.hg38, phastCons7way.UCSC.hg38 importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis, VariantFiltering suggestsMe: methrix, SNPassoc dependencyCount: 98 Package: GenomicSuperSignature Version: 1.2.1 Depends: R (>= 4.0), SummarizedExperiment Imports: ComplexHeatmap, ggplot2, methods, S4Vectors, Biobase, ggpubr, dplyr, plotly, BiocFileCache, grid, flextable 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: ff53b8611d6fc48d16390222f9217229 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: RELEASE_3_14 git_last_commit: e88625b git_last_commit_date: 2021-12-15 Date/Publication: 2021-12-16 source.ver: src/contrib/GenomicSuperSignature_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicSuperSignature_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicSuperSignature_1.2.1.tgz vignettes: vignettes/GenomicSuperSignature/inst/doc/GenomicSuperSignature_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/GenomicSuperSignature_Contents.R, vignettes/GenomicSuperSignature/inst/doc/Quickstart.R dependencyCount: 167 Package: GenomicTuples Version: 1.28.0 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 License: Artistic-2.0 MD5sum: f3ce8839dbf877c6c28b86b163dadf63 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: RELEASE_3_14 git_last_commit: 6527f88 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GenomicTuples_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenomicTuples_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenomicTuples_1.28.0.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: 18 Package: genotypeeval Version: 1.26.0 Depends: R (>= 3.4.0), VariantAnnotation Imports: ggplot2, rtracklayer, BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges, methods, BiocParallel, graphics, stats Suggests: rmarkdown, testthat, SNPlocs.Hsapiens.dbSNP141.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE Archs: i386, x64 MD5sum: b8bb6f5bd0b5b041053e8b412c9bc7c3 NeedsCompilation: no Title: QA/QC of a gVCF or VCF file Description: Takes in a gVCF or VCF and reports metrics to assess quality of calls. biocViews: Genetics, BatchEffect, Sequencing, SNP, VariantAnnotation, DataImport Author: Jennifer Tom [aut, cre] Maintainer: Jennifer Tom VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/genotypeeval git_branch: RELEASE_3_14 git_last_commit: 14bef66 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genotypeeval_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/genotypeeval_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/genotypeeval_1.26.0.tgz vignettes: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html vignetteTitles: genotypeeval_vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 112 Package: genphen Version: 1.22.0 Depends: R (>= 3.5.0), Rcpp (>= 0.12.17), methods, stats, graphics Imports: rstan (>= 2.17.3), ranger, parallel, foreach, doParallel, e1071, Biostrings, rPref Suggests: testthat, ggplot2, gridExtra, ape, ggrepel, knitr, reshape, xtable License: GPL (>= 2) MD5sum: a1e84fd546749ff21318d91124559255 NeedsCompilation: no Title: genphen: tool for quantification of genotype-phenotype associations in genome wide association studies (GWAS) Description: Genetic association studies help us discover relationships between genotypes and phenotype. genphen is a computational tool for quantification of genotype-phenotype associations using a hybrid approach based on statistical learning techniques and probabilistic models that are analyzed computationally by Bayes inference. biocViews: GenomeWideAssociation, Regression, Classification, SupportVectorMachine, Genetics, SequenceMatching, Bayesian, FeatureExtraction, Sequencing Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski BugReports: https://github.com/snaketron/genphen/issues git_url: https://git.bioconductor.org/packages/genphen git_branch: RELEASE_3_14 git_last_commit: 5802c38 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/genphen_1.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/genphen_1.22.0.tgz vignettes: vignettes/genphen/inst/doc/genphenManual.pdf vignetteTitles: genphen overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genphen/inst/doc/genphenManual.R dependencyCount: 85 Package: GenVisR Version: 1.26.0 Depends: R (>= 3.3.0), methods Imports: AnnotationDbi, biomaRt (>= 2.45.8), BiocGenerics, Biostrings, DBI, FField, 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: 1a454962065f2de56ffff7bd2de554c2 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: RELEASE_3_14 git_last_commit: 9bd43fb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GenVisR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GenVisR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GenVisR_1.26.0.tgz vignettes: vignettes/GenVisR/inst/doc/Intro.html, vignettes/GenVisR/inst/doc/Upcoming_Features.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html vignetteTitles: GenVisR: An introduction, Visualizing Small Variants, waterfall: function introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/Intro.R, vignettes/GenVisR/inst/doc/Upcoming_Features.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R dependencyCount: 119 Package: GeoDiff Version: 1.0.0 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 MD5sum: 6eb051ffdd001b8d5f74faf1dfd888c5 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: Lei Yang [aut], Zhi Yang [cre, aut] Maintainer: Zhi Yang 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: RELEASE_3_14 git_last_commit: cd343af git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeoDiff_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeoDiff_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeoDiff_1.0.0.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: 119 Package: GEOexplorer Version: 1.0.0 Depends: shiny, limma, Biobase, plotly, shinyBS Imports: DT, htmltools, factoextra, heatmaply, maptools, pheatmap, scales, shinyHeatmaply, shinybusy, ggplot2, stringr, umap, GEOquery, impute, grDevices, stats, graphics, utils Suggests: rmarkdown, knitr, usethis, testthat (>= 3.0.0) License: GPL-3 Archs: i386, x64 MD5sum: aec0ace054967993c39841f807208228 NeedsCompilation: no Title: GEOexplorer: an R/Bioconductor package for gene expression analysis and visualisation Description: GEOexplorer is a Shiny app that enables exploratory data analysis and differential gene expression of gene expression analysis on microarray gene expression datasets held on the GEO database. The outputs are interactive graphs that enable users to explore the results of the 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 Author: Guy Hunt [aut, cre] (), Rafael Henkin [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: RELEASE_3_14 git_last_commit: e22eaa4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GEOexplorer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEOexplorer_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEOexplorer_1.0.0.tgz vignettes: vignettes/GEOexplorer/inst/doc/GEOexplorer.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOexplorer/inst/doc/GEOexplorer.R dependencyCount: 182 Package: GEOfastq Version: 1.2.0 Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: f06ebd8632e8b994a11ec62013248ed7 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: RELEASE_3_14 git_last_commit: 866d6af git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GEOfastq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEOfastq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEOfastq_1.2.0.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: 40 Package: GEOmetadb Version: 1.56.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 8d6dd4b1781349ce232268bb55e18cae 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 URL: http://gbnci.abcc.ncifcrf.gov/geo/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEOmetadb git_branch: RELEASE_3_14 git_last_commit: 68547ef git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GEOmetadb_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEOmetadb_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEOmetadb_1.56.0.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: 60 Package: GeomxTools Version: 2.0.0 Depends: R (>= 3.6), Biobase, NanoStringNCTools, S4Vectors Imports: BiocGenerics, rjson, readxl, EnvStats, reshape2, methods, utils, stats, data.table, outliers, lmerTest, dplyr Suggests: rmarkdown, knitr, testthat (>= 3.0.0), parallel, ggiraph License: MIT MD5sum: f027856bf0c1a50997c2e28dbe63be5c 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 Author: Nicole Ortogero [cre, aut], Zhi Yang [aut] Maintainer: Nicole Ortogero VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeomxTools git_branch: RELEASE_3_14 git_last_commit: fee3ebb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GeomxTools_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GeomxTools_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GeomxTools_2.0.0.tgz vignettes: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.html vignetteTitles: Developer Introduction to the NanoStringGeoMxSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.R dependsOnMe: GeoMxWorkflows importsMe: GeoDiff, SpatialDecon dependencyCount: 109 Package: GEOquery Version: 2.62.2 Depends: methods, Biobase Imports: httr, readr (>= 1.3.1), xml2, dplyr, data.table, tidyr, magrittr, R.utils, limma Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr, markdown License: GPL-2 MD5sum: f1cbdb8b9171368599cfacab0616d7f5 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 Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery VignetteBuilder: knitr BugReports: https://github.com/seandavi/GEOquery/issues/new git_url: https://git.bioconductor.org/packages/GEOquery git_branch: RELEASE_3_14 git_last_commit: 1966c10 git_last_commit_date: 2022-01-10 Date/Publication: 2022-01-11 source.ver: src/contrib/GEOquery_2.62.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEOquery_2.62.2.zip mac.binary.ver: bin/macosx/contrib/4.1/GEOquery_2.62.2.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.html vignetteTitles: Using GEOquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE103322, GSE13015, GSE62944 importsMe: bigmelon, BioPlex, ChIPXpress, conclus, crossmeta, DExMA, EGAD, GAPGOM, GEOexplorer, MACPET, minfi, MoonlightR, phantasus, recount, SRAdb, BeadArrayUseCases, GSE13015, easyDifferentialGeneCoexpression, geneExpressionFromGEO, MetaIntegrator suggestsMe: AUCell, autonomics, ctsGE, dearseq, debCAM, diffcoexp, dyebias, EpiDISH, fgsea, GCSscore, GeneExpressionSignature, GenomicOZone, methylclock, multiClust, MultiDataSet, omicsPrint, PCAtools, quantiseqr, RegEnrich, RGSEA, Rnits, runibic, skewr, spatialHeatmap, TargetScore, zFPKM, airway, antiProfilesData, muscData, parathyroidSE, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, RegParallel, AnnoProbe, BED, maGUI, metaMA, MLML2R, NACHO, TcGSA, tinyarray dependencyCount: 51 Package: GEOsubmission Version: 1.46.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: 65f6674846840e72bc32cf2f7f49b61b 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: RELEASE_3_14 git_last_commit: 6984223 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GEOsubmission_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEOsubmission_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEOsubmission_1.46.0.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: gep2pep Version: 1.14.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 MD5sum: ad04165d35bcb425a16f190ac4207038 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: RELEASE_3_14 git_last_commit: f1a07c8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gep2pep_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gep2pep_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gep2pep_1.14.0.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: gespeR Version: 1.26.0 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: 692631a7ceb4ee0d97e8c2757e76eb21 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/gespeR git_branch: RELEASE_3_14 git_last_commit: e9a08c0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gespeR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gespeR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gespeR_1.26.0.tgz vignettes: vignettes/gespeR/inst/doc/gespeR.pdf vignetteTitles: An R package for deconvoluting off-target confounded RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gespeR/inst/doc/gespeR.R dependencyCount: 117 Package: getDEE2 Version: 1.4.0 Depends: R (>= 4.0) Imports: stats, utils, SummarizedExperiment, htm2txt Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: da0b5c83088a342be30544bb5a4cbf7b 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: RELEASE_3_14 git_last_commit: 210947c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-29 source.ver: src/contrib/getDEE2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/getDEE2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/getDEE2_1.4.0.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 dependencyCount: 26 Package: geva Version: 1.2.0 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: 53e3db26c1479e536f3f5354d3da282b 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: RELEASE_3_14 git_last_commit: 80d87c0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/geva_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/geva_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/geva_1.2.0.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: 1.38.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: a3e4d2488e89d7254a52ec43361d144f 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: RELEASE_3_14 git_last_commit: b941c3f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GEWIST_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GEWIST_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GEWIST_1.38.0.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: 87 Package: ggbio Version: 1.42.0 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: 08119148ca8684aa168dbaaebe6e8e7a 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: RELEASE_3_14 git_last_commit: 3540047 git_last_commit_date: 2021-10-27 Date/Publication: 2021-11-02 source.ver: src/contrib/ggbio_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggbio_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ggbio_1.42.0.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: cageminer, derfinderPlot, GenomicOZone, msgbsR, R3CPET, ReportingTools, RiboProfiling, scruff, SomaticSignatures suggestsMe: bambu, beadarray, ensembldb, gwascat, interactiveDisplay, NanoStringNCTools, Pi, regionReport, RnBeads, StructuralVariantAnnotation, universalmotif, NanoporeRNASeq, Single.mTEC.Transcriptomes, SomaticCancerAlterations dependencyCount: 152 Package: ggcyto Version: 1.22.0 Depends: methods, ggplot2(>= 3.3.0), flowCore(>= 1.41.5), ncdfFlow(>= 2.17.1), flowWorkspace(>= 3.33.1) Imports: plyr, scales, hexbin, data.table, RColorBrewer, gridExtra, rlang Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz, ggridges, vdiffr License: Artistic-2.0 MD5sum: 21b36fdcf82db518a345361748459b6d 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 ,Jake Wagner URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: RELEASE_3_14 git_last_commit: b36d93a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ggcyto_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggcyto_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ggcyto_1.22.0.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: FALSE 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 importsMe: CytoML suggestsMe: CATALYST, flowCore, flowStats, flowTime, flowWorkspace, openCyto dependencyCount: 84 Package: ggmsa Version: 1.0.0 Depends: R (>= 4.1.0) Imports: Biostrings, ggplot2, magrittr, tidyr, utils, stats, aplot, RColorBrewer, ggalt, ggforce, dplyr, R4RNA, grDevices, seqmagick, grid, methods Suggests: ggtreeExtra, ggtree (>= 1.17.1), ape, cowplot, knitr, BiocStyle, rmarkdown, readxl, ggnewscale, kableExtra, gggenes, testthat (>= 3.0.0), phangorn License: Artistic-2.0 MD5sum: bfd9fe52e29dce9df16d3be9c78b83c1 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: http://yulab-smu.top/ggmsa/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggmsa/issues git_url: https://git.bioconductor.org/packages/ggmsa git_branch: RELEASE_3_14 git_last_commit: 17e4f08 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ggmsa_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggmsa_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ggmsa_1.0.0.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 dependencyCount: 95 Package: GGPA Version: 1.6.0 Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats Imports: Rcpp (>= 0.11.3) LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle License: GPL (>= 2) MD5sum: 2717d2e52468a5a8a155e777f45d5dc5 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: RELEASE_3_14 git_last_commit: 59e21a3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GGPA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GGPA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GGPA_1.6.0.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: 60 Package: ggspavis Version: 1.0.0 Depends: ggplot2 Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, ggside, grid, methods, stats Suggests: BiocStyle, rmarkdown, knitr, STexampleData, BumpyMatrix, scater, scran, uwot, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: i386, x64 MD5sum: 6db49d2066cf74b78b3da41882f7b10e NeedsCompilation: no Title: Visualization functions for spatially resolved transcriptomics data Description: Visualization functions for spatially resolved transcriptomics datasets stored in SpatialExperiment format. Includes functions to create several types of plots for data from from spot-based (e.g. 10x Genomics Visium) and molecule-based (e.g. seqFISH) technological platforms. biocViews: SingleCell, Transcriptomics Author: Lukas M. Weber [aut, cre] (), Helena L. Crowell [aut] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/ggspavis VignetteBuilder: knitr BugReports: https://github.com/lmweber/ggspavis/issues git_url: https://git.bioconductor.org/packages/ggspavis git_branch: RELEASE_3_14 git_last_commit: 78f5f4b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ggspavis_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggspavis_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ggspavis_1.0.0.tgz vignettes: vignettes/ggspavis/inst/doc/ggspavis_overview.html vignetteTitles: ggspavis overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggspavis/inst/doc/ggspavis_overview.R dependencyCount: 111 Package: ggtree Version: 3.2.1 Depends: R (>= 3.5.0) Imports: ape, aplot (>= 0.0.4), dplyr, ggplot2 (>= 3.0.0), grid, magrittr, methods, purrr, rlang, ggfun, yulab.utils, tidyr, tidytree (>= 0.2.6), treeio (>= 1.8.0), utils, scales Suggests: emojifont, ggimage, ggplotify, grDevices, knitr, prettydoc, rmarkdown, stats, testthat, tibble License: Artistic-2.0 MD5sum: 9e14b31c3dac00961e5233755ca5d9fd 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] (), Yonghe Xia [ctb], Justin Silverman [ctb], Bradley Jones [ctb], Watal M. Iwasaki [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/treedata-book/ (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: RELEASE_3_14 git_last_commit: d3747e6 git_last_commit_date: 2021-11-14 Date/Publication: 2021-11-16 source.ver: src/contrib/ggtree_3.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggtree_3.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ggtree_3.2.1.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: tanggle importsMe: enrichplot, ggtreeExtra, LymphoSeq, miaViz, microbiomeMarker, MicrobiotaProcess, philr, singleCellTK, sitePath, systemPipeTools, treekoR, dowser, genBaRcode, harrietr, STraTUS suggestsMe: ggmsa, TreeAndLeaf, treeio, TreeSummarizedExperiment, universalmotif, aplot, CoOL, DAISIE, deeptime, ggimage, idiogramFISH, nosoi, oppr, PCMBase, Platypus, RAINBOWR, rhierbaps, yatah dependencyCount: 58 Package: ggtreeExtra Version: 1.4.2 Imports: ggplot2, utils, rlang, ggnewscale, stats, ggtree Suggests: treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc, markdown, testthat (>= 3.0.0), pillar, tidytree (>= 0.3.9) License: GPL-3 MD5sum: 5033bb2ab4afffae7d0a6c4ce1830aa0 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: RELEASE_3_14 git_last_commit: c5bde8b git_last_commit_date: 2022-03-06 Date/Publication: 2022-03-08 source.ver: src/contrib/ggtreeExtra_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/ggtreeExtra_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.1/ggtreeExtra_1.4.2.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 dependencyCount: 60 Package: GIGSEA Version: 1.12.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 85af59ce1910869e7b49e5d5be79ab82 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: RELEASE_3_14 git_last_commit: fbb5258 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GIGSEA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GIGSEA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GIGSEA_1.12.0.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: girafe Version: 1.46.0 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), graphics, grDevices, stats, utils, IRanges (>= 2.13.12) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 MD5sum: 2ab78244c001d40e50201fd30110697d 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: RELEASE_3_14 git_last_commit: 4803e9e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/girafe_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/girafe_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/girafe_1.46.0.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: 46 Package: GISPA Version: 1.18.0 Depends: R (>= 3.5) Imports: Biobase, changepoint, data.table, genefilter, graphics, GSEABase, HH, lattice, latticeExtra, plyr, scatterplot3d, stats Suggests: knitr License: GPL-2 Archs: i386, x64 MD5sum: 0303af6ce8fdf2287a953e12c94bd5da NeedsCompilation: no Title: GISPA: Method for Gene Integrated Set Profile Analysis Description: GISPA is a method intended for the researchers who are interested in defining gene sets with similar, a priori specified molecular profile. GISPA method has been previously published in Nucleic Acid Research (Kowalski et al., 2016; PMID: 26826710). biocViews: StatisticalMethod,GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GISPA git_branch: RELEASE_3_14 git_last_commit: 5182112 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GISPA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GISPA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GISPA_1.18.0.tgz vignettes: vignettes/GISPA/inst/doc/GISPA_manual.html vignetteTitles: GISPA:Method for Gene Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GISPA/inst/doc/GISPA_manual.R dependencyCount: 132 Package: GLAD Version: 2.58.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 MD5sum: 6013807420f8ce2032928e1599c603ac 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: RELEASE_3_14 git_last_commit: b91c49b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GLAD_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GLAD_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GLAD_2.58.0.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, seqCNA importsMe: ITALICS, MANOR, snapCGH suggestsMe: RnBeads, aroma.cn, aroma.core, cghRA dependencyCount: 4 Package: GladiaTOX Version: 1.10.3 Depends: R (>= 3.6.0), data.table (>= 1.9.4) Imports: DBI, RMySQL, RSQLite, numDeriv, RColorBrewer, parallel, stats, methods, graphics, grDevices, xtable, tools, brew, stringr, RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: dd06aad27cbd0742ea10c9e6cb4d3aab 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: RELEASE_3_14 git_last_commit: f196e41 git_last_commit_date: 2022-04-07 Date/Publication: 2022-04-10 source.ver: src/contrib/GladiaTOX_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/GladiaTOX_1.10.3.zip mac.binary.ver: bin/macosx/contrib/4.1/GladiaTOX_1.10.3.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: 2.4.0 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: 1b22fefc96529fa83f4b951fd6bf06e8 NeedsCompilation: no Title: Interactive HTML graphics Description: This package generates interactive visualisations for analysis of RNA-sequencing data using output from limma, edgeR or DESeq2 packages in an HTML page. The interactions are built on top of the popular static representations of analysis results in order to provide additional information. 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: RELEASE_3_14 git_last_commit: caa270e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Glimma_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Glimma_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Glimma_2.4.0.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, limma, singlecell 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: 1.6.0 Imports: Rcpp, DelayedMatrixStats, matrixStats, DelayedArray, HDF5Array, SummarizedExperiment, BiocGenerics, methods, stats, utils, splines 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 License: GPL-3 MD5sum: dd02d5a1f843d24139507215706f1159 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] (), 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: RELEASE_3_14 git_last_commit: 03df1e1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/glmGamPoi_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/glmGamPoi_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/glmGamPoi_1.6.0.tgz vignettes: vignettes/glmGamPoi/inst/doc/glmGamPoi.html vignetteTitles: glmGamPoi Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmGamPoi/inst/doc/glmGamPoi.R importsMe: transformGamPoi suggestsMe: DESeq2 dependencyCount: 35 Package: glmSparseNet Version: 1.12.0 Depends: R (>= 4.1), Matrix, MultiAssayExperiment, glmnet Imports: SummarizedExperiment, biomaRt, futile.logger, sparsebn, sparsebnUtils, forcats, dplyr, glue, readr, httr, ggplot2, survminer, reshape2, stringr, parallel, methods, loose.rock (>= 1.0.12) Suggests: testthat, knitr, rmarkdown, survival, survcomp, pROC, VennDiagram, BiocStyle, curatedTCGAData, TCGAutils License: GPL-3 Archs: i386, x64 MD5sum: 5d6ec2e75601012607f1b6181c0bb6ea 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: RELEASE_3_14 git_last_commit: d305a80 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/glmSparseNet_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/glmSparseNet_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/glmSparseNet_1.12.0.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: 177 Package: GlobalAncova Version: 4.12.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM Suggests: GO.db, golubEsets, hu6800.db, vsn, Rgraphviz License: GPL (>= 2) MD5sum: 33767fc2dacd2711624f55ed0d7332e4 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: RELEASE_3_14 git_last_commit: 16a2b80 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GlobalAncova_4.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GlobalAncova_4.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GlobalAncova_4.12.0.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: 84 Package: globalSeq Version: 1.22.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 Archs: i386, x64 MD5sum: 7c862cf3b0e9b6591582d464363af500 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: RELEASE_3_14 git_last_commit: 7b42a44 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/globalSeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/globalSeq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/globalSeq_1.22.0.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: 5.48.0 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: 3df150b6d71b854d5aa97f48628f6ebc 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: RELEASE_3_14 git_last_commit: 86c2c8f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/globaltest_5.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/globaltest_5.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/globaltest_5.48.0.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: gmapR Version: 1.36.0 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: eabe57475a89bb4c9e6cb6fa58a706b0 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: RELEASE_3_14 git_last_commit: 1e9cf9a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gmapR_1.36.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/gmapR_1.36.0.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 importsMe: MMAPPR2 suggestsMe: VariantTools, VariantToolsData dependencyCount: 98 Package: GmicR Version: 1.8.0 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: cfe413ca13576a7ed747b9c81f433592 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: RELEASE_3_14 git_last_commit: f795e9d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GmicR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GmicR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GmicR_1.8.0.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: 148 Package: gmoviz Version: 1.6.0 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: 8be6acaf3c1a71b547ad51d0700abb7f 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: RELEASE_3_14 git_last_commit: 22e8f55 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gmoviz_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gmoviz_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gmoviz_1.6.0.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: 111 Package: GMRP Version: 1.22.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) MD5sum: f9a457c40c437719b717047703ac1b36 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: RELEASE_3_14 git_last_commit: f374007 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GMRP_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GMRP_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GMRP_1.22.0.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: 21 Package: GNET2 Version: 1.10.0 Depends: R (>= 3.6) Imports: ggplot2,xgboost,Rcpp,reshape2,grid,DiagrammeR,methods,stats,matrixStats,graphics,SummarizedExperiment,dplyr,igraph, grDevices, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: Apache License 2.0 MD5sum: 2e54c4c39937717b7e33b84adfd8d832 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: RELEASE_3_14 git_last_commit: 905f61c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GNET2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GNET2_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GNET2_1.10.0.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: 91 Package: GOexpress Version: 1.28.0 Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) Archs: i386, x64 MD5sum: 4b38e8ce8a16d78b64538ccca595c4e3 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: RELEASE_3_14 git_last_commit: 2ffb103 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GOexpress_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOexpress_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOexpress_1.28.0.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: 1.14.0 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: a873a434bd5e48986c6526e7dd983f6d 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: RELEASE_3_14 git_last_commit: b3d445a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GOfuncR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOfuncR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOfuncR_1.14.0.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 importsMe: ABAEnrichment dependencyCount: 53 Package: GOpro Version: 1.20.0 Depends: R (>= 3.4) 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: i386, x64 MD5sum: 7fa67b56688705097d078b65abb98a3a 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: RELEASE_3_14 git_last_commit: 81cc5b2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GOpro_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOpro_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOpro_1.20.0.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: 95 Package: goProfiles Version: 1.56.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 Archs: i386, x64 MD5sum: c8b7ee90384892c1b844c096ae7f6ffc 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: RELEASE_3_14 git_last_commit: 8d389b2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/goProfiles_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/goProfiles_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/goProfiles_1.56.0.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 dependencyCount: 50 Package: GOSemSim Version: 2.20.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, GO.db, methods, utils LinkingTo: Rcpp Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat, ROCR License: Artistic-2.0 MD5sum: fd739a102b9e10c132d34323481f7dc4 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: RELEASE_3_14 git_last_commit: fa82442 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GOSemSim_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOSemSim_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOSemSim_2.20.0.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, BiSEp importsMe: clusterProfiler, DOSE, enrichplot, GAPGOM, meshes, Rcpi, rrvgo, simplifyEnrichment, ViSEAGO suggestsMe: BioCor, epiNEM, FELLA, SemDist, protr, rDNAse dependencyCount: 46 Package: goseq Version: 1.46.0 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) Archs: i386, x64 MD5sum: e0fe37149110265102c9e61f82316363 NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data biocViews: ImmunoOncology, Sequencing, GO, GeneExpression, Transcription, RNASeq Author: Matthew Young Maintainer: Matthew Young , Nadia Davidson git_url: https://git.bioconductor.org/packages/goseq git_branch: RELEASE_3_14 git_last_commit: 1fb5626 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/goseq_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/goseq_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/goseq_1.46.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd importsMe: ideal, SMITE suggestsMe: sparrow dependencyCount: 102 Package: GOSim Version: 1.32.0 Depends: GO.db, annotate Imports: org.Hs.eg.db, AnnotationDbi, topGO, cluster, flexmix, RBGL, graph, Matrix, corpcor, Rcpp LinkingTo: Rcpp Enhances: igraph License: GPL (>= 2) MD5sum: 64629b1f423d1b1bdce284a56dd40de6 NeedsCompilation: yes Title: Computation of functional similarities between GO terms and gene products; GO enrichment analysis Description: This package implements several functions useful for computing similarities between GO terms and gene products based on their GO annotation. Moreover it allows for computing a GO enrichment analysis biocViews: GO, Clustering, Software, Pathways Author: Holger Froehlich Maintainer: Holger Froehlich git_url: https://git.bioconductor.org/packages/GOSim git_branch: RELEASE_3_14 git_last_commit: 0c5092d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GOSim_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOSim_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOSim_1.32.0.tgz vignettes: vignettes/GOSim/inst/doc/GOSim.pdf vignetteTitles: GOsim hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSim/inst/doc/GOSim.R dependencyCount: 64 Package: goSTAG Version: 1.18.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: e43be6651aa0ac4a5ebf22ec98a3d97e 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: RELEASE_3_14 git_last_commit: a62fde0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/goSTAG_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/goSTAG_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/goSTAG_1.18.0.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: 72 Package: GOstats Version: 2.60.0 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 License: Artistic-2.0 MD5sum: c85b362ed87be4f15a7003358d63e6a1 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], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GOstats git_branch: RELEASE_3_14 git_last_commit: a20055c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GOstats_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOstats_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOstats_2.60.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf, vignettes/GOstats/inst/doc/GOstatsHyperG.pdf, vignettes/GOstats/inst/doc/GOvis.pdf vignetteTitles: Hypergeometric tests for less common model organisms, Hypergeometric Tests Using GOstats, Visualizing Data Using GOstats 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, PloGO2 importsMe: affycoretools, attract, categoryCompare, GmicR, ideal, MIGSA, miRLAB, pcaExplorer, scTensor, DNLC suggestsMe: a4, Category, fastLiquidAssociation, fgga, GSEAlm, interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe, DGCA, maGUI, sand dependencyCount: 62 Package: GOsummaries Version: 2.30.0 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) MD5sum: 1f2ee26d6ac40457470c113debed82a8 NeedsCompilation: yes Title: Word cloud summaries of GO enrichment analysis Description: A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data. biocViews: GeneExpression, Clustering, GO, Visualization Author: Raivo Kolde Maintainer: Raivo Kolde URL: https://github.com/raivokolde/GOsummaries git_url: https://git.bioconductor.org/packages/GOsummaries git_branch: RELEASE_3_14 git_last_commit: e5c3a3b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GOsummaries_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOsummaries_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOsummaries_2.30.0.tgz vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf vignetteTitles: GOsummaries basics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOsummaries/inst/doc/GOsummaries-basics.R dependencyCount: 48 Package: GOTHiC Version: 1.30.0 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 Archs: i386, x64 MD5sum: 7b97b1ae757251582b500fed3cd9ceed 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: RELEASE_3_14 git_last_commit: a2e9c22 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GOTHiC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GOTHiC_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GOTHiC_1.30.0.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 dependencyCount: 81 Package: goTools Version: 1.68.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: e8e48b255ea93ea4611c44aa175197c0 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: RELEASE_3_14 git_last_commit: 31a5f26 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/goTools_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/goTools_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/goTools_1.68.0.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: 1.6.0 Depends: R (>= 4.0.0), methods, graphics, Rcpp Imports: parallel, ggplot2, ggrepel, plyr, vegan, DT, shiny, shinyBS, stats, utils, grDevices LinkingTo: Rcpp Suggests: gpaExample License: GPL (>= 2) MD5sum: c1236736851efd79db5c6bbae7356762 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: RELEASE_3_14 git_last_commit: 825eeeb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GPA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GPA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GPA_1.6.0.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: 71 Package: gpart Version: 1.12.0 Depends: R (>= 3.5.0), grid, Homo.sapiens, TxDb.Hsapiens.UCSC.hg38.knownGene, Imports: igraph, biomaRt, Rcpp, data.table, OrganismDbi, AnnotationDbi, grDevices, stats, utils, GenomicRanges, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 15612d8c6cbf35ae4f83a69f4786d832 NeedsCompilation: yes Title: Human genome partitioning of dense sequencing data by identifying haplotype blocks Description: we provide a new SNP sequence partitioning method which partitions the whole SNP sequence based on not only LD block structures but also gene location information. The LD block construction for GPART is performed using Big-LD algorithm, with additional improvement from previous version reported in Kim et al.(2017). We also add a visualization tool to show the LD heatmap with the information of LD block boundaries and gene locations in the package. biocViews: Software, Clustering Author: Sun Ah Kim [aut, cre, cph], Yun Joo Yoo [aut, cph] Maintainer: Sun Ah Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gpart git_branch: RELEASE_3_14 git_last_commit: fea63e2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gpart_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gpart_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gpart_1.12.0.tgz vignettes: vignettes/gpart/inst/doc/gpart.html vignetteTitles: Your Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gpart/inst/doc/gpart.R dependencyCount: 107 Package: gpls Version: 1.66.0 Imports: stats Suggests: MASS License: Artistic-2.0 Archs: i386, x64 MD5sum: b85098c1110fe8f537c8a0ac5720417d 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: RELEASE_3_14 git_last_commit: 3c4eeec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gpls_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gpls_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gpls_1.66.0.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: gprege Version: 1.38.0 Depends: R (>= 2.10), gptk Suggests: spam License: AGPL-3 MD5sum: 10dce864205b521f17665d783c193290 NeedsCompilation: no Title: Gaussian Process Ranking and Estimation of Gene Expression time-series Description: The gprege package implements the methodology described in Kalaitzis & Lawrence (2011) "A simple approach to ranking differentially expressed gene expression time-courses through Gaussian process regression". The software fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS (Angelini et.al, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (http://www.biomedcentral.com/1471-2105/12/180). biocViews: Microarray, Preprocessing, Bioinformatics, DifferentialExpression, TimeCourse Author: Alfredo Kalaitzis Maintainer: Alfredo Kalaitzis BugReports: alkalait@gmail.com git_url: https://git.bioconductor.org/packages/gprege git_branch: RELEASE_3_14 git_last_commit: 4bf7d50 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gprege_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gprege_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gprege_1.38.0.tgz vignettes: vignettes/gprege/inst/doc/gprege_quick.pdf vignetteTitles: gprege Quick Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gprege/inst/doc/gprege_quick.R dependencyCount: 45 Package: gpuMagic Version: 1.10.0 Depends: R (>= 3.6.0), methods, utils Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 52f9bf64e806638136415a75148f9cf5 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 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: RELEASE_3_14 git_last_commit: d9b9915 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gpuMagic_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gpuMagic_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gpuMagic_1.10.0.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: 38 Package: granulator Version: 1.2.0 Depends: R (>= 4.1) Imports: cowplot, e1071, epiR, dplyr, dtangle, ggplot2, ggplotify, grDevices, limSolve, magrittr, MASS, nnls, parallel, pheatmap, purrr, rlang, stats, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 1a60340ed0bf825836aebe8fc5994c70 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: RELEASE_3_14 git_last_commit: aaeb345 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/granulator_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/granulator_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/granulator_1.2.0.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: 100 Package: graper Version: 1.10.0 Depends: R (>= 3.6) Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: 35a8ee5478c5dc5f27299602e20277cb 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: RELEASE_3_14 git_last_commit: 0b86dc6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/graper_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/graper_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/graper_1.10.0.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: 43 Package: graph Version: 1.72.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11) Imports: stats, stats4, utils Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster Enhances: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: f6bfbb24b04eb19d60213285f53fed0a 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, Elizabeth Whalen, W. Huber, S. Falcon Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/graph git_branch: RELEASE_3_14 git_last_commit: 7afbd26 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/graph_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/graph_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/graph_1.72.0.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.pdf, vignettes/graph/inst/doc/graph.pdf, vignettes/graph/inst/doc/graphAttributes.pdf, vignettes/graph/inst/doc/GraphClass.pdf, vignettes/graph/inst/doc/MultiGraphClass.pdf vignetteTitles: clusterGraph and distGraph, Graph, 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, gaggle, GOstats, GraphAT, GSEABase, hypergraph, maigesPack, MineICA, pathRender, Pigengene, pkgDepTools, PoTRA, RbcBook1, RBGL, RBioinf, RCyjs, Rgraphviz, ROntoTools, SRAdb, topGO, vtpnet, ppiData, SNAData, yeastExpData, cyjShiny, dlsem, gridGraphviz, GUIProfiler, hasseDiagram, msSurv, NFP, PairViz, PerfMeas, QuACN, RSeed, SubpathwayLNCE importsMe: alpine, AnnotationHubData, BgeeDB, BiocCheck, biocGraph, BiocOncoTK, BiocPkgTools, biocViews, BioPlex, bnem, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, CytoML, DAPAR, dce, DEGraph, DEsubs, epiNEM, EventPointer, fgga, flowCL, flowClust, flowUtils, flowWorkspace, gage, GAPGOM, GeneNetworkBuilder, GOSim, GraphAT, graphite, hyperdraw, KEGGgraph, keggorthology, MIGSA, mnem, NCIgraph, NeighborNet, netresponse, OncoSimulR, ontoProc, oposSOM, OrganismDbi, pathview, PFP, PhenStat, pkgDepTools, ppiStats, pwOmics, qpgraph, RCy3, RGraph2js, RpsiXML, rsbml, Rtreemix, SplicingGraphs, Streamer, trackViewer, VariantFiltering, abn, BayesNetBP, BiDAG, BNrich, ceg, CePa, classGraph, CodeDepends, cogmapr, dnet, eulerian, ggm, GGRidge, gRain, gRbase, gridDebug, HEMDAG, hmma, HydeNet, kpcalg, net4pg, netgsa, NetPreProc, pcalg, pcgen, rags2ridges, RANKS, SEMgraph, simPATHy, SourceSet, stablespec, topologyGSA, unifDAG, wiseR, zenplots suggestsMe: AnnotationDbi, DEGraph, EBcoexpress, ecolitk, GeneAnswers, gwascat, KEGGlincs, MLP, NetPathMiner, rBiopaxParser, rTRM, S4Vectors, SPIA, VariantTools, arulesViz, bnclassify, bnlearn, bnstruct, bsub, ccdrAlgorithm, ChoR, epoc, gbutils, GeneNet, gMCP, igraph, lava, loon, maGUI, psych, rEMM, rPref, sisal, sparsebnUtils, textplot, tidygraph dependencyCount: 6 Package: GraphAlignment Version: 1.58.0 License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: c23a343dfd293ecca12cda33819b876d 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: RELEASE_3_14 git_last_commit: bc70f85 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GraphAlignment_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GraphAlignment_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GraphAlignment_1.58.0.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: 1.66.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL Archs: i386, x64 MD5sum: 63bd894ea956925370365da87e8a2ce8 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: RELEASE_3_14 git_last_commit: 8a14fe8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GraphAT_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GraphAT_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GraphAT_1.66.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 18 Package: graphite Version: 1.40.0 Depends: R (>= 4.1), methods Imports: AnnotationDbi, checkmate, graph (>= 1.67.1), httr, rappdirs, stats, utils, graphics Suggests: a4Preproc, ALL, BiocStyle, clipper, 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: 2eaa57182525e638645144dd64e619da 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 VignetteBuilder: R.rsp git_url: https://git.bioconductor.org/packages/graphite git_branch: RELEASE_3_14 git_last_commit: 9ac9323 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/graphite_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/graphite_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/graphite_1.40.0.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 dependsOnMe: PoTRA importsMe: dce, EnrichmentBrowser, mogsa, multiGSEA, ReactomePA, StarBioTrek, ICDS, netgsa suggestsMe: clipper, InterCellar, metaboliteIDmapping, NFP, SourceSet dependencyCount: 49 Package: GraphPAC Version: 1.36.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 571924159b293896379ef048b3347d8e 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: RELEASE_3_14 git_last_commit: 06d3157 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GraphPAC_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GraphPAC_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GraphPAC_1.36.0.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: 39 Package: GRENITS Version: 1.46.0 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) MD5sum: 642ab90b34512d10243587e022fbcd49 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: RELEASE_3_14 git_last_commit: 46eff8a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GRENITS_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GRENITS_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GRENITS_1.46.0.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: 45 Package: GreyListChIP Version: 1.26.0 Depends: R (>= 4.0), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Archs: i386, x64 MD5sum: 6486ef6825c3290ddb64176cd3e5bbaa 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: Gord Brown Maintainer: Gordon Brown git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: RELEASE_3_14 git_last_commit: 51b06d0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GreyListChIP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GreyListChIP_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GreyListChIP_1.26.0.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: 46 Package: GRmetrics Version: 1.20.0 Depends: R (>= 4.0), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 MD5sum: 25cf33eac767554e867189ee0f39ed4d 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: RELEASE_3_14 git_last_commit: 594e094 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GRmetrics_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GRmetrics_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GRmetrics_1.20.0.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: 129 Package: groHMM Version: 1.28.0 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: 89abbeb41c91e0dcd278900656ff21a1 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: Anusha Nagari , 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: RELEASE_3_14 git_last_commit: 6432fd9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/groHMM_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/groHMM_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/groHMM_1.28.0.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: 45 Package: GRridge Version: 1.18.0 Depends: R (>= 3.2), penalized, Iso, survival, methods, graph,stats,glmnet,mvtnorm Suggests: testthat License: GPL-3 Archs: i386, x64 MD5sum: ff9262480cedcd3bb9d8590112660f41 NeedsCompilation: no Title: Better prediction by use of co-data: Adaptive group-regularized ridge regression Description: This package allows the use of multiple sources of co-data (e.g. external p-values, gene lists, annotation) to improve prediction of binary, continuous and survival response using (logistic, linear or Cox) group-regularized ridge regression. It also facilitates post-hoc variable selection and prediction diagnostics by cross-validation using ROC curves and AUC. biocViews: Classification, Regression, Survival, Bayesian, RNASeq, GenePrediction, GeneExpression, Pathways, GeneSetEnrichment, GO, KEGG, GraphAndNetwork, ImmunoOncology Author: Mark A. van de Wiel , Putri W. Novianti Maintainer: Mark A. van de Wiel git_url: https://git.bioconductor.org/packages/GRridge git_branch: RELEASE_3_14 git_last_commit: f42cfee git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GRridge_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GRridge_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GRridge_1.18.0.tgz vignettes: vignettes/GRridge/inst/doc/GRridge.pdf vignetteTitles: GRridge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRridge/inst/doc/GRridge.R dependencyCount: 24 Package: GSALightning Version: 1.22.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: acc813e6b6230e43482da70e6e4f465e 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: RELEASE_3_14 git_last_commit: f205f25 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSALightning_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSALightning_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSALightning_1.22.0.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: 1.28.0 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: 298418f49b341f28ef03de33cb8e3f5a 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: RELEASE_3_14 git_last_commit: c8d70ec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSAR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSAR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSAR_1.28.0.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: 11 Package: GSCA Version: 2.24.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 84cd53657f56e96e539c3539ccfec073 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: RELEASE_3_14 git_last_commit: 13354f9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSCA_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSCA_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSCA_2.24.0.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: 1.8.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: 73e43e08094c4f7cd6d30a2fb028ad9d 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: RELEASE_3_14 git_last_commit: 56f9c2a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gscreend_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gscreend_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gscreend_1.8.0.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: 74 Package: GSEABase Version: 1.56.0 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 License: Artistic-2.0 MD5sum: 05918aa9c692fe99a3f6364a98c66f7c 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, Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEABase git_branch: RELEASE_3_14 git_last_commit: ee7c3ca git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSEABase_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSEABase_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSEABase_1.56.0.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, CCPROMISE, Cepo, cpvSNP, npGSEA, PROMISE, splineTimeR, TissueEnrich, GSVAdata, OSCA.basic importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cellHTS2, cosmosR, EnrichmentBrowser, escape, gep2pep, GISPA, GlobalAncova, GmicR, GSRI, GSVA, MIGSA, miRSM, mogsa, oppar, phenoTest, PROMISE, RcisTarget, ReportingTools, scTGIF, signatureSearch, singleCellTK, singscore, slalom, sparrow, TFutils, vissE, msigdb, SingscoreAMLMutations, clustermole, immcp, RVA suggestsMe: BiocSet, gage, globaltest, GOstats, GSAR, MAST, phenoTest, TFEA.ChIP, BaseSet dependencyCount: 49 Package: GSEABenchmarkeR Version: 1.14.0 Depends: 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: d43d69e72062fa9ddaf1578f6a77983a 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: RELEASE_3_14 git_last_commit: e2d1e39 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSEABenchmarkeR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSEABenchmarkeR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSEABenchmarkeR_1.14.0.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 dependencyCount: 125 Package: GSEAlm Version: 1.54.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 5e180933da740b65e82f00a81fc81f77 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: RELEASE_3_14 git_last_commit: f6012d8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSEAlm_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSEAlm_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSEAlm_1.54.0.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: 1.4.0 Depends: R (>= 4.0) Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud, stringr, gridExtra, rlang, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat License: GPL-3 | file LICENSE MD5sum: 201b2c2b9058077d77dd0de7273cf134 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: RELEASE_3_14 git_last_commit: 19dee20 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSEAmining_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSEAmining_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSEAmining_1.4.0.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: 57 Package: gsean Version: 1.14.0 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, knitr, plotly, RANKS, WGCNA, rmarkdown License: Artistic-2.0 MD5sum: 43edbe0e932722401e4aeb9c1f98c5a5 NeedsCompilation: no 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: RELEASE_3_14 git_last_commit: b0c972b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gsean_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gsean_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gsean_1.14.0.tgz vignettes: vignettes/gsean/inst/doc/gsean.html vignetteTitles: gsean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gsean/inst/doc/gsean.R dependencyCount: 117 Package: GSgalgoR Version: 1.4.0 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: 29ffc018e292240888a08ba1b761d5ec 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: RELEASE_3_14 git_last_commit: f28b3d5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSgalgoR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSgalgoR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSgalgoR_1.4.0.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: 1.28.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 MD5sum: f351fed3ce779377769f2eeea2eb9829 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: RELEASE_3_14 git_last_commit: d346e69 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSReg_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSReg_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSReg_1.28.0.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: 104 Package: GSRI Version: 2.42.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 Archs: i386, x64 MD5sum: 20250c46d915dcd48b614878ac54dd84 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: RELEASE_3_14 git_last_commit: de1e393 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSRI_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSRI_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSRI_2.42.0.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: 64 Package: GSVA Version: 1.42.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, graphics, S4Vectors, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix, parallel, BiocParallel, SingleCellExperiment, 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) MD5sum: 57be6e8c86f10124d22735216bcec934 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: Justin Guinney [aut, cre], Robert Castelo [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb] Maintainer: Justin Guinney 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: RELEASE_3_14 git_last_commit: c99b10b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GSVA_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GSVA_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GSVA_1.42.0.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.html vignetteTitles: Gene set variation analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA.R dependsOnMe: MM2S importsMe: consensusOV, EGSEA, escape, oppar, singleCellTK, TBSignatureProfiler, TNBC.CMS, clustermole, DRviaSPCN, immcp, psSubpathway, scMappR, SIGN, SMDIC suggestsMe: decoupleR, MCbiclust, sparrow dependencyCount: 78 Package: gtrellis Version: 1.26.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, rmarkdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff License: MIT + file LICENSE Archs: i386, x64 MD5sum: 74013c1d042bb671edbaf431abce6e89 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 Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: RELEASE_3_14 git_last_commit: f2c3121 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gtrellis_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gtrellis_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gtrellis_1.26.0.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: 25 Package: GUIDEseq Version: 1.24.0 Depends: R (>= 3.2.0), GenomicRanges, BiocGenerics Imports: BiocParallel, Biostrings, CRISPRseek, ChIPpeakAnno, data.table, matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors (>= 0.9.6), GenomicAlignments (>= 1.7.3), GenomeInfoDb, Rsamtools, hash, limma,dplyr Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 3b4fb84c9c9f3bf53b1c6b255ee1ddf2 NeedsCompilation: no Title: GUIDE-seq analysis pipeline Description: The package implements GUIDE-seq analysis workflow including functions for 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. 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: RELEASE_3_14 git_last_commit: 17cac6b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GUIDEseq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GUIDEseq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GUIDEseq_1.24.0.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 importsMe: crisprseekplus dependencyCount: 152 Package: Guitar Version: 2.10.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 MD5sum: f6f13797197b3865d197206b5862a5b8 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: RELEASE_3_14 git_last_commit: cbc4814 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Guitar_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Guitar_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Guitar_2.10.0.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: 114 Package: Gviz Version: 1.38.4 Depends: R (>= 4.1), 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: 1480aa2a243a7d279ff39109a4c3a175 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: RELEASE_3_14 git_last_commit: 6cde2b9 git_last_commit_date: 2022-04-07 Date/Publication: 2022-04-10 source.ver: src/contrib/Gviz_1.38.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/Gviz_1.38.4.zip mac.binary.ver: bin/macosx/contrib/4.1/Gviz_1.38.4.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, coMET, cummeRbund, DMRforPairs, Pviz, methylationArrayAnalysis, rnaseqGene, csawBook importsMe: AllelicImbalance, ASpediaFI, ASpli, CAGEfightR, DMRcate, ELMER, GenomicInteractions, maser, mCSEA, MEAL, motifbreakR, PING, primirTSS, regutools, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, SPLINTER, srnadiff, STAN, trackViewer, TVTB, uncoverappLib, VariantFiltering, DMRcatedata suggestsMe: annmap, cellbaseR, CNEr, CNVRanger, DeepBlueR, ensembldb, GenomicRanges, gwascat, interactiveDisplay, InterMineR, Pi, pqsfinder, QuasR, RnBeads, segmenter, SplicingGraphs, TFutils, Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, chicane, RTIGER dependencyCount: 141 Package: GWAS.BAYES Version: 1.4.0 Depends: R (>= 4.0), Rcpp (>= 1.0.3), RcppEigen (>= 0.3.3.7.0), GA (>= 3.2), caret (>= 6.0-86), ggplot2 (>= 3.3.0), doParallel (>= 1.0.15), memoise (>= 1.1.0), reshape2 (>= 1.4.4), Matrix (>= 1.2-18) LinkingTo: RcppEigen (>= 0.3.3.7.0),Rcpp (>= 1.0.3) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP, qqman License: GPL-2 | GPL-3 MD5sum: 132f47fbc10ca31033b859bfcb84abb6 NeedsCompilation: yes Title: GWAS for Selfing Species Description: This package is built to perform GWAS analysis for selfing species. The research related to this package was supported in part by National Science Foundation Award 1853549. biocViews: AssayDomain, SNP Author: Jake Williams [aut, cre], Marco Ferreira [aut], Tieming Ji [aut] Maintainer: Jake Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWAS.BAYES git_branch: RELEASE_3_14 git_last_commit: 80818c9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GWAS.BAYES_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GWAS.BAYES_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GWAS.BAYES_1.4.0.tgz vignettes: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.html vignetteTitles: GWAS.BAYES hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.R dependencyCount: 89 Package: gwascat Version: 2.26.0 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, Gviz, Rsamtools, IRanges, rtracklayer, graph, ggbio, DelayedArray, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BiocStyle Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 Archs: i386, x64 MD5sum: b2070e880beb322b8fe1e2d78e0014b3 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: RELEASE_3_14 git_last_commit: 4098842 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gwascat_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gwascat_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gwascat_2.26.0.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: 128 Package: GWASTools Version: 1.40.0 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 License: Artistic-2.0 MD5sum: 2af1b40c28f0af4acede96ef5286f9c4 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, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Michael Lawrence, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp, Sarah Nelson, David Levine Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/GWASTools git_url: https://git.bioconductor.org/packages/GWASTools git_branch: RELEASE_3_14 git_last_commit: 438eb92 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GWASTools_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GWASTools_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GWASTools_1.40.0.tgz vignettes: vignettes/GWASTools/inst/doc/Affymetrix.pdf, vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf vignetteTitles: Preparing Affymetrix Data, GWAS Data Cleaning, Data formats in GWASTools 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 importsMe: GENESIS, gwasurvivr suggestsMe: podkat dependencyCount: 77 Package: gwasurvivr Version: 1.12.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 59addf00b3a4d56d2a94ae473ee1808d 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: RELEASE_3_14 git_last_commit: 5c94e32 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/gwasurvivr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/gwasurvivr_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/gwasurvivr_1.12.0.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: 134 Package: GWENA Version: 1.4.0 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: fb968ce4c9bf2989aa316cbf757c2ae0 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: RELEASE_3_14 git_last_commit: 5f37285 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/GWENA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/GWENA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/GWENA_1.4.0.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: 134 Package: h5vc Version: 2.28.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 1.99.1), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rhtslib (>= 1.15.3) Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics, rmarkdown License: GPL (>= 3) MD5sum: 56ced35d560168cfa85722d4215fe373 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: RELEASE_3_14 git_last_commit: c3e0cb5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/h5vc_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/h5vc_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/h5vc_2.28.0.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: 88 Package: hapFabia Version: 1.36.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 1e33f22174e31a4e86a5745962a56086 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: RELEASE_3_14 git_last_commit: b5514bc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hapFabia_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hapFabia_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hapFabia_1.36.0.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: 1.22.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.11.2), graphics, stats, methods LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE Archs: i386, x64 MD5sum: b5ab792d7ed687ee0f87689d0736db36 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: Josh Bowden [aut], Jason Ross [aut, cre], Yalchin Oytam [aut] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr BugReports: https://github.com/JasonR055/Harman/issues git_url: https://git.bioconductor.org/packages/Harman git_branch: RELEASE_3_14 git_last_commit: 07410e8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Harman_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Harman_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Harman_1.22.0.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 dependencyCount: 5 Package: Harshlight Version: 1.66.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 4e73c56dea22e093bb77835ea5a7cfc7 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: RELEASE_3_14 git_last_commit: 6971886 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Harshlight_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Harshlight_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Harshlight_1.66.0.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: 27 Package: hca Version: 1.2.3 Depends: R (>= 4.1) Imports: httr, jsonlite, dplyr, tibble, tidyr, readr, BiocFileCache, tools, utils, digest Suggests: futile.logger, LoomExperiment, SummarizedExperiment, SingleCellExperiment, S4Vectors, methods, testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: i386, x64 MD5sum: 1bd6c2917961281b2d8a2e1ee8536e95 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] () Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hca git_branch: RELEASE_3_14 git_last_commit: 6e7d450 git_last_commit_date: 2022-04-04 Date/Publication: 2022-04-05 source.ver: src/contrib/hca_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/hca_1.2.3.zip mac.binary.ver: bin/macosx/contrib/4.1/hca_1.2.3.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: 57 Package: HDF5Array Version: 1.22.1 Depends: R (>= 3.4), methods, DelayedArray (>= 0.15.16), rhdf5 (>= 2.31.6) Imports: utils, stats, tools, Matrix, rhdf5filters, BiocGenerics (>= 0.31.5), S4Vectors, IRanges LinkingTo: S4Vectors (>= 0.27.13), Rhdf5lib Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>= 1.15.1), h5vcData, ExperimentHub, TENxBrainData, zellkonverter, GenomicFeatures, RUnit, SingleCellExperiment License: Artistic-2.0 MD5sum: 6cc05a85678723341b292cecc9c50395 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: RELEASE_3_14 git_last_commit: b3f091f git_last_commit_date: 2021-11-13 Date/Publication: 2021-11-14 source.ver: src/contrib/HDF5Array_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/HDF5Array_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/HDF5Array_1.22.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: compartmap, MAGAR, TENxBrainData, TENxPBMCData importsMe: biscuiteer, bsseq, Cepo, clusterExperiment, cytomapper, DelayedTensor, DropletUtils, FRASER, GenomicScores, glmGamPoi, GSVA, LoomExperiment, methrix, minfi, MOFA2, netSmooth, NxtIRFcore, recountmethylation, scmeth, scry, signatureSearch, spatialHeatmap, transformGamPoi, MafH5.gnomAD.v3.1.1.GRCh38, curatedTCGAData, HCAData, imcdatasets, MethylSeqData, SingleCellMultiModal, TabulaMurisSenisData suggestsMe: beachmat, BiocSklearn, DelayedArray, DelayedMatrixStats, iSEE, MAST, mbkmeans, metabolomicsWorkbenchR, MultiAssayExperiment, PDATK, QFeatures, scMerge, scran, sesame, SummarizedExperiment, zellkonverter, BigDataStatMeth, digitalDLSorteR dependencyCount: 19 Package: HDTD Version: 1.28.0 Depends: R (>= 4.1) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: d8685afb3f66d091527d72bb5931e1a2 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: RELEASE_3_14 git_last_commit: b223583 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HDTD_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HDTD_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HDTD_1.28.0.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: 1.18.0 Depends: R (>= 3.4) 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 Archs: i386, x64 MD5sum: cfdbf1f4a5cb33dcd53335a445e6ddc7 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: RELEASE_3_14 git_last_commit: ea7317d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/heatmaps_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/heatmaps_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/heatmaps_1.18.0.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 dependencyCount: 40 Package: Heatplus Version: 3.2.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: e168f006d7037a76f3561a8fa3997752 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: RELEASE_3_14 git_last_commit: b0a95ee git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Heatplus_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Heatplus_3.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Heatplus_3.2.0.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: GeneAnswers, phenoTest, tRanslatome, heatmapFlex suggestsMe: mtbls2, RforProteomics, RAM dependencyCount: 4 Package: HelloRanges Version: 1.20.0 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 Imports: docopt, stats, tools, utils Suggests: HelloRangesData, BiocStyle License: GPL (>= 2) MD5sum: 08f841e9f338ce13953b6c406c0fd989 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: RELEASE_3_14 git_last_commit: 5f4a86d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HelloRanges_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HelloRanges_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HelloRanges_1.20.0.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: 99 Package: HELP Version: 1.52.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: 431596d54f602f7e126a0fdfcab8ce16 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: RELEASE_3_14 git_last_commit: 7d5ef19 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HELP_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HELP_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HELP_1.52.0.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: 1.66.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) MD5sum: cbec477767e4756f583297748f33c9b4 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: RELEASE_3_14 git_last_commit: c131900 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HEM_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HEM_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HEM_1.66.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 7 Package: HGC Version: 1.2.0 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: i386, x64 MD5sum: 09479f9de479990d507ec19e124183ac 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: RELEASE_3_14 git_last_commit: 47a92f6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HGC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HGC_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HGC_1.2.0.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: 54 Package: hiAnnotator Version: 1.28.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics, markdown License: GPL (>= 2) MD5sum: 0997c331a10821b438039d1f88901b90 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: RELEASE_3_14 git_last_commit: ac0785b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hiAnnotator_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hiAnnotator_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hiAnnotator_1.28.0.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: 81 Package: HIBAG Version: 1.30.2 Depends: R (>= 3.2.0) Imports: methods, RcppParallel LinkingTo: RcppParallel (>= 5.0.0) Suggests: parallel, ggplot2, reshape2, gdsfmt, SNPRelate, SeqArray, knitr, markdown, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: b8056d7891c55de3cd9d6f701af67ce5 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: http://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: RELEASE_3_14 git_last_commit: 9a23f40 git_last_commit_date: 2022-01-17 Date/Publication: 2022-01-18 source.ver: src/contrib/HIBAG_1.30.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/HIBAG_1.30.2.zip mac.binary.ver: bin/macosx/contrib/4.1/HIBAG_1.30.2.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: HiCcompare Version: 1.16.0 Depends: R (>= 3.4.0), dplyr Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet, GenomicRanges, IRanges, S4Vectors, BiocParallel, QDNAseq, KernSmooth, methods, utils, graphics, pheatmap, gtools, rhdf5 Suggests: knitr, rmarkdown, testthat, multiHiCcompare License: MIT + file LICENSE MD5sum: 721f196863def5a002c6ef55caf3ef62 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: John Stansfield , Kellen Cresswell , Mikhail Dozmorov Maintainer: John Stansfield , 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: RELEASE_3_14 git_last_commit: dafc05f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HiCcompare_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HiCcompare_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HiCcompare_1.16.0.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: 97 Package: HiCDCPlus Version: 1.2.1 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 Archs: i386, x64 MD5sum: fbb1e2f308b6df0b8a6c7e30ad8110ba 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: RELEASE_3_14 git_last_commit: 0d0adfa git_last_commit_date: 2022-01-23 Date/Publication: 2022-01-25 source.ver: src/contrib/HiCDCPlus_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/HiCDCPlus_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/HiCDCPlus_1.2.1.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: 156 Package: hierGWAS Version: 1.24.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: 2544fa7fd67a383e0f918113dc4f7b4b 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: RELEASE_3_14 git_last_commit: dc00525 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hierGWAS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hierGWAS_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hierGWAS_1.24.0.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: 1.12.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE Archs: i386, x64 MD5sum: 8d6603cdf3645eafa0718ccc139de4a4 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: RELEASE_3_14 git_last_commit: dd091df git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hierinf_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hierinf_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hierinf_1.12.0.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: 1.24.0 Depends: R (>= 3.1.2), 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: e69333bf8445e9488e3173fc7c826c24 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 Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: RELEASE_3_14 git_last_commit: e59f46c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HilbertCurve_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HilbertCurve_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HilbertCurve_1.24.0.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: 27 Package: HilbertVis Version: 1.52.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) MD5sum: 625c9a84a86064ebf19750c91c0ca802 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: RELEASE_3_14 git_last_commit: e972484 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HilbertVis_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HilbertVis_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HilbertVis_1.52.0.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: 1.52.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: 1105fe36646075745460def45ac3fc65 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: RELEASE_3_14 git_last_commit: 1d38e6f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HilbertVisGUI_1.52.0.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: 1.8.0 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: a2109119fec32f76299586f3b8fcdc12 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: RELEASE_3_14 git_last_commit: e333310 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HiLDA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HiLDA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HiLDA_1.8.0.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: 123 Package: hipathia Version: 2.10.0 Depends: R (>= 3.6), 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 Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 Archs: i386, x64 MD5sum: 7eee0f26bfbcfd5abf421211bed3df7d 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: RELEASE_3_14 git_last_commit: bc0e4d8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hipathia_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hipathia_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hipathia_2.10.0.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: 115 Package: HIPPO Version: 1.6.0 Depends: R (>= 3.6.0) Imports: ggplot2, graphics, stats, reshape2, gridExtra, Rtsne, umap, dplyr, rlang, magrittr, irlba, Matrix, SingleCellExperiment, ggrepel Suggests: knitr, rmarkdown License: GPL (>=2) Archs: i386, x64 MD5sum: 621c5b7d30c8bd06729f342fd8f8ad57 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: RELEASE_3_14 git_last_commit: c9e2f06 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HIPPO_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HIPPO_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HIPPO_1.6.0.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: 82 Package: hiReadsProcessor Version: 1.30.0 Depends: Biostrings, GenomicAlignments, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl, methods Suggests: knitr, testthat, markdown License: GPL-3 MD5sum: 4984f188ed581fba1abc38ec2d92b753 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: RELEASE_3_14 git_last_commit: 2f94596 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hiReadsProcessor_1.30.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/hiReadsProcessor_1.30.0.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: 90 Package: HIREewas Version: 1.12.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) MD5sum: 9cb0df6d5152e668f86e9043a01dae29 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: RELEASE_3_14 git_last_commit: 6319d9f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HIREewas_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HIREewas_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HIREewas_1.12.0.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: 1.38.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, GenomeInfoDb Suggests: BiocStyle, HiCDataHumanIMR90 License: Artistic-2.0 MD5sum: d8e4331074973e9a83673a959415b025 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: RELEASE_3_14 git_last_commit: 2b50c43 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HiTC_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HiTC_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HiTC_1.38.0.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: 45 Package: hmdbQuery Version: 1.14.2 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: d3dab0019eb5727dccfec59de46130eb 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: RELEASE_3_14 git_last_commit: 147a8b2 git_last_commit_date: 2022-01-05 Date/Publication: 2022-01-06 source.ver: src/contrib/hmdbQuery_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/hmdbQuery_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.1/hmdbQuery_1.14.2.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: 1.36.0 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 MD5sum: e860285a3d58e10ecdf749e601efeef2 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 , Sohrab Shah git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: RELEASE_3_14 git_last_commit: b37931c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HMMcopy_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HMMcopy_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HMMcopy_1.36.0.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: hopach Version: 2.54.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) MD5sum: 6f6bda95935fa0cb3f5abde0679b23bb 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: RELEASE_3_14 git_last_commit: 783c88d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hopach_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hopach_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hopach_2.54.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest, scClassify, treekoR suggestsMe: MicrobiotaProcess dependencyCount: 8 Package: HPAanalyze Version: 1.12.0 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 Archs: i386, x64 MD5sum: 5a779a9e8b08d774b0549dce5e15c155 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: RELEASE_3_14 git_last_commit: 3c34acc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HPAanalyze_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HPAanalyze_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HPAanalyze_1.12.0.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: 49 Package: hpar Version: 1.36.0 Depends: R (>= 3.5.0) Imports: utils Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 89410b42ec2ea0f559f55415d41626ae 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, Homo_sapiens, CellBiology Author: Laurent Gatto [cre, aut] (), Manon Martin [aut] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hpar git_branch: RELEASE_3_14 git_last_commit: 5d6f4a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hpar_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hpar_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hpar_1.36.0.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 dependsOnMe: proteomics importsMe: MetaboSignal suggestsMe: HPAStainR, pRoloc, RforProteomics dependencyCount: 1 Package: HPAStainR Version: 1.4.1 Depends: R (>= 4.1.0), dplyr, tidyr Imports: utils, stats, scales, stringr, tibble, shiny, data.table Suggests: knitr, BiocManager, qpdf, hpar, testthat, rmarkdown License: Artistic-2.0 MD5sum: d1530ffb45a34e474166c64a53941ef9 NeedsCompilation: no Title: Queries the Human Protein Atlas Staining Data for Multiple Proteins and Genes Description: This package is built around the HPAStainR function. The purpose of the HPAStainR function is to query the visual staining data in the Human Protein Atlas to return a table of staining ranked cell types. The function also has multiple arguments to personalize to output as well to include cancer data, csv readable names, modify the confidence levels of the results and more. The other functions exist exclusively to easily acquire the data required to run HPAStainR. biocViews: GeneExpression, GeneSetEnrichment Author: Tim O. Nieuwenhuis [aut, cre] () Maintainer: Tim O. Nieuwenhuis SystemRequirements: 4GB of RAM VignetteBuilder: knitr BugReports: https://github.com/tnieuwe/HPAstainR git_url: https://git.bioconductor.org/packages/HPAStainR git_branch: RELEASE_3_14 git_last_commit: 70ebd09 git_last_commit_date: 2021-11-25 Date/Publication: 2021-11-28 source.ver: src/contrib/HPAStainR_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/HPAStainR_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/HPAStainR_1.4.1.tgz vignettes: vignettes/HPAStainR/inst/doc/HPAStainR.html vignetteTitles: HPAStainR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HPAStainR/inst/doc/HPAStainR.R dependencyCount: 58 Package: HPiP Version: 1.0.0 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 Suggests: rmarkdown, colorspace, e1071, kernlab, ranger, SummarizedExperiment, Biostrings, randomForest, gprofiler2, gridExtra, ggthemes, BiocStyle, BiocGenerics, RUnit, tools, knitr License: MIT + file LICENSE MD5sum: 009b754c587f47a54958c17c039be487 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: RELEASE_3_14 git_last_commit: e3d7cf4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HPiP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HPiP_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HPiP_1.0.0.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: 102 Package: HTqPCR Version: 1.48.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: 3110562f278f3fbf7d61edb34ea18b75 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/HTqPCR git_branch: RELEASE_3_14 git_last_commit: 49e18e0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HTqPCR_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HTqPCR_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HTqPCR_1.48.0.tgz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R importsMe: nondetects, unifiedWMWqPCR dependencyCount: 20 Package: HTSeqGenie Version: 4.24.0 Depends: R (>= 3.0.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.9.25), IRanges (>= 1.21.39), GenomicRanges (>= 1.23.21), Rsamtools (>= 1.8.5), Biostrings (>= 2.24.1), 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 License: Artistic-2.0 MD5sum: 56eb90d3dd0f2ae0d5c332eb5ddec7dc 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: RELEASE_3_14 git_last_commit: eb291fb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HTSeqGenie_4.24.0.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: 107 Package: HTSFilter Version: 1.34.0 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: 1fad95828f4ed06d3812ec77bba69b28 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: RELEASE_3_14 git_last_commit: 5abf134 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HTSFilter_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HTSFilter_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HTSFilter_1.34.0.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: 95 Package: HubPub Version: 1.2.5 Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager, utils Suggests: AnnotationHubData, ExperimentHubData, testthat, knitr, rmarkdown, BiocStyle, License: Artistic-2.0 MD5sum: dd4442af3a12741a31072698a53f58fb 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: RELEASE_3_14 git_last_commit: 1328fc6 git_last_commit_date: 2022-04-07 Date/Publication: 2022-04-10 source.ver: src/contrib/HubPub_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/HubPub_1.2.5.zip mac.binary.ver: bin/macosx/contrib/4.1/HubPub_1.2.5.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: 80 Package: HumanTranscriptomeCompendium Version: 1.10.0 Depends: R (>= 3.6) Imports: shiny, ssrch, S4Vectors, SummarizedExperiment, utils Suggests: knitr, BiocStyle, beeswarm, tximportData, DT, tximport, dplyr, magrittr, BiocFileCache, testthat, rhdf5client, rmarkdown License: Artistic-2.0 MD5sum: ab3660f6582a6d27e6bb4c02c7a15570 NeedsCompilation: no Title: Tools to work with a Compendium of 181000 human transcriptome sequencing studies Description: Provide tools for working with a compendium of human transcriptome sequences (originally htxcomp). biocViews: Transcriptomics, Infrastructure Author: Sean Davis, Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HumanTranscriptomeCompendium git_branch: RELEASE_3_14 git_last_commit: 54b2ae3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HumanTranscriptomeCompendium_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HumanTranscriptomeCompendium_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HumanTranscriptomeCompendium_1.10.0.tgz vignettes: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.html vignetteTitles: HumanTranscriptomeCompendium -- a cloud-resident collection of sequenced human transcriptomes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.R dependencyCount: 60 Package: hummingbird Version: 1.4.0 Depends: R (>= 4.0) Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: f7163e24623f48d067284016e2111e3f 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: RELEASE_3_14 git_last_commit: 27df8a1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hummingbird_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hummingbird_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hummingbird_1.4.0.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: 26 Package: HybridMTest Version: 1.38.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: 80586d40fcb79709902ff6fd37e1c5d2 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: RELEASE_3_14 git_last_commit: 53e80d9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/HybridMTest_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/HybridMTest_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/HybridMTest_1.38.0.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 dependencyCount: 14 Package: hypeR Version: 1.10.0 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 Suggests: tidyverse, devtools, testthat, knitr License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 91620f04651007935c94532f50960c8a 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], 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: RELEASE_3_14 git_last_commit: 15670e7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hypeR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hypeR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hypeR_1.10.0.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: 104 Package: hyperdraw Version: 1.46.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 6f76da16e8417f0d3889c688e40c13c2 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: RELEASE_3_14 git_last_commit: 4d60ce6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hyperdraw_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hyperdraw_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hyperdraw_1.46.0.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: 1.66.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: aa56e66d62905601b73e533238b0af3a 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: RELEASE_3_14 git_last_commit: e9c4733 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/hypergraph_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/hypergraph_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/hypergraph_1.66.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs importsMe: BiGGR, hyperdraw, RpsiXML dependencyCount: 7 Package: iASeq Version: 1.38.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 3eea551838a70f5e37cb69a475435d5c 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: RELEASE_3_14 git_last_commit: e1d11db git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iASeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iASeq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iASeq_1.38.0.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: 1.12.0 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: fa50c1592953bf720d5069be4c2adbed 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: RELEASE_3_14 git_last_commit: 2c9b454 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iasva_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iasva_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iasva_1.12.0.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: 35 Package: iBBiG Version: 1.38.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 MD5sum: cf841ac475db4cf6b79058deac433df9 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: RELEASE_3_14 git_last_commit: 2d73690 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iBBiG_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iBBiG_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iBBiG_1.38.0.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: 55 Package: ibh Version: 1.42.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: 96a33147c8f0e0d4df1aab36144ce7fc 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: RELEASE_3_14 git_last_commit: d6505a0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ibh_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ibh_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ibh_1.42.0.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: 1.34.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: i386, x64 MD5sum: 70da203d2eae4cd8ea3b48433481e533 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: RELEASE_3_14 git_last_commit: 77b19ee git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iBMQ_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iBMQ_1.34.0.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: 40 Package: iCARE Version: 1.22.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE MD5sum: 28d82362e339269c172ce2e8dacb2ab3 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: RELEASE_3_14 git_last_commit: 55539bb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iCARE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iCARE_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iCARE_1.22.0.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: 70 Package: Icens Version: 1.66.0 Depends: survival Imports: graphics License: Artistic-2.0 Archs: i386, x64 MD5sum: 0b3544010419c2b4eed0a0cf9988eb21 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: RELEASE_3_14 git_last_commit: 9722aaa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Icens_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Icens_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Icens_1.66.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess, icensBKL, interval importsMe: PROcess, LTRCtrees suggestsMe: ReIns dependencyCount: 10 Package: icetea Version: 1.12.0 Depends: R (>= 4.0) Imports: stats, utils, methods, graphics, grDevices, ggplot2, GenomicFeatures, ShortRead, BiocParallel, Biostrings, S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments, GenomicRanges, rtracklayer, SummarizedExperiment, VariantAnnotation, limma, edgeR, csaw, DESeq2, TxDb.Dmelanogaster.UCSC.dm6.ensGene Suggests: knitr, rmarkdown, Rsubread (>= 1.29.0), testthat License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: ff2cb106791d94fd86ac275f6420f503 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: RELEASE_3_14 git_last_commit: 133907b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/icetea_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/icetea_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/icetea_1.12.0.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: 129 Package: iCheck Version: 1.24.0 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: f17905c0a2a1fc34021e836198d150e5 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: RELEASE_3_14 git_last_commit: 0c3a651 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iCheck_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iCheck_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iCheck_1.24.0.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: 179 Package: iChip Version: 1.48.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) MD5sum: 1114ce896274ea6f464b76d408f1e0a6 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: RELEASE_3_14 git_last_commit: bf5ace5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iChip_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iChip_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iChip_1.48.0.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: 6 Package: iClusterPlus Version: 1.30.0 Depends: R (>= 3.3.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 6ee5a73e387818cb68911e4fcd18c31c 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: RELEASE_3_14 git_last_commit: 37ea735 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iClusterPlus_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iClusterPlus_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iClusterPlus_1.30.0.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: 1.14.0 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 Archs: i386, x64 MD5sum: 701e8e1370727dcad366b641309d3846 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: RELEASE_3_14 git_last_commit: 8294aeb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iCNV_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iCNV_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iCNV_1.14.0.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: 90 Package: iCOBRA Version: 1.22.2 Depends: R (>= 4.0) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2 (>= 2.0.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR Suggests: knitr, rmarkdown, testthat License: GPL (>=2) Archs: i386, x64 MD5sum: d2eefe421c12c002849b94b8244e0f77 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: RELEASE_3_14 git_last_commit: b9283c0 git_last_commit_date: 2021-12-18 Date/Publication: 2021-12-19 source.ver: src/contrib/iCOBRA_1.22.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/iCOBRA_1.22.2.zip mac.binary.ver: bin/macosx/contrib/4.1/iCOBRA_1.22.2.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, SummarizedBenchmark dependencyCount: 83 Package: ideal Version: 1.18.1 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, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, edgeR License: MIT + file LICENSE MD5sum: 090e4a358bbbb2337cd15f0dbe5dca90 NeedsCompilation: no Title: Interactive Differential Expression AnaLysis Description: This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting 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 git_url: https://git.bioconductor.org/packages/ideal git_branch: RELEASE_3_14 git_last_commit: a6dc2e0 git_last_commit_date: 2021-12-01 Date/Publication: 2021-12-02 source.ver: src/contrib/ideal_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ideal_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ideal_1.18.1.tgz vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html vignetteTitles: ideal User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R dependencyCount: 205 Package: IdeoViz Version: 1.30.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 MD5sum: ea7eb01b79f0600b61913340feafe812 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: RELEASE_3_14 git_last_commit: dbdef2a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IdeoViz_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IdeoViz_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IdeoViz_1.30.0.tgz vignettes: vignettes/IdeoViz/inst/doc/Vignette.pdf vignetteTitles: IdeoViz: a package for plotting simple data along ideograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R dependencyCount: 45 Package: idiogram Version: 1.70.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 4b0f536f02f2ca6eaf478974f000f166 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: RELEASE_3_14 git_last_commit: 2d1c671 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/idiogram_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/idiogram_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/idiogram_1.70.0.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: 1.4.0 Depends: R (>= 4.0.0) 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, msa, ape, testthat, seqinr License: LGPL-3 MD5sum: c8d89b39390bb07299db01d65cc4ca9e 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. 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: RELEASE_3_14 git_last_commit: e5c0eac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/idpr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/idpr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/idpr_1.4.0.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: 57 Package: idr2d Version: 1.8.1 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: c1ddc6303a8c0e33d077e0f26bb92652 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: RELEASE_3_14 git_last_commit: aae18db git_last_commit_date: 2021-11-13 Date/Publication: 2021-11-14 source.ver: src/contrib/idr2d_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/idr2d_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/idr2d_1.8.1.tgz vignettes: vignettes/idr2d/inst/doc/idr1d.html, vignettes/idr2d/inst/doc/idr2d.html vignetteTitles: Identify reproducible genomic peaks from replicate ChIP-seq experiments, Identify reproducible genomic interactions from replicate ChIA-PET experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/idr2d/inst/doc/idr1d.R, vignettes/idr2d/inst/doc/idr2d.R dependencyCount: 69 Package: iGC Version: 1.24.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: 769ff16faa992d7b3f4e7184b0cb1f33 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: RELEASE_3_14 git_last_commit: fd26769 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iGC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iGC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iGC_1.24.0.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: 1.8.0 Depends: methods, R (>= 3.6.0), Rcpp (>= 0.12.0), SummarizedExperiment, StanHeaders (> 2.18.1) Imports: rstan (>= 2.19.2), reshape2 (>= 1.4.3) Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2, ggforce, gridExtra, ggrepel License: file LICENSE MD5sum: 3f4086837ec56c0fe4fbd74a4c571b93 NeedsCompilation: no Title: Differential gene usage in immune repertoires Description: Detection of biases in immunoglobulin (Ig) gene usage between adaptive immune repertoires that belong to different biological conditions is an important task in immune repertoire profiling. IgGeneUsage detects aberrant Ig gene usage using probabilistic model which is analyzed computationally by Bayes inference. biocViews: DifferentialExpression, Regression, Genetics, Bayesian Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski VignetteBuilder: knitr BugReports: https://github.com/snaketron/IgGeneUsage/issues git_url: https://git.bioconductor.org/packages/IgGeneUsage git_branch: RELEASE_3_14 git_last_commit: b71fcc0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IgGeneUsage_1.8.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/IgGeneUsage_1.8.0.tgz vignettes: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.html vignetteTitles: User Manual: IgGeneUsage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.R dependencyCount: 78 Package: igvR Version: 1.14.0 Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>= 2.9.1) Imports: methods, BiocGenerics, httpuv, utils, MotifDb, seqLogo, rtracklayer, VariantAnnotation, RColorBrewer Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 2b1d1956b2e4102a4e97baac68335f40 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: Paul Shannon URL: https://paul-shannon.github.io/igvR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: RELEASE_3_14 git_last_commit: 2d7b52a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/igvR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/igvR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/igvR_1.14.0.tgz vignettes: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.html, vignettes/igvR/inst/doc/basicIntro.html, vignettes/igvR/inst/doc/chooseStockOrCustomGenome.html, vignettes/igvR/inst/doc/ctcfChipSeq.html vignetteTitles: "Explore VCF variants,, GWAS snps,, promoters and histone marks around the MEF2C gene in Alzheimers Disease", "Introduction: a simple demo", "Choose a Stock or Custom Genome", "Explore ChIP-seq alignments from a bam file,, MACS2 narrowPeaks,, conservation,, H3K4me3 methylation and motif matching" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.R, vignettes/igvR/inst/doc/basicIntro.R, vignettes/igvR/inst/doc/chooseStockOrCustomGenome.R, vignettes/igvR/inst/doc/ctcfChipSeq.R dependencyCount: 107 Package: IHW Version: 1.22.0 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: 1be2e14f9192b95d2a9a8e1c2f3123da 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: RELEASE_3_14 git_last_commit: 0d30505 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IHW_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IHW_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IHW_1.22.0.tgz vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html vignetteTitles: "Introduction to IHW" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R dependsOnMe: IHWpaper importsMe: ideal suggestsMe: DEWSeq, metagenomeSeq, SummarizedBenchmark, BloodCancerMultiOmics2017, BisRNA, DGEobj.utils dependencyCount: 9 Package: illuminaio Version: 0.36.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 MD5sum: 123811b8393109831ff69ef515498eb0 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: RELEASE_3_14 git_last_commit: 01f422d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/illuminaio_0.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/illuminaio_0.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/illuminaio_0.36.0.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: 1.4.0 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 License: GPL-3 MD5sum: 8cb781b1f658476cbe3568bf4b0b1af1 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: RELEASE_3_14 git_last_commit: d0229aa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ILoReg_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ILoReg_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ILoReg_1.4.0.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: 116 Package: imageHTS Version: 1.44.1 Depends: R (>= 2.9.0), EBImage (>= 4.3.12), cellHTS2 (>= 2.10.0) Imports: tools, Biobase, hwriter, methods, vsn, stats, utils, e1071 Suggests: BiocStyle, MASS License: LGPL-2.1 Archs: i386, x64 MD5sum: 3773490c101e8b9955ac3c7c7dbb9965 NeedsCompilation: no Title: Analysis of high-throughput microscopy-based screens Description: imageHTS is an R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, imageHTS provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets. biocViews: ImmunoOncology, Software, CellBasedAssays, Preprocessing, Visualization Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber Maintainer: Joseph Barry git_url: https://git.bioconductor.org/packages/imageHTS git_branch: RELEASE_3_14 git_last_commit: 608b196 git_last_commit_date: 2022-01-06 Date/Publication: 2022-01-13 source.ver: src/contrib/imageHTS_1.44.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/imageHTS_1.44.1.zip mac.binary.ver: bin/macosx/contrib/4.1/imageHTS_1.44.1.tgz vignettes: vignettes/imageHTS/inst/doc/imageHTS-introduction.pdf vignetteTitles: Analysis of high-throughput microscopy-based screens with imageHTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageHTS/inst/doc/imageHTS-introduction.R dependencyCount: 103 Package: IMAS Version: 1.18.0 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: 8aa4a3f3e6bd2e7990a13a56ff532621 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: RELEASE_3_14 git_last_commit: b7c52db git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IMAS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IMAS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IMAS_1.18.0.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: 139 Package: imcRtools Version: 1.0.2 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 Suggests: CATALYST, grid, tidyr, BiocStyle, knitr, rmarkdown, markdown, testthat License: GPL-3 MD5sum: 206208ac6075d18c114be359adf82c0e 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, cre] (), Tobias Hoch [ctb], Vito Zanotelli [ctb], Jana Fischer [ctb], Daniel Schulz [ctb] Maintainer: Nils Eling 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: RELEASE_3_14 git_last_commit: a8d93e1 git_last_commit_date: 2021-12-15 Date/Publication: 2021-12-19 source.ver: src/contrib/imcRtools_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/imcRtools_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/imcRtools_1.0.2.tgz vignettes: vignettes/imcRtools/inst/doc/imcRtools.html vignetteTitles: "Tools for IMC data analysis" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imcRtools/inst/doc/imcRtools.R dependencyCount: 185 Package: IMMAN Version: 1.14.0 Imports: STRINGdb, Biostrings, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 9da8cca362ab898d604d12a780313253 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: RELEASE_3_14 git_last_commit: bf9080e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IMMAN_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IMMAN_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IMMAN_1.14.0.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: 58 Package: ImmuneSpaceR Version: 1.22.0 Depends: R (>= 3.5.0) Imports: utils, R6, data.table, curl, httr, Rlabkey (>= 2.3.1), Biobase, pheatmap, ggplot2 (>= 3.2.0), scales, stats, gplots, plotly, heatmaply (>= 0.7.0), jsonlite, rmarkdown, preprocessCore, flowCore, flowWorkspace, digest Suggests: knitr, testthat License: GPL-2 MD5sum: 4ec1378fc7e4760ee327c4287950de7f NeedsCompilation: no Title: A Thin Wrapper around the ImmuneSpace Database Description: Provides a convenient API for accessing data sets within ImmuneSpace (www.immunespace.org), the data repository and analysis platform of the Human Immunology Project Consortium (HIPC). biocViews: DataImport, DataRepresentation, ThirdPartyClient Author: Greg Finak [aut], Renan Sauteraud [aut], Mike Jiang [aut], Gil Guday [aut], Leo Dashevskiy [aut], Evan Henrich [aut], Ju Yeong Kim [aut], Lauren Wolfe [aut], Helen Miller [aut], Raphael Gottardo [aut], ImmuneSpace Package Maintainer [cre, cph] Maintainer: ImmuneSpace Package Maintainer URL: https://github.com/RGLab/ImmuneSpaceR VignetteBuilder: knitr BugReports: https://github.com/RGLab/ImmuneSpaceR/issues git_url: https://git.bioconductor.org/packages/ImmuneSpaceR git_branch: RELEASE_3_14 git_last_commit: 54aead9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ImmuneSpaceR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ImmuneSpaceR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ImmuneSpaceR_1.22.0.tgz vignettes: vignettes/ImmuneSpaceR/inst/doc/getDataset.html, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.html, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.html, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.html vignetteTitles: Downloading Datasets with getDataset, Handling Expression Matrices with ImmuneSpaceR, interactive_netrc() Function Walkthrough, An Introduction to the ImmuneSpaceR Package, SDY144: Correlation of HAI/Virus Neutralizition Titer and Cell Counts, SDY180: Abundance of Plasmablasts Measured by Multiparameter Flow Cytometry, SDY269: Correlating HAI with Flow Cytometry and ELISPOT Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImmuneSpaceR/inst/doc/getDataset.R, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.R, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.R, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.R dependencyCount: 135 Package: immunoClust Version: 1.26.0 Depends: R(>= 3.6), flowCore Imports: methods, stats, graphics, grid, lattice, grDevices Suggests: BiocStyle, utils, testthat License: Artistic-2.0 MD5sum: d47a911b35f07d06f0a87b1da16aad1d 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: RELEASE_3_14 git_last_commit: fe68208 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/immunoClust_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/immunoClust_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/immunoClust_1.26.0.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: 20 Package: immunotation Version: 1.2.0 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: ee4240ab821520667fff1eb492cfbcdd 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: RELEASE_3_14 git_last_commit: bf8ad64 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/immunotation_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/immunotation_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/immunotation_1.2.0.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: 68 Package: IMPCdata Version: 1.30.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE Archs: i386, x64 MD5sum: 9a7563381edb324a9e2052e8a85404d4 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: RELEASE_3_14 git_last_commit: 2bb1c0b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IMPCdata_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IMPCdata_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IMPCdata_1.30.0.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: 1.68.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 6ef4866aec5d36ba0757a7095e9cfe28 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: RELEASE_3_14 git_last_commit: fa4e4d8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/impute_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/impute_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/impute_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, FAMT, iC10, imputeLCMD, moduleColor, snpReady, swamp importsMe: biscuiteer, CancerSubtypes, cola, DAPAR, DExMA, doppelgangR, EGAD, fastLiquidAssociation, genefu, genomation, GEOexplorer, MAGAR, MatrixQCvis, MEAT, methylclock, MethylMix, miRLAB, MSnbase, netboost, Pigengene, pmp, POMA, REMP, RNAAgeCalc, Rnits, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, armada, DIscBIO, lilikoi, mi4p, PINSPlus, Rnmr1D, samr, speaq, WGCNA suggestsMe: BioNet, graphite, MethPed, MsCoreUtils, QFeatures, RnBeads, scp, TBSignatureProfiler, TCGAutils, DDPNA, DGCA, GeoTcgaData, GSA, maGUI dependencyCount: 0 Package: INDEED Version: 2.8.0 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: c654bf0110710f349725fdfc0736fa70 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: RELEASE_3_14 git_last_commit: 9f05217 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/INDEED_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/INDEED_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/INDEED_2.8.0.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: 86 Package: infercnv Version: 1.10.1 Depends: R(>= 4.0) Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, phyclust, Matrix, fastcluster, dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest, RANN, leiden, reshape, rjags, fitdistrplus, future, foreach, doParallel, BiocGenerics, SummarizedExperiment, SingleCellExperiment, tidyr, parallel, coda, gridExtra, argparse Suggests: BiocStyle, knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: dcc4180e439fc9000641515752274878 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: RELEASE_3_14 git_last_commit: 1bc58d2 git_last_commit_date: 2021-11-08 Date/Publication: 2021-11-08 source.ver: src/contrib/infercnv_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/infercnv_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/infercnv_1.10.1.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 dependencyCount: 114 Package: infinityFlow Version: 1.4.0 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: cb69bbf36405848d3199f569b8b5c60a 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: RELEASE_3_14 git_last_commit: 68a3242 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/infinityFlow_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/infinityFlow_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/infinityFlow_1.4.0.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: 42 Package: Informeasure Version: 1.4.0 Depends: R (>= 4.0) Imports: entropy Suggests: knitr, rmarkdown, testthat, SummarizedExperiment License: GPL-3 MD5sum: 6a5305d376929c6348230ee14ef1e846 NeedsCompilation: no Title: R implementation of Information measures Description: This package compiles most of the information measures currently available: mutual information, conditional mutual information, interaction information, partial information decomposition and part mutual information. All of these estimators can be used to quantify nonlinear dependence between variables in (biological regulatory) network inference. The first estimator is used to infer bivariate networks while the last four estimators are dedicated to analysis of trivariate networks. 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: RELEASE_3_14 git_last_commit: d4c8c48 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Informeasure_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Informeasure_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Informeasure_1.4.0.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: 2.2.0 Depends: R (>= 3.1), methods, Biobase, GenomicRanges, S4Vectors Imports: AnnotationDbi, BSgenome, cleanUpdTSeq, preprocessCore, IRanges, GenomeInfoDb, depmixS4, limma, BiocParallel, Biostrings, dplyr, magrittr, plyranges, readr, RSQLite, DBI, purrr, GenomicFeatures, ggplot2, reshape2 Suggests: RUnit, BiocGenerics, BiocManager, rtracklayer, BiocStyle, knitr, markdown, rmarkdown, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v79, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL (>= 2) MD5sum: 4ddbb62fbfcf79395119f8ade22ebdb1 NeedsCompilation: no Title: A Bioconductor package for identifying 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: RNASeq, Sequencing, AlternativeSplicing, Coverage, DifferentialSplicing, GeneRegulation, Transcription, ImmunoOncology 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: RELEASE_3_14 git_last_commit: 84281b7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/InPAS_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InPAS_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InPAS_2.2.0.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: 135 Package: INPower Version: 1.30.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 8f2c5feb269ffd6b235c040744917371 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: RELEASE_3_14 git_last_commit: da84c4f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/INPower_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/INPower_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/INPower_1.30.0.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: 3 Package: INSPEcT Version: 1.24.0 Depends: R (>= 3.6), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, KernSmooth, gdata, 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: 3fce05785adc80d24d0e7005601ef24f 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: RELEASE_3_14 git_last_commit: ed551fb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/INSPEcT_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/INSPEcT_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/INSPEcT_1.24.0.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: 140 Package: InTAD Version: 1.14.0 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: d79f11cf6dc535183064ecbc91d76176 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: RELEASE_3_14 git_last_commit: 25795d0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/InTAD_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InTAD_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InTAD_1.14.0.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: 133 Package: intansv Version: 1.34.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE MD5sum: c9bb10de49b7c7f685aa3122f1c9f986 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: RELEASE_3_14 git_last_commit: 499d4d8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/intansv_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/intansv_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/intansv_1.34.0.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: 153 Package: interacCircos Version: 1.4.0 Depends: R (>= 4.1) Imports: RColorBrewer, htmlwidgets, plyr, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: da598971cf92b6ee6d3de3d207922fde 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: RELEASE_3_14 git_last_commit: 8f87335 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/interacCircos_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/interacCircos_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/interacCircos_1.4.0.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: 14 Package: InteractionSet Version: 1.22.0 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: 14f2fa80c63e5a6813cb341789de7e1a 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: RELEASE_3_14 git_last_commit: 82bf308 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/InteractionSet_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InteractionSet_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InteractionSet_1.22.0.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, MACPET, sevenC, nullrangesData importsMe: CAGEfightR, ChIPpeakAnno, HiCcompare, nullranges, trackViewer suggestsMe: plotgardener, CAGEWorkflow dependencyCount: 26 Package: InteractiveComplexHeatmap Version: 1.2.0 Depends: R (>= 4.0.0), Imports: ComplexHeatmap (>= 2.7.10), 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: b8042dc6c826b04fbce4b0fa7d56fe26 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: RELEASE_3_14 git_last_commit: ce5e576 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/InteractiveComplexHeatmap_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InteractiveComplexHeatmap_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InteractiveComplexHeatmap_1.2.0.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 suggestsMe: simplifyEnrichment dependencyCount: 96 Package: interactiveDisplay Version: 1.32.0 Depends: R (>= 2.10), 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 Archs: i386, x64 MD5sum: d673bd6ea9abe3a506a81f2191ee1a70 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 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: RELEASE_3_14 git_last_commit: 2851932 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/interactiveDisplay_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/interactiveDisplay_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/interactiveDisplay_1.32.0.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: 106 Package: interactiveDisplayBase Version: 1.32.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny, DT Suggests: knitr, markdown Enhances: rstudioapi License: Artistic-2.0 MD5sum: e41b0d679bc35213a096ad9b03e8955e 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 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: RELEASE_3_14 git_last_commit: 0f88b2a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/interactiveDisplayBase_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/interactiveDisplayBase_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/interactiveDisplayBase_1.32.0.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: AnnotationHub, interactiveDisplay suggestsMe: recount3 dependencyCount: 41 Package: InterCellar Version: 2.0.0 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: 8c99da4381a597223e2e3bbbb3082741 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: RELEASE_3_14 git_last_commit: abf5698 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/InterCellar_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InterCellar_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InterCellar_2.0.0.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: 217 Package: IntEREst Version: 1.18.0 Depends: R (>= 3.4), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors Imports: seqLogo, Biostrings, GenomicFeatures (>= 1.39.4), 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: 8b444bf221b601ebaced02b81c264865 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: RELEASE_3_14 git_last_commit: 0a4f63a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IntEREst_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IntEREst_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IntEREst_1.18.0.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: 130 Package: InterMineR Version: 1.16.0 Depends: R (>= 3.4.1) Imports: Biostrings, RCurl, XML, xml2, RJSONIO, sqldf, igraph, httr, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, methods Suggests: BiocStyle, Gviz, knitr, rmarkdown, GeneAnswers, GO.db, org.Hs.eg.db License: LGPL MD5sum: 1f9b512c5c44fd75dbd572ec4581a08c NeedsCompilation: no Title: R Interface with InterMine-Powered Databases Description: Databases based on the InterMine platform such as FlyMine, modMine (modENCODE), RatMine, YeastMine, HumanMine and TargetMine are integrated databases of genomic, expression and protein data for various organisms. Integrating data makes it possible to run sophisticated data mining queries that span domains of biological knowledge. This R package provides interfaces with these databases through webservices. It makes most from the correspondence of the data frame object in R and the table object in databases, while hiding the details of data exchange through XML or JSON. biocViews: GeneExpression, SNP, GeneSetEnrichment, DifferentialExpression, GeneRegulation, GenomeAnnotation, GenomeWideAssociation, FunctionalPrediction, AlternativeSplicing, ComparativeGenomics, FunctionalGenomics, Proteomics, SystemsBiology, Microarray, MultipleComparison, Pathways, GO, KEGG, Reactome, Visualization Author: Bing Wang, Julie Sullivan, Rachel Lyne, Konstantinos Kyritsis, Celia Sanchez Maintainer: InterMine Team VignetteBuilder: knitr BugReports: https://github.com/intermine/intermineR/issues git_url: https://git.bioconductor.org/packages/InterMineR git_branch: RELEASE_3_14 git_last_commit: 275a095 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/InterMineR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/InterMineR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/InterMineR_1.16.0.tgz vignettes: vignettes/InterMineR/inst/doc/InterMineR.html vignetteTitles: InterMineR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterMineR/inst/doc/InterMineR.R dependencyCount: 59 Package: IntramiRExploreR Version: 1.16.0 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: 2707eb1e22b7385ab594e840408d11a9 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: RELEASE_3_14 git_last_commit: c1d31e7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IntramiRExploreR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IntramiRExploreR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IntramiRExploreR_1.16.0.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: 32 Package: inveRsion Version: 1.42.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) MD5sum: fdc504f00ada9646b7df4d84533819ef NeedsCompilation: yes Title: Inversions in genotype data Description: Package to find genetic inversions in genotype (SNP array) data. biocViews: Microarray, SNP Author: Alejandro Caceres Maintainer: Alejandro Caceres git_url: https://git.bioconductor.org/packages/inveRsion git_branch: RELEASE_3_14 git_last_commit: 476476f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/inveRsion_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/inveRsion_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/inveRsion_1.42.0.tgz vignettes: vignettes/inveRsion/inst/doc/inveRsion.pdf vignetteTitles: Quick start guide for inveRsion package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inveRsion/inst/doc/inveRsion.R dependencyCount: 81 Package: IONiseR Version: 2.18.0 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: 29d56bb548344538b430b98d7d3d05b0 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: RELEASE_3_14 git_last_commit: 9256877 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IONiseR_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IONiseR_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IONiseR_2.18.0.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: 85 Package: iPAC Version: 1.38.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 Archs: i386, x64 MD5sum: 91b6e9eb22195aafe84063c42bb13f52 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: RELEASE_3_14 git_last_commit: e34e9ed git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iPAC_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iPAC_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iPAC_1.38.0.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: 29 Package: iPath Version: 1.0.0 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: 37c41c94b38416f47307f38e34d102ad 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: RELEASE_3_14 git_last_commit: 10fca6d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iPath_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/iPath_1.0.0.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: 126 Package: ipdDb Version: 1.12.0 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: c0f2b9552913f3ac67ac62449caaa638 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: RELEASE_3_14 git_last_commit: da8cb12 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ipdDb_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ipdDb_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ipdDb_1.12.0.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: 87 Package: IPO Version: 1.20.0 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: d7258fa6561d16642a32790fab25b18a 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 Riebenbauer Maintainer: Thomas Riebenbauer 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: RELEASE_3_14 git_last_commit: f26cf5e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IPO_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IPO_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IPO_1.20.0.tgz 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: 129 Package: IRanges Version: 2.28.0 Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.39.2), S4Vectors (>= 0.29.19) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 MD5sum: 117ce87444bb569749e3eb97eaf9366b 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: H. Pagès, P. Aboyoun and M. Lawrence Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/IRanges BugReports: https://github.com/Bioconductor/IRanges/issues git_url: https://git.bioconductor.org/packages/IRanges git_branch: RELEASE_3_14 git_last_commit: d85ee90 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IRanges_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IRanges_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IRanges_2.28.0.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R dependsOnMe: AnnotationDbi, AnnotationHubData, BaalChIP, bambu, biomvRCNS, Biostrings, BiSeq, BSgenome, BubbleTree, bumphunter, CAFE, casper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, CODEX, consensusSeekeR, CSAR, CSSQ, customProDB, deepSNV, DelayedArray, DESeq2, DEXSeq, 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srnadiff, STAN, strandCheckR, StructuralVariantAnnotation, SummarizedExperiment, SynExtend, TAPseq, target, TarSeqQC, TCGAbiolinks, TCGAutils, TCseq, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, TnT, tracktables, trackViewer, transcriptR, TransView, TreeSummarizedExperiment, TRESS, tricycle, tRNA, tRNAdbImport, tRNAscanImport, tscR, TSRchitect, TVTB, txcutr, tximeta, UMI4Cats, Uniquorn, universalmotif, VanillaICE, VarCon, VariantAnnotation, VariantExperiment, VariantFiltering, VaSP, wavClusteR, wiggleplotr, xcms, XNAString, XVector, yamss, 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.v3.1.1.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.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, leeBamViews, MethylSeqData, pd.atdschip.tiling, SomaticCancerAlterations, spatialLIBD, systemPipeRdata, ActiveDriverWGS, alakazam, BinQuasi, crispRdesignR, ExomeDepth, geno2proteo, HiCfeat, hoardeR, ICAMS, intePareto, LoopRig, MAAPER, MAFDash, MitoHEAR, noisyr, oncoPredict, PACVr, RapidoPGS, RTIGER, Signac, simMP, SNPassoc, STRMPS, tidygenomics, utr.annotation, VALERIE suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, Chicago, ClassifyR, epivizrChart, Glimma, gwascat, GWASTools, HilbertVis, HilbertVisGUI, maftools, martini, MiRaGE, multicrispr, MungeSumstats, regionReport, RTCGA, S4Vectors, SigsPack, splatter, svaNUMT, svaRetro, systemPipeR, TFutils, yeastRNASeq, cancerTiming, fuzzyjoin, gkmSVM, LDheatmap, pagoo, Platypus, polyRAD, rliger, seqmagick, Seurat, sigminer, updog, valr linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments, GenomicRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 7 Package: IRISFGM Version: 1.2.0 Depends: R (>= 4.1) Imports: Rcpp (>= 1.0.0), MCL, anocva, Polychrome, RColorBrewer, colorspace, AnnotationDbi, ggplot2, org.Hs.eg.db, org.Mm.eg.db, pheatmap, AdaptGauss, DEsingle,DrImpute, Matrix, Seurat, SingleCellExperiment, clusterProfiler, ggpubr, ggraph, igraph, mixtools, scater, scran, stats, methods, grDevices, graphics, utils, knitr LinkingTo: Rcpp Suggests: rmarkdown License: GPL-2 MD5sum: 55207048a269521e03e40dff09c85b39 NeedsCompilation: yes Title: Comprehensive Analysis of Gene Interactivity Networks Based on Single-Cell RNA-Seq Description: Single-cell RNA-Seq data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its availability is limited to a C implementation, and its applicative power is affected by only a few downstream analyses functionalities. We developed an R package named IRIS-FGM (integrative scRNA-Seq interpretation system for functional gene module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can identify co-expressed and co-regulated FGMs, predict types/clusters, identify differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM also applies Seurat objects that can be easily used in the Seurat vignettes. biocViews: Software, GeneExpression, SingleCell, Clustering, DifferentialExpression, Preprocessing, DimensionReduction, Visualization, Normalization, DataImport Author: Yuzhou Chang [aut, cre], Qin Ma [aut], Carter Allen [aut], Dongjun Chung [aut] Maintainer: Yuzhou Chang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IRISFGM git_branch: RELEASE_3_14 git_last_commit: d9e123f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IRISFGM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IRISFGM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IRISFGM_1.2.0.tgz vignettes: vignettes/IRISFGM/inst/doc/IRISFGM_Rpackage.html vignetteTitles: IRIS-FGM vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRISFGM/inst/doc/IRISFGM_Rpackage.R dependencyCount: 288 Package: ISAnalytics Version: 1.4.3 Depends: R (>= 4.1), magrittr Imports: utils, dplyr, readr, tidyr, purrr, rlang, tibble, BiocParallel, stringr, fs, lubridate, lifecycle, ggplot2, ggrepel, stats, psych, data.table, readxl, tools, Rcapture, grDevices, zip Suggests: testthat, covr, knitr, BiocStyle, sessioninfo, rmarkdown, roxygen2, vegan, withr, extraDistr, ggalluvial, scales, gridExtra, R.utils, RefManageR, flexdashboard, DT, circlize, plotly, gtools, eulerr License: CC BY 4.0 Archs: i386, x64 MD5sum: c1978209ce2029b34bd7967ba11c0c97 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: Andrea Calabria [aut, cre], Giulio Spinozzi [aut], Giulia Pais [aut] Maintainer: Andrea Calabria URL: https://calabrialab.github.io/ISAnalytics, https://github.com//calabrialab/isanalytics VignetteBuilder: knitr BugReports: https://github.com/calabrialab/ISAnalytics/issues git_url: https://git.bioconductor.org/packages/ISAnalytics git_branch: RELEASE_3_14 git_last_commit: 51c47b9 git_last_commit_date: 2022-01-13 Date/Publication: 2022-01-16 source.ver: src/contrib/ISAnalytics_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/ISAnalytics_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.1/ISAnalytics_1.4.3.tgz vignettes: vignettes/ISAnalytics/inst/doc/aggregate_function_usage.html, vignettes/ISAnalytics/inst/doc/collision_removal.html, vignettes/ISAnalytics/inst/doc/how_to_import_functions.html, vignettes/ISAnalytics/inst/doc/sharing_analyses.html vignetteTitles: aggregate_function_usage, collision_removal, import_functions_howto, sharing_analyses hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISAnalytics/inst/doc/aggregate_function_usage.R, vignettes/ISAnalytics/inst/doc/collision_removal.R, vignettes/ISAnalytics/inst/doc/how_to_import_functions.R, vignettes/ISAnalytics/inst/doc/sharing_analyses.R dependencyCount: 77 Package: iSEE Version: 2.6.0 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, ComplexHeatmap, circlize, grid Suggests: testthat, BiocStyle, knitr, rmarkdown, scRNAseq, TENxPBMCData, scater, DelayedArray, HDF5Array, RColorBrewer, viridis, htmltools License: MIT + file LICENSE Archs: i386, x64 MD5sum: beb9aab2824b35beeccdc9af9592ffe1 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: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays 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: RELEASE_3_14 git_last_commit: 9c0e8e0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iSEE_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iSEE_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iSEE_2.6.0.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: iSEEu, OSCA.advanced suggestsMe: schex, DuoClustering2018, HCAData, TabulaMurisData, TabulaMurisSenisData dependencyCount: 105 Package: iSEEu Version: 1.6.0 Depends: iSEE Imports: methods, S4Vectors, IRanges, shiny, SummarizedExperiment, SingleCellExperiment, ggplot2, 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: c2833217093b186344f1a8bdee7e32a8 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: RELEASE_3_14 git_last_commit: cb50d30 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iSEEu_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iSEEu_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iSEEu_1.6.0.tgz vignettes: vignettes/iSEEu/inst/doc/universe.html vignetteTitles: Panel universe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEu/inst/doc/universe.R dependencyCount: 106 Package: iSeq Version: 1.46.0 Depends: R (>= 2.10.0) License: GPL (>= 2) MD5sum: 0a550785910f429f02b4f3239059b82b 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: RELEASE_3_14 git_last_commit: 991ffa0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iSeq_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iSeq_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iSeq_1.46.0.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: isobar Version: 1.40.0 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: cd0daa8fbf70e1f877752426d24ca8f0 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: RELEASE_3_14 git_last_commit: 3dd1987 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/isobar_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/isobar_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/isobar_1.40.0.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 suggestsMe: RforProteomics dependencyCount: 92 Package: IsoCorrectoR Version: 1.12.0 Depends: R (>= 3.5) Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr, tibble, tools, utils, pracma, WriteXLS Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 359cbe934e57ee01621863e9076d5690 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: RELEASE_3_14 git_last_commit: fc45423 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IsoCorrectoR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IsoCorrectoR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IsoCorrectoR_1.12.0.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: 43 Package: IsoCorrectoRGUI Version: 1.10.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 3ef7104114bca36e1f3767272a9fd05c 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: RELEASE_3_14 git_last_commit: d29ebaf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IsoCorrectoRGUI_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IsoCorrectoRGUI_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IsoCorrectoRGUI_1.10.0.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: 46 Package: IsoformSwitchAnalyzeR Version: 1.16.0 Depends: R (>= 3.6), limma, DEXSeq, ggplot2 Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>= 2.50.0), IRanges, GenomicRanges, DRIMSeq, 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 Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, rmarkdown License: GPL (>= 2) MD5sum: 95f373a138f94e0f5a767c47446610fe NeedsCompilation: yes Title: Identify, Annotate and Visualize Alternative Splicing and 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] () 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: RELEASE_3_14 git_last_commit: 5eb5b2b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IsoformSwitchAnalyzeR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IsoformSwitchAnalyzeR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IsoformSwitchAnalyzeR_1.16.0.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: 160 Package: IsoGeneGUI Version: 2.30.0 Depends: tcltk, xlsx Imports: Rcpp, tkrplot, multtest, relimp, geneplotter, RColorBrewer, Iso, IsoGene, ORCME, ORIClust, orQA, goric, ff, Biobase, jpeg Suggests: RUnit License: GPL-2 MD5sum: 7efb9c31c0339f9dc7c5b4c18224648c NeedsCompilation: no Title: A graphical user interface to conduct a dose-response analysis of microarray data Description: The IsoGene Graphical User Interface (IsoGene-GUI) is a user friendly interface of the IsoGene package which is aimed to identify for genes with a monotonic trend in the expression levels with respect to the increasing doses. Additionally, GUI extension of original package contains various tools to perform clustering of dose-response profiles. Testing is addressed through several test statistics: global likelihood ratio test (E2), Bartholomew 1961, Barlow et al. 1972 and Robertson et al. 1988), Williams (1971, 1972), Marcus (1976), the M (Hu et al. 2005) and the modified M (Lin et al. 2007). The p-values of the global likelihood ratio test (E2) are obtained using the exact distribution and permutations. The other four test statistics are obtained using permutations. Several p-values adjustment are provided: Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for controlling the family-wise Type I error rate (FWER), and BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2001) procedures are used for controlling the FDR. The inference is based on resampling methods, which control the False Discovery Rate (FDR), for both permutations (Ge et al., 2003) and the Significance Analysis of Microarrays (SAM, Tusher et al., 2001). Clustering methods are outsourced from CRAN packages ORCME, ORIClust. The package ORCME is based on delta-clustering method (Cheng and Church, 2000) and ORIClust on Order Restricted Information Criterion (Liu et al., 2009), both perform same task but from different perspective and their outputs are clusters of genes. Additionally, profile selection for given gene based on Generalized ORIC (Kuiper et al., 2014) from package goric and permutation test for E2 based on package orQA are included in IsoGene-GUI. None of these four packages has GUI. biocViews: Microarray, DifferentialExpression, GUI Author: Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke, Martin Otava Maintainer: Setia Pramana URL: http://ibiostat.be/online-resources/online-resources/isogenegui/isogenegui-package git_url: https://git.bioconductor.org/packages/IsoGeneGUI git_branch: RELEASE_3_14 git_last_commit: 55c03bf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IsoGeneGUI_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IsoGeneGUI_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IsoGeneGUI_2.30.0.tgz vignettes: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.pdf vignetteTitles: IsoGeneGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.R dependencyCount: 79 Package: ISoLDE Version: 1.22.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) Archs: i386, x64 MD5sum: ab5d2ee06f1d268650e41876f6e3e3d4 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: RELEASE_3_14 git_last_commit: fcaeb58 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ISoLDE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ISoLDE_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ISoLDE_1.22.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: isomiRs Version: 1.22.1 Depends: R (>= 3.5), DiscriMiner, SummarizedExperiment Imports: AnnotationDbi, assertive.sets, 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: 1aabc8a59c2ab426c0b3e3aea63e03ff 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: RELEASE_3_14 git_last_commit: fe2f868 git_last_commit_date: 2022-03-02 Date/Publication: 2022-03-03 source.ver: src/contrib/isomiRs_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/isomiRs_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/isomiRs_1.22.1.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: 2.54.0 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 Archs: i386, x64 MD5sum: c15fd46d717d474106a5726e2cd21097 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: RELEASE_3_14 git_last_commit: 108e69a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ITALICS_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ITALICS_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ITALICS_2.54.0.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: 59 Package: iterativeBMA Version: 1.52.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: 60f5779a8664077e87d10ec04783610e 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: RELEASE_3_14 git_last_commit: 94f9f84 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iterativeBMA_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iterativeBMA_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iterativeBMA_1.52.0.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: 1.52.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: 6c09fac029743dab85aa6a39ac9d17a9 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: RELEASE_3_14 git_last_commit: 9eebd34 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iterativeBMAsurv_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iterativeBMAsurv_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iterativeBMAsurv_1.52.0.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: iterClust Version: 1.16.0 Depends: R (>= 3.4.1) Imports: Biobase, cluster, stats, methods Suggests: tsne, bcellViper License: file LICENSE MD5sum: dd3608945dfb06b34d4a7a4ccb25c941 NeedsCompilation: no Title: Iterative Clustering Description: A framework for performing clustering analysis iteratively. biocViews: StatisticalMethod, Clustering Author: Hongxu Ding and Andrea Califano Maintainer: Hongxu Ding URL: https://github.com/hd2326/iterClust BugReports: https://github.com/hd2326/iterClust/issues git_url: https://git.bioconductor.org/packages/iterClust git_branch: RELEASE_3_14 git_last_commit: 9b050e4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iterClust_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iterClust_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iterClust_1.16.0.tgz vignettes: vignettes/iterClust/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iterClust/inst/doc/introduction.R dependencyCount: 8 Package: iteremoval Version: 1.14.0 Depends: R (>= 3.5.0), ggplot2 (>= 2.2.1) Imports: magrittr, graphics, utils, GenomicRanges, SummarizedExperiment Suggests: testthat, knitr License: GPL-2 MD5sum: a9ca587e662dbaf276aa9ea1d937253c NeedsCompilation: no Title: Iteration removal method for feature selection Description: The package provides a flexible algorithm to screen features of two distinct groups in consideration of overfitting and overall performance. It was originally tailored for methylation locus screening of NGS data, and it can also be used as a generic method for feature selection. Each step of the algorithm provides a default method for simple implemention, and the method can be replaced by a user defined function. biocViews: StatisticalMethod Author: Jiacheng Chuan [aut, cre] Maintainer: Jiacheng Chuan URL: https://github.com/cihga39871/iteremoval VignetteBuilder: knitr BugReports: https://github.com/cihga39871/iteremoval/issues git_url: https://git.bioconductor.org/packages/iteremoval git_branch: RELEASE_3_14 git_last_commit: 59d81e1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/iteremoval_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/iteremoval_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/iteremoval_1.14.0.tgz vignettes: vignettes/iteremoval/inst/doc/iteremoval.html vignetteTitles: An introduction to iteremoval hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iteremoval/inst/doc/iteremoval.R dependencyCount: 55 Package: IVAS Version: 2.14.0 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: 0f060c7dadfe43a51875ccd9a11673c5 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: RELEASE_3_14 git_last_commit: cb2ad75 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IVAS_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IVAS_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IVAS_2.14.0.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 importsMe: ASpediaFI dependencyCount: 137 Package: ivygapSE Version: 1.16.0 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 License: Artistic-2.0 MD5sum: a70333c5ab46b39651ce283d94fbd1c2 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: RELEASE_3_14 git_last_commit: 268c7d4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ivygapSE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ivygapSE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ivygapSE_1.16.0.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: 158 Package: IWTomics Version: 1.18.0 Depends: GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: knitr License: GPL (>=2) MD5sum: 4eace2270ee0fe295c036c1dd1d0b424 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: RELEASE_3_14 git_last_commit: f4d7331 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/IWTomics_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/IWTomics_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/IWTomics_1.18.0.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: 72 Package: karyoploteR Version: 1.20.3 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: 02d582fdfa3ee52fccf732d897810e33 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 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: RELEASE_3_14 git_last_commit: 1a34c68 git_last_commit_date: 2022-01-19 Date/Publication: 2022-01-20 source.ver: src/contrib/karyoploteR_1.20.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/karyoploteR_1.20.3.zip mac.binary.ver: bin/macosx/contrib/4.1/karyoploteR_1.20.3.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, RIPAT suggestsMe: Category, MitoHEAR dependencyCount: 144 Package: KBoost Version: 1.2.0 Depends: R (>= 4.1), stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 | GPL-3 MD5sum: 58d67f5b4262b681b6b916ea6120c204 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: RELEASE_3_14 git_last_commit: a207b80 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/KBoost_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KBoost_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KBoost_1.2.0.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: 2.52.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: cff45d8e4f03b5fde5ebf651640ac18c 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: RELEASE_3_14 git_last_commit: e6b33a9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/KCsmart_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KCsmart_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KCsmart_2.52.0.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: 1.28.1 Depends: R (>= 3.2.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.7.3), S4Vectors (>= 0.27.3), e1071, LiblineaR, graphics, grDevices, utils, apcluster LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, Biobase, BiocGenerics, knitr License: GPL (>= 2.1) MD5sum: 21b1883481143d5d39c9357a7d87d208 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 Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/kebabs/ https://github.com/UBod/kebabs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kebabs git_branch: RELEASE_3_14 git_last_commit: 1f83eb1 git_last_commit_date: 2022-01-25 Date/Publication: 2022-01-27 source.ver: src/contrib/kebabs_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/kebabs_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.1/kebabs_1.28.1.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 suggestsMe: spiky dependencyCount: 29 Package: KEGGgraph Version: 1.54.0 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: 5c8d8a1fb15978f9699b8c4610466e9f 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 Maintainer: Jitao David Zhang URL: http://www.nextbiomotif.com git_url: https://git.bioconductor.org/packages/KEGGgraph git_branch: RELEASE_3_14 git_last_commit: 135ee3d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/KEGGgraph_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KEGGgraph_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KEGGgraph_1.54.0.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: ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal, MWASTools, NCIgraph, pathview, PFP, iCARH, kangar00, NFP, pathfindR suggestsMe: DEGraph, GenomicRanges, maGUI, rags2ridges dependencyCount: 13 Package: KEGGlincs Version: 1.20.0 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 Archs: i386, x64 MD5sum: 4e78fea9803ca2c9e3ccc37c391a7b8e 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: RELEASE_3_14 git_last_commit: 65205d4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/KEGGlincs_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KEGGlincs_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KEGGlincs_1.20.0.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: 60 Package: keggorthology Version: 2.46.0 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: ad7bb2355a08000d960969986437ad37 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: RELEASE_3_14 git_last_commit: 1d77032 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/keggorthology_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/keggorthology_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/keggorthology_2.46.0.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: 1.34.0 Depends: R (>= 3.5.0) Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr, markdown License: Artistic-2.0 MD5sum: 48b5de4a953a0759609e29a7c7788fa6 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 server. 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], Jeremy Volkening [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGREST git_branch: RELEASE_3_14 git_last_commit: 2056750 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/KEGGREST_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KEGGREST_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KEGGREST_1.34.0.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: GeneAnswers, pairkat, ROntoTools, Hiiragi2013 importsMe: ADAM, adSplit, AnnotationDbi, attract, BiocSet, ChIPpeakAnno, CNEr, EnrichmentBrowser, famat, FELLA, gage, MetaboSignal, MWASTools, PADOG, pathview, SBGNview, SMITE, transomics2cytoscape, YAPSA, g2f, MetaDBparse, omu, pathfindR suggestsMe: Category, categoryCompare, GenomicRanges, globaltest, iSEEu, MLP, padma, RTopper, CALANGO, maGUI, ptm, scDiffCom dependencyCount: 27 Package: KinSwingR Version: 1.12.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 MD5sum: dcf0b7578f7d79a6d8317f88ed3e07be 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: RELEASE_3_14 git_last_commit: 527597d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/KinSwingR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/KinSwingR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/KinSwingR_1.12.0.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: 34 Package: kissDE Version: 1.14.0 Imports: aod, Biobase, DESeq2, DSS, ggplot2, gplots, graphics, grDevices, matrixStats, stats, utils, foreach, doParallel, parallel Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: e9526407e2525861ba5479e3a31203f1 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 git_url: https://git.bioconductor.org/packages/kissDE git_branch: RELEASE_3_14 git_last_commit: ed7e372 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/kissDE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/kissDE_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/kissDE_1.14.0.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: 126 Package: KnowSeq Version: 1.8.1 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: edac865c1e94a01cc212dfd46f96a73e 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: RELEASE_3_14 git_last_commit: 9e61007 git_last_commit_date: 2022-04-08 Date/Publication: 2022-04-10 source.ver: src/contrib/KnowSeq_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/KnowSeq_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/KnowSeq_1.8.1.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: 169 Package: LACE Version: 1.6.0 Depends: R (>= 4.1.0) Imports: data.tree, graphics, grDevices, igraph, parallel, RColorBrewer, Rfast, stats, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: 5d29d9bd46519ff167f20fc3cb7174d9 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] () 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: RELEASE_3_14 git_last_commit: 4a9a702 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LACE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LACE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LACE_1.6.0.tgz vignettes: vignettes/LACE/inst/doc/vignette.pdf vignetteTitles: LACE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LACE/inst/doc/vignette.R dependencyCount: 38 Package: lapmix Version: 1.60.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: a20b7a8d466340377da51b6237d63cfa 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: RELEASE_3_14 git_last_commit: d9f410e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/lapmix_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lapmix_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lapmix_1.60.0.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: 1.62.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: d2d29fe40f78e8510379614f4b61c792 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: RELEASE_3_14 git_last_commit: 343d246 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LBE_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LBE_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LBE_1.62.0.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: 1.24.0 Depends: R (>= 3.5), methods Imports: Matrix, snpStats, VariantAnnotation, GenomeInfoDb, httr, ensembldb, EnsDb.Hsapiens.v75, Rsamtools, GenomicFiles (>= 1.13.6), BiocGenerics (>= 0.25.1) Suggests: RUnit, knitr, BiocStyle, gwascat, rmarkdown License: Artistic-2.0 MD5sum: 25b829b6637df7639623bacb7f3758a7 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: RELEASE_3_14 git_last_commit: e757bbd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ldblock_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ldblock_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ldblock_1.24.0.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: 107 Package: LEA Version: 3.6.0 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 MD5sum: af19271d4b3e6f35b8915e97fd66db45 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. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). {\tt LEA} also performs imputation of missing genotypes, and computes predictive values of genetic offsets based on new or future environments. The package includes factor methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). LEA is mainly based on optimized programs that can scale with the dimension 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: RELEASE_3_14 git_last_commit: d44b9c2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LEA_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LEA_3.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LEA_3.6.0.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: 1.28.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 27e92d98b55b363170a55b2723686ba9 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: RELEASE_3_14 git_last_commit: 10c50dd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LedPred_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LedPred_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LedPred_1.28.0.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: 1.4.0 Depends: SummarizedExperiment, R (>= 4.0.0) Imports: coin, MASS, ggplot2, stats, methods Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle, testthat, pkgdown, covr, withr License: Artistic-2.0 Archs: i386, x64 MD5sum: fe922f36ca545298bb63e50b71156175 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] (), Levi Waldron [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: RELEASE_3_14 git_last_commit: ef39953 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/lefser_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lefser_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lefser_1.4.0.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 dependencyCount: 66 Package: les Version: 1.44.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 2362620c7de80633cae2693a45723380 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: RELEASE_3_14 git_last_commit: f6e261b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/les_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/les_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/les_1.44.0.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: 1.12.0 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 License: GPL (>= 2) MD5sum: afdb5cf1428d1fe9cfca2732ca4b77d7 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: RELEASE_3_14 git_last_commit: 917a849 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/levi_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/levi_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/levi_1.12.0.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: 1.24.0 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 MD5sum: 92d0e72f4bca5cc60b7fe5b4a774b00b NeedsCompilation: yes Title: Logistic Factor Analysis for Categorical Data Description: LFA is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. biocViews: SNP, DimensionReduction, PrincipalComponent Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey 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: RELEASE_3_14 git_last_commit: 252f54c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/lfa_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lfa_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lfa_1.24.0.tgz vignettes: vignettes/lfa/inst/doc/lfa.pdf vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lfa/inst/doc/lfa.R importsMe: gcatest, jackstraw suggestsMe: popkin dependencyCount: 2 Package: limma Version: 3.50.3 Depends: R (>= 3.6.0) Imports: grDevices, graphics, stats, utils, methods Suggests: affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db, gplots, illuminaio, locfit, MASS, org.Hs.eg.db, splines, statmod (>= 1.2.2), vsn License: GPL (>=2) Archs: i386, x64 MD5sum: 4e8058fea270bb8e30ee361e863becdd NeedsCompilation: yes Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray 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], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limma git_url: https://git.bioconductor.org/packages/limma git_branch: RELEASE_3_14 git_last_commit: 724632d git_last_commit_date: 2022-04-06 Date/Publication: 2022-04-07 source.ver: src/contrib/limma_3.50.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/limma_3.50.3.zip mac.binary.ver: bin/macosx/contrib/4.1/limma_3.50.3.tgz vignettes: vignettes/limma/inst/doc/intro.pdf, vignettes/limma/inst/doc/usersguide.pdf vignetteTitles: Limma One Page Introduction, usersguide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ASpli, BLMA, cghMCR, codelink, convert, Cormotif, deco, DrugVsDisease, edgeR, ExiMiR, GEOexplorer, HTqPCR, IsoformSwitchAnalyzeR, maigesPack, marray, metagenomeSeq, metaseqR2, mpra, NeuCA, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits, splineTimeR, TOAST, tRanslatome, TurboNorm, variancePartition, wateRmelon, CCl4, Fletcher2013a, HD2013SGI, ReactomeGSA.data, EGSEA123, maEndToEnd, methylationArrayAnalysis, RNAseq123, OSCA.advanced, OSCA.basic, OSCA.workflows, BALLI, BioInsight, CEDA, CORM, countTransformers, cp4p, DAAGbio, DRomics, PerfMeas importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO, animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics, artMS, ASpediaFI, ATACseqQC, attract, autonomics, AWFisher, ballgown, BatchQC, beadarray, benchdamic, biotmle, BloodGen3Module, bnem, bsseq, BubbleTree, bumphunter, CancerSubtypes, casper, ChAMP, clusterExperiment, CNVRanger, combi, compcodeR, consensusDE, consensusOV, crlmm, crossmeta, csaw, cTRAP, ctsGE, CytoTree, DAMEfinder, DaMiRseq, DAPAR, debrowser, DEP, derfinderPlot, DEsubs, DExMA, DiffBind, diffcyt, diffHic, diffloop, diffUTR, distinct, DMRcate, Doscheda, DRIMSeq, eegc, EGAD, EGSEA, eisaR, EnrichmentBrowser, epigraHMM, erccdashboard, escape, EventPointer, EWCE, ExploreModelMatrix, flowBin, gCrisprTools, GDCRNATools, genefu, GeneSelectMMD, GEOquery, Glimma, GOsummaries, hipathia, HTqPCR, icetea, iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, KnowSeq, limmaGUI, Linnorm, lipidr, lmdme, mAPKL, MatrixQCvis, MBQN, mCSEA, MEAL, methylKit, MethylMix, microbiomeExplorer, microbiomeMarker, MIGSA, miloR, minfi, miRLAB, missMethyl, MLSeq, moanin, monocle, MoonlightR, msImpute, msqrob2, MSstats, MSstatsTMT, MultiDataSet, muscat, NADfinder, NanoMethViz, NanoTube, nethet, nondetects, NormalyzerDE, OLIN, omicRexposome, oppti, OVESEG, PAA, PADOG, PathoStat, pcaExplorer, PECA, pepStat, phantasus, phenoTest, PhosR, polyester, POMA, POWSC, projectR, psichomics, pwrEWAS, qPLEXanalyzer, qsea, RegEnrich, regsplice, Ringo, RNAinteract, ROSeq, RTCGAToolbox, RTN, RTopper, satuRn, scClassify, scone, scran, SEPIRA, seqsetvis, shinyepico, SimBindProfiles, SingleCellSignalR, singleCellTK, snapCGH, sparrow, spatialHeatmap, SPsimSeq, STATegRa, sva, timecourse, TimeSeriesExperiment, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, vsn, weitrix, Wrench, yamss, yarn, BeadArrayUseCases, DmelSGI, signatureSearchData, ExpHunterSuite, ExpressionNormalizationWorkflow, recountWorkflow, aliases2entrez, batchtma, BPM, Cascade, CIDER, cinaR, DCGL, DGEobj.utils, DiPALM, dsb, easyDifferentialGeneCoexpression, GWASbyCluster, immcp, INCATome, lilikoi, limorhyde2, lipidomeR, metaMA, mi4p, MiDA, miRtest, MKmisc, MKomics, nlcv, Patterns, plfMA, RANKS, RPPanalyzer, scBio, scRNAtools, SQDA, ssizeRNA, statVisual, tinyarray, wrProteo suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocSet, BioNet, BioQC, Category, categoryCompare, celaref, CellBench, CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar, dearseq, DEGreport, derfinder, DEScan2, dyebias, easyreporting, fgsea, fishpond, gage, geva, glmGamPoi, GSRI, GSVA, Harman, Heatplus, isobar, ivygapSE, les, lumi, MAST, methylumi, MLP, npGSEA, NxtIRFcore, oligo, oppar, piano, PREDA, proDA, puma, QFeatures, randRotation, Rcade, recountmethylation, ribosomeProfilingQC, rtracklayer, stageR, subSeq, SummarizedBenchmark, systemPipeR, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, ViSEAGO, zFPKM, BloodCancerMultiOmics2017, GeuvadisTranscriptExpr, mammaPrintData, msigdb, seventyGeneData, arrays, CAGEWorkflow, fluentGenomics, simpleSingleCell, AnnoProbe, aroma.affymetrix, canvasXpress, COCONUT, corncob, dnet, GeoTcgaData, hexbin, langevitour, limorhyde, LPS, maGUI, NACHO, Platypus, propr, protti, seqgendiff, Seurat, simphony, st, wrGraph, wrMisc, wrTopDownFrag dependencyCount: 5 Package: limmaGUI Version: 1.70.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) Archs: i386, x64 MD5sum: f25676972ac8ee98db24a59416eeb76a 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: RELEASE_3_14 git_last_commit: 8368a84 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/limmaGUI_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/limmaGUI_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/limmaGUI_1.70.0.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: 10 Package: LineagePulse Version: 1.14.0 Imports: BiocParallel, circlize, compiler, ComplexHeatmap, ggplot2, gplots, grDevices, grid, knitr, Matrix, methods, RColorBrewer, SingleCellExperiment, splines, stats, SummarizedExperiment, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: 7d70f048ceab5a423fc53aba5c2c910e NeedsCompilation: no Title: Differential expression analysis and model fitting for single-cell RNA-seq data Description: LineagePulse is a differential expression and expression model fitting package tailored to single-cell RNA-seq data (scRNA-seq). LineagePulse accounts for batch effects, drop-out and variable sequencing depth. One can use LineagePulse to perform longitudinal differential expression analysis across pseudotime as a continuous coordinate or between discrete groups of cells (e.g. pre-defined clusters or experimental conditions). Expression model fits can be directly extracted from LineagePulse. biocViews: ImmunoOncology, Software, StatisticalMethod, TimeCourse, Sequencing, DifferentialExpression, GeneExpression, CellBiology, CellBasedAssays, SingleCell Author: David S Fischer [aut, cre], Fabian Theis [ctb], Nir Yosef [ctb] Maintainer: David S Fischer VignetteBuilder: knitr BugReports: https://github.com/YosefLab/LineagePulse/issues git_url: https://git.bioconductor.org/packages/LineagePulse git_branch: RELEASE_3_14 git_last_commit: 23b2bf4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LineagePulse_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LineagePulse_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LineagePulse_1.14.0.tgz vignettes: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.html vignetteTitles: LineagePulse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.R dependencyCount: 89 Package: LinkHD Version: 1.8.0 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: 31cf42d14612d7388156620dcae7ca77 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: RELEASE_3_14 git_last_commit: 308b647 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LinkHD_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LinkHD_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LinkHD_1.8.0.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: 141 Package: Linnorm Version: 2.18.0 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 License: MIT + file LICENSE MD5sum: 1f384b44717315992f6701390acf901b 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 , Panwen Wang , Jean-Pierre Kocher , Pak Chung Sham , Junwen Wang Maintainer: Ken Shun Hang Yip URL: https://doi.org/10.1093/nar/gkx828 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Linnorm git_branch: RELEASE_3_14 git_last_commit: 2e5102d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Linnorm_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Linnorm_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Linnorm_2.18.0.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 dependencyCount: 69 Package: lionessR Version: 1.8.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE MD5sum: b5b07f54e2c0819eb38bec6a321818d8 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: RELEASE_3_14 git_last_commit: 7fce753 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/lionessR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lionessR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lionessR_1.8.0.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: 25 Package: lipidr Version: 2.8.1 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, iheatmapr, spelling, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: dfae4e538e76d2d9a0acfe0db8aaa26b 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: RELEASE_3_14 git_last_commit: 658506f git_last_commit_date: 2022-02-14 Date/Publication: 2022-02-15 source.ver: src/contrib/lipidr_2.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/lipidr_2.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/lipidr_2.8.1.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 dependencyCount: 90 Package: LiquidAssociation Version: 1.48.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: 31ed7cc428dd659bd4357871db6b8511 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: RELEASE_3_14 git_last_commit: c3c74ec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LiquidAssociation_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LiquidAssociation_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LiquidAssociation_1.48.0.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: 84 Package: lisaClust Version: 1.2.0 Depends: R (>= 4.1) Imports: ggplot2, class, concaveman, grid, BiocParallel, spatstat.core, spatstat.geom, BiocGenerics, S4Vectors, methods, spicyR, purrr, stats, data.table, dplyr, tidyr Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 25c5040ecb3d56a719d072b562245743 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 Author: Ellis Patrick [aut, cre], Nicolas Canete [aut] Maintainer: Ellis Patrick VignetteBuilder: knitr BugReports: https://github.com/ellispatrick/lisaClust/issues git_url: https://git.bioconductor.org/packages/lisaClust git_branch: RELEASE_3_14 git_last_commit: 94e92c6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/lisaClust_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lisaClust_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lisaClust_1.2.0.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 dependencyCount: 109 Package: lmdme Version: 1.36.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 76544592d5bcec4c0f4d6a5767036743 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: RELEASE_3_14 git_last_commit: e9e4b97 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/lmdme_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lmdme_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lmdme_1.36.0.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: 8 Package: LOBSTAHS Version: 1.20.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: e420fa0c15e24e0475fbf286c05d1954 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: RELEASE_3_14 git_last_commit: f4e6a04 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LOBSTAHS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LOBSTAHS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LOBSTAHS_1.20.0.tgz 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: 127 Package: loci2path Version: 1.14.0 Depends: R (>= 3.4) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 2ae574edd708af22c9825985adc557c1 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: RELEASE_3_14 git_last_commit: 47ab6ff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/loci2path_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/loci2path_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/loci2path_1.14.0.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: 42 Package: logicFS Version: 2.14.0 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: e7abbe044f8b19fd6fbf968093f629a2 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: RELEASE_3_14 git_last_commit: 397925a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/logicFS_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/logicFS_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/logicFS_2.14.0.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: logitT Version: 1.52.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) MD5sum: 4c34badaad3772c57559880c0f5c3788 NeedsCompilation: yes Title: logit-t Package Description: The logitT library implements the Logit-t algorithm introduced in --A high performance test of differential gene expression for oligonucleotide arrays-- by William J Lemon, Sandya Liyanarachchi and Ming You for use with Affymetrix data stored in an AffyBatch object in R. biocViews: Microarray, DifferentialExpression Author: Tobias Guennel Maintainer: Tobias Guennel URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/logitT git_branch: RELEASE_3_14 git_last_commit: fbaf96c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/logitT_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/logitT_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/logitT_1.52.0.tgz vignettes: vignettes/logitT/inst/doc/logitT.pdf vignetteTitles: logitT primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logitT/inst/doc/logitT.R dependencyCount: 12 Package: LOLA Version: 1.24.0 Depends: R (>= 2.10) 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: 230fc7a6ae40db7873b6f73caf41a2fb 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: RELEASE_3_14 git_last_commit: 31e3a51 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LOLA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LOLA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LOLA_1.24.0.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, DeepBlueR, MAGAR, MIRA, ramr dependencyCount: 24 Package: LoomExperiment Version: 1.12.0 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 Archs: i386, x64 MD5sum: 77c6f3162979585435395e2b7c8ff40b 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: RELEASE_3_14 git_last_commit: d254fd4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LoomExperiment_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LoomExperiment_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LoomExperiment_1.12.0.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: hca dependencyCount: 35 Package: LowMACA Version: 1.24.0 Depends: R (>= 2.10) Imports: cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation, BiocParallel, motifStack, Biostrings, httr, grid, gridBase Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 4c248cb70e64ad32a862802dcfceb05e NeedsCompilation: no Title: LowMACA - Low frequency Mutation Analysis via Consensus Alignment Description: The LowMACA package is a simple suite of tools to investigate and analyze the mutation profile of several proteins or pfam domains via consensus alignment. You can conduct an hypothesis driven exploratory analysis using our package simply providing a set of genes or pfam domains of your interest. biocViews: SomaticMutation, SequenceMatching, WholeGenome, Sequencing, Alignment, DataImport, MultipleSequenceAlignment Author: Stefano de Pretis , Giorgio Melloni Maintainer: Stefano de Pretis , Giorgio Melloni SystemRequirements: clustalo, gs, perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LowMACA git_branch: RELEASE_3_14 git_last_commit: b991ace git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LowMACA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LowMACA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LowMACA_1.24.0.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.html vignetteTitles: Bioconductor style for HTML documents hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R dependencyCount: 84 Package: LPE Version: 1.68.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: 0467d4519145a2bd419b81edc0d7a226 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: RELEASE_3_14 git_last_commit: 7e6a952 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LPE_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LPE_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LPE_1.68.0.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: LPEadj, PLPE importsMe: LPEadj suggestsMe: ABarray dependencyCount: 1 Package: LPEadj Version: 1.54.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 455339375b75cf9573e93540896e7809 NeedsCompilation: no Title: A correction of the local pooled error (LPE) method to replace the asymptotic variance adjustment with an unbiased adjustment based on sample size. Description: Two options are added to the LPE algorithm. The original LPE method sets all variances below the max variance in the ordered distribution of variances to the maximum variance. in LPEadj this option is turned off by default. The second option is to use a variance adjustment based on sample size rather than pi/2. By default the LPEadj uses the sample size based variance adjustment. biocViews: Microarray, Proteomics Author: Carl Murie , Robert Nadon Maintainer: Carl Murie git_url: https://git.bioconductor.org/packages/LPEadj git_branch: RELEASE_3_14 git_last_commit: 3ce0fd6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LPEadj_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LPEadj_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LPEadj_1.54.0.tgz vignettes: vignettes/LPEadj/inst/doc/LPEadj.pdf vignetteTitles: LPEadj test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPEadj/inst/doc/LPEadj.R dependencyCount: 2 Package: lpNet Version: 2.26.0 Depends: lpSolve License: Artistic License 2.0 MD5sum: b5e1afe7c6125a258038f22086a5ad7a 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: RELEASE_3_14 git_last_commit: a12de94 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/lpNet_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lpNet_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lpNet_2.26.0.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: 1 Package: lpsymphony Version: 1.22.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr, testthat Enhances: slam License: EPL MD5sum: be135e0a6f4c8734639bdf61de3a1d31 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: RELEASE_3_14 git_last_commit: 49017bb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/lpsymphony_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lpsymphony_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lpsymphony_1.22.0.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, Maaslin2 suggestsMe: oppr, prioritizr, TestDesign dependencyCount: 0 Package: LRBaseDbi Version: 2.4.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: testthat, BiocStyle License: Artistic-2.0 MD5sum: a9b4fb918d5e83524ea14483741cd7a3 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: RELEASE_3_14 git_last_commit: 92f5155 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LRBaseDbi_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LRBaseDbi_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LRBaseDbi_2.4.0.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: 1.2.0 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 Archs: i386, x64 MD5sum: 87e01c18095379826b98c7007f344272 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: RELEASE_3_14 git_last_commit: 37fee9c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LRcell_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LRcell_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LRcell_1.2.0.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: 113 Package: lumi Version: 2.46.0 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: 1b37d1b65f33a086ae27d4482b3dde72 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: RELEASE_3_14 git_last_commit: a68932c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/lumi_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/lumi_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/lumi_2.46.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: iCheck, wateRmelon, lumiHumanIDMapping, lumiMouseIDMapping, lumiRatIDMapping, ffpeExampleData, lumiBarnes, MAQCsubset, MAQCsubsetILM, mvoutData importsMe: arrayMvout, ffpe, MineICA suggestsMe: beadarray, blima, Harman, methylumi, tigre, beadarrayFilter, maGUI dependencyCount: 162 Package: LymphoSeq Version: 1.22.0 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: 5619b1e27062d4cd85065cdfba722ad4 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: RELEASE_3_14 git_last_commit: c0b2fd4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/LymphoSeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/LymphoSeq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/LymphoSeq_1.22.0.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: 91 Package: M3C Version: 1.16.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach, doParallel, matrixcalc, Rtsne, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: 1d11f1d9f78694465e6eab483ca4e353 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: RELEASE_3_14 git_last_commit: 2cc4373 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/M3C_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/M3C_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/M3C_1.16.0.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: HCV, lilikoi suggestsMe: parameters dependencyCount: 63 Package: M3Drop Version: 1.20.0 Depends: R (>= 3.4), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods Suggests: ROCR, knitr, M3DExampleData, scater, SingleCellExperiment, monocle, Seurat, Biobase License: GPL (>=2) MD5sum: 44e7c33ec0c9a4735c334cf8de2f713b NeedsCompilation: no Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq Description: This package fits a Michaelis-Menten 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. 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: RELEASE_3_14 git_last_commit: 7233577 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/M3Drop_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/M3Drop_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/M3Drop_1.20.0.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: 101 Package: m6Aboost Version: 1.0.0 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: 88379dd00ce013f776a750dd18aaea12 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: RELEASE_3_14 git_last_commit: 07140d0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/m6Aboost_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/m6Aboost_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/m6Aboost_1.0.0.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: 164 Package: maanova Version: 1.64.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) MD5sum: 9e1df43b62212a451d539188909863b5 NeedsCompilation: yes Title: Tools for analyzing Micro Array experiments Description: Analysis of N-dye Micro Array experiment using mixed model effect. Containing analysis of variance, permutation and bootstrap, cluster and consensus tree. biocViews: Microarray, DifferentialExpression, Clustering Author: Hao Wu, modified by Hyuna Yang and Keith Sheppard with ideas from Gary Churchill, Katie Kerr and Xiangqin Cui. Maintainer: Keith Sheppard URL: http://research.jax.org/faculty/churchill git_url: https://git.bioconductor.org/packages/maanova git_branch: RELEASE_3_14 git_last_commit: b81753d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/maanova_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maanova_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maanova_1.64.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 7 Package: Maaslin2 Version: 1.8.0 Depends: R (>= 3.6) Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, lpsymphony, pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap, logging, data.table, lmerTest, hash, optparse, grDevices, stats, utils, glmmTMB, MASS, cplm, pscl, lme4 Suggests: knitr, testthat (>= 2.1.0), rmarkdown License: MIT + file LICENSE MD5sum: 1fcbe40ca174a2d79e536efdf21e9437 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: RELEASE_3_14 git_last_commit: 6e36871 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Maaslin2_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Maaslin2_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Maaslin2_1.8.0.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: MMUPHin dependencyCount: 144 Package: macat Version: 1.68.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 Archs: i386, x64 MD5sum: 8b560aa96709d51374e985e43d559169 NeedsCompilation: no Title: MicroArray Chromosome Analysis Tool Description: This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'. biocViews: Microarray, DifferentialExpression, Visualization Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling Maintainer: Joern Toedling git_url: https://git.bioconductor.org/packages/macat git_branch: RELEASE_3_14 git_last_commit: 741a67b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/macat_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/macat_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/macat_1.68.0.tgz vignettes: vignettes/macat/inst/doc/macat.pdf vignetteTitles: MicroArray Chromosome Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/macat/inst/doc/macat.R dependencyCount: 48 Package: maCorrPlot Version: 1.64.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: 7662af7970a3be74ebbb837c917fb508 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: RELEASE_3_14 git_last_commit: aed9b24 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/maCorrPlot_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maCorrPlot_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maCorrPlot_1.64.0.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: MACPET Version: 1.14.0 Depends: R (>= 3.6.1), InteractionSet (>= 1.13.0), bigmemory (>= 4.5.33), BH (>= 1.66.0.1), Rcpp (>= 1.0.1) Imports: intervals (>= 0.15.1), plyr (>= 1.8.4), Rsamtools (>= 2.1.3), stats (>= 3.6.1), utils (>= 3.6.1), methods (>= 3.6.1), GenomicRanges (>= 1.37.14), S4Vectors (>= 0.23.17), IRanges (>= 2.19.10), GenomeInfoDb (>= 1.21.1), gtools (>= 3.8.1), GenomicAlignments (>= 1.21.4), knitr (>= 1.23), rtracklayer (>= 1.45.1), BiocParallel (>= 1.19.0), Rbowtie (>= 1.25.0), GEOquery (>= 2.53.0), Biostrings (>= 2.53.2), ShortRead (>= 1.43.0), futile.logger (>= 1.4.3) LinkingTo: Rcpp, bigmemory, BH Suggests: ggplot2 (>= 3.2.0), igraph (>= 1.2.4.1), rmarkdown (>= 1.14), reshape2 (>= 1.4.3), BiocStyle (>= 2.13.2) License: GPL-3 MD5sum: 1619adfe1cadac97dffc41c924c6f87f NeedsCompilation: yes Title: Model based analysis for paired-end data Description: The MACPET package can be used for complete interaction analysis for ChIA-PET data. MACPET reads ChIA-PET data in BAM or SAM format and separates the data into Self-ligated, Intra- and Inter-chromosomal PETs. Furthermore, MACPET breaks the genome into regions and applies 2D mixture models for identifying candidate peaks/binding sites using skewed generalized students-t distributions (SGT). It then uses a local poisson model for finding significant binding sites. Finally it runs an additive interaction-analysis model for calling for significant interactions between those peaks. MACPET is mainly written in C++, and it also supports the BiocParallel package. biocViews: Software, DNA3DStructure, PeakDetection, StatisticalMethod, Clustering, Classification, HiC Author: Ioannis Vardaxis Maintainer: Ioannis Vardaxis SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACPET git_branch: RELEASE_3_14 git_last_commit: d1b9a5c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MACPET_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MACPET_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MACPET_1.14.0.tgz vignettes: vignettes/MACPET/inst/doc/MACPET.pdf vignetteTitles: MACPET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACPET/inst/doc/MACPET.R dependencyCount: 107 Package: MACSQuantifyR Version: 1.8.0 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: 9332f75309cb4d86b8f055d6b717e36f 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: RELEASE_3_14 git_last_commit: 9396dba git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MACSQuantifyR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MACSQuantifyR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MACSQuantifyR_1.8.0.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: 80 Package: MACSr Version: 1.2.0 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: 4b255821a7c9405da9b58771c9500a90 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: RELEASE_3_14 git_last_commit: 78f9ee1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MACSr_1.2.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/MACSr_1.2.0.tgz vignettes: vignettes/MACSr/inst/doc/MACSr.html vignetteTitles: MACSr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MACSr/inst/doc/MACSr.R dependencyCount: 98 Package: made4 Version: 1.68.0 Depends: RColorBrewer,gplots,scatterplot3d, Biobase, SummarizedExperiment Imports: ade4 Suggests: affy, BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 425c90bf655f4947aa453812d71294bc 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: RELEASE_3_14 git_last_commit: b32f979 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/made4_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/made4_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/made4_1.68.0.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: deco, omicade4 dependencyCount: 35 Package: MADSEQ Version: 1.20.0 Depends: R(>= 3.4), 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: 49c975fd97fab13c38036b35ed50fcb4 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: RELEASE_3_14 git_last_commit: 2665662 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MADSEQ_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MADSEQ_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MADSEQ_1.20.0.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: 115 Package: maftools Version: 2.10.05 Depends: R (>= 3.3) Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib, survival 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 MD5sum: 72000a82877a12d14e2e81f2a84ee85f 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: RELEASE_3_14 git_last_commit: 3e3625a git_last_commit_date: 2022-02-23 Date/Publication: 2022-02-24 source.ver: src/contrib/maftools_2.10.05.tar.gz win.binary.ver: bin/windows/contrib/4.1/maftools_2.10.05.zip mac.binary.ver: bin/macosx/contrib/4.1/maftools_2.10.05.tgz vignettes: vignettes/maftools/inst/doc/cancer_hotspots.html, vignettes/maftools/inst/doc/maftools.html, vignettes/maftools/inst/doc/oncoplots.html vignetteTitles: 03: Somatic status of cancer hotspots, 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/maftools.R, vignettes/maftools/inst/doc/oncoplots.R dependsOnMe: MAFDash importsMe: CIMICE, musicatk, TCGAbiolinksGUI, TCGAWorkflow, oncoPredict, pathwayTMB, PMAPscore, Rediscover, sigminer, SMDIC suggestsMe: MultiAssayExperiment, survtype, TCGAbiolinks dependencyCount: 14 Package: MAGAR Version: 1.2.0 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, utils, stats Suggests: gridExtra, VennDiagram, qqman, LOLA, RUnit, rmutil, rmarkdown, JASPAR2018, TFBSTools, seqLogo, knitr, devtools, BiocGenerics, BiocManager License: GPL-3 MD5sum: e0b0c30ee944cf6189b13aaf690f497f 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: RELEASE_3_14 git_last_commit: fed74d7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MAGAR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MAGAR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MAGAR_1.2.0.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: 197 Package: MAGeCKFlute Version: 1.14.0 Depends: R (>= 3.5) Imports: Biobase, clusterProfiler (>= 3.16.1), enrichplot, gridExtra, ggplot2, ggrepel, grDevices, grid, reshape2, stats, utils Suggests: biomaRt, BiocStyle, DOSE, dendextend, graphics, knitr, msigdbr, pheatmap, png, pathview, scales, sva, testthat, License: GPL (>=3) Archs: i386, x64 MD5sum: 33c57c969cfef642333e716bbfd947c4 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: RELEASE_3_14 git_last_commit: e03a467 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MAGeCKFlute_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MAGeCKFlute_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MAGeCKFlute_1.14.0.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: SpidermiR dependencyCount: 125 Package: MAI Version: 1.0.0 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: f977508bf05bdccbbce9f49b29de90d3 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: RELEASE_3_14 git_last_commit: 0953c88 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MAI_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MAI_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MAI_1.0.0.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: 161 Package: maigesPack Version: 1.58.0 Depends: R (>= 2.10), convert, graph, limma, marray, methods Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML, rgl, som License: GPL (>= 2) MD5sum: 4d6255f8e6ffc321d20b57ff921894f7 NeedsCompilation: yes Title: Functions to handle cDNA microarray data, including several methods of data analysis Description: This package uses functions of various other packages together with other functions in a coordinated way to handle and analyse cDNA microarray data biocViews: Microarray, TwoChannel, Preprocessing, ThirdPartyClient, DifferentialExpression, Clustering, Classification, GraphAndNetwork Author: Gustavo H. Esteves , with contributions from Roberto Hirata Jr , E. Jordao Neves , Elier B. Cristo , Ana C. Simoes and Lucas Fahham Maintainer: Gustavo H. Esteves URL: http://www.maiges.org/en/software/ git_url: https://git.bioconductor.org/packages/maigesPack git_branch: RELEASE_3_14 git_last_commit: 455a3b6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/maigesPack_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maigesPack_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maigesPack_1.58.0.tgz vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf vignetteTitles: maigesPack Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maigesPack/inst/doc/maigesPack_tutorial.R dependencyCount: 12 Package: MAIT Version: 1.28.0 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: c293d4615db298948fb2ec8a849878fe 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: RELEASE_3_14 git_last_commit: 70509d4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MAIT_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MAIT_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MAIT_1.28.0.tgz 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: 209 Package: makecdfenv Version: 1.70.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: i386, x64 MD5sum: 4c338c8c7f98d3524fe54b5c9028fe52 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: RELEASE_3_14 git_last_commit: 82ecd0f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/makecdfenv_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/makecdfenv_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/makecdfenv_1.70.0.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: 1.66.0 Depends: R (>= 2.10) Imports: GLAD, graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, bookdown License: GPL-2 MD5sum: 5c78326721cdaa963de209cd92373a0d 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: RELEASE_3_14 git_last_commit: 36739b5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MANOR_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MANOR_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MANOR_1.66.0.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: 1.64.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) Archs: i386, x64 MD5sum: ad354c46fabf9d1df1df54ade4c52864 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: RELEASE_3_14 git_last_commit: 7836271 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MantelCorr_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MantelCorr_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MantelCorr_1.64.0.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: mAPKL Version: 1.24.0 Depends: R (>= 3.6.0), Biobase Imports: multtest, clusterSim, apcluster, limma, e1071, AnnotationDbi, methods, parmigene,igraph,reactome.db Suggests: BiocStyle, knitr, mAPKLData, hgu133plus2.db, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 1d728ba01cd0cb77235a3a9478582326 NeedsCompilation: no Title: A Hybrid Feature Selection method for gene expression data Description: We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. biocViews: FeatureExtraction, DifferentialExpression, Microarray, GeneExpression Author: Argiris Sakellariou Maintainer: Argiris Sakellariou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mAPKL git_branch: RELEASE_3_14 git_last_commit: b9e43cb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mAPKL_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mAPKL_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mAPKL_1.24.0.tgz vignettes: vignettes/mAPKL/inst/doc/mAPKL.pdf vignetteTitles: mAPKL Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mAPKL/inst/doc/mAPKL.R dependencyCount: 79 Package: maPredictDSC Version: 1.32.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: 35f743741f8316dbf76968f0f7245b22 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: RELEASE_3_14 git_last_commit: 5141953 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/maPredictDSC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maPredictDSC_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maPredictDSC_1.32.0.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: 131 Package: mapscape Version: 1.18.0 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: 089538ec78fd11d58757ec901059b697 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: RELEASE_3_14 git_last_commit: 5b48ada git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mapscape_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mapscape_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mapscape_1.18.0.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: 17 Package: marr Version: 1.4.0 Depends: R (>= 4.0) Imports: Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, covr License: GPL (>= 3) MD5sum: 052182d7e4b80843e6b735a962eb7fdb 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: RELEASE_3_14 git_last_commit: d3da7f2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/marr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/marr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/marr_1.4.0.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: 60 Package: marray Version: 1.72.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 21c115c5c76430f201b54a3c424225d3 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: RELEASE_3_14 git_last_commit: da35e8b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/marray_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/marray_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/marray_1.72.0.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, maigesPack, MineICA, nnNorm, OLIN, RBM, stepNorm, TurboNorm, beta7, dyebiasexamples importsMe: arrayQuality, ChAMP, MSstats, nnNorm, OLIN, OLINgui, piano, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz, hexbin dependencyCount: 6 Package: martini Version: 1.14.0 Depends: R (>= 4.0) Imports: igraph (>= 1.0.1), Matrix, 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), memoise (>= 2.0.0), knitr, testthat, readr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 00ffa3a8a02ef3073538bce65322b102 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: RELEASE_3_14 git_last_commit: 2777da8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/martini_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/martini_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/martini_1.14.0.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: 18 Package: maser Version: 1.12.1 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: d2951879a8bba73292149044d1291994 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: RELEASE_3_14 git_last_commit: b243c41 git_last_commit_date: 2022-02-08 Date/Publication: 2022-02-10 source.ver: src/contrib/maser_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/maser_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/maser_1.12.1.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: 149 Package: maSigPro Version: 1.66.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) Archs: i386, x64 MD5sum: 71d4e58ab655c680e02a582e464e483f 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: RELEASE_3_14 git_last_commit: cd427cd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/maSigPro_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maSigPro_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maSigPro_1.66.0.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: 1.38.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) MD5sum: a388c73d86845ec59ac14aac61b79933 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: RELEASE_3_14 git_last_commit: 41dc20e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/maskBAD_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/maskBAD_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/maskBAD_1.38.0.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: 25 Package: MassArray Version: 1.46.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: 6b9fc54e88a1b6fd356a2d6898253abc 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: RELEASE_3_14 git_last_commit: d4621b4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MassArray_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MassArray_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MassArray_1.46.0.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: 1.30.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: 59234e810005f012e8d5ba6659e94b31 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: RELEASE_3_14 git_last_commit: 2f04917 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/massiR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/massiR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/massiR_1.30.0.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: 1.60.1 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) MD5sum: 6930e1d66fb7a3db0176b98a60a72d7d NeedsCompilation: yes Title: Mass spectrum processing by wavelet-based algorithms Description: Processing Mass Spectrometry spectrum by using wavelet based algorithm biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Pan Du, Warren Kibbe, Simon Lin Maintainer: Sergio Oller git_url: https://git.bioconductor.org/packages/MassSpecWavelet git_branch: RELEASE_3_14 git_last_commit: 78ed31d git_last_commit_date: 2022-04-04 Date/Publication: 2022-04-05 source.ver: src/contrib/MassSpecWavelet_1.60.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/MassSpecWavelet_1.60.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MassSpecWavelet_1.60.1.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.pdf vignetteTitles: MassSpecWavelet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms, Rnmr1D, speaq dependencyCount: 5 Package: MAST Version: 1.20.0 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 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, Matrix, HDF5Array, zinbwave, dplyr License: GPL(>= 2) MD5sum: 73127d316cfa9f2914bd8caea503ec2c 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: RELEASE_3_14 git_last_commit: d4387f9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MAST_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MAST_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MAST_1.20.0.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 suggestsMe: clusterExperiment, Seurat dependencyCount: 67 Package: matchBox Version: 1.36.0 Depends: R (>= 2.8.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: bb5d4850e7053b47affc7d85e7f6c2de 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: RELEASE_3_14 git_last_commit: d178621 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/matchBox_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/matchBox_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/matchBox_1.36.0.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: 1.6.0 Depends: matrixStats (>= 0.60.1) Imports: methods Suggests: sparseMatrixStats, DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: d93e3d75d314733da20c179cd6bb41bd 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: RELEASE_3_14 git_last_commit: 4588a60 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MatrixGenerics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MatrixGenerics_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MatrixGenerics_1.6.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles, sparseMatrixStats, SummarizedExperiment, VariantAnnotation importsMe: CoreGx, MinimumDistance, PDATK, RaggedExperiment, scone, scPCA, tLOH, transformGamPoi, VanillaICE suggestsMe: MungeSumstats dependencyCount: 2 Package: MatrixQCvis Version: 1.2.4 Depends: SummarizedExperiment (>= 1.20.0), plotly (>= 4.9.3), shiny (>= 1.6.0) Imports: ComplexHeatmap (>= 2.7.9), dplyr (>= 1.0.5), 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), methods (>= 4.1.0), openxlsx (>= 4.2.3), pcaMethods (>= 1.83.0), proDA (>= 1.5.0), rlang (>= 0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), S4Vectors (>= 0.29.15), 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), vegan (>= 2.5-7), vsn (>= 3.59.1) Suggests: BiocGenerics (>= 0.37.4), BiocStyle (>= 2.19.2), hexbin (>= 1.28.2), knitr (>= 1.33), testthat (>= 3.0.2) License: GPL (>= 3) MD5sum: 39b7379d789b103acf6acb70457b3d5d 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, GUI, DimensionReduction, Metabolomics, Proteomics Author: Thomas Naake [aut, cre], Wolfgang Huber [aut] Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MatrixQCvis git_branch: RELEASE_3_14 git_last_commit: 9cccc5b git_last_commit_date: 2022-04-09 Date/Publication: 2022-04-10 source.ver: src/contrib/MatrixQCvis_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/MatrixQCvis_1.2.4.zip mac.binary.ver: bin/macosx/contrib/4.1/MatrixQCvis_1.2.4.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: 163 Package: MatrixRider Version: 1.26.0 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: 8d942c0e9704453adf55f6303e0cf1aa 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: RELEASE_3_14 git_last_commit: 3907aeb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MatrixRider_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MatrixRider_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MatrixRider_1.26.0.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: 123 Package: matter Version: 1.20.0 Depends: R (>= 3.5), BiocParallel, Matrix, methods, stats, biglm Imports: BiocGenerics, ProtGenerics, digest, irlba, utils Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 3f6f76bb0e96a93829282e59fd3c0d99 NeedsCompilation: yes Title: A framework for rapid prototyping with file-based data structures Description: Memory-efficient reading, writing, and manipulation of structured binary data as file-based vectors, matrices, arrays, lists, and data frames. biocViews: Infrastructure, DataRepresentation Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: https://github.com/kuwisdelu/matter git_url: https://git.bioconductor.org/packages/matter git_branch: RELEASE_3_14 git_last_commit: 60a913c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/matter_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/matter_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/matter_1.20.0.tgz vignettes: vignettes/matter/inst/doc/matter-supp1.pdf, vignettes/matter/inst/doc/matter-supp2.pdf, vignettes/matter/inst/doc/matter.pdf vignetteTitles: matter: Supplementary 1 - Simulations and comparative benchmarks, matter: Supplementary 2 - 3D mass spectrometry imaging case study, matter: Rapid prototyping with data on disk hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matter/inst/doc/matter-supp1.R, vignettes/matter/inst/doc/matter-supp2.R, vignettes/matter/inst/doc/matter.R importsMe: Cardinal dependencyCount: 22 Package: MBAmethyl Version: 1.28.0 Depends: R (>= 2.15) License: Artistic-2.0 Archs: i386, x64 MD5sum: c0616ac390ed721840915b5ead0c91c0 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: RELEASE_3_14 git_last_commit: fb9b322 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MBAmethyl_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBAmethyl_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MBAmethyl_1.28.0.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: 1.28.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: ba061f3d2ebb39d8c86c061227348836 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: RELEASE_3_14 git_last_commit: 785b3c0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MBASED_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBASED_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MBASED_1.28.0.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: 34 Package: MBCB Version: 1.48.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: 8e74b5032457d7094c1088f63faa0d7c 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: Jeff Allen URL: http://www.utsouthwestern.edu git_url: https://git.bioconductor.org/packages/MBCB git_branch: RELEASE_3_14 git_last_commit: ad6a077 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MBCB_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBCB_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MBCB_1.48.0.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: mbkmeans Version: 1.10.0 Depends: R (>= 3.6) Imports: methods, DelayedArray, Rcpp, S4Vectors, SingleCellExperiment, SummarizedExperiment, ClusterR, benchmarkme, Matrix, BiocParallel LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData, scater, DelayedMatrixStats, bluster, knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: 58b3c70756ce08532ed66b5331f89fec 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: RELEASE_3_14 git_last_commit: 1503619 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mbkmeans_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mbkmeans_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mbkmeans_1.10.0.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 importsMe: clusterExperiment suggestsMe: bluster, scDblFinder dependencyCount: 89 Package: mBPCR Version: 1.48.0 Depends: oligoClasses, GWASTools Imports: Biobase, graphics, methods, utils, grDevices Suggests: xtable License: GPL (>= 2) MD5sum: 51cbe7cc303bc2eb93fb1053baca4414 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: RELEASE_3_14 git_last_commit: 910c922 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mBPCR_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mBPCR_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mBPCR_1.48.0.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: 100 Package: MBQN Version: 2.6.2 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 Archs: i386, x64 MD5sum: 9ef82996db6bb69397a8528ee2a78615 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: RELEASE_3_14 git_last_commit: e96b07e git_last_commit_date: 2022-03-11 Date/Publication: 2022-03-13 source.ver: src/contrib/MBQN_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBQN_2.6.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MBQN_2.6.2.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: 106 Package: MBttest Version: 1.22.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 62d8397959f9c6d37a729d8156b3b502 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: RELEASE_3_14 git_last_commit: 5d1a791 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MBttest_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MBttest_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MBttest_1.22.0.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: 1.18.0 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: 7b002121b1d9aa7781243c677fc9351e 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: RELEASE_3_14 git_last_commit: a6d489e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MCbiclust_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MCbiclust_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MCbiclust_1.18.0.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: 130 Package: mCSEA Version: 1.14.0 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: ccd9c18a805d4e402f7eca9023fa0b3a 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: RELEASE_3_14 git_last_commit: 75e5e1d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mCSEA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mCSEA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mCSEA_1.14.0.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: 154 Package: mdp Version: 1.14.0 Depends: R (>= 4.0) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager License: GPL-3 MD5sum: 6e03f22dee7241919c94f7de8a43faf3 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: RELEASE_3_14 git_last_commit: a3d72f8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mdp_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mdp_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mdp_1.14.0.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: 39 Package: mdqc Version: 1.56.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 3a8d8b873eb1c3c59f1dae9df36179c0 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: RELEASE_3_14 git_last_commit: 96973e9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mdqc_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mdqc_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mdqc_1.56.0.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: 1.14.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: e9d61deef776a35bc578f67828d86b8b 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: RELEASE_3_14 git_last_commit: 2527d7a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MDTS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MDTS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MDTS_1.14.0.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: 43 Package: MEAL Version: 1.24.0 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: cf6fce5ba20d1403292ed36845249830 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: RELEASE_3_14 git_last_commit: 003c324 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MEAL_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEAL_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEAL_1.24.0.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: 212 Package: MeasurementError.cor Version: 1.66.0 License: LGPL MD5sum: 16635e71f77e3a25125a5e9af6877086 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: RELEASE_3_14 git_last_commit: 83dd7ec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MeasurementError.cor_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MeasurementError.cor_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MeasurementError.cor_1.66.0.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: 1.6.0 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: 379947477a8a8fb60646a820d2f773ee 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: RELEASE_3_14 git_last_commit: 5086e40 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MEAT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEAT_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEAT_1.6.0.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: 177 Package: MEB Version: 1.8.0 Depends: R (>= 3.6.0) Imports: e1071, SummarizedExperiment Suggests: knitr,rmarkdown,BiocStyle License: GPL-2 MD5sum: 2aa803b9d408ddd568042171d013b2d5 NeedsCompilation: no Title: A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq data Description: Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work. The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data. We transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a normalization-invariant minimum enclosing ball (NIMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed NIMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the NIMEB method could be easily extended to different species without normalization. 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: RELEASE_3_14 git_last_commit: f808247 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MEB_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEB_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEB_1.8.0.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: 29 Package: MEDIPS Version: 1.46.0 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: 1eabbbc5734729d0891c42fbe571ac84 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: RELEASE_3_14 git_last_commit: 9d2b4a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MEDIPS_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEDIPS_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEDIPS_1.46.0.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: 102 Package: MEDME Version: 1.54.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) MD5sum: c64e9065b52604dc9d69d8806535c66d 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: RELEASE_3_14 git_last_commit: d28d334 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MEDME_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEDME_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEDME_1.54.0.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: 108 Package: megadepth Version: 1.4.0 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: 59a45891f430d47b1dcfdc6450527c14 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: RELEASE_3_14 git_last_commit: 0e998eb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/megadepth_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/megadepth_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/megadepth_1.4.0.tgz vignettes: vignettes/megadepth/inst/doc/megadepth.html vignetteTitles: megadepth quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/megadepth/inst/doc/megadepth.R importsMe: dasper, ODER dependencyCount: 84 Package: MEIGOR Version: 1.28.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR, knitr License: GPL-3 Archs: i386, x64 MD5sum: 7f95fa592c52902942adbb15b658f604 NeedsCompilation: no Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization Description: Global Optimization biocViews: SystemsBiology Author: Jose A. Egea, David Henriques, Alexandre Fdez. Villaverde, Thomas Cokelaer Maintainer: Jose A. Egea VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEIGOR git_branch: RELEASE_3_14 git_last_commit: 2debaf5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MEIGOR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MEIGOR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MEIGOR_1.28.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.pdf vignetteTitles: Main vignette:Global Optimization for Bioinformatics and Systems Biology hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R importsMe: bioOED dependencyCount: 61 Package: Melissa Version: 1.10.0 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: daa578cf424775357c6a551c5f7f8b37 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: RELEASE_3_14 git_last_commit: 585e70a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Melissa_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Melissa_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Melissa_1.10.0.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: 107 Package: memes Version: 1.2.5 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: a8e3554cb58d0eb187b4ef7937eac1bc 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: RELEASE_3_14 git_last_commit: a1d3271 git_last_commit_date: 2022-02-03 Date/Publication: 2022-02-06 source.ver: src/contrib/memes_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/memes_1.2.5.zip mac.binary.ver: bin/macosx/contrib/4.1/memes_1.2.5.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: 109 Package: Mergeomics Version: 1.22.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: b2caf7f2c2caa470c99ed9652efb2438 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: RELEASE_3_14 git_last_commit: 2e57828 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Mergeomics_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Mergeomics_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Mergeomics_1.22.0.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: 1.30.0 Depends: R (>= 3.0.1) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: testthat License: Artistic-2.0 MD5sum: 410087554071b377cc954919df04823f 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: RELEASE_3_14 git_last_commit: cc39bc4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MeSHDbi_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MeSHDbi_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MeSHDbi_1.30.0.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: 1.20.0 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: 25fb044d7c85c4c31dc96c2ceb5a45e5 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: RELEASE_3_14 git_last_commit: d4c5f09 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/meshes_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/meshes_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/meshes_1.20.0.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: 151 Package: meshr Version: 2.0.2 Depends: R (>= 4.1.0) Imports: markdown, rmarkdown, BiocStyle, knitr, methods, stats, utils, fdrtool, MeSHDbi, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 MD5sum: 5162088c3eed3a6baaa734c8daaab2e0 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: RELEASE_3_14 git_last_commit: 269c2e7 git_last_commit_date: 2021-12-16 Date/Publication: 2021-12-16 source.ver: src/contrib/meshr_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/meshr_2.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/meshr_2.0.2.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: 83 Package: MesKit Version: 1.4.0 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: 24e925308f63bcb8c373ea8fd652d628 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: RELEASE_3_14 git_last_commit: 0782512 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MesKit_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MesKit_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MesKit_1.4.0.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 dependencyCount: 103 Package: messina Version: 1.30.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) MD5sum: 90c8975c87cffba925f0191b2fad4483 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: RELEASE_3_14 git_last_commit: 85fba7b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/messina_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/messina_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/messina_1.30.0.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: 44 Package: Metab Version: 1.28.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 05a366183407708b88dbb817f57faae0 NeedsCompilation: no Title: Metab: An R Package for a High-Throughput Analysis of Metabolomics Data Generated by GC-MS. Description: Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) (http://chemdata.nist.gov/mass-spc/amdis/downloads/). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, AMDIS, GCMS Author: Raphael Aggio Maintainer: Raphael Aggio git_url: https://git.bioconductor.org/packages/Metab git_branch: RELEASE_3_14 git_last_commit: e97e5d9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Metab_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Metab_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Metab_1.28.0.tgz vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf vignetteTitles: Applying Metab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Metab/inst/doc/MetabPackage.R dependencyCount: 99 Package: metabCombiner Version: 1.4.0 Depends: R (>= 4.0), dplyr (>= 1.0) Imports: methods, mgcv, caret, S4Vectors, stats, utils, rlang, graphics, matrixStats, tidyr Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: e2663be966479107a5690641902b61a2 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: RELEASE_3_14 git_last_commit: a9fbf51 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metabCombiner_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metabCombiner_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metabCombiner_1.4.0.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: 85 Package: MetaboCoreUtils Version: 1.2.0 Depends: R (>= 4.0) Imports: stringr, utils, MsCoreUtils Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: f000a785182173e95cb57ce90780fd22 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] 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: RELEASE_3_14 git_last_commit: 5f7f0aa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MetaboCoreUtils_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaboCoreUtils_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaboCoreUtils_1.2.0.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 dependencyCount: 18 Package: metabolomicsWorkbenchR Version: 1.4.0 Depends: R (>= 4.0) Imports: data.table, httr, jsonlite, methods, MultiAssayExperiment, struct, SummarizedExperiment, utils Suggests: BiocStyle, covr, knitr, HDF5Array, rmarkdown, structToolbox, testthat, pmp, grid, png License: GPL-3 Archs: i386, x64 MD5sum: cd850b419b8b59759c8ec2491312aac3 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: RELEASE_3_14 git_last_commit: e369ac1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metabolomicsWorkbenchR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metabolomicsWorkbenchR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metabolomicsWorkbenchR_1.4.0.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: 68 Package: metabomxtr Version: 1.28.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 MD5sum: 9008227a181459a83f215bcdc499fc74 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: RELEASE_3_14 git_last_commit: dd76cf2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metabomxtr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metabomxtr_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metabomxtr_1.28.0.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: 56 Package: MetaboSignal Version: 1.24.0 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: cb6637ecbf0a745a3258fbf4767ce0a2 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: RELEASE_3_14 git_last_commit: 4e4f9da git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MetaboSignal_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaboSignal_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaboSignal_1.24.0.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: 195 Package: metaCCA Version: 1.22.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 9340a12ecaf24aa90c9edfa6dfe4eb63 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: RELEASE_3_14 git_last_commit: 6089272 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metaCCA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metaCCA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metaCCA_1.22.0.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: 1.16.0 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: 6707d9034cbacbb3370bd7a67ee11589 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: RELEASE_3_14 git_last_commit: 7e80a3f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MetaCyto_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaCyto_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaCyto_1.16.0.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: 193 Package: metagene Version: 2.26.0 Depends: R (>= 3.5.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, muStat, Rsamtools, matrixStats, purrr, data.table, magrittr, methods, utils, ensembldb, EnsDb.Hsapiens.v86, stringr Suggests: BiocGenerics, similaRpeak, RUnit, knitr, BiocStyle, rmarkdown, similaRpeak License: Artistic-2.0 | file LICENSE MD5sum: 639385cc36ecf4e43cfb6458b070855b NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare the behavior of DNA-interacting proteins at selected groups of genes/features. Bam files are used to increase the resolution. Multiple combination of group of bam files and/or group of genomic regions can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Charles Joly Beauparlant , Fabien Claude Lamaze , Rawane Samb , Cedric Lippens , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/metagene/issues git_url: https://git.bioconductor.org/packages/metagene git_branch: RELEASE_3_14 git_last_commit: a09472f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metagene_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metagene_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metagene_2.26.0.tgz vignettes: vignettes/metagene/inst/doc/metagene_rnaseq.html, vignettes/metagene/inst/doc/metagene.html vignetteTitles: RNA-seq exp ext, Introduction to metagene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metagene/inst/doc/metagene_rnaseq.R, vignettes/metagene/inst/doc/metagene.R dependencyCount: 121 Package: metagene2 Version: 1.10.0 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: 53a99f4a9a5718ec9c0cb5b59bff5a7c 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: RELEASE_3_14 git_last_commit: f703798 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metagene2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metagene2_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metagene2_1.10.0.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: 83 Package: metagenomeSeq Version: 1.36.0 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: f82c1bc269af5315cbf8ca4adc850b87 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: RELEASE_3_14 git_last_commit: 682fd7a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metagenomeSeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metagenomeSeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metagenomeSeq_1.36.0.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: metavizr, microbiomeExplorer, etec16s importsMe: benchdamic, Maaslin2, microbiomeDASim, microbiomeMarker, MetaLonDA suggestsMe: interactiveDisplay, phyloseq, scTreeViz, Wrench dependencyCount: 30 Package: metahdep Version: 1.52.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 MD5sum: 52f88272988f3014be1c05da5d3438ac 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: RELEASE_3_14 git_last_commit: c335e38 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metahdep_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metahdep_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metahdep_1.52.0.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: 1.30.0 Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats, utils Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: 7f5889cd8dfb94bfc1f4df15fb5aa8d9 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: RELEASE_3_14 git_last_commit: fbf75d2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metaMS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metaMS_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metaMS_1.30.0.tgz 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: 127 Package: MetaNeighbor Version: 1.14.0 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 Archs: i386, x64 MD5sum: e22acfdcccecc80e280205d2a2497e05 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: RELEASE_3_14 git_last_commit: 9ed0bce git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MetaNeighbor_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaNeighbor_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaNeighbor_1.14.0.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: 68 Package: metapod Version: 1.2.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 740e3f5dbf9a3d5569d652ebf4450257 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: RELEASE_3_14 git_last_commit: 0da37b6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metapod_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metapod_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metapod_1.2.0.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, scran suggestsMe: TSCAN dependencyCount: 3 Package: metapone Version: 1.0.0 Depends: R (>= 4.1.0), BiocParallel, fields, markdown Imports: methods Suggests: rmarkdown, knitr License: Artistic-2.0 MD5sum: 0dd119ce2862d13f4f35c45c33cb5167 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: Tianwei Yu [aut], Tianwei Yu [cre] Maintainer: Tianwei Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapone git_branch: RELEASE_3_14 git_last_commit: 12059ca git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metapone_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metapone_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metapone_1.0.0.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: 56 Package: metaSeq Version: 1.34.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 Archs: i386, x64 MD5sum: 664ae876ba895b8397f4a1115bac49b0 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: RELEASE_3_14 git_last_commit: 08a332b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metaSeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metaSeq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metaSeq_1.34.0.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: 1.6.1 Depends: R (>= 4.0.0), DESeq2, limma, locfit, splines Imports: ABSSeq, baySeq, 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, utils, VennDiagram, vsn, yaml, zoo Suggests: BiocManager, BSgenome, knitr, RMySQL, RUnit Enhances: TCC License: GPL (>= 3) MD5sum: 89179e21f38580a9a1cf6c33d13f0079 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: RELEASE_3_14 git_last_commit: a87443a git_last_commit_date: 2021-11-26 Date/Publication: 2021-11-28 source.ver: src/contrib/metaseqR2_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/metaseqR2_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/metaseqR2_1.6.1.tgz vignettes: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.html, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.html vignetteTitles: Building an annotation database for metaseqR2, RNA-Seq data analysis with metaseqR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.R, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.R dependencyCount: 225 Package: metavizr Version: 1.18.0 Depends: R (>= 3.4), metagenomeSeq (>= 1.17.1), methods, data.table, Biobase, digest Imports: epivizr, epivizrData, epivizrServer, epivizrStandalone, vegan, GenomeInfoDb, phyloseq, httr Suggests: knitr, BiocStyle, matrixStats, msd16s (>= 0.109.1), etec16s, testthat, gss, ExperimentHub, tidyr, rmarkdown License: MIT + file LICENSE MD5sum: 4ba5637f1220a648bb70975743643c09 NeedsCompilation: no Title: R Interface to the metaviz web app for interactive metagenomics data analysis and visualization Description: This package provides Websocket communication to the metaviz web app (http://metaviz.cbcb.umd.edu) for interactive visualization of metagenomics data. Objects in R/bioc interactive sessions can be displayed in plots and data can be explored using a facetzoom visualization. Fundamental Bioconductor data structures are supported (e.g., MRexperiment objects), while providing an easy mechanism to support other data structures. Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI, Metagenomics, ImmunoOncology Author: Hector Corrada Bravo [cre, aut], Florin Chelaru [aut], Justin Wagner [aut], Jayaram Kancherla [aut], Joseph Paulson [aut] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metavizr git_branch: RELEASE_3_14 git_last_commit: e961e56 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/metavizr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/metavizr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/metavizr_1.18.0.tgz vignettes: vignettes/metavizr/inst/doc/IntroToMetavizr.html vignetteTitles: Introduction to metavizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metavizr/inst/doc/IntroToMetavizr.R dependencyCount: 161 Package: MetaVolcanoR Version: 1.8.0 Depends: R (>= 4.1.1) Imports: methods, data.table, dplyr, tidyr, plotly, ggplot2, cowplot, parallel, metafor, metap, rlang, topconfects, grDevices, graphics, stats, htmlwidgets Suggests: knitr, markdown, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 723cd174b31bc0be58982082defc4531 NeedsCompilation: no Title: Gene Expression Meta-analysis Visualization Tool Description: MetaVolcanoR combines differential gene expression results. It implements three strategies to summarize differential gene expression from different studies. i) Random Effects Model (REM) approach, ii) a p-value combining-approach, and iii) a vote-counting approach. In all cases, MetaVolcano exploits the Volcano plot reasoning to visualize the gene expression meta-analysis results. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, mRNAMicroarray, RNASeq Author: Cesar Prada [aut, cre], Diogenes Lima [aut], Helder Nakaya [aut, ths] Maintainer: Cesar Prada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaVolcanoR git_branch: RELEASE_3_14 git_last_commit: 87a1591 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MetaVolcanoR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetaVolcanoR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetaVolcanoR_1.8.0.tgz vignettes: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.html, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.html vignetteTitles: MetaVolcanoR: Differential expression meta-analysis tool, MetaVolcanoR inputs: differential expression examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.R, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.R Package: MetCirc Version: 1.24.0 Depends: R (>= 3.5), amap (>= 0.8), circlize (>= 0.3.9), scales (>= 0.3.0), shiny (>= 1.0.0), MSnbase (>= 2.15.3), Imports: ggplot2 (>= 3.2.1), S4Vectors (>= 0.22.0) Suggests: BiocGenerics, graphics (>= 3.5), grDevices (>= 3.5), knitr (>= 1.11), methods (>= 3.5), stats (>= 3.5), testthat (>= 2.2.1) License: GPL (>= 3) MD5sum: 51da291e3b0e62a5bf0fe5770e5a937c NeedsCompilation: no Title: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data Description: MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectrum2 and MSpectra infrastructure defined in the package MSnbase 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: ImmunoOncology, 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: RELEASE_3_14 git_last_commit: 6909c65 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MetCirc_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetCirc_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetCirc_1.24.0.tgz vignettes: vignettes/MetCirc/inst/doc/MetCirc.pdf vignetteTitles: Workflow for Metabolomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R dependencyCount: 101 Package: methimpute Version: 1.16.0 Depends: R (>= 3.4.0), 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: 63d01b57b2ffb2e5e6da96e54f9ea89a 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: RELEASE_3_14 git_last_commit: 23d0b49 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methimpute_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methimpute_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methimpute_1.16.0.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: 58 Package: methInheritSim Version: 1.16.0 Depends: R (>= 3.4) Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 MD5sum: 3cf2c5859c5df749b967e8879ac5d817 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: RELEASE_3_14 git_last_commit: 9b33cdf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methInheritSim_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methInheritSim_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methInheritSim_1.16.0.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: 98 Package: MethPed Version: 1.22.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: d511cadc0474f05c4ed336e55f903680 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: RELEASE_3_14 git_last_commit: fa70983 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MethPed_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethPed_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethPed_1.22.0.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: 1.4.0 Depends: R (>= 4.0) Imports: dplyr, plyr, GenomicRanges, SummarizedExperiment, DelayedArray, ggplot2, ggpubr, tibble, tidyr, S4Vectors, sesameData, stringr, readr, methods, stats, Matrix, MASS, rlang, pscl, IRanges, sfsmisc, progress, utils Suggests: rmarkdown, BiocStyle, testthat (>= 2.1.0), parallel, downloader, R.utils, doParallel, reshape2, JASPAR2020, TFBSTools, motifmatchr, matrixStats, biomaRt, dorothea, viper, stageR, BiocFileCache, png, htmltools, knitr, jpeg, sesame, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 Archs: i386, x64 MD5sum: 155506f3bec56fa244a4f33fc25fa136 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: RELEASE_3_14 git_last_commit: 4dae66b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MethReg_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethReg_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethReg_1.4.0.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: 173 Package: methrix Version: 1.8.0 Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment Imports: rtracklayer, DelayedArray, HDF5Array, BSgenome, DelayedMatrixStats, parallel, methods, ggplot2, matrixStats, graphics, stats, utils, GenomicRanges, IRanges Suggests: knitr, rmarkdown, DSS, bsseq, plotly, BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, GenomicScores, Biostrings, RColorBrewer, GenomeInfoDb, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 250c55516dab5a5ba749a3c91cc40487 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: RELEASE_3_14 git_last_commit: 19065b7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methrix_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methrix_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methrix_1.8.0.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 dependencyCount: 82 Package: MethTargetedNGS Version: 1.26.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: 26f995a6e6d24c6c16ba1708d797c6d6 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: RELEASE_3_14 git_last_commit: 3ffc14b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MethTargetedNGS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethTargetedNGS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethTargetedNGS_1.26.0.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: 34 Package: MethylAid Version: 1.28.0 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: e0b777b2ee3809c56faccfe9faa487d1 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: RELEASE_3_14 git_last_commit: 640611d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MethylAid_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethylAid_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethylAid_1.28.0.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: 167 Package: methylCC Version: 1.8.0 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: CC BY 4.0 MD5sum: 978214a1ca3bd44eda9abd0e4bdbff21 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: RELEASE_3_14 git_last_commit: 2ad87f7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methylCC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylCC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylCC_1.8.0.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: 157 Package: methylclock Version: 1.0.1 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, preprocessCore, dynamicTreeCut LinkingTo: Rcpp Suggests: BiocStyle, knitr, GEOquery, rmarkdown License: MIT + file LICENSE MD5sum: c41092a2a8d5b5eb37db9771ba64c354 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: RELEASE_3_14 git_last_commit: 4e5ab20 git_last_commit_date: 2021-11-21 Date/Publication: 2021-11-23 source.ver: src/contrib/methylclock_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylclock_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/methylclock_1.0.1.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: 279 Package: methylGSA Version: 1.12.0 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: 83aca88e08779c9b44b9b0ebe306d97c 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: RELEASE_3_14 git_last_commit: c7abc3c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methylGSA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylGSA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylGSA_1.12.0.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: 215 Package: methylInheritance Version: 1.18.0 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: 5161573e8097a0dd114b1ebcfc436ebc 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: RELEASE_3_14 git_last_commit: 0387c10 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methylInheritance_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylInheritance_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylInheritance_1.18.0.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: 101 Package: methylKit Version: 1.20.0 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 Archs: i386, x64 MD5sum: 8509ba91f7669981c2611e4e30621e97 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 Gosdschan [aut] Maintainer: Altuna Akalin , Alexander Gosdschan URL: http://code.google.com/p/methylkit/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylKit git_branch: RELEASE_3_14 git_last_commit: a69c0a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methylKit_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylKit_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylKit_1.20.0.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: 94 Package: MethylMix Version: 2.24.0 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: 4cbaef2b551495788d430f480129d5da 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: RELEASE_3_14 git_last_commit: 6ec065d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MethylMix_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethylMix_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethylMix_2.24.0.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: 53 Package: methylMnM Version: 1.32.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 MD5sum: d3466caa4f603b0e9d61a20a8d2925cb 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: RELEASE_3_14 git_last_commit: c518605 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methylMnM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylMnM_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylMnM_1.32.0.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: methylscaper Version: 1.2.0 Depends: R (>= 4.1.0) Imports: shiny, shinyjs, seriation, BiocParallel, seqinr, Biostrings, Rfast, grDevices, graphics, stats, utils, tools, methods, shinyFiles, data.table, SummarizedExperiment Suggests: knitr, rmarkdown, devtools License: GPL-2 Archs: i386, x64 MD5sum: b147ed265b62e4a8c7d73aab9561f844 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, PrincipalComponent, Visualization, SingleCell, NucleosomePositioning Author: Bacher Rhonda [aut, cre], Parker Knight [aut] Maintainer: Bacher Rhonda VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylscaper git_branch: RELEASE_3_14 git_last_commit: b56a8c2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methylscaper_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylscaper_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylscaper_1.2.0.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: 93 Package: MethylSeekR Version: 1.34.0 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: c03a0f7bb825bca2777ff69e8105a843 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: RELEASE_3_14 git_last_commit: 6a74421 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MethylSeekR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MethylSeekR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MethylSeekR_1.34.0.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: RnBeads dependencyCount: 77 Package: methylSig Version: 1.6.0 Depends: R (>= 3.6) Imports: bsseq, DelayedArray, DelayedMatrixStats, DSS, IRanges, GenomeInfoDb, GenomicRanges, methods, parallel, stats, S4Vectors Suggests: BiocStyle, bsseqData, knitr, rmarkdown, testthat (>= 2.1.0), covr License: GPL-3 Archs: i386, x64 MD5sum: d9b0c5b59d77e8bfab1e27982dfc7af9 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: RELEASE_3_14 git_last_commit: 61d61b1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/methylSig_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylSig_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/methylSig_1.6.0.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: 75 Package: methylumi Version: 2.40.1 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: 97da07be5110080a319008629dc7b0a0 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: RELEASE_3_14 git_last_commit: 2110392 git_last_commit_date: 2021-10-30 Date/Publication: 2021-10-31 source.ver: src/contrib/methylumi_2.40.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/methylumi_2.40.1.zip mac.binary.ver: bin/macosx/contrib/4.1/methylumi_2.40.1.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: 156 Package: MetID Version: 1.12.0 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: c6623dc42fee13e1fad58706d5b7ec11 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: RELEASE_3_14 git_last_commit: 928f6cd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MetID_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetID_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MetID_1.12.0.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: 115 Package: MetNet Version: 1.12.2 Depends: R (>= 4.0), S4Vectors (>= 0.28.1), SummarizedExperiment (>= 1.20.0) Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), dplyr (>= 1.0.3), ggplot2 (>= 3.3.3), GENIE3 (>= 1.7.0), methods (>= 3.5), mpmi (>= 0.42), parmigene (>= 1.0.2), ppcor (>= 1.1), 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) License: GPL (>= 3) MD5sum: 031f01751c037fc32e460c75195ce9bc 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: RELEASE_3_14 git_last_commit: 5ef3d25 git_last_commit_date: 2022-04-09 Date/Publication: 2022-04-10 source.ver: src/contrib/MetNet_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MetNet_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MetNet_1.12.2.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: 84 Package: mfa Version: 1.16.3 Depends: R (>= 3.4.0) Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack, MCMCglmm, coda, magrittr, tibble, Biobase LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: 2fa3a67574fc53d25a1fc67ef91d65f9 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: RELEASE_3_14 git_last_commit: 7e443d8 git_last_commit_date: 2022-03-21 Date/Publication: 2022-03-22 source.ver: src/contrib/mfa_1.16.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/mfa_1.16.3.zip mac.binary.ver: bin/macosx/contrib/4.1/mfa_1.16.3.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: 68 Package: Mfuzz Version: 2.54.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: b95fffa5f39141120390c7d1c6eb8230 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: RELEASE_3_14 git_last_commit: e97b74f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Mfuzz_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Mfuzz_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Mfuzz_2.54.0.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, TimiRGeN importsMe: DAPAR, Patterns suggestsMe: pwOmics dependencyCount: 16 Package: MGFM Version: 1.28.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: f65f4cd08ee9c806665e37c0a4fb6840 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: RELEASE_3_14 git_last_commit: 7d190dc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MGFM_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MGFM_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MGFM_1.28.0.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: 1.20.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 Archs: i386, x64 MD5sum: 04b058edb6118157238de33456a248bf 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: RELEASE_3_14 git_last_commit: a5ccf6a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MGFR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MGFR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MGFR_1.20.0.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: 73 Package: mgsa Version: 1.42.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 MD5sum: db843f9b593131017e3ef54940c2825c 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: RELEASE_3_14 git_last_commit: 1e5d21e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mgsa_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mgsa_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mgsa_1.42.0.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R dependencyCount: 9 Package: mia Version: 1.2.7 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 Suggests: testthat, knitr, patchwork, BiocStyle, yaml, phyloseq, dada2, stringr, biomformat, reldist, ade4, microbiomeDataSets, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: cfafd49022e571482fa5c03c2cb585ba 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, cre] (), Sudarshan A. Shetty [aut] (), Tuomas Borman [aut] (), 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] Maintainer: Felix G.M. Ernst 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: RELEASE_3_14 git_last_commit: 217dc43 git_last_commit_date: 2022-02-06 Date/Publication: 2022-02-08 source.ver: src/contrib/mia_1.2.7.tar.gz win.binary.ver: bin/windows/contrib/4.1/mia_1.2.7.zip mac.binary.ver: bin/macosx/contrib/4.1/mia_1.2.7.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: curatedMetagenomicData suggestsMe: philr dependencyCount: 117 Package: miaSim Version: 1.0.0 Depends: SummarizedExperiment Imports: deSolve, stats, poweRlaw Suggests: rmarkdown, knitr, BiocStyle, testthat License: Artistic-2.0 | file LICENSE MD5sum: f66cdd5360f9f497831d3bf127426fb2 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 SummarizedExperiment or TreeSummarizedExperiment objects. biocViews: Microbiome, Software, Sequencing, DNASeq, ATACSeq, Coverage, Network Author: Karoline Faust [aut], Yu Gao [aut], Emma Gheysen [aut], Daniel Rios Garza [aut], Yagmur Simsek [cre, 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: RELEASE_3_14 git_last_commit: 82852ca git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miaSim_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miaSim_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miaSim_1.0.0.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: 29 Package: miaViz Version: 1.2.1 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 Suggests: knitr, rmarkdown, BiocStyle, testthat, patchwork, microbiomeDataSets License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 1f42f284129f32def3683abdd4f478c7 NeedsCompilation: no Title: Microbiome Analysis Plotting and Visualization Description: The miaViz package implements plotting function to work with TreeSummarizedExperiment and related objects in a context of microbiome analysis. Among others this includes plotting tree, graph and microbiome series data. The package is part of the broader miaverse framework. biocViews: Microbiome, Software, Visualization Author: Felix G.M. Ernst [aut, cre] (), Tuomas Borman [aut] (), Leo Lahti [aut] () Maintainer: Felix G.M. Ernst VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miaViz git_branch: RELEASE_3_14 git_last_commit: 093001e git_last_commit_date: 2021-12-31 Date/Publication: 2022-01-02 source.ver: src/contrib/miaViz_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/miaViz_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/miaViz_1.2.1.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 dependencyCount: 134 Package: MiChip Version: 1.48.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: cef58535dec009da9aaf57bccfa1705a 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: RELEASE_3_14 git_last_commit: 26cc5a6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MiChip_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MiChip_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MiChip_1.48.0.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: 1.16.0 Depends: R (>= 3.6.0), phyloseq, ggplot2 Imports: dplyr, reshape2, Rtsne, scales, stats, tibble, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE MD5sum: ae72f754bd35e723747d37cd760b02d7 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: RELEASE_3_14 git_last_commit: a7b74b7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/microbiome_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/microbiome_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/microbiome_1.16.0.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: ANCOMBC dependencyCount: 84 Package: microbiomeDASim Version: 1.8.0 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: adbf2c369b9c92b2d16190c48396ab0c 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: RELEASE_3_14 git_last_commit: d52287e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/microbiomeDASim_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/microbiomeDASim_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/microbiomeDASim_1.8.0.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: 95 Package: microbiomeExplorer Version: 1.4.0 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: 988c905b1fa5d189071d9e266bffad4d 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: RELEASE_3_14 git_last_commit: 556ec83 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/microbiomeExplorer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/microbiomeExplorer_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/microbiomeExplorer_1.4.0.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: 207 Package: microbiomeMarker Version: 1.0.2 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 Suggests: testthat, covr, glmnet, Matrix, kernlab, e1071, ranger, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 03472c75a9eecd0eccc61a019b54ffbc 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: RELEASE_3_14 git_last_commit: ea467f1 git_last_commit_date: 2022-03-07 Date/Publication: 2022-03-08 source.ver: src/contrib/microbiomeMarker_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/microbiomeMarker_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/microbiomeMarker_1.0.2.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: 226 Package: MicrobiomeProfiler Version: 1.0.0 Depends: R (>= 4.1.0) Imports: clusterProfiler (>= 4.0.2), config, DT, enrichplot, golem, magrittr, shiny (>= 1.6.0), shinyWidgets, shinycustomloader, htmltools, ggplot2, graphics, utils Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-2 MD5sum: 7bc55f2caff19d088d5dbb6e8e785beb 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: Meijun Chen [aut, cre] (), Guangchuang Yu [aut, ths] () 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: RELEASE_3_14 git_last_commit: 3740c10 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MicrobiomeProfiler_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MicrobiomeProfiler_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MicrobiomeProfiler_1.0.0.tgz vignettes: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.html vignetteTitles: MicrobiomeProfiler hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.R dependencyCount: 171 Package: MicrobiotaProcess Version: 1.6.6 Depends: R (>= 4.0.0) Imports: ape, tidyr, ggplot2, magrittr, dplyr, Biostrings, ggrepel, vegan, zoo, ggtree, tidytree (>= 0.3.5), MASS, methods, rlang, tibble, grDevices, stats, utils, coin, ggsignif, patchwork, ggstar, tidyselect, SummarizedExperiment, foreach, treeio (>= 1.17.2), pillar, dtplyr, ggtreeExtra Suggests: rmarkdown, prettydoc, testthat, knitr, nlme, phangorn, plyr, DECIPHER, randomForest, biomformat, scales, yaml, withr, S4Vectors, purrr, seqmagick, glue, corrr, ggupset, ggVennDiagram, gghalves, ggalluvial (>= 0.11.1), forcats, cli, phyloseq, aplot, ggnewscale, ggside, ggh4x, hopach License: GPL (>= 3.0) MD5sum: bd1ee524f8a56b97d83dce3fa73312d4 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 analsys 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: RELEASE_3_14 git_last_commit: 1623d4f git_last_commit_date: 2022-04-06 Date/Publication: 2022-04-07 source.ver: src/contrib/MicrobiotaProcess_1.6.6.tar.gz win.binary.ver: bin/windows/contrib/4.1/MicrobiotaProcess_1.6.6.zip mac.binary.ver: bin/macosx/contrib/4.1/MicrobiotaProcess_1.6.6.tgz vignettes: vignettes/MicrobiotaProcess/inst/doc/Introduction.html vignetteTitles: Introduction to MicrobiotaProcess hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiotaProcess/inst/doc/Introduction.R dependencyCount: 99 Package: microRNA Version: 1.52.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 MD5sum: 1e6b5ce6d7122ef4716bf2feec2649fa 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: RELEASE_3_14 git_last_commit: ec6dfa1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/microRNA_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/microRNA_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/microRNA_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rtracklayer dependencyCount: 18 Package: midasHLA Version: 1.2.0 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: bac08f12461101f522ad4dc1258dc9f1 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: RELEASE_3_14 git_last_commit: 811d9d5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/midasHLA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/midasHLA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/midasHLA_1.2.0.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: 113 Package: MIGSA Version: 1.18.0 Depends: R (>= 3.4), methods, BiocGenerics Imports: AnnotationDbi, Biobase, BiocParallel, compiler, data.table, edgeR, futile.logger, ggdendro, ggplot2, GO.db, GOstats, graph, graphics, grDevices, grid, GSEABase, ismev, jsonlite, limma, matrixStats, org.Hs.eg.db, RBGL, reshape2, Rgraphviz, stats, utils, vegan Suggests: BiocStyle, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, knitr, mGSZ, MIGSAdata, RUnit License: GPL (>= 2) MD5sum: b9f4ab48b750981199ae79d894b62c49 NeedsCompilation: no Title: Massive and Integrative Gene Set Analysis Description: Massive and Integrative Gene Set Analysis. The MIGSA package allows to perform a massive and integrative gene set analysis over several expression and gene sets simultaneously. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required to perform both singular and gene set enrichment analyses in an integrative manner by means of the best available methods, i.e. dEnricher and mGSZ respectively. The greatest strengths of this big omics data tool are the availability of several functions to explore, analyze and visualize its results in order to facilitate the data mining task over huge information sources. MIGSA package also provides several functions that allow to easily load the most updated gene sets from several repositories. biocViews: Software, GeneSetEnrichment, Visualization, GeneExpression, Microarray, RNASeq, KEGG Author: Juan C. Rodriguez, Cristobal Fresno, Andrea S. Llera and Elmer A. Fernandez Maintainer: Juan C. Rodriguez URL: https://github.com/jcrodriguez1989/MIGSA/ BugReports: https://github.com/jcrodriguez1989/MIGSA/issues git_url: https://git.bioconductor.org/packages/MIGSA git_branch: RELEASE_3_14 git_last_commit: 13f31de git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MIGSA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MIGSA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MIGSA_1.18.0.tgz vignettes: vignettes/MIGSA/inst/doc/gettingPbcmcData.pdf, vignettes/MIGSA/inst/doc/gettingTcgaData.pdf, vignettes/MIGSA/inst/doc/MIGSA.pdf vignetteTitles: Getting pbcmc datasets, Getting TCGA datasets, Massive and Integrative Gene Set Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIGSA/inst/doc/gettingPbcmcData.R, vignettes/MIGSA/inst/doc/gettingTcgaData.R, vignettes/MIGSA/inst/doc/MIGSA.R dependencyCount: 108 Package: miloR Version: 1.2.0 Depends: R (>= 4.0.0), edgeR Imports: BiocNeighbors, SingleCellExperiment, Matrix (>= 1.3-0), S4Vectors, stats, stringr, methods, igraph, irlba, cowplot, BiocParallel, BiocSingular, limma, ggplot2, tibble, matrixStats, ggraph, gtools, SummarizedExperiment, patchwork, tidyr, dplyr, ggrepel, ggbeeswarm, RColorBrewer, grDevices Suggests: testthat, MASS, mvtnorm, scater, scran, covr, knitr, rmarkdown, uwot, BiocStyle, MouseGastrulationData, magick, RCurl, curl, graphics License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: a7f60672a41a27d56278f108c532f347 NeedsCompilation: no Title: Differential neighbourhood abundance testing on a graph Description: This package performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a negative bionomial generalized linear 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: RELEASE_3_14 git_last_commit: 00e5e3b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miloR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miloR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miloR_1.2.0.tgz vignettes: vignettes/miloR/inst/doc/milo_demo.html, vignettes/miloR/inst/doc/milo_gastrulation.html vignetteTitles: Differential abundance testing with Milo, Differential abundance testing with Milo - Mouse gastrulation example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miloR/inst/doc/milo_demo.R, vignettes/miloR/inst/doc/milo_gastrulation.R dependencyCount: 101 Package: mimager Version: 1.18.0 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: cec44dc71b2d4861d71e6c838076642d 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: RELEASE_3_14 git_last_commit: ec54b8c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mimager_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mimager_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mimager_1.18.0.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: 66 Package: MIMOSA Version: 1.32.0 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales, dplyr, tidyr, rlang LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: MIT + file LICENSE MD5sum: 87b6821905011abb6ea9280e1341f72f NeedsCompilation: yes Title: Mixture Models for Single-Cell Assays Description: Modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Greg Finak Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MIMOSA git_branch: RELEASE_3_14 git_last_commit: 4b385ad git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MIMOSA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MIMOSA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MIMOSA_1.32.0.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R dependencyCount: 88 Package: mina Version: 1.2.0 Depends: R (>= 4.0.0) Imports: methods, stats, Rcpp, MCL, RSpectra, apcluster, bigmemory, foreach, ggplot2, parallel, parallelDist, reshape2, plyr, biganalytics, stringr, Hmisc, utils LinkingTo: Rcpp, RcppParallel, RcppArmadillo Suggests: knitr, rmarkdown Enhances: doMC License: GPL MD5sum: 4f942b670329a50b8953cef874030542 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: RELEASE_3_14 git_last_commit: 0c8ce8b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mina_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mina_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mina_1.2.0.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: 89 Package: MineICA Version: 1.34.0 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: 16d4e430646d64b069b621284455d9d8 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: RELEASE_3_14 git_last_commit: 602994d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MineICA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MineICA_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MineICA_1.34.0.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: 208 Package: minet Version: 3.52.0 Imports: infotheo License: Artistic-2.0 Archs: i386, x64 MD5sum: a3dd882cad47692d5da726627e91c006 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: RELEASE_3_14 git_last_commit: 8208e60 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/minet_3.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/minet_3.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/minet_3.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: BioNERO, epiNEM, netOmics, RTN, TCGAWorkflow, TGS suggestsMe: CNORfeeder, predictionet, TCGAbiolinks, dnapath, WGCNA dependencyCount: 1 Package: minfi Version: 1.40.0 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 Archs: i386, x64 MD5sum: b1db4aae291caefbdcb1667d718c89d1 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: RELEASE_3_14 git_last_commit: 17fa2b5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/minfi_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/minfi_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/minfi_1.40.0.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, ChAMP, conumee, methylumi, REMP, shinyMethyl, IlluminaHumanMethylation27kanno.ilmn12.hg19, IlluminaHumanMethylation27kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICanno.ilm10b3.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, 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: DMRcate, EnMCB, funtooNorm, MEAL, MEAT, MethylAid, methylCC, methylclock, methylumi, missMethyl, quantro, recountmethylation, shinyepico, skewr suggestsMe: epivizr, epivizrChart, Harman, mCSEA, MultiDataSet, planet, RnBeads, sesame, brgedata, GSE159526, MLML2R dependencyCount: 141 Package: MinimumDistance Version: 1.38.0 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: 6f823aa1d6b5946603adf30aa3b716fd 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: RELEASE_3_14 git_last_commit: 48aab1f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MinimumDistance_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MinimumDistance_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MinimumDistance_1.38.0.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: 85 Package: MiPP Version: 1.66.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 4caa0828f0f1403bb22759dbf11edd36 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: RELEASE_3_14 git_last_commit: 9935b39 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MiPP_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MiPP_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MiPP_1.66.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 11 Package: miQC Version: 1.2.0 Depends: R (>= 3.5.0) Imports: SingleCellExperiment, flexmix, ggplot2, splines Suggests: scRNAseq, scater, biomaRt, BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 0d6edaa37f4cd12199ba1fee4d5f3968 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: RELEASE_3_14 git_last_commit: ad367e9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miQC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miQC_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miQC_1.2.0.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: 59 Package: MIRA Version: 1.16.0 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: 2a993222a73697b0c5e2229396ab12e0 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: RELEASE_3_14 git_last_commit: 40eceb4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MIRA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MIRA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MIRA_1.16.0.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: 91 Package: MiRaGE Version: 1.36.0 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: e4476391cfc4426d82cc60d033a05fd8 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: RELEASE_3_14 git_last_commit: 231e9f7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MiRaGE_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MiRaGE_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MiRaGE_1.36.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R dependencyCount: 46 Package: miRBaseConverter Version: 1.18.0 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown License: GPL (>= 2) MD5sum: 55e87373fc4ed314c0addd178384b568 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: RELEASE_3_14 git_last_commit: 4be6d07 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miRBaseConverter_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRBaseConverter_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRBaseConverter_1.18.0.tgz vignettes: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R importsMe: ExpHunterSuite dependencyCount: 1 Package: miRcomp Version: 1.24.0 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: af5ee47e07736eba477203386d79b5d4 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: RELEASE_3_14 git_last_commit: d61d466 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miRcomp_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRcomp_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRcomp_1.24.0.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: 1.24.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: 47215ca6627c5120d37467c55f570612 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: RELEASE_3_14 git_last_commit: 891cc25 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mirIntegrator_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mirIntegrator_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mirIntegrator_1.24.0.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: 78 Package: miRLAB Version: 1.24.0 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: 15cc69af8c20fa0ac7240326923d952e 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: Vu Viet Hoang Pham URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRLAB git_branch: RELEASE_3_14 git_last_commit: 023c639 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miRLAB_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRLAB_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRLAB_1.24.0.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: 188 Package: miRmine Version: 1.16.0 Depends: R (>= 3.4), SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, DESeq2 License: GPL (>= 3) MD5sum: 26810ab7b3dd7ae0d0df0fd6a2e16f16 NeedsCompilation: no Title: Data package with miRNA-seq datasets from miRmine database as RangedSummarizedExperiment Description: miRmine database is a collection of expression profiles from different publicly available miRNA-seq datasets, Panwar et al (2017) miRmine: A Database of Human miRNA Expression, prepared with this data package as RangedSummarizedExperiment. biocViews: Homo_sapiens_Data, RNASeqData, SequencingData, ExpressionData Author: Dusan Randjelovic [aut, cre] Maintainer: Dusan Randjelovic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRmine git_branch: RELEASE_3_14 git_last_commit: 81e7f81 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miRmine_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRmine_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRmine_1.16.0.tgz vignettes: vignettes/miRmine/inst/doc/miRmine.html vignetteTitles: miRmine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRmine/inst/doc/miRmine.R dependencyCount: 25 Package: miRNAmeConverter Version: 1.22.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: da4ffd79d8d323be6d563901e3be6be5 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: RELEASE_3_14 git_last_commit: ca6bcbb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miRNAmeConverter_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRNAmeConverter_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRNAmeConverter_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 52 Package: miRNApath Version: 1.54.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 123f690a44a50a6e31f5b83604ff858e 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: RELEASE_3_14 git_last_commit: 43147ac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miRNApath_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRNApath_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRNApath_1.54.0.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: 1.28.0 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: 8acb8f25f2b0a712678f1f0ac0268478 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: RELEASE_3_14 git_last_commit: a67c80b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miRNAtap_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRNAtap_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRNAtap_1.28.0.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: SpidermiR, miRNAtap.db dependencyCount: 53 Package: miRSM Version: 1.12.0 Depends: R (>= 3.5.0) Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, linkcomm, MCL, NMF, biclust, iBBiG, fabia, BicARE, isa2, s4vd, BiBitR, rqubic, Biobase, PMA, stats, dbscan, subspace, mclust, SOMbrero, ppclust, miRspongeR, Rcpp, utils, SummarizedExperiment, GSEABase, org.Hs.eg.db, MatrixCorrelation, energy Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 3c5cb038b28988ef8979423e1a0e28cb NeedsCompilation: yes Title: Inferring miRNA sponge modules in heterogeneous data Description: The package aims to identify miRNA sponge modules in heterogeneous data. It provides several functions to study miRNA sponge modules, including popular methods for inferring gene modules (candidate miRNA sponge modules), and a function to identify miRNA sponge modules, 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: RELEASE_3_14 git_last_commit: c8fb2c3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/miRSM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRSM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/miRSM_1.12.0.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: 252 Package: miRspongeR Version: 1.20.1 Depends: R (>= 3.5.0) Imports: corpcor, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, varhandle, linkcomm, utils, Rcpp, org.Hs.eg.db Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 9e1a1774c13bb58166ac3b1e7eee1b6c NeedsCompilation: yes Title: Identification and analysis of miRNA sponge interaction networks and modules Description: This package provides several functions to study miRNA sponge (also called ceRNA or miRNA decoy), including popular methods for identifying miRNA sponge interactions, and the 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 modules, and conduct survival analysis of modules. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software 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: RELEASE_3_14 git_last_commit: d2a41b3 git_last_commit_date: 2022-02-08 Date/Publication: 2022-02-10 source.ver: src/contrib/miRspongeR_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/miRspongeR_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/miRspongeR_1.20.1.tgz vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html vignetteTitles: miRspongeR: identification and analysis of miRNA sponge interaction networks and modules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R importsMe: miRSM dependencyCount: 140 Package: mirTarRnaSeq Version: 1.2.0 Depends: R (>= 4.1.0) Imports: purrr, MASS, pscl, assertthat, caTools, dplyr, pheatmap, reshape2, corrplot, grDevices, graphics, stats, utils, data.table, R.utils Suggests: BiocStyle, knitr, rmarkdown, R.cache License: MIT MD5sum: 9c3d5306243576dc4d2eb4c6b4419a70 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: RELEASE_3_14 git_last_commit: fdd7bf7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mirTarRnaSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mirTarRnaSeq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mirTarRnaSeq_1.2.0.tgz vignettes: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.pdf vignetteTitles: mirTarRnaSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.R dependencyCount: 50 Package: missMethyl Version: 1.28.0 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 Archs: x64 MD5sum: 3ba44ee91f6fece77fc3e772312231a6 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: RELEASE_3_14 git_last_commit: 6a36aee git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/missMethyl_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/missMethyl_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/missMethyl_1.28.0.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: 166 Package: missRows Version: 1.14.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 0d11f45f7ade89e84d77f7985a657240 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: RELEASE_3_14 git_last_commit: d2af164 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/missRows_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/missRows_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/missRows_1.14.0.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: 65 Package: mistyR Version: 1.2.1 Depends: R (>= 4.0) Imports: assertthat, caret, deldir, digest, distances, dplyr, filelock, furrr (>= 0.2.0), ggplot2, MASS, purrr, ranger, readr, rlang, rlist, R.utils, stats, stringr, tibble, tidyr, withr Suggests: BiocStyle, covr, future, igraph, knitr, Matrix, progeny, rmarkdown, sctransform, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, testthat (>= 3.0.0) License: GPL-3 MD5sum: 392fc8ba69045ad123d8c291b1fa35d4 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: RELEASE_3_14 git_last_commit: d929603 git_last_commit_date: 2021-10-29 Date/Publication: 2021-10-29 source.ver: src/contrib/mistyR_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/mistyR_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/mistyR_1.2.1.tgz vignettes: vignettes/mistyR/inst/doc/mistySpatialExperiment.pdf, vignettes/mistyR/inst/doc/mistyR.html vignetteTitles: mistyR and SpatialExperiment, Getting started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mistyR/inst/doc/mistyR.R, vignettes/mistyR/inst/doc/mistySpatialExperiment.R dependencyCount: 105 Package: mitch Version: 1.6.0 Depends: R (>= 4.0) Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2, parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2, gplots, beeswarm, echarts4r Suggests: stringi, testthat (>= 2.1.0) License: CC BY-SA 4.0 + file LICENSE MD5sum: 8dd6e2240e0ffc5b95d0bba28f9d9dd7 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. 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 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: RELEASE_3_14 git_last_commit: 429d65d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mitch_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mitch_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mitch_1.6.0.tgz vignettes: vignettes/mitch/inst/doc/mitchWorkflow.html vignetteTitles: mitch Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mitch/inst/doc/mitchWorkflow.R dependencyCount: 97 Package: mitoClone2 Version: 1.0.0 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: 4e0de5c5c21b464a50b045247c3f1d89 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: RELEASE_3_14 git_last_commit: 2b6203f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mitoClone2_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mitoClone2_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mitoClone2_1.0.0.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: 117 Package: mixOmics Version: 6.18.1 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: ad8908fc61599be89e19cc097961e2d1 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], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb] Maintainer: Al J Abadi URL: http://www.mixOmics.org VignetteBuilder: knitr BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/ git_url: https://git.bioconductor.org/packages/mixOmics git_branch: RELEASE_3_14 git_last_commit: 5ef4960 git_last_commit_date: 2021-11-17 Date/Publication: 2021-11-18 source.ver: src/contrib/mixOmics_6.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/mixOmics_6.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/mixOmics_6.18.1.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, DepecheR, multiSight, POMA, MetabolomicsBasics, MSclassifR, plsmod, plsRcox, RVAideMemoire suggestsMe: autonomics, netOmics, SelectBoost dependencyCount: 67 Package: MLInterfaces Version: 1.74.0 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 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, testthat Enhances: parallel License: LGPL MD5sum: 7f001925a425a2ef920170971dee96e5 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: Vince Carey , Robert Gentleman, Jess Mar, and contributions from Jason Vertrees and Laurent Gatto Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: RELEASE_3_14 git_last_commit: 5ee73b6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MLInterfaces_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MLInterfaces_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MLInterfaces_1.74.0.tgz vignettes: vignettes/MLInterfaces/inst/doc/MLint_devel.pdf, vignettes/MLInterfaces/inst/doc/MLInterfaces.pdf, vignettes/MLInterfaces/inst/doc/MLprac2_2.pdf, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf vignetteTitles: MLInterfaces devel for schema-based MLearn, MLInterfaces Primer, A machine learning tutorial: applications of the Bioconductor MLInterfaces package to expression and ChIP-Seq data, MLInterfaces Computer Cluster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLInterfaces.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: pRoloc, SigCheck, proteomics, dGAselID, nlcv dependencyCount: 112 Package: MLP Version: 1.42.0 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, KEGGREST, annotate, Rgraphviz, GOstats, graph, limma, mouse4302.db, reactome.db License: GPL-3 Archs: i386, x64 MD5sum: a9a1a77771f4fac3b6d96a4f48e438a0 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] Maintainer: Tobias Verbeke git_url: https://git.bioconductor.org/packages/MLP git_branch: RELEASE_3_14 git_last_commit: 6946b4f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MLP_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MLP_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MLP_1.42.0.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: 49 Package: MLSeq Version: 2.12.0 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: 823bedd9dc669922e8f1ce7678493e75 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: RELEASE_3_14 git_last_commit: be20d3a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MLSeq_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MLSeq_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MLSeq_2.12.0.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: 153 Package: MMAPPR2 Version: 1.8.0 Depends: R (>= 3.6.0) Imports: ensemblVEP (>= 1.20.0), gmapR, Rsamtools, VariantAnnotation, BiocParallel, Biobase, BiocGenerics, dplyr, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, tidyr, VariantTools, magrittr, methods, grDevices, graphics, stats, utils, stringr, data.table Suggests: testthat, mockery, roxygen2, knitr, rmarkdown, BiocStyle, MMAPPR2data License: GPL-3 OS_type: unix MD5sum: 7b6f7b944f95641198e603cde1ea2e6a NeedsCompilation: no Title: Mutation Mapping Analysis Pipeline for Pooled RNA-Seq Description: MMAPPR2 maps mutations resulting from pooled RNA-seq data from the F2 cross of forward genetic screens. Its predecessor is described in a paper published in Genome Research (Hill et al. 2013). MMAPPR2 accepts aligned BAM files as well as a reference genome as input, identifies loci of high sequence disparity between the control and mutant RNA sequences, predicts variant effects using Ensembl's Variant Effect Predictor, and outputs a ranked list of candidate mutations. biocViews: RNASeq, PooledScreens, DNASeq, VariantDetection Author: Kyle Johnsen [aut], Nathaniel Jenkins [aut], Jonathon Hill [cre] Maintainer: Jonathon Hill URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3613585/, https://github.com/kjohnsen/MMAPPR2 SystemRequirements: Ensembl VEP, Samtools VignetteBuilder: knitr BugReports: https://github.com/kjohnsen/MMAPPR2/issues git_url: https://git.bioconductor.org/packages/MMAPPR2 git_branch: RELEASE_3_14 git_last_commit: 6ae1611 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MMAPPR2_1.8.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/MMAPPR2_1.8.0.tgz vignettes: vignettes/MMAPPR2/inst/doc/MMAPPR2.html vignetteTitles: An Introduction to MMAPPR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMAPPR2/inst/doc/MMAPPR2.R dependencyCount: 104 Package: MMDiff2 Version: 1.22.0 Depends: R (>= 3.3), 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: 857d6a8877cc65eadcc9fab0e631f561 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: RELEASE_3_14 git_last_commit: 62b1cce git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MMDiff2_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MMDiff2_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MMDiff2_1.22.0.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: 95 Package: MMUPHin Version: 1.8.2 Depends: R (>= 3.6) Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr, cowplot, utils, stats, grDevices Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan, phyloseq, curatedMetagenomicData, genefilter License: MIT + file LICENSE MD5sum: 73191145ffdc92c56cdea3a2d666964c 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMUPHin git_branch: RELEASE_3_14 git_last_commit: 6c224c9 git_last_commit_date: 2022-03-31 Date/Publication: 2022-04-03 source.ver: src/contrib/MMUPHin_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MMUPHin_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MMUPHin_1.8.1.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: 155 Package: mnem Version: 1.10.0 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 License: GPL-3 MD5sum: 7a85ef254a56d479c4581b97e7c4a6eb 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: RELEASE_3_14 git_last_commit: 6b5bed1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mnem_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mnem_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mnem_1.10.0.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: 84 Package: moanin Version: 1.2.0 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: 27df5111899bf6d7d440c9abc0516e3f 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: RELEASE_3_14 git_last_commit: 66fb668 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/moanin_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/moanin_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/moanin_1.2.0.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: 95 Package: MODA Version: 1.20.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: abaf53bb514240e860de9bcea1bc771e 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: RELEASE_3_14 git_last_commit: c3b2e62 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MODA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MODA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MODA_1.20.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 111 Package: ModCon Version: 1.2.0 Depends: data.table, parallel, utils, stats, R (>= 4.1) Suggests: testthat, knitr, rmarkdown, dplyr, shinycssloaders, shiny, shinyFiles, shinydashboard, shinyjs License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 9b4fe5a4fb4117c45564788fd7232495 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: RELEASE_3_14 git_last_commit: 8ba4dc3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ModCon_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ModCon_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ModCon_1.2.0.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: 1.10.0 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: 6c6dc6b91813c1f8f6ba7f39fda101b4 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: RELEASE_3_14 git_last_commit: 88e7263 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Modstrings_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Modstrings_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Modstrings_1.10.0.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: 23 Package: MOFA2 Version: 1.4.0 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, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix License: GPL (>= 2) + file LICENSE MD5sum: 15837f8d5907a75e539570318be856da 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] (), Damien Arnol [aut] (), Danila Bredikhin [aut] (), Britta Velten [aut, cre] () Maintainer: Britta Velten 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: RELEASE_3_14 git_last_commit: 2aa2c46 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MOFA2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MOFA2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MOFA2_1.4.0.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: TRUE 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: 89 Package: MOGAMUN Version: 1.4.0 Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit, BiocParallel, igraph Suggests: knitr, markdown License: GPL-3 + file LICENSE MD5sum: 088c91391f5fb6d91bc9cb31e6c0eb61 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: RELEASE_3_14 git_last_commit: 5ae0a7f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MOGAMUN_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MOGAMUN_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MOGAMUN_1.4.0.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: 64 Package: mogsa Version: 1.28.0 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: a7a48b5d9678283f4856ee3e623dce08 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: RELEASE_3_14 git_last_commit: 2ddd4d5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mogsa_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mogsa_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mogsa_1.28.0.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: 68 Package: MOMA Version: 1.6.0 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: c5c41da87f54859dbb4b69ee7eaa9150 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: RELEASE_3_14 git_last_commit: 2d64f18 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MOMA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MOMA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MOMA_1.6.0.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: 95 Package: monaLisa Version: 1.0.0 Depends: R (>= 4.1) Imports: methods, stats, utils, grDevices, graphics, BiocGenerics, GenomicRanges, TFBSTools, Biostrings, IRanges, stabs, BSgenome, glmnet, S4Vectors, SummarizedExperiment, BiocParallel, grid, circlize, ComplexHeatmap (>= 2.0.0), XVector, GenomeInfoDb, tools, vioplot Suggests: JASPAR2020, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, knitr, rmarkdown, testthat, BiocStyle, gridExtra License: GPL-3 MD5sum: 6af2fed04549ebba77bee38e2c458c2a 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: RELEASE_3_14 git_last_commit: bec40ca git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/monaLisa_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/monaLisa_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/monaLisa_1.0.0.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: 141 Package: monocle Version: 2.22.0 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, densityClust (>= 0.3), Rtsne, MASS, reshape2, limma, tibble, dplyr, qlcMatrix, 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: aaf72312ada737f5ad5dad52c6aa5da9 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: RELEASE_3_14 git_last_commit: 224c423 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/monocle_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/monocle_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/monocle_2.22.0.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, ctgGEM, phemd importsMe: uSORT suggestsMe: M3Drop, scran, sincell, Seurat dependencyCount: 85 Package: MoonlightR Version: 1.20.0 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 License: GPL (>= 3) MD5sum: 1bdeb472dd149dd0d4c2724be7f9dc86 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*, Catharina Olsen*, Claudia Cava, Thilde Terkelsen, Laura Cantini, Andre Olsen, Gloria Bertoli, Andrei Zinovyev, Emmanuel Barillot, Isabella Castiglioni, Elena Papaleo, Gianluca Bontempi Maintainer: Antonio Colaprico , Catharina Olsen URL: https://github.com/ibsquare/MoonlightR VignetteBuilder: knitr BugReports: https://github.com/ibsquare/MoonlightR/issues git_url: https://git.bioconductor.org/packages/MoonlightR git_branch: RELEASE_3_14 git_last_commit: 3854c2d git_last_commit_date: 2021-10-26 Date/Publication: 2021-12-19 source.ver: src/contrib/MoonlightR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MoonlightR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MoonlightR_1.20.0.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: 180 Package: mosaics Version: 2.32.0 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: 7554c579fb3fb4ef3fd409befdf8518f 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: RELEASE_3_14 git_last_commit: 32605c7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mosaics_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mosaics_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mosaics_2.32.0.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: 41 Package: mosbi Version: 1.0.3 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: i386, x64 MD5sum: e378a334f74231a8746ed03648a1b3d9 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] Maintainer: Tim Daniel Rose SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mosbi git_branch: RELEASE_3_14 git_last_commit: a2f4fce git_last_commit_date: 2022-01-05 Date/Publication: 2022-01-06 source.ver: src/contrib/mosbi_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/mosbi_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/mosbi_1.0.3.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: 1.8.0 Depends: R (>= 3.6) Imports: HiddenMarkov, zoo, methods, matrixStats, dplyr, stringi, lazyeval, rlang, stats, utils, purrr, scales, stringr, tibble, tidyr, ggplot2, Biobase, IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 00463cbf735499a4b0ada15b83d90bb7 NeedsCompilation: no 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: Carlos Martínez [cre, aut], Sonia Tarazona [aut] Maintainer: Carlos Martínez URL: https://github.com/Neurergus/MOSim VignetteBuilder: knitr BugReports: https://github.com/Neurergus/MOSim/issues git_url: https://git.bioconductor.org/packages/MOSim git_branch: RELEASE_3_14 git_last_commit: f86be7d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MOSim_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MOSim_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MOSim_1.8.0.tgz vignettes: vignettes/MOSim/inst/doc/MOSim.pdf vignetteTitles: MOSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSim/inst/doc/MOSim.R dependencyCount: 56 Package: motifbreakR Version: 2.8.0 Depends: R (>= 3.5.0), grid, MotifDb Imports: methods, compiler, grDevices, grImport, stringr, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings, BSgenome, rtracklayer, VariantAnnotation, BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue, SummarizedExperiment Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP142.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: a4f36ac86f33efbfc01d17075c7a6f44 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 22). biocViews: ChIPSeq, Visualization, MotifAnnotation 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: RELEASE_3_14 git_last_commit: 58931f1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/motifbreakR_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/motifbreakR_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/motifbreakR_2.8.0.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: 151 Package: motifcounter Version: 1.18.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 Archs: i386, x64 MD5sum: e29b1fa1804ec336fcf23ab632f47b66 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: RELEASE_3_14 git_last_commit: 6628a11 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/motifcounter_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/motifcounter_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/motifcounter_1.18.0.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: 18 Package: MotifDb Version: 1.36.0 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: f520edc4a524c4a2dfeaf3d60b7e4d24 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: RELEASE_3_14 git_last_commit: 7e45029 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MotifDb_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MotifDb_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MotifDb_1.36.0.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, trena, generegulation importsMe: igvR, rTRMui suggestsMe: ATACseqQC, DiffLogo, enhancerHomologSearch, memes, MMDiff2, motifcounter, motifStack, profileScoreDist, PWMEnrich, rTRM, TFutils, universalmotif, vtpnet dependencyCount: 46 Package: motifmatchr Version: 1.16.0 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: 9cf5b760ff3ad1dc2170d5a3c1841e5b 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: RELEASE_3_14 git_last_commit: c191ebc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/motifmatchr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/motifmatchr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/motifmatchr_1.16.0.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: enhancerHomologSearch, enrichTF, esATAC, pageRank, spatzie suggestsMe: chromVAR, MethReg, CAGEWorkflow, Signac dependencyCount: 124 Package: motifStack Version: 1.38.0 Depends: R (>= 2.15.1), methods, grid Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets, stats, stats4, utils, XML Suggests: grImport, grImport2, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr, RUnit, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: a48d439b72fb6f1f0b18d96d51be0594 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: RELEASE_3_14 git_last_commit: 0c4bc16 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/motifStack_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/motifStack_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/motifStack_1.38.0.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, LowMACA, motifbreakR, ribosomeProfilingQC, TCGAWorkflow suggestsMe: ChIPpeakAnno, TFutils, tripr, universalmotif dependencyCount: 60 Package: MouseFM Version: 1.4.2 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: 08871f0bea69aec7579d90fa386ecb0c 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: RELEASE_3_14 git_last_commit: 0e54f23 git_last_commit_date: 2022-02-28 Date/Publication: 2022-03-01 source.ver: src/contrib/MouseFM_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MouseFM_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MouseFM_1.4.2.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: 97 Package: MPFE Version: 1.30.0 License: GPL (>= 3) Archs: i386, x64 MD5sum: f1270f279861670b1fcbeba4a465f091 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: RELEASE_3_14 git_last_commit: 0c60129 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MPFE_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MPFE_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MPFE_1.30.0.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: 1.16.0 Depends: R (>= 3.4.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: aaf617d37657d7edc21f5cd68a902401 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: RELEASE_3_14 git_last_commit: 3dfdbbb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mpra_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mpra_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mpra_1.16.0.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: 38 Package: MPRAnalyze Version: 1.12.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 MD5sum: 90e926e44666a03022c834d35d2a07d7 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: RELEASE_3_14 git_last_commit: 490525a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MPRAnalyze_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MPRAnalyze_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MPRAnalyze_1.12.0.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: 45 Package: MQmetrics Version: 1.2.0 Imports: ggplot2, readr, magrittr, dplyr, purrr, reshape2, gridExtra, utils, stringr, ggpubr, stats, cowplot, RColorBrewer, ggridges, tidyr, scales, grid, rlang, ggforce, grDevices, gtable, plyr, knitr, rmarkdown, ggrepel, gghalves, tools Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: 0da40e1d0928e3ba0697aa270de234b3 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/MQmetrics git_branch: RELEASE_3_14 git_last_commit: ba5d543 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MQmetrics_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MQmetrics_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MQmetrics_1.2.0.tgz vignettes: vignettes/MQmetrics/inst/doc/MQmetrics.html vignetteTitles: MQmetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MQmetrics/inst/doc/MQmetrics.R dependencyCount: 122 Package: msa Version: 1.26.0 Depends: R (>= 3.1.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape, phangorn License: GPL (>= 2) MD5sum: cfe56f4efe394bb852a2b842da8acb60 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, Christoph Horejs-Kainrath, Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/msa/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/msa git_branch: RELEASE_3_14 git_last_commit: 2b1349d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/msa_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msa_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msa_1.26.0.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 suggestsMe: idpr, bio3d, datelife dependencyCount: 19 Package: MsBackendMassbank Version: 1.2.0 Depends: R (>= 4.0), Spectra (>= 1.0) Imports: BiocParallel, S4Vectors, IRanges, methods, ProtGenerics, MsCoreUtils, DBI, utils Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, rmarkdown License: Artistic-2.0 MD5sum: c52b9c359f7e3f2fcd5f2a16545b394c 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: RELEASE_3_14 git_last_commit: 83d84a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MsBackendMassbank_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MsBackendMassbank_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MsBackendMassbank_1.2.0.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: 27 Package: MsBackendMgf Version: 1.2.0 Depends: R (>= 4.0), Spectra (>= 1.0) Imports: BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: a14aee106669d3b8e2fd89ea7d53a2a1 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] () 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: RELEASE_3_14 git_last_commit: cf07dc3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MsBackendMgf_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MsBackendMgf_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MsBackendMgf_1.2.0.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: xcms dependencyCount: 26 Package: MsBackendRawFileReader Version: 1.0.0 Depends: R (>= 4.1), methods, Spectra (>= 1.2) Imports: MsCoreUtils, S4Vectors, IRanges, rawrr (>= 1.1.14), utils, BiocParallel Suggests: BiocStyle (>= 2.5), ExperimentHub, knitr, lattice, mzR, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 Archs: i386, x64 MD5sum: 285e09f8034d993f7977fd6f91562a14 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 (Kockmann T. et al. (2020) ) 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] () 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: RELEASE_3_14 git_last_commit: a1aa867 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MsBackendRawFileReader_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MsBackendRawFileReader_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MsBackendRawFileReader_1.0.0.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: 27 Package: MsCoreUtils Version: 1.6.2 Depends: R (>= 3.6.0) Imports: methods, S4Vectors, MASS, stats, clue LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD, impute, norm, pcaMethods, vsn, preprocessCore License: Artistic-2.0 MD5sum: 7e042dfd68a985924c5d34cdd3663369 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), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...) as well as 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] (), Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake [ctb], Josep Maria Badia Aparicio [ctb] (), Michael Witting [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: RELEASE_3_14 git_last_commit: 0d9fa88 git_last_commit_date: 2022-02-23 Date/Publication: 2022-02-24 source.ver: src/contrib/MsCoreUtils_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MsCoreUtils_1.6.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MsCoreUtils_1.6.2.tgz vignettes: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html vignetteTitles: Core Utils for Mass Spectrometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.R importsMe: MetaboCoreUtils, MsBackendMassbank, MsBackendMgf, MsBackendRawFileReader, MsFeatures, MSnbase, QFeatures, scp, Spectra, xcms suggestsMe: msqrob2 dependencyCount: 12 Package: MsFeatures Version: 1.2.0 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: e1da309dc723e2c5e5a8f1fc06e7bc73 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: RELEASE_3_14 git_last_commit: 264aee0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MsFeatures_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MsFeatures_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MsFeatures_1.2.0.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 dependencyCount: 31 Package: msgbsR Version: 1.18.0 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 MD5sum: 53a6472027bd8f76c43c248c61e19b72 NeedsCompilation: no Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions Description: Pipeline for the anaysis of a MS-GBS experiment. biocViews: ImmunoOncology, DifferentialMethylation, DataImport, Epigenetics, MethylSeq Author: Benjamin Mayne Maintainer: Benjamin Mayne git_url: https://git.bioconductor.org/packages/msgbsR git_branch: RELEASE_3_14 git_last_commit: 092f4e7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/msgbsR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msgbsR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msgbsR_1.18.0.tgz vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf vignetteTitles: msgbsR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R dependencyCount: 165 Package: MSGFgui Version: 1.28.0 Depends: mzR, xlsx Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools Suggests: knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 8df2c34a02ddedb4d983f75dcdd7f73d NeedsCompilation: no Title: A shiny GUI for MSGFplus Description: This package makes it possible to perform analyses using the MSGFplus package in a GUI environment. Furthermore it enables the user to investigate the results using interactive plots, summary statistics and filtering. Lastly it exposes the current results to another R session so the user can seamlessly integrate the gui into other workflows. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/MSGFgui git_branch: RELEASE_3_14 git_last_commit: 2725a95 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSGFgui_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSGFgui_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSGFgui_1.28.0.tgz vignettes: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R dependencyCount: 62 Package: MSGFplus Version: 1.28.0 Depends: methods Imports: mzID, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: b77ac127aedbead5d4590a3c5872ef3d NeedsCompilation: no Title: An interface between R and MS-GF+ Description: This package contains function to perform peptide identification using the MS-GF+ algorithm. The package contains functionality for building up a parameter set both in code and through a simple GUI, as well as running the algorithm in batches, potentially asynchronously. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/MSGFplus git_branch: RELEASE_3_14 git_last_commit: d0bdc5b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSGFplus_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSGFplus_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSGFplus_1.28.0.tgz vignettes: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R importsMe: MSGFgui dependencyCount: 12 Package: msImpute Version: 1.4.0 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: 46f7a7aa1aaff455836f12b7c1bbb298 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: RELEASE_3_14 git_last_commit: 01913c0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/msImpute_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msImpute_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msImpute_1.4.0.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: 89 Package: msmsEDA Version: 1.32.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: 1ba465fecbcdde825dd7f3cd2b3522b7 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: RELEASE_3_14 git_last_commit: d560187 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/msmsEDA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msmsEDA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msmsEDA_1.32.0.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: 82 Package: msmsTests Version: 1.32.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: 0f74840a9029fbd9df945afe262d1f9f 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: RELEASE_3_14 git_last_commit: 04b6de9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/msmsTests_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msmsTests_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msmsTests_1.32.0.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: 90 Package: MSnbase Version: 2.20.4 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.19.6), S4Vectors, ProtGenerics (>= 1.25.1) Imports: MsCoreUtils, BiocParallel, IRanges (>= 2.13.28), plyr, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, XML, 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, shiny, magrittr, SummarizedExperiment License: Artistic-2.0 MD5sum: 9e84c59bdd2652a3b892e0bad7bed5bc 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 and Lieven Clement. 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: RELEASE_3_14 git_last_commit: c86ac8b git_last_commit_date: 2022-01-13 Date/Publication: 2022-01-16 source.ver: src/contrib/MSnbase_2.20.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSnbase_2.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MSnbase_2.20.0.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: MetCirc, msmsEDA, msmsTests, pRoloc, pRolocGUI, qPLEXanalyzer, xcms, pRolocdata, RforProteomics, proteomics importsMe: cliqueMS, CluMSID, DAPAR, DEP, MSnID, MSstatsQC, peakPantheR, POMA, PrInCE, ProteomicsAnnotationHubData, ptairMS, topdownr, DAPARdata, qPLEXdata suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, msPurity, msqrob2, proDA, qcmetrics, wpm, msdata, enviGCMS, pmd, RAMClustR dependencyCount: 76 Package: MSnID Version: 1.28.0 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: 1bcfcb57ac64c37336f8c933bff93398 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: RELEASE_3_14 git_last_commit: 0e6de89 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSnID_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSnID_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSnID_1.28.0.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 dependsOnMe: proteomics suggestsMe: RforProteomics dependencyCount: 160 Package: MSPrep Version: 1.4.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, S4Vectors, pcaMethods (>= 1.24.0), VIM, crmn, preprocessCore, sva, dplyr (>= 0.7), tidyr, tibble (>= 1.2), magrittr, rlang, stats, stringr, methods, ddpcr, missForest Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 1.0.2) License: GPL-3 MD5sum: b735d462c5f7361d9fb5da253fca0e8b 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: RELEASE_3_14 git_last_commit: 34c95d3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSPrep_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSPrep_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSPrep_1.4.0.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: 190 Package: msPurity Version: 1.20.0 Depends: Rcpp Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite, uuid, jsonlite Suggests: MSnbase, testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA, RPostgres, RMySQL License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: bbf3bee6663fcf3186bd36c504b063fd NeedsCompilation: no Title: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics Description: msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation. biocViews: MassSpectrometry, Metabolomics, Software Author: Thomas N. Lawson [aut, cre] (), 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 git_url: https://git.bioconductor.org/packages/msPurity git_branch: RELEASE_3_14 git_last_commit: 82cfdcb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/msPurity_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msPurity_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msPurity_1.20.0.tgz vignettes: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html, vignettes/msPurity/inst/doc/msPurity-vignette.html vignetteTitles: msPurity spectral matching, msPurity spectral database schema, msPurity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R, vignettes/msPurity/inst/doc/msPurity-vignette.R dependencyCount: 75 Package: msqrob2 Version: 1.2.0 Depends: R (>= 4.1), QFeatures (>= 1.1.2) Imports: stats, methods, lme4, purrr, BiocParallel, Matrix, MASS, limma, SummarizedExperiment, codetools Suggests: multcomp, gridExtra, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown, testthat, tidyverse, plotly, msdata, MSnbase, matrixStats, MsCoreUtils License: Artistic-2.0 MD5sum: 46e78e508a6b456824dac410fc971305 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: RELEASE_3_14 git_last_commit: 815b2a6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/msqrob2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/msqrob2_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/msqrob2_1.2.0.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: 117 Package: MSstats Version: 4.2.0 Depends: R (>= 4.0) Imports: MSstatsConvert, data.table, checkmate, MASS, limma, lme4, preprocessCore, survival, utils, Rcpp, ggplot2, ggrepel, gplots, marray, stats, grDevices, graphics, methods LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, MSstatsBioData, tinytest, covr, markdown License: Artistic-2.0 MD5sum: 6e52c7584b63b7178b7569f88967ab9b 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: RELEASE_3_14 git_last_commit: e67107f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSstats_4.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstats_4.1.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstats_4.2.0.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, MSstatsLiP, MSstatsPTM, MSstatsSampleSize, MSstatsTMT dependencyCount: 78 Package: MSstatsConvert Version: 1.4.1 Depends: R (>= 4.0) Imports: data.table, log4r, methods, checkmate, utils, stringi Suggests: tinytest, covr, knitr, rmarkdown License: Artistic-2.0 MD5sum: c7683c5fb656fcd5581d07d093498b80 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: RELEASE_3_14 git_last_commit: 4593789 git_last_commit_date: 2022-02-14 Date/Publication: 2022-02-15 source.ver: src/contrib/MSstatsConvert_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsConvert_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsConvert_1.4.1.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, MSstatsLiP, MSstatsPTM, MSstatsTMT dependencyCount: 9 Package: MSstatsLiP Version: 1.0.0 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 License: Artistic-2.0 MD5sum: 6b77d641d8543f360a754e121d549acf 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: RELEASE_3_14 git_last_commit: 93a0da8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSstatsLiP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsLiP_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsLiP_1.0.0.tgz vignettes: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html vignetteTitles: MSstatsLiP Workflow: An example workflow and analysis of the MSstatsLiP package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.R dependencyCount: 189 Package: MSstatsLOBD Version: 1.2.0 Depends: R (>= 4.0) Imports: minpack.lm, ggplot2, utils, stats, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, dplyr License: Artistic-2.0 Archs: i386, x64 MD5sum: 90ccd3c569b24e12490460bf96487a4e 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: RELEASE_3_14 git_last_commit: 6bb9f32 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSstatsLOBD_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsLOBD_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsLOBD_1.2.0.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: 40 Package: MSstatsPTM Version: 1.4.2 Depends: R (>= 4.0) Imports: dplyr, gridExtra, stringr, stats, ggplot2, grDevices, MSstatsTMT, MSstatsConvert, MSstats, data.table, Rcpp, Biostrings, checkmate, ggrepel LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, tinytest, covr License: Artistic-2.0 MD5sum: dd87d78c52143f6df93113a607a8c76a 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, 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: RELEASE_3_14 git_last_commit: 19f6303 git_last_commit_date: 2021-12-21 Date/Publication: 2021-12-23 source.ver: src/contrib/MSstatsPTM_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsPTM_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsPTM_1.4.2.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: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.R, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.R importsMe: MSstatsLiP dependencyCount: 98 Package: MSstatsQC Version: 2.12.0 Depends: R (>= 3.5.0) Imports: dplyr,plotly,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics Suggests: knitr,rmarkdown, testthat, RforProteomics License: Artistic License 2.0 Archs: i386, x64 MD5sum: 8b981391e81b9098b0245da9ee542dd3 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: RELEASE_3_14 git_last_commit: db3d95c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSstatsQC_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsQC_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsQC_2.12.0.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: 126 Package: MSstatsQCgui Version: 1.14.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid Suggests: knitr License: Artistic License 2.0 MD5sum: 94b46c90c2b50e4d786d304c3e798fc4 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: RELEASE_3_14 git_last_commit: c6f7e2c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSstatsQCgui_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsQCgui_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsQCgui_1.14.0.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: 128 Package: MSstatsSampleSize Version: 1.8.0 Depends: R (>= 3.6) Imports: ggplot2, BiocParallel, caret, gridExtra, reshape2, stats, utils, grDevices, graphics, MSstats Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 2c91b279701d4dd11f739a8f9f8676af NeedsCompilation: no Title: Simulation tool for optimal design of high-dimensional MS-based proteomics experiment Description: The packages estimates the variance in the input protein abundance data and simulates data with predefined number of biological replicates based on the variance estimation. It reports the mean predictive accuracy of the classifier and mean protein importance over multiple iterations of the simulation. biocViews: MassSpectrometry, Proteomics, Software, DifferentialExpression, Classification, PrincipalComponent, ExperimentalDesign, Visualization Author: Ting Huang [aut, cre], Meena Choi [aut], Olga Vitek [aut] Maintainer: Ting Huang URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsSampleSize git_branch: RELEASE_3_14 git_last_commit: 15829c4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MSstatsSampleSize_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsSampleSize_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsSampleSize_1.8.0.tgz vignettes: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.html vignetteTitles: MSstatsSampleSize User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.R dependencyCount: 124 Package: MSstatsTMT Version: 2.2.7 Depends: R (>= 4.1) 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: aee8eedc173d3fa02c8b8f267b9c5870 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: Ting Huang [aut, cre], Meena Choi [aut], Mateusz Staniak [aut], Sicheng Hao [aut], Olga Vitek [aut] Maintainer: Ting Huang 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: RELEASE_3_14 git_last_commit: 11f937a git_last_commit_date: 2022-02-18 Date/Publication: 2022-02-20 source.ver: src/contrib/MSstatsTMT_2.2.7.tar.gz win.binary.ver: bin/windows/contrib/4.1/MSstatsTMT_2.2.7.zip mac.binary.ver: bin/macosx/contrib/4.1/MSstatsTMT_2.2.7.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 dependencyCount: 81 Package: Mulcom Version: 1.44.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 MD5sum: aa0aac9b829d47775dd13cfaa69f9715 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: RELEASE_3_14 git_last_commit: 318bf6c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Mulcom_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Mulcom_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Mulcom_1.44.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 46 Package: MultiAssayExperiment Version: 1.20.0 Depends: R (>= 4.0.0), SummarizedExperiment (>= 1.3.81) Imports: methods, GenomicRanges (>= 1.25.93), BiocGenerics, S4Vectors (>= 0.23.19), IRanges, Biobase, stats, tidyr, utils Suggests: BiocStyle, HDF5Array (>= 1.19.17), knitr, maftools (>= 2.7.10), rmarkdown, R.rsp, RaggedExperiment, UpSetR, survival, survminer, testthat License: Artistic-2.0 MD5sum: bcf7c70f048651ee9011856b885a8d8d NeedsCompilation: no Title: Software for the integration of multi-omics experiments in Bioconductor Description: MultiAssayExperiment harmonizes 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], 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: RELEASE_3_14 git_last_commit: c543a7c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MultiAssayExperiment_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MultiAssayExperiment_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MultiAssayExperiment_1.20.0.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: CAGEr, cBioPortalData, ClassifyR, evaluomeR, glmSparseNet, hipathia, InTAD, mia, midasHLA, missRows, QFeatures, TimiRGeN, curatedTCGAData, microbiomeDataSets, OMICsPCAdata, SingleCellMultiModal importsMe: AffiXcan, AMARETTO, animalcules, autonomics, ClassifyR, CoreGx, corral, ELMER, FindIT2, GOpro, LinkHD, metabolomicsWorkbenchR, MOMA, MultiBaC, OMICsPCA, omicsPrint, padma, PDATK, PharmacoGx, scp, TCGAutils, curatedTBData, HMP2Data suggestsMe: BiocOncoTK, CNVRanger, deco, maftools, MOFA2, MultiDataSet, RaggedExperiment, brgedata, MOFAdata dependencyCount: 45 Package: MultiBaC Version: 1.4.0 Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics, methods, plotrix Suggests: knitr, rmarkdown, BiocStyle, devtools License: GPL-3 MD5sum: 1f83caa811dcbe3e73426f5634e21969 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: Manuel Ugidos [aut, cre], Sonia Tarazona [aut], María José Nueda [aut] Maintainer: Manuel Ugidos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiBaC git_branch: RELEASE_3_14 git_last_commit: cfad776 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MultiBaC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MultiBaC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MultiBaC_1.4.0.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: 70 Package: multiClust Version: 1.24.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, rmarkdown, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) Archs: i386, x64 MD5sum: 2b1c70da9be0e9d85721efa169b3af7d 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: RELEASE_3_14 git_last_commit: a435bdc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/multiClust_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiClust_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiClust_1.24.0.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: 47 Package: multicrispr Version: 1.4.3 Depends: R (>= 4.0) Imports: assertive, 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: 46b16252daba4582aec2d8cdc0266c42 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], 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: RELEASE_3_14 git_last_commit: fae9a9e git_last_commit_date: 2021-12-23 Date/Publication: 2021-12-26 source.ver: src/contrib/multicrispr_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/multicrispr_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.1/multicrispr_1.4.3.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: 190 Package: MultiDataSet Version: 1.22.0 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: 2d24c6668782de7422a89a26f76d89c8 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: RELEASE_3_14 git_last_commit: d737fbe git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MultiDataSet_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MultiDataSet_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MultiDataSet_1.22.0.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, ropls dependencyCount: 60 Package: multiGSEA Version: 1.4.0 Depends: R (>= 4.0.0) Imports: magrittr, graphite, AnnotationDbi, 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, metaboliteIDmapping, knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: GPL-3 MD5sum: a9ad99299928bce92e2fc530b2c3a6b7 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: RELEASE_3_14 git_last_commit: 28622fe git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/multiGSEA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiGSEA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiGSEA_1.4.0.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: 117 Package: multiHiCcompare Version: 1.12.0 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: 08958919300b97f41d85f36d0f791d95 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: John Stansfield , Mikhail Dozmorov Maintainer: John Stansfield , 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: RELEASE_3_14 git_last_commit: 718f593 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/multiHiCcompare_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiHiCcompare_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiHiCcompare_1.12.0.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 suggestsMe: HiCcompare dependencyCount: 104 Package: MultiMed Version: 2.16.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: d8336578c095ef03b00f5a60f0507690 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: RELEASE_3_14 git_last_commit: 0d50891 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MultiMed_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MultiMed_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MultiMed_2.16.0.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: 1.16.0 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: 668e7bd2f2d6c25dd7c8fdd1d30e1523 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: RELEASE_3_14 git_last_commit: 56f3190 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/multiMiR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiMiR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiMiR_1.16.0.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 dependencyCount: 57 Package: multiOmicsViz Version: 1.18.0 Depends: R (>= 3.3.2) Imports: methods, parallel, doParallel, foreach, grDevices, graphics, utils, SummarizedExperiment, stats Suggests: BiocGenerics License: LGPL MD5sum: edd87d1b698f4ee0c186d82f1fdf6af3 NeedsCompilation: no Title: Plot the effect of one omics data on other omics data along the chromosome Description: Calculate the spearman correlation between the source omics data and other target omics data, identify the significant correlations and plot the significant correlations on the heat map in which the x-axis and y-axis are ordered by the chromosomal location. biocViews: Software, Visualization, SystemsBiology Author: Jing Wang Maintainer: Jing Wang git_url: https://git.bioconductor.org/packages/multiOmicsViz git_branch: RELEASE_3_14 git_last_commit: 5ffded9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/multiOmicsViz_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiOmicsViz_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiOmicsViz_1.18.0.tgz vignettes: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.pdf vignetteTitles: multiOmicsViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.R dependencyCount: 30 Package: multiscan Version: 1.54.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) MD5sum: e52b64f79e57b190708d634efecc965b 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: RELEASE_3_14 git_last_commit: 731f206 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/multiscan_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiscan_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiscan_1.54.0.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: multiSight Version: 1.2.0 Depends: R (>= 4.1) Imports: golem, config, R6, shiny, shinydashboard, DT, dplyr, stringr, anyLib, caret, biosigner, mixOmics, stats, DESeq2, clusterProfiler, rWikiPathways, ReactomePA, enrichplot, ppcor, metap, infotheo, igraph, networkD3, easyPubMed, utils, htmltools, rmarkdown Suggests: org.Mm.eg.db, rlang, markdown, attempt, processx, testthat, knitr, BiocStyle License: CeCILL + file LICENSE MD5sum: ca168299ee7e72901e34c6c7654ac1cc NeedsCompilation: no Title: Multi-omics Classification, Functional Enrichment and Network Inference analysis Description: multiSight is an R package providing an user-friendly graphical interface to analyze your omic datasets in a multi-omics manner based on Stouffer's p-value pooling and multi-block statistical methods. For each omic dataset you furnish, multiSight provides classification models with feature selection you can use as biosignature: (i) To forecast phenotypes (e.g. to diagnostic tasks, histological subtyping), (ii) To design Pathways and gene ontology enrichments (Over Representation Analysis), (iii) To build Network inference linked to PubMed querying to make assumptions easier and data-driven. biocViews: Software, RNASeq, miRNA, Network, NetworkInference, DifferentialExpression, Classification, Pathways, GeneSetEnrichment Author: Florian Jeanneret [cre, aut] (), Stephane Gazut [aut] Maintainer: Florian Jeanneret VignetteBuilder: knitr BugReports: https://github.com/Fjeanneret/multiSight/issues git_url: https://git.bioconductor.org/packages/multiSight git_branch: RELEASE_3_14 git_last_commit: 371993c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/multiSight_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multiSight_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multiSight_1.2.0.tgz vignettes: vignettes/multiSight/inst/doc/multiSight.html vignetteTitles: multiSight quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiSight/inst/doc/multiSight.R dependencyCount: 276 Package: multtest Version: 2.50.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL MD5sum: 60a8d74fae0081224a69f65d88795d3b 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: RELEASE_3_14 git_last_commit: 1de9664 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/multtest_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/multtest_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/multtest_2.50.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: aCGH, BicARE, iPAC, KCsmart, PREDA, rain, REDseq, siggenes, webbioc, cp4p, DiffCorr, GExMap, PCS importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, ChIPpeakAnno, IsoGeneGUI, mAPKL, metabomxtr, microbiomeMarker, nethet, OCplus, phyloseq, RTopper, SingleCellSignalR, singleCellTK, webbioc, hddplot, INCATome, MetaIntegrator, mutoss, nlcv, pRF, TcGSA suggestsMe: annaffy, ecolitk, factDesign, GOstats, GSEAlm, maigesPack, ropls, topGO, xcms, cherry, POSTm dependencyCount: 14 Package: mumosa Version: 1.2.0 Depends: SingleCellExperiment Imports: stats, utils, methods, igraph, Matrix, BiocGenerics, BiocParallel, IRanges, S4Vectors, DelayedArray, DelayedMatrixStats, SummarizedExperiment, BiocNeighbors, BiocSingular, ScaledMatrix, beachmat, scuttle, metapod, scran, batchelor, uwot Suggests: testthat, knitr, BiocStyle, rmarkdown, scater, bluster, DropletUtils, scRNAseq License: GPL-3 Archs: i386, x64 MD5sum: ee887ed457dd5a4e0e76b8f8c17638af 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: RELEASE_3_14 git_last_commit: 8c9f2e8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mumosa_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mumosa_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mumosa_1.2.0.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 dependsOnMe: OSCA.advanced dependencyCount: 66 Package: MungeSumstats Version: 1.2.4 Depends: R(>= 4.1) Imports: magrittr, data.table, utils, R.utils, dplyr, stats, GenomicRanges, GenomeInfoDb, BSgenome, Biostrings, VariantAnnotation, stringr, googleAuthR, httr, jsonlite, methods, parallel, rtracklayer, RCurl Suggests: SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BiocGenerics, IRanges, S4Vectors, rmarkdown, markdown, knitr, testthat (>= 3.0.0), UpSetR, BiocStyle, covr, seqminer, Rsamtools, MatrixGenerics License: Artistic-2.0 MD5sum: cf73c17c8fabcbef5fa38dc509b7ac71 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 removes duplicates across SNPs. 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: RELEASE_3_14 git_last_commit: a8af342 git_last_commit_date: 2022-03-23 Date/Publication: 2022-03-24 source.ver: src/contrib/MungeSumstats_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/MungeSumstats_1.2.4.zip mac.binary.ver: bin/macosx/contrib/4.1/MungeSumstats_1.2.4.tgz vignettes: vignettes/MungeSumstats/inst/doc/MungeSumstats.html, vignettes/MungeSumstats/inst/doc/OpenGWAS.html vignetteTitles: Standardise the format of summary statistics from GWAS with MungeSumstats, OpenGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MungeSumstats/inst/doc/MungeSumstats.R, vignettes/MungeSumstats/inst/doc/OpenGWAS.R dependencyCount: 106 Package: muscat Version: 1.8.2 Depends: R (>= 4.1) 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 Archs: i386, x64 MD5sum: ba17ad452f267d0da54496153c115273 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: RELEASE_3_14 git_last_commit: 498aea3 git_last_commit_date: 2022-03-09 Date/Publication: 2022-03-10 source.ver: src/contrib/muscat_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/muscat_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.1/muscat_1.8.2.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: muscData dependencyCount: 182 Package: muscle Version: 3.36.0 Depends: Biostrings License: Unlimited MD5sum: 257784e68aeadb7d2e0e16a7f400e8bd 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: RELEASE_3_14 git_last_commit: d0481ec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/muscle_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/muscle_3.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/muscle_3.36.0.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 importsMe: ptm suggestsMe: seqmagick dependencyCount: 18 Package: musicatk Version: 1.4.0 Depends: R (>= 4.0.0), NMF Imports: SummarizedExperiment, VariantAnnotation, cowplot, 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, withr, plotly, utils, factoextra, cluster, ComplexHeatmap, philentropy, shinydashboard, sortable, maftools, shiny, shinyjs, shinyalert, shinybusy, shinyBS, TCGAbiolinks, shinyjqui, stringi Suggests: testthat, BiocStyle, knitr, rmarkdown, survival, XVector, qpdf, covr License: LGPL-3 MD5sum: 5153ea9a8cc2ee6af15e859db7054ef3 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 [cre] (0000-0002-3968-9250), Joshua D. Campbell [aut] () Maintainer: Aaron Chevalier VignetteBuilder: knitr BugReports: https://github.com/campbio/musicatk/issues git_url: https://git.bioconductor.org/packages/musicatk git_branch: RELEASE_3_14 git_last_commit: eee66e7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/musicatk_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/musicatk_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/musicatk_1.4.0.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: 3.4.1 Depends: R (>= 4.1.0), 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: 7e8eebbcb4703ca488ed1da3bb7bc8f7 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/10.1186/s13073-018-0539-0 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MutationalPatterns git_branch: RELEASE_3_14 git_last_commit: 7ee7100 git_last_commit_date: 2022-02-14 Date/Publication: 2022-02-15 source.ver: src/contrib/MutationalPatterns_3.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/MutationalPatterns_3.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/MutationalPatterns_3.4.1.tgz 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 dependencyCount: 133 Package: MVCClass Version: 1.68.0 Depends: R (>= 2.1.0), methods License: LGPL Archs: i386, x64 MD5sum: 49886a0af1a14c7dbef69ecdd8edc6f0 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: RELEASE_3_14 git_last_commit: e6aecb4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MVCClass_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MVCClass_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MVCClass_1.68.0.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: 1.18.0 Depends: R(>= 3.4) 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 MD5sum: 27d8b0055020910d83d9746640f06a19 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: RELEASE_3_14 git_last_commit: 17465ce git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/MWASTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/MWASTools_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/MWASTools_1.18.0.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: 135 Package: mygene Version: 1.30.0 Depends: R (>= 3.2.1), GenomicFeatures, Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf, plyr Suggests: BiocStyle License: Artistic-2.0 MD5sum: 129672106ef5a28c1fe3d8d22b12c2d2 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: RELEASE_3_14 git_last_commit: 106bf9f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mygene_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mygene_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mygene_1.30.0.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 dependencyCount: 138 Package: myvariant Version: 1.24.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 8aee2c3ca821008a61a2a237d765d497 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: RELEASE_3_14 git_last_commit: 85d3ce1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/myvariant_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/myvariant_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/myvariant_1.24.0.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: 136 Package: mzID Version: 1.32.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 96693aca9ed85ab376c958fa164d93e0 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: RELEASE_3_14 git_last_commit: d414638 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mzID_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mzID_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mzID_1.32.0.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 dependsOnMe: proteomics importsMe: MSGFgui, MSGFplus, MSnbase, MSnID, TargetDecoy suggestsMe: mzR, RforProteomics dependencyCount: 11 Package: mzR Version: 2.28.0 Depends: Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3), ncdf4 LinkingTo: Rcpp, zlibbioc, Rhdf5lib (>= 1.1.4) Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML, rmarkdown License: Artistic-2.0 MD5sum: 928b7b2be2752399f55267209932c4e3 NeedsCompilation: yes Title: parser for netCDF, mzXML, mzData 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 wrapper for the ISB random access parser for mass spectrometry mzXML, mzData and mzML files. The package contains the original code written by the ISB, and a subset of the proteowizard library for 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 , Laurent Gatto , Qiakng Kou 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: RELEASE_3_14 git_last_commit: bee7d6f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/mzR_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/mzR_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/mzR_2.28.0.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: MSGFgui, MSnbase, proteomics importsMe: adductomicsR, CluMSID, DIAlignR, MSnID, msPurity, peakPantheR, ProteomicsAnnotationHubData, SIMAT, TargetDecoy, topdownr, xcms, yamss suggestsMe: AnnotationHub, MsBackendRawFileReader, qcmetrics, Spectra, msdata, RforProteomics, erah dependencyCount: 11 Package: NADfinder Version: 1.18.0 Depends: R (>= 3.4), 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: aab662a2f9a0771b756d9351e6b5c545 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: RELEASE_3_14 git_last_commit: 6f8cb3d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NADfinder_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NADfinder_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NADfinder_1.18.0.tgz vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R dependencyCount: 219 Package: NanoMethViz Version: 2.0.0 Depends: R (>= 4.0.0), methods, ggplot2 Imports: cpp11 (>= 0.2.5), readr, S4Vectors, SummarizedExperiment, BiocSingular, bsseq, forcats, assertthat, AnnotationDbi, Rcpp, dplyr, data.table, e1071, fs, GenomicRanges, ggthemes, glue, limma (>= 3.44.0), patchwork, purrr, rlang, RSQLite, Rsamtools, scales, scico, stats, stringr, tibble, tidyr, utils, withr, zlibbioc LinkingTo: Rcpp Suggests: DSS, Mus.musculus, Homo.sapiens, knitr, rmarkdown, testthat (>= 3.0.0), covr License: Apache License (>= 2.0) MD5sum: efbacc715d8f621f45e9c039f7780bf4 NeedsCompilation: yes Title: Visualise methlation 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, Visualization, DifferentialMethylation Author: Shian Su [cre, aut] Maintainer: Shian Su URL: https://github.com/shians/NanoMethViz SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Shians/NanoMethViz/issues git_url: https://git.bioconductor.org/packages/NanoMethViz git_branch: RELEASE_3_14 git_last_commit: ea1692c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NanoMethViz_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NanoMethViz_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NanoMethViz_2.0.0.tgz vignettes: vignettes/NanoMethViz/inst/doc/DimensionalityReduction.html, vignettes/NanoMethViz/inst/doc/ExonAnnotations.html, vignettes/NanoMethViz/inst/doc/ImportingData.html, vignettes/NanoMethViz/inst/doc/Introduction.html vignetteTitles: Dimensionality Reduction, Exon Annotations, Importing Data, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoMethViz/inst/doc/DimensionalityReduction.R, vignettes/NanoMethViz/inst/doc/ExonAnnotations.R, vignettes/NanoMethViz/inst/doc/ImportingData.R, vignettes/NanoMethViz/inst/doc/Introduction.R dependencyCount: 138 Package: NanoStringDiff Version: 1.24.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL Archs: i386, x64 MD5sum: 40e30f03926fc41e3cda3bd000d009ce 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: RELEASE_3_14 git_last_commit: c141f8c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NanoStringDiff_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NanoStringDiff_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NanoStringDiff_1.24.0.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: 1.2.0 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 MD5sum: 9b8cf5c5153df98dbc84896162171a62 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 [cre], Zhi Yang [ctb] Maintainer: Nicole Ortogero VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringNCTools git_branch: RELEASE_3_14 git_last_commit: 9dc7ccb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NanoStringNCTools_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NanoStringNCTools_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NanoStringNCTools_1.2.0.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: 70 Package: NanoStringQCPro Version: 1.26.0 Depends: R (>= 3.2), methods Imports: AnnotationDbi (>= 1.26.0), org.Hs.eg.db (>= 2.14.0), Biobase (>= 2.24.0), knitr (>= 1.12), NMF (>= 0.20.5), RColorBrewer (>= 1.0-5), png (>= 0.1-7) Suggests: roxygen2 (>= 4.0.1), testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 1c476d7bcbc6b1921377262b157a46dd NeedsCompilation: no Title: Quality metrics and data processing methods for NanoString mRNA gene expression data Description: NanoStringQCPro provides a set of quality metrics that can be used to assess the quality of NanoString mRNA gene expression data -- i.e. to identify outlier probes and outlier samples. It also provides different background subtraction and normalization approaches for this data. It outputs suggestions for flagging samples/probes and an easily sharable html quality control output. biocViews: Microarray, mRNAMicroarray, Preprocessing, Normalization, QualityControl, ReportWriting Author: Dorothee Nickles , Thomas Sandmann , Robert Ziman , Richard Bourgon Maintainer: Robert Ziman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringQCPro git_branch: RELEASE_3_14 git_last_commit: 42d2b8e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NanoStringQCPro_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NanoStringQCPro_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NanoStringQCPro_1.26.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: vignetteNanoStringQCPro.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 95 Package: nanotatoR Version: 1.10.0 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 Archs: i386, x64 MD5sum: 29fec19ced2b2cf3eaa46f28aa9c0b0c NeedsCompilation: no 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 Author: Surajit Bhattacharya, Hayk Barsheghyan, Emmanuele C Delot and Eric Vilain Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/nanotatoR VignetteBuilder: knitr BugReports: https://github.com/VilainLab/nanotatoR/issues git_url: https://git.bioconductor.org/packages/nanotatoR git_branch: RELEASE_3_14 git_last_commit: 821a44d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/nanotatoR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nanotatoR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nanotatoR_1.10.0.tgz vignettes: vignettes/nanotatoR/inst/doc/nanotatoR.html vignetteTitles: nanotatoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nanotatoR/inst/doc/nanotatoR.R dependencyCount: 147 Package: NanoTube Version: 1.0.0 Depends: R (>= 4.1), Biobase, ggplot2 Imports: fgsea, limma, methods, reshape, stats, utils Suggests: grid, kableExtra, knitr, NanoStringDiff, pheatmap, plotly, rlang, rmarkdown, ruv, qusage, shiny, testthat, xlsx License: GPL-3 MD5sum: d690e5ed00f13073a5b543f6913393d8 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] () Maintainer: Caleb Class VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoTube git_branch: RELEASE_3_14 git_last_commit: f066025 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NanoTube_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NanoTube_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NanoTube_1.0.0.tgz vignettes: vignettes/NanoTube/inst/doc/NanoTube.html vignetteTitles: NanoTube Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoTube/inst/doc/NanoTube.R dependencyCount: 56 Package: NBAMSeq Version: 1.10.0 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: f56bafbce8bff97d2d88f6237385c112 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: RELEASE_3_14 git_last_commit: 68cc51c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NBAMSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NBAMSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NBAMSeq_1.10.0.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: 93 Package: NBSplice Version: 1.12.0 Depends: R (>= 3.5), methods Imports: edgeR, stats, MASS, car, mppa, BiocParallel, ggplot2, reshape2 Suggests: knitr, RUnit, BiocGenerics, BiocStyle, rmarkdown, markdown License: GPL (>=2) MD5sum: 0cb32c26d61e05f0392808cab120408d NeedsCompilation: no Title: Negative Binomial Models to detect Differential Splicing Description: The package proposes a differential splicing evaluation method based on isoform quantification. It applies generalized linear models with negative binomial distribution to infer changes in isoform relative expression. biocViews: Software, StatisticalMethod, AlternativeSplicing, Regression, DifferentialExpression, DifferentialSplicing, RNASeq, ImmunoOncology Author: Gabriela A. Merino and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NBSplice git_branch: RELEASE_3_14 git_last_commit: 9f992af git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NBSplice_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NBSplice_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NBSplice_1.12.0.tgz vignettes: vignettes/NBSplice/inst/doc/NBSplice-vignette.html vignetteTitles: NBSplice-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBSplice/inst/doc/NBSplice-vignette.R dependencyCount: 100 Package: ncdfFlow Version: 2.40.0 Depends: R (>= 2.14.0), flowCore(>= 1.51.7), RcppArmadillo, methods, BH Imports: Biobase,BiocGenerics,flowCore,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH, Rhdf5lib Suggests: testthat,parallel,flowStats,knitr License: file LICENSE MD5sum: e75e4b99a5a3c862aa0834fd99bebff3 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 , Jake Wagner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: RELEASE_3_14 git_last_commit: 631576d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ncdfFlow_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ncdfFlow_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ncdfFlow_2.40.0.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R dependsOnMe: ggcyto importsMe: flowStats, flowWorkspace suggestsMe: COMPASS, cydar, gateR dependencyCount: 19 Package: ncGTW Version: 1.8.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 MD5sum: cd7f96ff657bdcc6d429c76899760d57 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: RELEASE_3_14 git_last_commit: b4a5445 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ncGTW_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ncGTW_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ncGTW_1.8.0.tgz vignettes: vignettes/ncGTW/inst/doc/ncGTW.html vignetteTitles: ncGTW User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncGTW/inst/doc/ncGTW.R dependencyCount: 95 Package: NCIgraph Version: 1.42.0 Depends: R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 3c9befc042cc964868aba02fc13cc0d6 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: RELEASE_3_14 git_last_commit: a3876c0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NCIgraph_1.42.0.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: 53 Package: ncRNAtools Version: 1.4.0 Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges, GenomicRanges, S4Vectors Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics License: GPL-3 MD5sum: a42403539b3f068a4cbee694b6c19641 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: RELEASE_3_14 git_last_commit: d780c4c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ncRNAtools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ncRNAtools_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ncRNAtools_1.4.0.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: 58 Package: ndexr Version: 1.16.0 Depends: igraph Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD Archs: i386, x64 MD5sum: 6703a9a7ad9c190bf8c99f29ea6bc4d8 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 , Frank Kramer , Alex Ishkin , Dexter Pratt 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: RELEASE_3_14 git_last_commit: 26abbfb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ndexr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ndexr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ndexr_1.16.0.tgz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R suggestsMe: netgsa dependencyCount: 39 Package: nearBynding Version: 1.4.0 Depends: R (>= 4.0) Imports: R.utils, matrixStats, plyranges, transport, Rsamtools, S4Vectors, grDevices, graphics, rtracklayer, dplyr, GenomeInfoDb, methods, GenomicRanges, utils, stats, magrittr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, ggplot2, gplots, BiocGenerics, rlang Suggests: knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: bf23451194dcc70fc7bcd66bab2534b6 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: RELEASE_3_14 git_last_commit: 8fe94e0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/nearBynding_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nearBynding_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nearBynding_1.4.0.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: 123 Package: Nebulosa Version: 1.4.0 Depends: R (>= 4.0), ggplot2, patchwork Imports: Seurat, SingleCellExperiment, SummarizedExperiment, ks, Matrix, stats, methods Suggests: testthat, BiocStyle, knitr, rmarkdown, covr, scater, scran, DropletUtils, igraph, BiocFileCache, SeuratObject License: GPL-3 MD5sum: ca49e649e64eaa6cb279b5baf178465f 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: RELEASE_3_14 git_last_commit: ae4a997 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Nebulosa_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Nebulosa_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Nebulosa_1.4.0.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 dependencyCount: 167 Package: NeighborNet Version: 1.12.0 Depends: methods Imports: graph, stats License: CC BY-NC-ND 4.0 MD5sum: d8fb4b447d79d34974da2bbe1ad9dc49 NeedsCompilation: no Title: Neighbor_net analysis Description: Identify the putative mechanism explaining the active interactions between genes in the investigated phenotype. biocViews: Software, GeneExpression, StatisticalMethod, GraphAndNetwork Author: Sahar Ansari and Sorin Draghici Maintainer: Sahar Ansari git_url: https://git.bioconductor.org/packages/NeighborNet git_branch: RELEASE_3_14 git_last_commit: 35d43ce git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NeighborNet_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NeighborNet_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NeighborNet_1.12.0.tgz vignettes: vignettes/NeighborNet/inst/doc/neighborNet.pdf vignetteTitles: NeighborNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NeighborNet/inst/doc/neighborNet.R dependencyCount: 7 Package: nempi Version: 1.2.0 Depends: R (>= 4.1), mnem Imports: e1071, nnet, randomForest, naturalsort, graphics, stats, utils, matrixStats, epiNEM Suggests: knitr, BiocGenerics, rmarkdown, RUnit License: GPL-3 MD5sum: 562c14a6eea9fb2b24ff9cd29b25d05a 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: RELEASE_3_14 git_last_commit: 6e2291d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/nempi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nempi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nempi_1.2.0.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: 110 Package: netbiov Version: 1.28.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: 6bc7d8c1c6d6db3c2e3e65fa5d40f6eb NeedsCompilation: no Title: A package for visualizing complex biological network Description: A package that provides an effective visualization of large biological networks biocViews: GraphAndNetwork, Network, Software, Visualization Author: Shailesh tripathi and Frank Emmert-Streib Maintainer: Shailesh tripathi URL: http://www.bio-complexity.com git_url: https://git.bioconductor.org/packages/netbiov git_branch: RELEASE_3_14 git_last_commit: 27d072e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/netbiov_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netbiov_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netbiov_1.28.0.tgz vignettes: vignettes/netbiov/inst/doc/netbiov-intro.pdf vignetteTitles: netbiov: An R package for visualizing biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbiov/inst/doc/netbiov-intro.R dependencyCount: 11 Package: netboost Version: 2.2.0 Depends: R (>= 4.0.0) Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats, utils, dynamicTreeCut, WGCNA, impute, colorspace, methods, R.utils LinkingTo: Rcpp, RcppParallel Suggests: knitr, markdown, rmarkdown License: GPL-3 OS_type: unix MD5sum: dc7df9af5be8460d2d2ef9ae6c5f1475 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: https://github.com/PascalSchlosser/netboost/issues git_url: https://git.bioconductor.org/packages/netboost git_branch: RELEASE_3_14 git_last_commit: 1735c8f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/netboost_2.2.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/netboost_2.2.0.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: 113 Package: netboxr Version: 1.6.0 Depends: R (>= 4.0.0), igraph (>= 1.2.4.1), parallel Imports: RColorBrewer, DT, stats, clusterProfiler, data.table, gplots, jsonlite, plyr Suggests: paxtoolsr, BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, cgdsr License: LGPL-3 + file LICENSE MD5sum: e2c923fdf19c3610483fe52d1f695569 NeedsCompilation: no Title: netboxr Description: NetBox is a network-based approach that combines prior knowledge with a network clustering algorithm. The algorithm allows for the identification of functional modules and allows for combining multiple data types, such as mutations and copy number alterations. NetBox performs network analysis on human interaction networks, and comes pre-loaded with a Human Interaction Network (HIN) derived from four literature curated data sources, including the Human Protein Reference Database (HPRD), Reactome, NCI-Nature Pathway Interaction (PID) Database, and the MSKCC Cancer Cell Map. biocViews: Software,Network,Pathways,GraphAndNetwork,Reactome, SystemsBiology, GeneSetEnrichment, NetworkEnrichment, KEGG Author: Eric Minwei Liu [aut,cre], Augustin Luna [aut], Ethan Cerami [aut] Maintainer: Eirc Minwei Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netboxr git_branch: RELEASE_3_14 git_last_commit: b7882f1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/netboxr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netboxr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netboxr_1.6.0.tgz vignettes: vignettes/netboxr/inst/doc/netboxrTutorial.html vignetteTitles: NetBoxR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netboxr/inst/doc/netboxrTutorial.R dependencyCount: 138 Package: netDx Version: 1.6.0 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: 7ce5d48b51c4ee81dac15b789422f258 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: RELEASE_3_14 git_last_commit: 85f6f3b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/netDx_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/netDx_1.6.0.tgz vignettes: vignettes/netDx/inst/doc/RawDataConversion.html, vignettes/netDx/inst/doc/ThreeWayClassifier.html vignetteTitles: 02. Running netDx with data in table format, 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/RawDataConversion.R, vignettes/netDx/inst/doc/ThreeWayClassifier.R dependencyCount: 110 Package: nethet Version: 1.26.0 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: 820fdcba70840e9a96b39ce54c4434bd 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: RELEASE_3_14 git_last_commit: b1233b3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/nethet_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nethet_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nethet_1.26.0.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: 76 Package: netOmics Version: 1.0.0 Depends: R (>= 4.1) Imports: dplyr, ggplot2, igraph, magrittr, minet, purrr, tibble, tidyr, AnnotationDbi, GO.db, RandomWalkRestartMH, gprofiler2, methods, stats Suggests: mixOmics, timeOmics, tidyverse, BiocStyle, testthat, covr, rmarkdown, knitr License: GPL-3 MD5sum: cd6a98a9360c9f60553884d638140aa1 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/netOmics git_branch: RELEASE_3_14 git_last_commit: 00eaf6b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/netOmics_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netOmics_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netOmics_1.0.0.tgz vignettes: vignettes/netOmics/inst/doc/netOmics.html vignetteTitles: netOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netOmics/inst/doc/netOmics.R dependencyCount: 110 Package: NetPathMiner Version: 1.30.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 90c6c33a8d7c6976340a16ae254f5af8 NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed , Tim Hancock , Ichigaku Takigawa , Nicolas Wicker Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetPathMiner git_branch: RELEASE_3_14 git_last_commit: 087bcbc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NetPathMiner_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NetPathMiner_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NetPathMiner_1.30.0.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R dependencyCount: 11 Package: netprioR Version: 1.20.0 Depends: methods, graphics, R(>= 3.3) Imports: stats, Matrix, dplyr, doParallel, foreach, parallel, sparseMVN, ggplot2, gridExtra, pROC Suggests: knitr, BiocStyle, pander License: GPL-3 Archs: i386, x64 MD5sum: 4d190675f943d49f6f8a8df8ccaab6ca 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: RELEASE_3_14 git_last_commit: 6cfe988 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/netprioR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netprioR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netprioR_1.20.0.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: 52 Package: netresponse Version: 1.54.0 Depends: R (>= 2.15.1), BiocStyle, Rgraphviz, rmarkdown, methods, minet, mclust, reshape2 Imports: dmt, ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer Suggests: knitr License: GPL (>=2) MD5sum: 89bc70771ac99baf1a3cefc735021846 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: RELEASE_3_14 git_last_commit: 48f1fd7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/netresponse_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netresponse_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netresponse_1.54.0.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: 75 Package: NetSAM Version: 1.34.0 Depends: R (>= 3.0.0), seriation (>= 1.0-6), igraph (>= 0.6-1), 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: f01c46bd33e2e371db4248d345cc2ae8 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: RELEASE_3_14 git_last_commit: a0ea6d4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NetSAM_1.34.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/NetSAM_1.34.0.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: 131 Package: netSmooth Version: 1.14.0 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: i386, x64 MD5sum: 193c465fa25a7eef61fcb5eff52efc3d 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: RELEASE_3_14 git_last_commit: 1cbeacd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/netSmooth_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/netSmooth_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/netSmooth_1.13.0.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: 167 Package: networkBMA Version: 2.34.0 Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3), RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1), leaps LinkingTo: Rcpp, RcppArmadillo, RcppEigen, BH License: GPL (>= 2) MD5sum: a57f513948eb7aba9d552088e1db2b1e NeedsCompilation: yes Title: Regression-based network inference using Bayesian Model Averaging Description: An extension of Bayesian Model Averaging (BMA) for network construction using time series gene expression data. Includes assessment functions and sample test data. biocViews: GraphsAndNetwork, NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: Chris Fraley, Wm. Chad Young, Ling-Hong Hung, Kaiyuan Shi, Ka Yee Yeung, Adrian Raftery (with contributions from Kenneth Lo) Maintainer: Ka Yee Yeung SystemRequirements: liblapack-dev git_url: https://git.bioconductor.org/packages/networkBMA git_branch: RELEASE_3_14 git_last_commit: cb0f234 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/networkBMA_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/networkBMA_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/networkBMA_2.34.0.tgz vignettes: vignettes/networkBMA/inst/doc/networkBMA.pdf vignetteTitles: networkBMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/networkBMA/inst/doc/networkBMA.R suggestsMe: DREAM4 dependencyCount: 23 Package: NeuCA Version: 1.0.0 Depends: R(>= 3.5.0), keras, limma, e1071, SingleCellExperiment Suggests: BiocStyle, knitr, rmarkdown, networkD3 License: GPL-2 MD5sum: 8b3c8e0f61fb2797a8bc97d982507007 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: RELEASE_3_14 git_last_commit: 0bea933 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NeuCA_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/NeuCA_1.0.0.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: 63 Package: NewWave Version: 1.4.0 Depends: R (>= 4.0), SummarizedExperiment Imports: methods, SingleCellExperiment, parallel, irlba, Matrix, DelayedArray, BiocSingular, SharedObject, stats Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp, BiocStyle, knitr License: GPL-3 Archs: i386, x64 MD5sum: 39b7492aacf39009809ff9c135fd45e7 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: RELEASE_3_14 git_last_commit: b524b24 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NewWave_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NewWave_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NewWave_1.4.0.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: 41 Package: ngsReports Version: 1.10.0 Depends: R (>= 4.1.0), BiocGenerics, ggplot2 (>= 3.3.5), tibble (>= 1.3.1) Imports: Biostrings, checkmate, dplyr (>= 1.0.0), DT, forcats, ggdendro, grDevices (>= 3.6.0), grid, lifecycle, lubridate, methods, pander, plotly (>= 4.9.4), readr, reshape2, rmarkdown, scales, stats, stringr, tidyr, tidyselect (>= 0.2.3), utils, zoo Suggests: BiocStyle, Cairo, knitr, testthat, truncnorm License: file LICENSE MD5sum: 0fec49efac4ef6352b9aeef7d71498d7 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: Steve Pederson [aut, cre], Christopher Ward [aut], Thu-Hien To [aut] Maintainer: Steve Pederson URL: https://github.com/steveped/ngsReports VignetteBuilder: knitr BugReports: https://github.com/steveped/ngsReports/issues git_url: https://git.bioconductor.org/packages/ngsReports git_branch: RELEASE_3_14 git_last_commit: b18d9df git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ngsReports_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ngsReports_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ngsReports_1.10.0.tgz vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html vignetteTitles: An Introduction To ngsReports hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R dependencyCount: 107 Package: nnNorm Version: 2.58.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL Archs: i386, x64 MD5sum: 249c267cf6fb0cb448c8fb21900a85ab 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: RELEASE_3_14 git_last_commit: 746510a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/nnNorm_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nnNorm_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nnNorm_2.58.0.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: 8 Package: NOISeq Version: 2.38.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: a2fdcb2c3ba21375b82e91a831a61a4f 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: RELEASE_3_14 git_last_commit: 07db1af git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NOISeq_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NOISeq_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NOISeq_2.38.0.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: CNVPanelizer, ExpHunterSuite suggestsMe: compcodeR dependencyCount: 11 Package: nondetects Version: 2.24.0 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 MD5sum: 469e6f33d25971e2fd67c09e5bc2873f NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/nondetects git_branch: RELEASE_3_14 git_last_commit: f63e146 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/nondetects_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nondetects_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nondetects_2.24.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R dependencyCount: 70 Package: NoRCE Version: 1.6.0 Depends: R (>= 4.0) Imports: KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices, 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: 1dcbb2a6d6644c880fc6930c351f2b40 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: RELEASE_3_14 git_last_commit: b9eb356 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NoRCE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NoRCE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NoRCE_1.6.0.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: 123 Package: normalize450K Version: 1.22.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: 7c93d6839e720f2b98f5c5926b937d69 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: RELEASE_3_14 git_last_commit: 21f95db git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/normalize450K_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/normalize450K_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/normalize450K_1.22.0.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: 1.12.0 Depends: R (>= 3.6) Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods, Biobase, RcmdrMisc, raster, utils, stats, SummarizedExperiment, matrixStats, ggforce Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle License: Artistic-2.0 MD5sum: 472881055372c992caa16cd92e242c68 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: RELEASE_3_14 git_last_commit: bc472c8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NormalyzerDE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NormalyzerDE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NormalyzerDE_1.12.0.tgz vignettes: vignettes/NormalyzerDE/inst/doc/vignette.pdf 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: 154 Package: NormqPCR Version: 1.40.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: 28011193f46478922d0fe10f62292737 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: RELEASE_3_14 git_last_commit: 214616e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NormqPCR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NormqPCR_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NormqPCR_1.40.0.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: 36 Package: normr Version: 1.20.0 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: 4ec5af57b4f64337acd17944be0fe875 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: RELEASE_3_14 git_last_commit: 048cdd3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/normr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/normr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/normr_1.20.0.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: 80 Package: NPARC Version: 1.6.0 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: f03311f98bdc02a919c2bac7f8ae8060 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: RELEASE_3_14 git_last_commit: 59fa586 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NPARC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NPARC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NPARC_1.6.0.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: 57 Package: npGSEA Version: 1.30.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: ca6552da82d78c9e7a79c8046ccf66dc 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: RELEASE_3_14 git_last_commit: 6ba8dbd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/npGSEA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/npGSEA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/npGSEA_1.30.0.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: 1.44.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 Archs: i386, x64 MD5sum: 6592ccbd23c03f0282b6d9b748f4fcf2 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: RELEASE_3_14 git_last_commit: a2d49d9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NTW_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NTW_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NTW_1.44.0.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: 4 Package: nucleoSim Version: 1.22.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 27855d6f59ffc816efcc68c40c3b6880 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 user has choice between three different distributions for the read positioning: Normal, Student and Uniform. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes 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: RELEASE_3_14 git_last_commit: 4ff08d2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/nucleoSim_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nucleoSim_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nucleoSim_1.22.0.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: 2.26.0 Depends: 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: b5485248a17c0b905c8548c0551876ab 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: RELEASE_3_14 git_last_commit: 25bc1aa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/nucleR_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nucleR_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nucleR_2.26.0.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: 76 Package: nuCpos Version: 1.12.0 Depends: R (>= 3.6) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: file LICENSE MD5sum: f98f0cc2439b6541c93ca873b9d49699 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. In nuCpos, a duration hidden Markov model is trained with a chemical map of nucleosomes either from budding yeast, fission yeast, or mouse embryonic stem cells. nuCpos outputs the Viterbi (most probable) path of nucleosome-linker states, predicted nucleosome occupancy scores and histone binding affinity (HBA) scores as NuPoP does. nuCpos can also calculate local and whole nucleosomal HBA scores for a given 147-bp sequence. Furthermore, effect of genetic alterations on nucleosome occupancy can be predicted with this package. The parental package NuPoP, which is based on an MNase-seq-based map of budding yeast nucleosomes, was developed by Ji-Ping Wang and Liqun Xi, licensed under GPL-2. biocViews: Genetics, Epigenetics, NucleosomePositioning, HiddenMarkovModel, ImmunoOncology Author: Hiroaki Kato, Takeshi Urano Maintainer: Hiroaki Kato git_url: https://git.bioconductor.org/packages/nuCpos git_branch: RELEASE_3_14 git_last_commit: ff31780 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/nuCpos_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/nuCpos_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/nuCpos_1.12.0.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: 1.0.1 Imports: stats, IRanges, GenomicRanges, GenomeInfoDb, methods, rlang, S4Vectors, scales, InteractionSet, ggplot2, grDevices, plyranges, ks, speedglm, data.table, progress, ggridges Suggests: testthat, knitr, rmarkdown, DNAcopy, RcppHMM, AnnotationHub, nullrangesData, excluderanges, EnsDb.Hsapiens.v86, microbenchmark, patchwork, plotgardener, magrittr, cobalt License: GPL-3 MD5sum: 22740ff6243b317c995f0774d30076e1 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 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/nullranges/ git_url: https://git.bioconductor.org/packages/nullranges git_branch: RELEASE_3_14 git_last_commit: c94c0c1 git_last_commit_date: 2021-11-05 Date/Publication: 2021-11-07 source.ver: src/contrib/nullranges_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/nullranges_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/nullranges_1.0.1.tgz vignettes: vignettes/nullranges/inst/doc/matching_ginteractions.html, vignettes/nullranges/inst/doc/matching_granges.html, vignettes/nullranges/inst/doc/matching_ranges.html, vignettes/nullranges/inst/doc/nullranges.html, vignettes/nullranges/inst/doc/segmented_boot_ranges.html, vignettes/nullranges/inst/doc/unseg_boot_ranges.html vignetteTitles: 3. Case study II: CTCF orientation, 2. Case study I: CTCF occupancy, 1. Overview of matchRanges, 0. Introduction to nullranges, 4. Segmented block bootstrap, 5. Unsegmented block bootstrap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nullranges/inst/doc/matching_ginteractions.R, vignettes/nullranges/inst/doc/matching_granges.R, vignettes/nullranges/inst/doc/matching_ranges.R, vignettes/nullranges/inst/doc/nullranges.R, vignettes/nullranges/inst/doc/segmented_boot_ranges.R, vignettes/nullranges/inst/doc/unseg_boot_ranges.R dependencyCount: 98 Package: NuPoP Version: 2.2.0 Depends: R (>= 4.0) Imports: graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 3876925b44123c7ef60ec68f956e7cb3 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 http://nucleosome.stats.northwestern.edu. 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: RELEASE_3_14 git_last_commit: ab10a61 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NuPoP_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NuPoP_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NuPoP_2.2.0.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: NxtIRFcore Version: 1.0.0 Depends: R (>= 3.5.0), NxtIRFdata Imports: methods, stats, utils, tools, parallel, magrittr, Rcpp (>= 1.0.5), data.table, fst, ggplot2, AnnotationHub, BiocFileCache, BiocGenerics, BiocParallel, Biostrings, BSgenome, DelayedArray, DelayedMatrixStats, genefilter, GenomeInfoDb, GenomicRanges, HDF5Array, IRanges, plotly, R.utils, rhdf5, rtracklayer, SummarizedExperiment, S4Vectors LinkingTo: Rcpp, zlibbioc, RcppProgress Suggests: knitr, rmarkdown, pheatmap, shiny, openssl, crayon, egg, DESeq2, limma, DoubleExpSeq, Rsubread, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: i386, x64 MD5sum: c67eb3df50a96458df68bc18131945b6 NeedsCompilation: yes Title: Core Engine for NxtIRF: a User-Friendly Intron Retention and Alternative Splicing Analysis using the IRFinder Engine Description: Interactively analyses Intron Retention and Alternative Splicing Events (ASE) in RNA-seq data. NxtIRF quantifies ASE events in BAM files aligned to the genome using a splice-aware aligner such as STAR. The core quantitation algorithm relies on the IRFinder/C++ engine ported via Rcpp for multi-platform compatibility. In addition, NxtIRF provides convenient pipelines for downstream analysis and publication-ready visualisation tools. biocViews: Software, Transcriptomics, RNASeq, AlternativeSplicing, Coverage, DifferentialSplicing Author: Alex Chit Hei Wong [aut, cre, cph], William Ritchie [aut, cph], Ulf Schmitz [ctb] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/NxtIRFcore SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/NxtIRFcore git_branch: RELEASE_3_14 git_last_commit: 933ccae git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/NxtIRFcore_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/NxtIRFcore_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/NxtIRFcore_1.0.0.tgz vignettes: vignettes/NxtIRFcore/inst/doc/NxtIRF.html vignetteTitles: NxtIRFcore: Differential Alternative Splicing and Intron Retention analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NxtIRFcore/inst/doc/NxtIRF.R dependencyCount: 146 Package: occugene Version: 1.54.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: fe7433a1e536e26ff0824769c46013f7 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: RELEASE_3_14 git_last_commit: a2b44a8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/occugene_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/occugene_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/occugene_1.54.0.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: 1.68.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, akima License: LGPL Archs: i386, x64 MD5sum: 7c39366ea7748e95659940b595b2e23e 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: RELEASE_3_14 git_last_commit: 17e0e51 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OCplus_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OCplus_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OCplus_1.68.0.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: 17 Package: ODER Version: 1.0.0 Depends: R (>= 4.1) Imports: BiocGenerics, BiocFileCache, dasper, derfinder, dplyr, IRanges, GenomeInfoDb, GenomicRanges, ggplot2, ggpubr, ggrepel, magrittr, rtracklayer, S4Vectors, stringr, data.table, megadepth, methods, plyr, purrr, tibble, utils Suggests: BiocStyle, covr, knitr, recount, RefManageR, rmarkdown, sessioninfo, SummarizedExperiment, testthat (>= 3.0.0), GenomicFeatures, xfun License: Artistic-2.0 Archs: i386, x64 MD5sum: 6fe0110955eb926316b2c7b37a875b53 NeedsCompilation: no Title: Optimising the Definition of Expressed Regions Description: The aim of ODER is to identify previously unannotated expressed regions (ERs) using RNA-sequencing data. For this purpose, ODER defines and optimises the definition of ERs, then connected these ERs to genes using junction data. In this way, ODER improves gene annotation. Gene annotation is a staple input of many bioinformatic pipelines and a more complete gene annotation can enable more accurate interpretation of disease associated variants. biocViews: Software, GenomeAnnotation, Transcriptomics, RNASeq, GeneExpression, Sequencing, DataImport Author: Emmanuel Olagbaju [aut], David Zhang [aut, cre] (), Sebastian Guelfi [ctb], Siddharth Sethi [ctb] Maintainer: David Zhang URL: https://github.com/eolagbaju/ODER VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/ODER git_url: https://git.bioconductor.org/packages/ODER git_branch: RELEASE_3_14 git_last_commit: 53a24c4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ODER_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ODER_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ODER_1.0.0.tgz vignettes: vignettes/ODER/inst/doc/ODER_overview.html vignetteTitles: Introduction to ODER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ODER/inst/doc/ODER_overview.R dependencyCount: 213 Package: odseq Version: 1.22.0 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: dc206d8f6f3ce49fbc5effa0890df0d9 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: RELEASE_3_14 git_last_commit: 7cd0985 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/odseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/odseq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/odseq_1.22.0.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: 32 Package: oligo Version: 1.58.0 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: 851a184174a24d36b1664fa2e9d50342 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: RELEASE_3_14 git_last_commit: 24a02c2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/oligo_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oligo_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oligo_1.58.0.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 dependencyCount: 52 Package: oligoClasses Version: 1.56.0 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: 1a9e52836c44d47c9af6b39f6d74e708 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: RELEASE_3_14 git_last_commit: 6e6c7b4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/oligoClasses_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oligoClasses_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oligoClasses_1.56.0.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: 48 Package: OLIN Version: 1.72.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 00ff48d8419f558610c7d8d10cd741ec 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: RELEASE_3_14 git_last_commit: 039cf2e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OLIN_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OLIN_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OLIN_1.72.0.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 suggestsMe: maigesPack dependencyCount: 10 Package: OLINgui Version: 1.68.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 Archs: i386, x64 MD5sum: dbd7f505b1f9d08c85fe98e31fd1c768 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: RELEASE_3_14 git_last_commit: 82f8574 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OLINgui_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OLINgui_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OLINgui_1.68.0.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: 16 Package: OmaDB Version: 2.10.0 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: ff4500554b914c4b7156634a5f12125e 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: RELEASE_3_14 git_last_commit: 753099f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OmaDB_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OmaDB_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OmaDB_2.10.0.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 dependencyCount: 57 Package: omicade4 Version: 1.34.0 Depends: R (>= 3.0.0), ade4 Imports: made4, Biobase Suggests: BiocStyle License: GPL-2 MD5sum: 500f1e90afb2c3c75b5c7962c83da995 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: RELEASE_3_14 git_last_commit: a236a9c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/omicade4_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/omicade4_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/omicade4_1.34.0.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, ropls dependencyCount: 36 Package: OmicCircos Version: 1.32.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: d590a207028e8073d2095461fb326e1b 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: RELEASE_3_14 git_last_commit: 318b95c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OmicCircos_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OmicCircos_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OmicCircos_1.32.0.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: 16 Package: omicplotR Version: 1.14.0 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: 0d98519239848e3f26bf933c25fee440 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: RELEASE_3_14 git_last_commit: ffa4a1d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/omicplotR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/omicplotR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/omicplotR_1.14.0.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: 97 Package: omicRexposome Version: 1.16.0 Depends: R (>= 3.4), 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: f3b5758550e2d3e64d330f3212ab56d6 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: RELEASE_3_14 git_last_commit: 9b3cd50 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-28 source.ver: src/contrib/omicRexposome_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/omicRexposome_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/omicRexposome_1.16.0.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: 207 Package: OmicsLonDA Version: 1.10.0 Depends: R(>= 3.6) Imports: SummarizedExperiment, gss, plyr, zoo, pracma, ggplot2, BiocParallel, parallel, grDevices, graphics, stats, utils, methods, BiocGenerics Suggests: knitr, rmarkdown, testthat, devtools, BiocManager License: MIT + file LICENSE MD5sum: a40978062e8646603218a681b5ea676f NeedsCompilation: no Title: Omics Longitudinal Differential Analysis Description: Statistical method that provides robust identification of time intervals where omics features (such as proteomics, lipidomics, metabolomics, transcriptomics, microbiome, as well as physiological parameters captured by wearable sensors such as heart rhythm, body temperature, and activity level) are significantly different between groups. biocViews: TimeCourse, Survival, Microbiome, Metabolomics, Proteomics, Lipidomics, Transcriptomics, Regression Author: Ahmed A. Metwally, Tom Zhang, Michael Snyder Maintainer: Ahmed A. Metwally URL: https://github.com/aametwally/OmicsLonDA VignetteBuilder: knitr BugReports: https://github.com/aametwally/OmicsLonDA/issues git_url: https://git.bioconductor.org/packages/OmicsLonDA git_branch: RELEASE_3_14 git_last_commit: a145163 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OmicsLonDA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OmicsLonDA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OmicsLonDA_1.10.0.tgz vignettes: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.html vignetteTitles: OmicsLonDA Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.R dependencyCount: 68 Package: OMICsPCA Version: 1.12.0 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: 608be4bb1d13b8bd15d24da98896bf1d 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: RELEASE_3_14 git_last_commit: 5161764 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OMICsPCA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OMICsPCA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OMICsPCA_1.12.0.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: 216 Package: omicsPrint Version: 1.14.0 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: 51fc651be5f37f831220ea6e6940e554 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: RELEASE_3_14 git_last_commit: ffd8d57 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/omicsPrint_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/omicsPrint_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/omicsPrint_1.14.0.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: 48 Package: Omixer Version: 1.4.0 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: fc1cd3cf590aa86cc3ad79ec65aeb820 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: RELEASE_3_14 git_last_commit: 7149ae5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Omixer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Omixer_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Omixer_1.4.0.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: 57 Package: OmnipathR Version: 3.2.8 Depends: R(>= 4.0) Imports: checkmate, crayon, curl, digest, dplyr, httr, igraph, jsonlite, later, logger, magrittr, progress, purrr, rappdirs, readr(>= 2.0.0), readxl, rlang, stats, stringr, tibble, tidyr, tidyselect, tools, utils, xml2, yaml Suggests: BiocStyle, dnet, ggplot2, ggraph, gprofiler2, knitr, mlrMBO, parallelMap, ParamHelpers, Rgraphviz, rmarkdown, smoof, supraHex, testthat License: MIT + file LICENSE MD5sum: 220c8cc49cb70fd80c1884d7e80d2f74 NeedsCompilation: no Title: OmniPath web service client and more Description: A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). biocViews: GraphAndNetwork, Network, Pathways, Software, ThirdPartyClient, DataImport, DataRepresentation, GeneSignaling, GeneRegulation, SystemsBiology, Transcriptomics, SingleCell, Annotation, KEGG Author: Alberto Valdeolivas [aut] (), Denes Turei [cre, aut] (), Attila Gabor [aut] () Maintainer: Denes Turei URL: https://saezlab.github.io/OmnipathR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/OmnipathR/issues git_url: https://git.bioconductor.org/packages/OmnipathR git_branch: RELEASE_3_14 git_last_commit: f3fbd35 git_last_commit_date: 2022-02-23 Date/Publication: 2022-02-24 source.ver: src/contrib/OmnipathR_3.2.8.tar.gz win.binary.ver: bin/windows/contrib/4.1/OmnipathR_3.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OmnipathR_3.2.8.tgz vignettes: vignettes/OmnipathR/inst/doc/bioc_workshop.html, vignettes/OmnipathR/inst/doc/drug_targets.html, vignettes/OmnipathR/inst/doc/nichenet.html, vignettes/OmnipathR/inst/doc/omnipath_intro.html, vignettes/OmnipathR/inst/doc/paths.html vignetteTitles: OmniPath Bioconductor workshop, Building networks around drug-targets using OmnipathR, Using NicheNet with OmnipathR, OmnipathR: an R client for the OmniPath web service, Pathway construction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmnipathR/inst/doc/bioc_workshop.R, vignettes/OmnipathR/inst/doc/drug_targets.R, vignettes/OmnipathR/inst/doc/nichenet.R, vignettes/OmnipathR/inst/doc/omnipath_intro.R, vignettes/OmnipathR/inst/doc/paths.R importsMe: wppi suggestsMe: decoupleR dependencyCount: 61 Package: oncomix Version: 1.16.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: bf077e47f80c3877e0ce8a781444956c 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: RELEASE_3_14 git_last_commit: 251f244 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/oncomix_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oncomix_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oncomix_1.16.0.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: 58 Package: OncoScore Version: 1.22.0 Depends: R (>= 4.1.0), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: e0408944321dc5a62ee15b52c8add7e8 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 [aut] (), Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [cre, 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: RELEASE_3_14 git_last_commit: 43e0fc4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OncoScore_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OncoScore_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OncoScore_1.22.0.tgz vignettes: vignettes/OncoScore/inst/doc/vignette.pdf vignetteTitles: OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OncoScore/inst/doc/vignette.R dependencyCount: 71 Package: OncoSimulR Version: 3.2.0 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: 9378e551867a22b37d70634ae8b46763 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 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 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], Mark Taylor [ctb], Arash Partow [ctb], Sophie Brouillet [ctb], Sebastian Matuszewski [ctb], Harry Annoni [ctb], Luca Ferretti [ctb], Guillaume Achaz [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], Niklas Endres [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: RELEASE_3_14 git_last_commit: 7521636 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OncoSimulR_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OncoSimulR_3.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OncoSimulR_3.2.0.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: 96 Package: oneSENSE Version: 1.16.0 Depends: R (>= 3.4), webshot, shiny, shinyFiles, scatterplot3d Imports: Rtsne, plotly, gplots, grDevices, graphics, stats, utils, methods, flowCore Suggests: knitr, rmarkdown License: GPL (>=3) MD5sum: 644e273215a60bb71f78d0c3219b3ab5 NeedsCompilation: no Title: One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (OneSENSE) Description: A graphical user interface that facilitates the dimensional reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. Each dimension is informative and can be annotated through the use of heatplots aligned in parallel to each axis, allowing for simultaneous visualization of two catergories across a two-dimensional plot. The cellular occupancy of the resulting plots alllows for direct assessment of the relationships between the categories. biocViews: ImmunoOncology, Software, FlowCytometry, GUI, DimensionReduction Author: Cheng Yang, Evan Newell, Yong Kee Tan Maintainer: Yong Kee Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oneSENSE git_branch: RELEASE_3_14 git_last_commit: 9a0de26 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/oneSENSE_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/oneSENSE_1.16.0.tgz vignettes: vignettes/oneSENSE/inst/doc/quickstart.html vignetteTitles: Introduction to oneSENSE GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oneSENSE/inst/doc/quickstart.R dependencyCount: 100 Package: onlineFDR Version: 2.2.0 Imports: stats, Rcpp, RcppProgress, dplyr, tidyr, ggplot2, progress LinkingTo: Rcpp, RcppProgress Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: 7634aa5e45d5d94587d108e87e56f014 NeedsCompilation: yes Title: Online error control Description: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online hypothesis testing, where hypotheses arrive sequentially in a stream. In this framework, a null hypothesis is rejected based only on the previous decisions, as the future p-values and the number of hypotheses to be tested are unknown. 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: RELEASE_3_14 git_last_commit: 74c705e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/onlineFDR_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/onlineFDR_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/onlineFDR_2.2.0.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: 49 Package: ontoProc Version: 1.16.0 Depends: R (>= 3.5), ontologyIndex Imports: Biobase, S4Vectors, methods, AnnotationDbi, stats, utils, BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT, igraph, AnnotationHub Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle, SingleCellExperiment, celldex, rmarkdown License: Artistic-2.0 MD5sum: da54d0b3056713b1545b8177859aa930 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: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ontoProc git_branch: RELEASE_3_14 git_last_commit: 45a31ef git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ontoProc_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ontoProc_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ontoProc_1.16.0.tgz vignettes: vignettes/ontoProc/inst/doc/ontoProc.html vignetteTitles: ontoProc: some ontology-oriented utilites with single-cell focus for Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R importsMe: pogos, tenXplore suggestsMe: BiocOncoTK, SingleRBook, scDiffCom dependencyCount: 95 Package: openCyto Version: 2.6.0 Depends: R (>= 3.5.0) Imports: methods,Biobase,BiocGenerics,gtools,flowCore(>= 1.99.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace(>= 3.99.1),flowStats(>= 3.99.1),flowClust(>= 3.11.4),MASS,clue,plyr,RBGL,graph,data.table,ks,RColorBrewer,lattice,rrcov,R.utils LinkingTo: Rcpp Suggests: flowWorkspaceData, knitr, rmarkdown, markdown, testthat, utils, tools, parallel, ggcyto, CytoML License: file LICENSE MD5sum: 9ce09576d4a8218bd609c54776b80f9c 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 ,Jake Wagner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: RELEASE_3_14 git_last_commit: f3e02cd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/openCyto_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/openCyto_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/openCyto_2.6.0.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: 122 Package: openPrimeR Version: 1.16.0 Depends: R (>= 4.0.0) Imports: Biostrings (>= 2.38.4), 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), distr (>= 2.6), distrEx (>= 2.6), fitdistrplus (>= 1.0-7), 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 Archs: i386, x64 MD5sum: 200b9ceea01e26ee047c61363812a56f 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: RELEASE_3_14 git_last_commit: 25c3eda git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/openPrimeR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/openPrimeR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/openPrimeR_1.16.0.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: 117 Package: openPrimeRui Version: 1.16.0 Depends: R (>= 4.0.0), openPrimeR (>= 0.99.0) Imports: shiny (>= 1.0.2), shinyjs (>= 0.9), shinyBS (>= 0.61), DT (>= 0.2), rmarkdown (>= 1.0) Suggests: knitr (>= 1.13) License: GPL-2 Archs: i386, x64 MD5sum: d6e94a31ee63b871c4d04ba307bcbfa4 NeedsCompilation: no Title: Shiny Application for Multiplex PCR Primer Design and Analysis Description: A Shiny application providing methods for designing, evaluating, and comparing primer sets for multiplex polymerase chain reaction. 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. biocViews: Software, Technology Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeRui git_branch: RELEASE_3_14 git_last_commit: 2cbc62a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/openPrimeRui_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/openPrimeRui_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/openPrimeRui_1.16.0.tgz vignettes: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.html vignetteTitles: openPrimeRui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.R dependencyCount: 139 Package: OpenStats Version: 1.6.0 Depends: nlme Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist, summarytools, graphics, stats, utils Suggests: rmarkdown License: GPL (>= 2) MD5sum: b17a8d3d66d5c8883db86677ab2ca072 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: Hamed Haseli Mashhadi URL: https://git.io/Jv5w0 VignetteBuilder: knitr BugReports: https://git.io/Jv5wg git_url: https://git.bioconductor.org/packages/OpenStats git_branch: RELEASE_3_14 git_last_commit: 82252de git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OpenStats_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OpenStats_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OpenStats_1.6.0.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: 128 Package: oposSOM Version: 2.12.0 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: i386, x64 MD5sum: 9afdd78e39f86fe52236b6bb3581c6c0 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: RELEASE_3_14 git_last_commit: 4d67968 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/oposSOM_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oposSOM_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oposSOM_2.12.0.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: 85 Package: oppar Version: 1.22.0 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: daae1c21e1a8ec03adf57762a1ac81a8 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: RELEASE_3_14 git_last_commit: 7e04b67 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/oppar_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oppar_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oppar_1.22.0.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: 79 Package: oppti Version: 1.8.0 Depends: R (>= 3.5) Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer, pheatmap, knitr, methods, devtools, parallelDist, Suggests: markdown License: MIT MD5sum: 488c6631f70451e47d5163c444c052c8 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: RELEASE_3_14 git_last_commit: 778ee5d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/oppti_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/oppti_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/oppti_1.8.0.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: 102 Package: optimalFlow Version: 1.6.0 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 MD5sum: 53e31b7af573debe1759431498e096b1 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: RELEASE_3_14 git_last_commit: ea8a7af git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/optimalFlow_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/optimalFlow_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/optimalFlow_1.6.0.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: 89 Package: OPWeight Version: 1.16.0 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 Archs: i386, x64 MD5sum: 8e0564d96bcd1807b47429174c726e8e 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: RELEASE_3_14 git_last_commit: 7c45eb0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OPWeight_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OPWeight_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OPWeight_1.16.0.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: 45 Package: OrderedList Version: 1.66.0 Depends: R (>= 3.6.1), Biobase, twilight Imports: methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 9d89b148ae378dd350748b73e9aa258d 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: RELEASE_3_14 git_last_commit: 78ae944 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OrderedList_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OrderedList_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OrderedList_1.66.0.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: 1.2.0 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: Artistic-2.0 MD5sum: dc70cd66638eb368ed1a06d090a050f2 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: RELEASE_3_14 git_last_commit: f091ec7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ORFhunteR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ORFhunteR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ORFhunteR_1.2.0.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: 55 Package: ORFik Version: 1.14.7 Depends: R (>= 3.6.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1), GenomicAlignments (>= 1.19.0) Imports: AnnotationDbi (>= 1.45.0), Biostrings (>= 2.51.1), biomartr, BiocGenerics (>= 0.29.1), BiocParallel (>= 1.19.0), BSgenome, cowplot (>= 1.0.0), data.table (>= 1.11.8), DESeq2 (>= 1.24.0), fst (>= 0.9.2), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), GGally (>= 1.4.0), 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, utils, xml2 (>= 1.2.0) LinkingTo: Rcpp Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: 2e54b60ea54df36ab8d5e61a4d284679 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], Evind 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: RELEASE_3_14 git_last_commit: 3eb90dd git_last_commit_date: 2022-01-17 Date/Publication: 2022-01-20 source.ver: src/contrib/ORFik_1.14.7.tar.gz win.binary.ver: bin/windows/contrib/4.1/ORFik_1.14.7.zip mac.binary.ver: bin/macosx/contrib/4.1/ORFik_1.14.7.tgz vignettes: vignettes/ORFik/inst/doc/Annotation_Alignment.html, vignettes/ORFik/inst/doc/ORFikExperiment.html, vignettes/ORFik/inst/doc/ORFikOverview.html, vignettes/ORFik/inst/doc/Ribo-seq_pipeline.html vignetteTitles: Annotation_Alignment.html, ORFikExperiment.html, ORFik Overview, Ribo-seq_pipeline.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ORFik/inst/doc/Annotation_Alignment.R, vignettes/ORFik/inst/doc/ORFikExperiment.R, vignettes/ORFik/inst/doc/ORFikOverview.R, vignettes/ORFik/inst/doc/Ribo-seq_pipeline.R dependsOnMe: RiboCrypt dependencyCount: 143 Package: Organism.dplyr Version: 1.22.1 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, BiocStyle, ggplot2 License: Artistic-2.0 MD5sum: 26ffe489cc93e9d0acaec61333c1a0ef 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 git_url: https://git.bioconductor.org/packages/Organism.dplyr git_branch: RELEASE_3_14 git_last_commit: f324c0f git_last_commit_date: 2021-10-28 Date/Publication: 2021-10-29 source.ver: src/contrib/Organism.dplyr_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/Organism.dplyr_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/Organism.dplyr_1.22.1.tgz vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html vignetteTitles: Organism.dplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R dependsOnMe: annotation importsMe: Ularcirc dependencyCount: 98 Package: OrganismDbi Version: 1.36.0 Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.39.4) Imports: Biobase, BiocManager, GenomicRanges (>= 1.31.13), graph, IRanges, RBGL, DBI, S4Vectors (>= 0.9.25), stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, mirbase.db, rtracklayer, biomaRt, RUnit, RMariaDB License: Artistic-2.0 MD5sum: 5bda6d1a7ade0d3a4b65aba4cdbf686b 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, Hervé Pagès, Martin Morgan, Valerie Obenchain Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/OrganismDbi git_branch: RELEASE_3_14 git_last_commit: 3e7a90d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OrganismDbi_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OrganismDbi_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OrganismDbi_1.36.0.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf 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, gpart, uncoverappLib suggestsMe: ChIPpeakAnno, epivizrStandalone dependencyCount: 99 Package: orthogene Version: 1.0.2 Depends: R (>= 4.1) Imports: dplyr, methods, stats, utils, Matrix, jsonlite, homologene, gprofiler2, babelgene, data.table, parallel, ggplot2, ggpubr, patchwork, DelayedArray, DelayedMatrixStats, Matrix.utils, grr, repmis, GenomeInfoDbData, tools Suggests: remotes, knitr, BiocStyle, covr, markdown, rmarkdown, here, testthat (>= 3.0.0), piggyback, badger, magick License: GPL-3 Archs: i386, x64 MD5sum: c414160e3b1e5ae8b8a82caf0c66b7df 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 interspecies gene ortholog mappings across 700+ organisms. It also provides various utility functions to map common objects (e.g. data.frames, gene expression matrices, lists) onto 1:1 gene orthologs from any other 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: RELEASE_3_14 git_last_commit: be43cf5 git_last_commit_date: 2022-04-08 Date/Publication: 2022-04-10 source.ver: src/contrib/orthogene_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/orthogene_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/orthogene_1.0.2.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 dependencyCount: 145 Package: OSAT Version: 1.42.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 07a47b12a36f3174ab295bc89e6e2653 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: RELEASE_3_14 git_last_commit: ff4490a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OSAT_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OSAT_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OSAT_1.42.0.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 dependencyCount: 2 Package: Oscope Version: 1.24.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: d5f79b3091bf18749b518f2a29b7927a 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: RELEASE_3_14 git_last_commit: 9c26bea git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Oscope_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Oscope_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Oscope_1.24.0.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 dependencyCount: 56 Package: OTUbase Version: 1.44.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: 35e8f32249ddc0b7c609990050378ec0 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: RELEASE_3_14 git_last_commit: 3a0915a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OTUbase_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OTUbase_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OTUbase_1.44.0.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: 51 Package: OUTRIDER Version: 1.12.0 Depends: R (>= 3.6), BiocParallel, GenomicFeatures, SummarizedExperiment, data.table, methods Imports: BBmisc, BiocGenerics, DESeq2 (>= 1.16.1), generics, GenomicRanges, ggplot2, grDevices, heatmaply, pheatmap, graphics, IRanges, matrixStats, plotly, plyr, pcaMethods, PRROC, RColorBrewer, Rcpp, reshape2, S4Vectors, scales, splines, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB, AnnotationDbi, beeswarm, covr License: MIT + file LICENSE MD5sum: 0fa91a3534614ccf856a348e1dcb5369 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 git_url: https://git.bioconductor.org/packages/OUTRIDER git_branch: RELEASE_3_14 git_last_commit: 2aa8599 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OUTRIDER_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OUTRIDER_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OUTRIDER_1.12.0.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: 156 Package: OVESEG Version: 1.10.0 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: ae90ae8dfa8e7031fcaa2cfe0dbcc2fd 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: RELEASE_3_14 git_last_commit: e045bb6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/OVESEG_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/OVESEG_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/OVESEG_1.10.0.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: 36 Package: PAA Version: 1.28.0 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: 4317b9ee7912184aefd78d9dda53ad6e 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: RELEASE_3_14 git_last_commit: 7503154 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PAA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PAA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PAA_1.28.0.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: 82 Package: packFinder Version: 1.6.0 Depends: R (>= 4.0.0) Imports: Biostrings, GenomicRanges, kmer, ape, methods, IRanges, S4Vectors Suggests: biomartr, knitr, rmarkdown, testthat, dendextend, biocViews, BiocCheck, BiocStyle License: GPL-2 MD5sum: a658f05a1d914700905c7a69a50c8a16 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: RELEASE_3_14 git_last_commit: e72ab2e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/packFinder_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/packFinder_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/packFinder_1.6.0.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: 30 Package: padma Version: 1.4.0 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: i386, x64 MD5sum: 2555df265c6b8870564b8ec853110338 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: RELEASE_3_14 git_last_commit: 399d1e0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/padma_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/padma_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/padma_1.4.0.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: 121 Package: PADOG Version: 1.36.0 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: i386, x64 MD5sum: dd1cc0259887c438f35ef611f9ea988b 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: RELEASE_3_14 git_last_commit: 8cac9c8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PADOG_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PADOG_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PADOG_1.36.0.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 dependencyCount: 61 Package: pageRank Version: 1.4.0 Depends: R (>= 4.0) Imports: GenomicRanges, igraph, motifmatchr, stats, utils, grDevices, graphics Suggests: bcellViper, BSgenome.Hsapiens.UCSC.hg19, JASPAR2018, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, TFBSTools, GenomicFeatures, annotate License: GPL-2 Archs: i386, x64 MD5sum: 561cb73b889839e7dce6f0928039976f 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: RELEASE_3_14 git_last_commit: 8f6a7ff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pageRank_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pageRank_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pageRank_1.4.0.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: 126 Package: PAIRADISE Version: 1.10.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 3a1720b5e31488e0f07655d242762850 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: RELEASE_3_14 git_last_commit: 96ce11a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PAIRADISE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PAIRADISE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PAIRADISE_1.10.0.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: 66 Package: paircompviz Version: 1.32.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: 546f15db0330e1350d1f06d7f0eaaf93 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: RELEASE_3_14 git_last_commit: 18e2cee git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/paircompviz_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/paircompviz_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/paircompviz_1.32.0.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: pairkat Version: 1.0.0 Depends: R (>= 4.1), KEGGREST, S4Vectors, SummarizedExperiment, igraph, data.table, methods, stats Imports: dplyr, magrittr, CompQuadForm, tibble Suggests: rmarkdown, knitr, BiocStyle License: GPL-3 MD5sum: b18197dce3d0f78c00afa07b9a5faa44 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 [cre, aut] Maintainer: Cameron Severn VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/pairkat/issues git_url: https://git.bioconductor.org/packages/pairkat git_branch: RELEASE_3_14 git_last_commit: 3a57a26 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pairkat_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pairkat_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pairkat_1.0.0.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: 56 Package: pandaR Version: 1.26.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 975f81f17e208cda9fb989f06b0f96f1 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: RELEASE_3_14 git_last_commit: 9f17ba5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pandaR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pandaR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pandaR_1.26.0.tgz vignettes: vignettes/pandaR/inst/doc/pandaR.html vignetteTitles: pandaR Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R dependencyCount: 47 Package: panelcn.mops Version: 1.16.0 Depends: R (>= 3.4), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: 2b51376a6f23231e9f2a7fa175fd737a 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: RELEASE_3_14 git_last_commit: 4de5991 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/panelcn.mops_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/panelcn.mops_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/panelcn.mops_1.16.0.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: 32 Package: panp Version: 1.64.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 12e45766880d43c6c4db568cc7724d31 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: RELEASE_3_14 git_last_commit: 9cd25be git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/panp_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/panp_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/panp_1.64.0.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: 1.40.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: f043ee102d94cddb68785de0796c54ac 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: RELEASE_3_14 git_last_commit: fb085e2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PANR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PANR_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PANR_1.40.0.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: 14 Package: PanVizGenerator Version: 1.22.0 Depends: methods Imports: shiny, tools, jsonlite, pcaMethods, FindMyFriends, igraph, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, digest License: GPL (>= 2) MD5sum: eeb807171dba41264a7e703064aaf459 NeedsCompilation: no Title: Generate PanViz visualisations from your pangenome Description: PanViz is a JavaScript based visualisation tool for functionaly annotated pangenomes. PanVizGenerator is a companion for PanViz that facilitates the necessary data preprocessing step necessary to create a working PanViz visualization. The output is fully self-contained so the recipient of the visualization does not need R or PanVizGenerator installed. biocViews: ComparativeGenomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/PanVizGenerator VignetteBuilder: knitr BugReports: https://github.com/thomasp85/PanVizGenerator/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/PanVizGenerator git_branch: RELEASE_3_14 git_last_commit: dafc6c3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PanVizGenerator_1.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/PanVizGenerator_1.22.0.tgz vignettes: vignettes/PanVizGenerator/inst/doc/panviz_howto.html vignetteTitles: Creating PanViz visualizations with PanVizGenerator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PanVizGenerator/inst/doc/panviz_howto.R dependencyCount: 45 Package: parglms Version: 1.26.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 732f02369bf47db91d082215f744aa51 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: RELEASE_3_14 git_last_commit: d69d65f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/parglms_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/parglms_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/parglms_1.26.0.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: 36 Package: parody Version: 1.52.0 Depends: R (>= 3.5.0), tools, utils Suggests: knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 MD5sum: bfd389b4be70884636a15d26744e8cf5 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: RELEASE_3_14 git_last_commit: 992af39 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/parody_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/parody_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/parody_1.52.0.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: PAST Version: 1.10.0 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: 43fcaab436cf0b37767f1f38f5c15499 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: RELEASE_3_14 git_last_commit: bf2a213 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PAST_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PAST_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PAST_1.10.0.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: 87 Package: Path2PPI Version: 1.24.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: c7711724f92928d9fefbb77e30fc7793 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: RELEASE_3_14 git_last_commit: 3eadc0d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Path2PPI_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Path2PPI_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Path2PPI_1.24.0.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: 11 Package: pathifier Version: 1.32.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: eafc7d0dcbf9cef10c1cf3fbbe85c72d 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: RELEASE_3_14 git_last_commit: 40c6b3c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pathifier_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathifier_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathifier_1.32.0.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: PathNet Version: 1.34.0 Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: fa1108abdb0d59859bbf92add20a09d0 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: RELEASE_3_14 git_last_commit: 6569bfd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PathNet_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PathNet_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PathNet_1.34.0.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: 1.20.0 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: 598c07d52071cda4100b1be35060c115 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: RELEASE_3_14 git_last_commit: 468d049 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PathoStat_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PathoStat_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PathoStat_1.20.0.tgz vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html vignetteTitles: PathoStat intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R dependencyCount: 206 Package: pathRender Version: 1.62.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: 516c702c762a8fb03172f4e1d3b766a4 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: RELEASE_3_14 git_last_commit: fe37bb7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pathRender_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathRender_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathRender_1.62.0.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: pathVar Version: 1.24.0 Depends: R (>= 3.3.0), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table, stats, grDevices, graphics, utils License: LGPL (>= 2.0) MD5sum: a7071145fedd85971b559b2b0bdc92ef NeedsCompilation: no Title: Methods to Find Pathways with Significantly Different Variability Description: This package contains the functions to find the pathways that have significantly different variability than a reference gene set. It also finds the categories from this pathway that are significant where each category is a cluster of genes. The genes are separated into clusters by their level of variability. biocViews: GeneticVariability, GeneSetEnrichment, Pathways Author: Laurence de Torrente, Samuel Zimmerman, Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/pathVar git_branch: RELEASE_3_14 git_last_commit: 1585e11 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pathVar_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathVar_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathVar_1.24.0.tgz vignettes: vignettes/pathVar/inst/doc/pathVar.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{PathVar} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathVar/inst/doc/pathVar.R dependencyCount: 43 Package: pathview Version: 1.34.0 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, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 50632d6b3003af87bac21ff5866354c3 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: RELEASE_3_14 git_last_commit: a878890 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pathview_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathview_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathview_1.34.0.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: BioNetStat, EGSEA, RNASeqR, SBGNview importsMe: debrowser, EnrichmentBrowser, GDCRNATools, TCGAbiolinksGUI, TCGAWorkflow, lilikoi suggestsMe: gage, MAGeCKFlute, TCGAbiolinks, gageData, CAGEWorkflow dependencyCount: 51 Package: pathwayPCA Version: 1.10.0 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: 9e3ae0ac850a368f93c492a48f5baff6 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: RELEASE_3_14 git_last_commit: 1a2a816 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pathwayPCA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pathwayPCA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pathwayPCA_1.10.0.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 importsMe: fcoex dependencyCount: 12 Package: paxtoolsr Version: 1.28.0 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 MD5sum: 2669fa1f3a3a596d63ca2773e5560d5c NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/paxtoolsr git_branch: RELEASE_3_14 git_last_commit: ccd6010 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/paxtoolsr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/paxtoolsr_1.28.0.zip vignettes: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html vignetteTitles: Using PaxtoolsR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R suggestsMe: netboxr dependencyCount: 53 Package: pcaExplorer Version: 2.20.2 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, airway, org.Hs.eg.db, htmltools License: MIT + file LICENSE MD5sum: 665e0b7e3b90c495cad939099004f363 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 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: RELEASE_3_14 git_last_commit: 92f6e27 git_last_commit_date: 2022-03-21 Date/Publication: 2022-03-22 source.ver: src/contrib/pcaExplorer_2.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/pcaExplorer_2.20.2.zip mac.binary.ver: bin/macosx/contrib/4.1/pcaExplorer_2.20.2.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 importsMe: ideal dependencyCount: 182 Package: pcaMethods Version: 1.86.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) MD5sum: 8fb729a4854e146f02f3d5fa7fa6ca6f 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: RELEASE_3_14 git_last_commit: 9419cfa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pcaMethods_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pcaMethods_1.86.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pcaMethods_1.86.0.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: autonomics, consensusDE, DAPAR, destiny, FRASER, MAI, MatrixQCvis, MSnbase, MSPrep, OUTRIDER, PanVizGenerator, PhosR, pmp, scde, SomaticSignatures, ADAPTS, geneticae, LOST, MetabolomicsBasics, missCompare, multiDimBio, polyRAD, RAMClustR, santaR, scMappR suggestsMe: MsCoreUtils, QFeatures, mtbls2, pagoda2, rsvddpd dependencyCount: 9 Package: PCAN Version: 1.22.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: b2fc4793c026fc73c52d94989e9af8dc 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: RELEASE_3_14 git_last_commit: 102b5fb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PCAN_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PCAN_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PCAN_1.22.0.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: 12 Package: PCAtools Version: 2.6.0 Depends: ggplot2, ggrepel Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng LinkingTo: Rcpp, beachmat, BH, dqrng Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2, airway, org.Hs.eg.db, magrittr, rmarkdown License: GPL-3 MD5sum: 8707552e1bbf1b6b001141d53e9ea568 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: RELEASE_3_14 git_last_commit: 9c497b5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PCAtools_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PCAtools_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PCAtools_2.6.0.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 dependsOnMe: OSCA.advanced suggestsMe: scDataviz dependencyCount: 70 Package: pcxn Version: 2.16.0 Depends: R (>= 3.4), pcxnData Imports: methods, grDevices, utils, pheatmap Suggests: igraph, annotate, org.Hs.eg.db License: MIT + file LICENSE MD5sum: 9323495c2ea5d0d89f3763e3b2c50fb2 NeedsCompilation: no Title: Exploring, analyzing and visualizing functions utilizing the pcxnData package Description: Discover the correlated pathways/gene sets of a single pathway/gene set or discover correlation relationships among multiple pathways/gene sets. Draw a heatmap or create a network of your query and extract members of each pathway/gene set found in the available collections (MSigDB H hallmark, MSigDB C2 Canonical pathways, MSigDB C5 GO BP and Pathprint). biocViews: ExperimentData, ExpressionData, MicroarrayData, GEO, Homo_sapiens_Data, OneChannelData, PathwayInteractionDatabase Author: Sokratis Kariotis, Yered Pita-Juarez, Winston Hide, Wenbin Wei Maintainer: Sokratis Kariotis git_url: https://git.bioconductor.org/packages/pcxn git_branch: RELEASE_3_14 git_last_commit: c538d53 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pcxn_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pcxn_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pcxn_2.16.0.tgz vignettes: vignettes/pcxn/inst/doc/using_pcxn.pdf vignetteTitles: pcxn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcxn/inst/doc/using_pcxn.R suggestsMe: pcxnData dependencyCount: 20 Package: PDATK Version: 1.2.0 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 Archs: i386, x64 MD5sum: 375d774275813b614f0e0e03fca2335b 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: RELEASE_3_14 git_last_commit: ecf1881 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PDATK_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PDATK_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PDATK_1.2.0.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: 260 Package: pdInfoBuilder Version: 1.58.0 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 MD5sum: d980612537692f0c0c602cf6930977d9 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: RELEASE_3_14 git_last_commit: 0988869 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pdInfoBuilder_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pdInfoBuilder_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pdInfoBuilder_1.58.0.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: 53 Package: PeacoQC Version: 1.4.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2, grDevices, grid, gridExtra, methods, plyr, stats, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=3) Archs: i386, x64 MD5sum: 1b5ba2a0e9eaef520d41ef44fe3c39b7 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: RELEASE_3_14 git_last_commit: c117e80 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PeacoQC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PeacoQC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PeacoQC_1.4.0.tgz vignettes: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.html vignetteTitles: PeacoQC_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.R dependencyCount: 96 Package: peakPantheR Version: 1.8.0 Depends: R (>= 4.1) Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>= 3.3.0), 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), shinythemes (>= 1.1.1), shinycssloaders (>= 1.0.0), DT (>= 0.15), pracma (>= 2.2.3), utils Suggests: testthat, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle License: GPL-3 MD5sum: 3323a68e94f63a7fe4390f98a87a4e6e 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: RELEASE_3_14 git_last_commit: eb2c999 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/peakPantheR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/peakPantheR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/peakPantheR_1.8.0.tgz vignettes: vignettes/peakPantheR/inst/doc/getting-started.html, vignettes/peakPantheR/inst/doc/parallel-annotation.html, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.html, vignettes/peakPantheR/inst/doc/real-time-annotation.html vignetteTitles: Getting Started with the peakPantheR package, Parallel Annotation, peakPantheR Graphical User Interface, Real Time Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peakPantheR/inst/doc/getting-started.R, vignettes/peakPantheR/inst/doc/parallel-annotation.R, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.R, vignettes/peakPantheR/inst/doc/real-time-annotation.R dependencyCount: 109 Package: PECA Version: 1.30.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: 6171021801abd7cf1c1b5df985bb70c4 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: RELEASE_3_14 git_last_commit: a61a19f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PECA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PECA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PECA_1.30.0.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: 85 Package: peco Version: 1.6.0 Depends: R (>= 2.10) Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso (>= 1.4), graphics, methods, parallel, scater, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown License: GPL (>= 3) Archs: i386, x64 MD5sum: 65acb8b1bff5fff287ac6bd730ead322 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: RELEASE_3_14 git_last_commit: bbd7e91 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/peco_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/peco_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/peco_1.6.0.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: 96 Package: pengls Version: 1.0.0 Depends: R (>= 4.0) Imports: glmnet, nlme, stats, BiocParallel Suggests: knitr,rmarkdown,testthat License: GPL-2 MD5sum: ea7a1af730d0f72c87e18dadcb347b75 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 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: RELEASE_3_14 git_last_commit: 1916a95 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pengls_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pengls_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pengls_1.0.0.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: 26 Package: PepsNMR Version: 1.12.0 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: 51c15a23d57ae91474b074cb176a88b0 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: RELEASE_3_14 git_last_commit: 17f8b0f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PepsNMR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PepsNMR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PepsNMR_1.12.0.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: 49 Package: pepStat Version: 1.28.0 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: 3101cde6184d7d75a368ba50a24e810b 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: RELEASE_3_14 git_last_commit: 66118b0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pepStat_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pepStat_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pepStat_1.28.0.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: 61 Package: pepXMLTab Version: 1.28.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d0a190817560499fd3e444db7c6c0371 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: RELEASE_3_14 git_last_commit: 947bec8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pepXMLTab_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pepXMLTab_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pepXMLTab_1.28.0.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: PERFect Version: 1.8.0 Depends: R (>= 3.6.0), sn (>= 1.5.2) Imports: ggplot2 (>= 3.0.0), phyloseq (>= 1.28.0), zoo (>= 1.8.3), psych (>= 1.8.4), stats (>= 3.6.0), Matrix (>= 1.2.14), fitdistrplus (>= 1.0.12), parallel (>= 3.6.0) Suggests: knitr, rmarkdown, kableExtra, ggpubr License: Artistic-2.0 MD5sum: 80de0f89530ed287e6acc4401cd6fc63 NeedsCompilation: no Title: Permutation filtration for microbiome data Description: PERFect is a novel permutation filtering approach designed to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds, and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering. biocViews: Software, Microbiome, Sequencing, Classification, Metagenomics Author: Ekaterina Smirnova , Quy Cao Maintainer: Quy Cao URL: https://github.com/cxquy91/PERFect VignetteBuilder: knitr BugReports: https://github.com/cxquy91/PERFect/issues git_url: https://git.bioconductor.org/packages/PERFect git_branch: RELEASE_3_14 git_last_commit: f35aedd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PERFect_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PERFect_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PERFect_1.8.0.tgz vignettes: vignettes/PERFect/inst/doc/MethodIllustration.html vignetteTitles: Method Illustration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PERFect/inst/doc/MethodIllustration.R dependencyCount: 87 Package: periodicDNA Version: 1.4.0 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: 0e2f56ee622c6c82a7f7aa796daac689 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: RELEASE_3_14 git_last_commit: 9944cc9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/periodicDNA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/periodicDNA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/periodicDNA_1.4.0.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: 76 Package: PFP Version: 1.2.0 Depends: R (>= 4.0) Imports: graph, igraph, KEGGgraph, clusterProfiler, ggplot2, plyr, tidyr, magrittr, stats, methods, utils Suggests: knitr, testthat, rmarkdown, org.Hs.eg.db License: GPL-2 MD5sum: b319fb945d26631cc29f08635e703074 NeedsCompilation: no Title: Pathway Fingerprint Framework in R Description: An implementation of the pathway fingerprint framework that introduced in paper "Pathway Fingerprint: a novel pathway knowledge and topology based method for biomarker discovery and characterization". This method provides a systematic comparisons between a gene set (such as a list of differentially expressed genes) and well-studied "basic pathway networks" (KEGG pathways), measuring the importance of pathways and genes for the gene set. The package is helpful for researchers to find the biomarkers and its function. biocViews: Software, Pathways, RNASeq Author: XC Zhang [aut, cre] Maintainer: XC Zhang URL: https://github.com/aib-group/PFP VignetteBuilder: knitr BugReports: https://github.com/aib-group/PFP/issues git_url: https://git.bioconductor.org/packages/PFP git_branch: RELEASE_3_14 git_last_commit: 0d1251a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PFP_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PFP_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PFP_1.2.0.tgz vignettes: vignettes/PFP/inst/doc/PFP.html vignetteTitles: Pathway fingerprint: a tool for biomarker discovery based on gene expression data and pathway knowledge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PFP/inst/doc/PFP.R dependencyCount: 129 Package: pgca Version: 1.18.0 Imports: utils, stats Suggests: knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: a63799859a6c985f692a805569ae7538 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: RELEASE_3_14 git_last_commit: cbf40c6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pgca_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pgca_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pgca_1.18.0.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: phantasus Version: 1.14.0 Depends: R (>= 3.5) Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools, httpuv, jsonlite, limma, opencpu, assertthat, methods, httr, rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4), svglite, gtable, stats, Matrix, pheatmap, scales, ccaPP, grid, grDevices, AnnotationDbi, DESeq2, curl Suggests: testthat, BiocStyle, knitr, rmarkdown, data.table License: MIT + file LICENSE MD5sum: bc7f9d387ced0f791f39962d79fa71e8 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://genome.ifmo.ru/phantasus, https://artyomovlab.wustl.edu/phantasus VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasus/issues git_url: https://git.bioconductor.org/packages/phantasus git_branch: RELEASE_3_14 git_last_commit: 26a4f40 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-28 source.ver: src/contrib/phantasus_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phantasus_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phantasus_1.14.0.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: 148 Package: PharmacoGx Version: 2.6.0 Depends: R (>= 3.6), CoreGx Imports: BiocGenerics, Biobase, S4Vectors, SummarizedExperiment, MultiAssayExperiment, BiocParallel, ggplot2, magicaxis, RColorBrewer, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, reshape2, jsonlite, data.table, glue, checkmate Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat, markdown License: Artistic-2.0 MD5sum: c384a6b3cf4f8b10f394078bb286184c NeedsCompilation: no 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], Zhaleh Safikhani [aut], Christopher Eeles [aut], Mark Freeman [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: RELEASE_3_14 git_last_commit: 0cb3c82 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PharmacoGx_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PharmacoGx_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PharmacoGx_2.6.0.tgz vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.pdf, vignettes/PharmacoGx/inst/doc/PharmacoGx.pdf vignetteTitles: Creating a PharmacoSet Object, 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/PharmacoGx.R importsMe: Xeva suggestsMe: ToxicoGx dependencyCount: 134 Package: phemd Version: 1.10.0 Depends: R (>= 3.5), monocle, Seurat Imports: SingleCellExperiment, RColorBrewer, igraph, transport, pracma, cluster, Rtsne, destiny, RANN, ggplot2, maptree, pheatmap, scatterplot3d, VGAM, methods, grDevices, graphics, stats, utils, cowplot, S4Vectors, BiocGenerics, SummarizedExperiment, Biobase, phateR, reticulate Suggests: knitr License: GPL-2 MD5sum: 13a6518d2905280ce07e272e083a27b0 NeedsCompilation: no Title: Phenotypic EMD for comparison of single-cell samples Description: Package for comparing and generating a low-dimensional embedding of multiple single-cell samples. biocViews: Clustering, ComparativeGenomics, Proteomics, Transcriptomics, Sequencing, DimensionReduction, SingleCell, DataRepresentation, Visualization, MultipleComparison Author: William S Chen Maintainer: William S Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phemd git_branch: RELEASE_3_14 git_last_commit: ac301f6 git_last_commit_date: 2022-04-03 Date/Publication: 2022-04-05 source.ver: src/contrib/phemd_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phemd_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phemd_1.10.0.tgz vignettes: vignettes/phemd/inst/doc/phemd.html vignetteTitles: PhEMD vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phemd/inst/doc/phemd.R dependencyCount: 236 Package: PhenoGeneRanker Version: 1.2.0 Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils, parallel Suggests: knitr, rmarkdown License: Creative Commons Attribution 4.0 International License MD5sum: 5891237ee58652481246b802b2eac67f 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: RELEASE_3_14 git_last_commit: 5133f17 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PhenoGeneRanker_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhenoGeneRanker_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PhenoGeneRanker_1.2.0.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: 32 Package: phenopath Version: 1.18.0 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: dc33f2c266efce8ab8ce25b91dc66bbc 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: RELEASE_3_14 git_last_commit: 2e3261b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/phenopath_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phenopath_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phenopath_1.18.0.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: 62 Package: phenoTest Version: 1.42.0 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: d08617a2a8d591e2f0debdaa53c6bc71 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: RELEASE_3_14 git_last_commit: 28925db git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/phenoTest_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phenoTest_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phenoTest_1.42.0.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: 138 Package: PhenStat Version: 2.30.0 Depends: R (>= 3.5.0) Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf, knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4, graphics, grDevices, utils, stats Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: 2e3c460aef24aa2799b03f9a1eee621a 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: RELEASE_3_14 git_last_commit: 145ef9d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PhenStat_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhenStat_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PhenStat_2.30.0.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: 105 Package: philr Version: 1.20.1 Imports: ape, phangorn, tidyr, ggplot2, ggtree, methods Suggests: testthat, knitr, ecodist, rmarkdown, BiocStyle, phyloseq, SummarizedExperiment, TreeSummarizedExperiment, glmnet, dplyr, mia License: GPL-3 MD5sum: 0907d36b669e42771d3e39b0f416dd16 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: RELEASE_3_14 git_last_commit: 65af4a9 git_last_commit_date: 2022-01-02 Date/Publication: 2022-01-09 source.ver: src/contrib/philr_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/philr_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/philr_1.20.1.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: 63 Package: PhIPData Version: 1.2.0 Depends: R (>= 4.1.0), SummarizedExperiment (>= 1.3.81) Imports: BiocGenerics, methods, GenomicRanges, IRanges, S4Vectors, edgeR, cli, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, covr, dplyr, readr, withr License: MIT + file LICENSE Archs: i386, x64 MD5sum: efe66eb73514b07e43ade528a5d8e804 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: RELEASE_3_14 git_last_commit: 4e37055 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PhIPData_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhIPData_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PhIPData_1.2.0.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 dependencyCount: 31 Package: phosphonormalizer Version: 1.18.0 Depends: R (>= 4.0) Imports: plyr, stats, graphics, matrixStats, methods Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: 610a75c2a3a0ece34fc01bba15eacf03 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: RELEASE_3_14 git_last_commit: da5b544 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/phosphonormalizer_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phosphonormalizer_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phosphonormalizer_1.18.0.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: 1.4.0 Depends: R (>= 4.1.0) 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 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: c74e51b729a48422c65d52b18a9dbcb4 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: RELEASE_3_14 git_last_commit: 4366e01 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PhosR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhosR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PhosR_1.4.0.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: 146 Package: PhyloProfile Version: 1.8.6 Depends: R (>= 4.1.0) Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table, DT, energy, ExperimentHub, ggplot2, gridExtra, pbapply, RColorBrewer, RCurl, shiny, shinyBS, shinyFiles, shinyjs, OmaDB, plyr, xml2, zoo, yaml Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 949c6ca989e2a309553a5b44d81c1e9b 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: RELEASE_3_14 git_last_commit: fe576e8 git_last_commit_date: 2022-02-17 Date/Publication: 2022-02-20 source.ver: src/contrib/PhyloProfile_1.8.6.tar.gz win.binary.ver: bin/windows/contrib/4.1/PhyloProfile_1.8.6.zip mac.binary.ver: bin/macosx/contrib/4.1/PhyloProfile_1.8.6.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: 139 Package: phyloseq Version: 1.38.0 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: f48aeb80a125f0e5da8726c03c8fa931 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: RELEASE_3_14 git_last_commit: 1e2409a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/phyloseq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/phyloseq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/phyloseq_1.38.0.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, phyloseqGraphTest importsMe: ANCOMBC, benchdamic, combi, metavizr, microbiomeDASim, microbiomeMarker, PathoStat, PERFect, RCM, reconsi, RPA, SPsimSeq, HMP2Data, adaptiveGPCA, breakaway, corncob, fido, HTSSIP, microbial, MicrobiomeStat, mixKernel, SigTree, treeDA suggestsMe: decontam, mia, MicrobiotaProcess, MMUPHin, philr, HMP16SData, file2meco, metacoder, PLNmodels dependencyCount: 76 Package: Pi Version: 2.6.0 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 MD5sum: 151b1282ee6524c8e7b5f98de4f6c3ae NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/Pi git_branch: RELEASE_3_14 git_last_commit: 0c06e8e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Pi_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Pi_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Pi_2.6.0.tgz vignettes: vignettes/Pi/inst/doc/Pi_vignettes.html vignetteTitles: Pi User Manual (R/Bioconductor package) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pi/inst/doc/Pi_vignettes.R dependencyCount: 142 Package: piano Version: 2.10.1 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: c539cb71197a1438d94af810baccabd9 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: RELEASE_3_14 git_last_commit: 4dcb74a git_last_commit_date: 2022-03-30 Date/Publication: 2022-03-31 source.ver: src/contrib/piano_2.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/piano_2.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/piano_2.10.1.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: 93 Package: pickgene Version: 1.66.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 4110330a2104f0ee6f0887e2a918d70b 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: RELEASE_3_14 git_last_commit: 3dfb8a2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pickgene_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pickgene_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pickgene_1.66.0.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: 2.38.0 Depends: R (>= 3.0.0) Imports: utils, stats, graphics, grDevices, methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments Suggests: rtracklayer, parallel, knitr License: Artistic-2.0 MD5sum: acae1359d89476cb137c084c44a5054f 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: RELEASE_3_14 git_last_commit: d9c7084 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PICS_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PICS_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PICS_2.38.0.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: 38 Package: Pigengene Version: 1.20.0 Depends: R (>= 4.0.3), graph, BiocStyle (>= 2.18.1) 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 Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>= 2.30.0), knitr, AnnotationDbi, energy, DOSE License: GPL (>=2) MD5sum: 0e563f6824af894c9d999b37ee136995 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, and Neda Emami Maintainer: Habil Zare VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pigengene git_branch: RELEASE_3_14 git_last_commit: 91cef1f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Pigengene_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Pigengene_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Pigengene_1.20.0.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 dependencyCount: 183 Package: PING Version: 2.38.0 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: 42bf5c31f6890dbcabdad3ed97270144 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: RELEASE_3_14 git_last_commit: 55c01b3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PING_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PING_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PING_2.38.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 162 Package: pipeComp Version: 1.4.0 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: b03d6bd29643cc55aa8752eafc4087aa 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: RELEASE_3_14 git_last_commit: 79b7c02 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pipeComp_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pipeComp_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pipeComp_1.4.0.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: 204 Package: pipeFrame Version: 1.10.0 Depends: R (>= 4.0.0), Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings, GenomeInfoDb, parallel, stats, utils, rmarkdown Suggests: BiocManager, knitr, rtracklayer, testthat License: GPL-3 Archs: i386, x64 MD5sum: 32b1a34033942ec089ad89015297e4f9 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: RELEASE_3_14 git_last_commit: 8a2b23e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pipeFrame_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pipeFrame_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pipeFrame_1.10.0.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: enrichTF, esATAC dependencyCount: 69 Package: pkgDepTools Version: 1.60.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocManager License: GPL-2 MD5sum: 08ccd8efd9ed4e50b9673c1efe54b2a1 NeedsCompilation: no Title: Package Dependency Tools Description: This package provides tools for computing and analyzing dependency relationships among R packages. It provides tools for building a graph-based representation of the dependencies among all packages in a list of CRAN-style package repositories. There are also utilities for computing installation order of a given package. If the RCurl package is available, an estimate of the download size required to install a given package and its dependencies can be obtained. biocViews: Infrastructure, GraphAndNetwork Author: Seth Falcon [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/pkgDepTools git_branch: RELEASE_3_14 git_last_commit: 2be9378 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pkgDepTools_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pkgDepTools_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pkgDepTools_1.60.0.tgz vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf vignetteTitles: How to Use pkgDepTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pkgDepTools/inst/doc/pkgDepTools.R dependencyCount: 9 Package: planet Version: 1.2.0 Depends: R (>= 4.0) Imports: methods, tibble, magrittr, dplyr Suggests: ggplot2, testthat, tidyr, scales, minfi, EpiDISH, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: cde03e2879151a7dcdd840009affe1c3 NeedsCompilation: no Title: Placental DNA methylation analysis tools Description: This package contains R functions to infer additional biological variables to supplemental DNA methylation analysis of placental data. This includes inferring ethnicity/ancestry, gestational age, and cell composition from placental DNA methylation array (450k/850k) data. The package comes with an example processed placental dataset. 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: RELEASE_3_14 git_last_commit: 336f863 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/planet_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/planet_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/planet_1.2.0.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 dependencyCount: 21 Package: plethy Version: 1.32.0 Depends: R (>= 3.1.0), methods, DBI (>= 0.5-1), RSQLite (>= 1.1), BiocGenerics, S4Vectors Imports: Streamer, IRanges, reshape2, plyr, RColorBrewer,ggplot2, Biobase Suggests: RUnit, BiocStyle License: GPL-3 MD5sum: 213fe347904a3f564f080422609c3bcd NeedsCompilation: no Title: R framework for exploration and analysis of respirometry data Description: This package provides the infrastructure and tools to import, query and perform basic analysis of whole body plethysmography and metabolism data. Currently support is limited to data derived from Buxco respirometry instruments as exported by their FinePointe software. biocViews: DataImport, biocViews, Infastructure, DataRepresentation,TimeCourse Author: Daniel Bottomly [aut, cre], Marty Ferris [ctb], Beth Wilmot [aut], Shannon McWeeney [aut] Maintainer: Daniel Bottomly git_url: https://git.bioconductor.org/packages/plethy git_branch: RELEASE_3_14 git_last_commit: 81dc611 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/plethy_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/plethy_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/plethy_1.32.0.tgz vignettes: vignettes/plethy/inst/doc/plethy.pdf vignetteTitles: plethy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plethy/inst/doc/plethy.R dependencyCount: 63 Package: plgem Version: 1.66.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS, methods License: GPL-2 MD5sum: f89843c152ac540310a42af2bcd2f4da 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: RELEASE_3_14 git_last_commit: d7f821f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/plgem_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/plgem_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/plgem_1.66.0.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: 1.64.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) MD5sum: a56905e2e3dbfcb9d057ff8c2f4b8772 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: RELEASE_3_14 git_last_commit: 7e49f3a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/plier_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/plier_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/plier_1.64.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 12 Package: PloGO2 Version: 1.6.0 Depends: R (>= 4.0), GO.db, GOstats Imports: lattice, httr, openxlsx, xtable License: GPL-2 MD5sum: 3c17d12ff7e900ad2a99c406add9b522 NeedsCompilation: no Title: Plot Gene Ontology and KEGG pathway Annotation and Abundance Description: Functions for enrichment analysis and plotting gene ontology or KEGG pathway information for multiple data subsets at the same time. It also enables encorporating multiple conditions and abundance data. biocViews: Annotation, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison, Pathways, Software, Visualization Author: Dana Pascovici, Jemma Wu Maintainer: Jemma Wu , Dana Pascovici git_url: https://git.bioconductor.org/packages/PloGO2 git_branch: RELEASE_3_14 git_last_commit: b720cbf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PloGO2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PloGO2_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PloGO2_1.6.0.tgz vignettes: vignettes/PloGO2/inst/doc/PloGO2_vignette.pdf, vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PloGO2/inst/doc/PloGO2_vignette.R, vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.R dependencyCount: 66 Package: plotgardener Version: 1.0.17 Depends: R (>= 4.1.0) Imports: curl, data.table, dplyr, grDevices, grid, ggplotify, IRanges, methods, plyranges, purrr, Rcpp, RColorBrewer, rlang, stats, strawr, tools, utils LinkingTo: Rcpp Suggests: AnnotationDbi, AnnotationHub, BSgenome, BSgenome.Hsapiens.UCSC.hg19, ComplexHeatmap, GenomicFeatures, GenomeInfoDb, GenomicRanges, ggplot2, InteractionSet, knitr, org.Hs.eg.db, rtracklayer, plotgardenerData, png, rmarkdown, scales, showtext, testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg19.knownGene License: MIT + file LICENSE MD5sum: 28634f0a2b8b3172a2dd1000f87e58be 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: RELEASE_3_14 git_last_commit: 36d2d67 git_last_commit_date: 2022-03-16 Date/Publication: 2022-03-17 source.ver: src/contrib/plotgardener_1.0.17.tar.gz win.binary.ver: bin/windows/contrib/4.1/plotgardener_1.0.17.zip mac.binary.ver: bin/macosx/contrib/4.1/plotgardener_1.0.17.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 suggestsMe: nullranges dependencyCount: 85 Package: plotGrouper Version: 1.12.0 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: 4e0fd7cfa7d595d3f873c8d872c3f6ff 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: RELEASE_3_14 git_last_commit: 6936cf8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/plotGrouper_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/plotGrouper_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/plotGrouper_1.12.0.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: 144 Package: PLPE Version: 1.54.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 4e640f637f32f00050a30718b0037fea 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: RELEASE_3_14 git_last_commit: c78aa94 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PLPE_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PLPE_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PLPE_1.54.0.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: plyranges Version: 1.14.0 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: 616b5ceb787371b7c657f70f68f9d4c6 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, cre] (), Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb] () Maintainer: Stuart Lee VignetteBuilder: knitr BugReports: https://github.com/sa-lee/plyranges git_url: https://git.bioconductor.org/packages/plyranges git_branch: RELEASE_3_14 git_last_commit: eb60114 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/plyranges_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/plyranges_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/plyranges_1.14.0.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: BUSpaRse, dasper, InPAS, methylCC, multicrispr, nearBynding, nullranges, plotgardener, fluentGenomics suggestsMe: memes, svaNUMT, svaRetro dependencyCount: 61 Package: pmm Version: 1.26.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 Archs: i386, x64 MD5sum: 41186e604fa4546781c5cb007eb556f0 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: RELEASE_3_14 git_last_commit: b153a8e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pmm_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pmm_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pmm_1.26.0.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: 1.6.0 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: fbe4e787a1d080448335ecd48f4f96ce 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: RELEASE_3_14 git_last_commit: c407dac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pmp_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pmp_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pmp_1.6.0.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: 69 Package: PoDCall Version: 1.2.0 Depends: R (>= 4.1) Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT, LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: a4599f9a7b99556a427be3a310b00bf3 NeedsCompilation: no Title: Positive Droplet Calling for DNA Methylation Droplet Digital PCR Description: Reads files exported from '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: RELEASE_3_14 git_last_commit: 11015b2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PoDCall_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PoDCall_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PoDCall_1.2.0.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: 85 Package: podkat Version: 1.26.0 Depends: R (>= 3.5.0), methods, Rsamtools (>= 1.99.1), GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats, 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: 59dd9209bc6e7e7f4af727e108519287 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 Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/podkat/ https://github.com/UBod/podkat SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/podkat git_branch: RELEASE_3_14 git_last_commit: 92a94b9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/podkat_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/podkat_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/podkat_1.26.0.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: 46 Package: pogos Version: 1.14.1 Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1) Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics Suggests: knitr, DT, ontologyPlot, testthat, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 90ceeee524ff8dcc12be94bbfb2b14b2 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: RELEASE_3_14 git_last_commit: b38f307 git_last_commit_date: 2022-03-26 Date/Publication: 2022-03-27 source.ver: src/contrib/pogos_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/pogos_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pogos_1.14.1.tgz vignettes: vignettes/pogos/inst/doc/pogos.html vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with emphasis on ontology hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pogos/inst/doc/pogos.R suggestsMe: BiocOncoTK dependencyCount: 111 Package: polyester Version: 1.30.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma, zlibbioc Suggests: knitr, ballgown, markdown License: Artistic-2.0 MD5sum: 8680d9f6f29f361a281289380697a19f NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/polyester git_branch: RELEASE_3_14 git_last_commit: dc3cd74 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/polyester_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/polyester_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/polyester_1.30.0.tgz vignettes: vignettes/polyester/inst/doc/polyester.html vignetteTitles: The Polyester package for simulating RNA-seq reads hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R dependencyCount: 20 Package: POMA Version: 1.4.0 Depends: R (>= 4.0) Imports: broom, caret, clisymbols, ComplexHeatmap, crayon, dplyr, e1071, ggcorrplot, ggplot2, ggraph, ggrepel, glasso (>= 1.11), glmnet, impute, knitr, limma, magrittr, mixOmics, MSnbase (>= 2.12), patchwork, qpdf, randomForest, RankProd (>= 3.14), rmarkdown, tibble, tidyr, vegan Suggests: Biobase, BiocStyle, covr, plotly, tidyverse, testthat (>= 2.3.2) License: GPL-3 MD5sum: 39f6507dc4dd2f2603da7022b2443bc9 NeedsCompilation: no Title: User-friendly Workflow for Metabolomics and Proteomics Data Analysis Description: A structured, reproducible and easy-to-use workflow for the visualization, pre-processing, exploratory data analysis, and statistical analysis of metabolomics and proteomics data. The main aim of POMA is to enable a flexible data cleaning and statistical analysis processes in one comprehensible and user-friendly R package. This package also has a Shiny app version that implements all POMA functions. See https://github.com/pcastellanoescuder/POMAShiny. biocViews: MassSpectrometry, Metabolomics, Proteomics, Software, Visualization, Preprocessing, Normalization, ReportWriting Author: Pol Castellano-Escuder [aut, cre] (), Cristina Andrés-Lacueva [aut] (), Alex Sánchez-Pla [aut] () 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: RELEASE_3_14 git_last_commit: c549d40 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/POMA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/POMA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/POMA_1.4.0.tgz vignettes: vignettes/POMA/inst/doc/POMA-demo.html, vignettes/POMA/inst/doc/POMA-eda.html, vignettes/POMA/inst/doc/POMA-normalization.html vignetteTitles: POMA Workflow, POMA EDA Example, POMA Normalization Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POMA/inst/doc/POMA-demo.R, vignettes/POMA/inst/doc/POMA-eda.R, vignettes/POMA/inst/doc/POMA-normalization.R suggestsMe: fobitools dependencyCount: 172 Package: PoTRA Version: 1.10.0 Depends: R (>= 3.6.0), stats, BiocGenerics, org.Hs.eg.db, igraph, graph, graphite Suggests: BiocStyle, knitr, rmarkdown, colr, metap, repmis License: LGPL Archs: i386, x64 MD5sum: 039f7c7bc66ffccd14080f10aee3a954 NeedsCompilation: no Title: PoTRA: Pathways of Topological Rank Analysis Description: The PoTRA analysis is based on topological ranks of genes in biological pathways. PoTRA can be used to detect pathways involved in disease (Li, Liu & Dinu, 2018). We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fishers Exact test to determine if the number of hub genes in each pathway is altered from normal to cancer (Li, Liu & Dinu, 2018). Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer (Li, Liu & Dinu, 2018). Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes (Li, Liu & Dinu, 2018). PoTRA can be used with the KEGG, Reactome, SMPDB and PharmGKB, Panther, PathBank, etc databases from the devel graphite library. biocViews: GraphAndNetwork, StatisticalMethod, GeneExpression, DifferentialExpression, Pathways, Reactome, Network, KEGG, PathBank, Panther Author: Chaoxing Li, Li Liu and Valentin Dinu Maintainer: Margaret Linan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoTRA git_branch: RELEASE_3_14 git_last_commit: 7598d95 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PoTRA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PoTRA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PoTRA_1.10.0.tgz vignettes: vignettes/PoTRA/inst/doc/PoTRA.html vignetteTitles: Pathways of Topological Rank Analysis (PoTRA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoTRA/inst/doc/PoTRA.R dependencyCount: 56 Package: powerTCR Version: 1.14.0 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 1149c6f1287afa5e874f460727cc3452 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: RELEASE_3_14 git_last_commit: f165527 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/powerTCR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/powerTCR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/powerTCR_1.14.0.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 importsMe: scRepertoire dependencyCount: 32 Package: POWSC Version: 1.2.0 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: a12d43197d457c9f9cdcf4f6a01028b7 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: RELEASE_3_14 git_last_commit: c5947c0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/POWSC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/POWSC_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/POWSC_1.2.0.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: 70 Package: ppcseq Version: 1.2.0 Depends: R (>= 4.1.0) Imports: methods, Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>= 2.0.0), tibble, dplyr, magrittr, purrr, future, furrr, tidyr (>= 0.8.3.9000), lifecycle, ggplot2, foreach, tidybayes, edgeR, benchmarkme, parallel, rlang, stats, utils, graphics LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: knitr, testthat, BiocStyle, rmarkdown License: GPL-3 MD5sum: c150d4f9a4d081a0b200d4f4c6a72112 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 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/stemangiola/ppcseq/issues git_url: https://git.bioconductor.org/packages/ppcseq git_branch: RELEASE_3_14 git_last_commit: 91373e3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ppcseq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ppcseq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ppcseq_1.2.0.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: 100 Package: PPInfer Version: 1.20.4 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData Imports: httr, grDevices, graphics, stats, utils License: Artistic-2.0 MD5sum: 9a398dd299d79608e90c7145be3a85fb 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: RELEASE_3_14 git_last_commit: 403294a git_last_commit_date: 2021-12-01 Date/Publication: 2021-12-02 source.ver: src/contrib/PPInfer_1.20.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/PPInfer_1.20.4.zip mac.binary.ver: bin/macosx/contrib/4.1/PPInfer_1.20.4.tgz vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf vignetteTitles: User manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R dependsOnMe: gsean dependencyCount: 116 Package: ppiStats Version: 1.60.0 Imports: Biobase, Category, graph, graphics, grDevices, lattice, methods, RColorBrewer, stats Suggests: yeastExpData, xtable, ppiData, ScISI License: Artistic-2.0 MD5sum: fb2bb150925210c0306bff020ee3ce38 NeedsCompilation: no Title: Protein-Protein Interaction Statistical Package Description: Tools for the analysis of protein interaction data. biocViews: Proteomics, GraphAndNetwork, Network, NetworkInference Author: T. Chiang and D. Scholtens with contributions from W. Huber and L. Wang Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/ppiStats git_branch: RELEASE_3_14 git_last_commit: 2b0ed01 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ppiStats_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ppiStats_1.59.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ppiStats_1.60.0.tgz vignettes: vignettes/ppiStats/inst/doc/ppiStats.pdf vignetteTitles: ppiStats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppiStats/inst/doc/ppiStats.R suggestsMe: RpsiXML, ppiData dependencyCount: 60 Package: pqsfinder Version: 2.10.1 Depends: 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: 39497ea3ce7d3cc622ca5584bd53365a 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: RELEASE_3_14 git_last_commit: 1f91f71 git_last_commit_date: 2021-12-22 Date/Publication: 2021-12-23 source.ver: src/contrib/pqsfinder_2.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/pqsfinder_2.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/pqsfinder_2.10.1.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: 21 Package: pram Version: 1.10.0 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) Archs: i386, x64 MD5sum: dda9fcd1c929fd06dbfb21058cdcee62 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: RELEASE_3_14 git_last_commit: 65986cf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pram_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pram_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pram_1.10.0.tgz vignettes: vignettes/pram/inst/doc/pram.pdf vignetteTitles: Pooling RNA-seq and Assembling Models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pram/inst/doc/pram.R dependencyCount: 45 Package: prebs Version: 1.34.0 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: 216cad2a27219426a37ffa7a0bede4cf 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: RELEASE_3_14 git_last_commit: 88e2979 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/prebs_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/prebs_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/prebs_1.34.0.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: 114 Package: preciseTAD Version: 1.4.0 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: 450d17fab5221df6ebe957edf56b9b1f 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: RELEASE_3_14 git_last_commit: 676dd8e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/preciseTAD_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/preciseTAD_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/preciseTAD_1.4.0.tgz vignettes: vignettes/preciseTAD/inst/doc/preciseTAD.html vignetteTitles: preciseTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/preciseTAD/inst/doc/preciseTAD.R suggestsMe: preciseTADhub dependencyCount: 179 Package: PrecisionTrialDrawer Version: 1.10.0 Depends: R (>= 3.6) Imports: graphics, grDevices, stats, utils, methods, cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, BiocParallel, magrittr, biomaRt, XML, httr, jsonlite, ggplot2, ggrepel, grid, S4Vectors, IRanges, GenomicRanges, LowMACAAnnotation, googleVis, shiny, shinyBS, DT, brglm, matrixStats Suggests: BiocStyle, knitr, rmarkdown, dplyr License: GPL-3 Archs: i386, x64 MD5sum: d1e04fc75ebb5c275e2c2cfbe04481c1 NeedsCompilation: no Title: A Tool to Analyze and Design NGS Based Custom Gene Panels Description: A suite of methods to design umbrella and basket trials for precision oncology. biocViews: SomaticMutation, WholeGenome, Sequencing, DataImport, GeneExpression Author: Giorgio Melloni, Alessandro Guida, Luca Mazzarella Maintainer: Giorgio Melloni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PrecisionTrialDrawer git_branch: RELEASE_3_14 git_last_commit: a02b24e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PrecisionTrialDrawer_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PrecisionTrialDrawer_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PrecisionTrialDrawer_1.10.0.tgz vignettes: vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.html vignetteTitles: Bioconductor style for HTML documents hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.R dependencyCount: 127 Package: PREDA Version: 1.40.0 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 MD5sum: 750011ca43e1f2c2e660ec3210564613 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: RELEASE_3_14 git_last_commit: 65d1da0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PREDA_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PREDA_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PREDA_1.40.0.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: predictionet Version: 1.40.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: 5b2cb02331c7e7bb8dab3b3cbcf2e355 NeedsCompilation: yes Title: Inference for predictive networks designed for (but not limited to) genomic data Description: This package contains a set of functions related to network inference combining genomic data and prior information extracted from biomedical literature and structured biological databases. The main function is able to generate networks using Bayesian or regression-based inference methods; while the former is limited to < 100 of variables, the latter may infer networks with hundreds of variables. Several statistics at the edge and node levels have been implemented (edge stability, predictive ability of each node, ...) in order to help the user to focus on high quality subnetworks. Ultimately, this package is used in the 'Predictive Networks' web application developed by the Dana-Farber Cancer Institute in collaboration with Entagen. biocViews: GraphAndNetwork, NetworkInference Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Catharina Olsen URL: http://compbio.dfci.harvard.edu, http://www.ulb.ac.be/di/mlg PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/predictionet git_branch: RELEASE_3_14 git_last_commit: 7abbaec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/predictionet_1.40.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/predictionet_1.40.0.tgz vignettes: vignettes/predictionet/inst/doc/predictionet.pdf vignetteTitles: predictionet hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/predictionet/inst/doc/predictionet.R dependencyCount: 23 Package: preprocessCore Version: 1.56.0 Imports: stats License: LGPL (>= 2) MD5sum: a8cd1a8d485a7e54240dea553f40cd27 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: RELEASE_3_14 git_last_commit: 8f32722 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/preprocessCore_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/preprocessCore_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/preprocessCore_1.56.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, cqn, crlmm, RefPlus, SCATE importsMe: affy, BloodGen3Module, bnbc, cn.farms, DAPAR, EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools, hipathia, iCheck, ImmuneSpaceR, InPAS, lumi, MADSEQ, MBCB, MBQN, MEDIPS, methylclock, mimager, minfi, MSPrep, MSstats, NormalyzerDE, oligo, PECA, PhosR, Pigengene, proBatch, qPLEXanalyzer, quantiseqr, sesame, soGGi, tidybulk, yarn, GSE13015, ADAPTS, bulkAnalyseR, cinaR, FARDEEP, HEMDAG, lilikoi, MetaIntegrator, MiDA, noise, noisyr, oncoPredict, RAMClustR, retriever, SMDIC, WGCNA suggestsMe: MsCoreUtils, multiClust, QFeatures, scp, splatter, wateRmelon, aroma.affymetrix, aroma.core, glycanr, wrMisc, wrTopDownFrag linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: primirTSS Version: 1.12.0 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: 76cbb37798cb9878ce118b2b25ad3b51 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: RELEASE_3_14 git_last_commit: fdc79dc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/primirTSS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/primirTSS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/primirTSS_1.12.0.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: 190 Package: PrInCE Version: 1.10.0 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: 74d920826d69fcf02bad95218fffa235 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: RELEASE_3_14 git_last_commit: d2e07dc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PrInCE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PrInCE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PrInCE_1.10.0.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: 138 Package: proActiv Version: 1.4.0 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 Suggests: testthat, rmarkdown, knitr, Rtsne, gridExtra License: MIT + file LICENSE MD5sum: 30810f98d20d0d011fea826cdd360320 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: RELEASE_3_14 git_last_commit: 6198397 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/proActiv_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proActiv_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proActiv_1.4.0.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: 123 Package: proBAMr Version: 1.28.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: be75b9e17f11d24327c8cc78b5c68dc8 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: RELEASE_3_14 git_last_commit: f72b73a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/proBAMr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proBAMr_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proBAMr_1.28.0.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: 96 Package: proBatch Version: 1.10.0 Depends: R (>= 3.6) Imports: Biobase, corrplot, dplyr, data.table, ggfortify, ggplot2, grDevices, lazyeval, lubridate, magrittr, pheatmap, preprocessCore, purrr, pvca, RColorBrewer, reshape2, rlang, scales, stats, sva, tidyr, tibble, tools, utils, viridis, wesanderson, WGCNA Suggests: knitr, rmarkdown, devtools, ggpubr, gtable, gridExtra, roxygen2, testthat (>= 2.1.0), spelling License: GPL-3 MD5sum: 47c136c578eb4741c231ecc9616bc47a NeedsCompilation: no Title: Tools for Diagnostics and Corrections of Batch Effects in Proteomics Description: These tools facilitate batch effects analysis and correction in high-throughput experiments. It was developed primarily for mass-spectrometry proteomics (DIA/SWATH), but could also be applicable to most omic data with minor adaptations. The package contains functions for diagnostics (proteome/genome-wide and feature-level), correction (normalization and batch effects correction) and quality control. Non-linear fitting based approaches were also included to deal with complex, mass spectrometry-specific signal drifts. biocViews: BatchEffect, Normalization, Preprocessing, Software, MassSpectrometry,Proteomics, QualityControl Author: Jelena Cuklina , Chloe H. Lee , Patrick Pedrioli Maintainer: Chloe H. Lee URL: https://github.com/symbioticMe/proBatch VignetteBuilder: knitr BugReports: https://github.com/symbioticMe/proBatch/issues git_url: https://git.bioconductor.org/packages/proBatch git_branch: RELEASE_3_14 git_last_commit: 22462ff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/proBatch_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proBatch_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proBatch_1.10.0.tgz vignettes: vignettes/proBatch/inst/doc/proBatch.pdf vignetteTitles: proBatch package overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBatch/inst/doc/proBatch.R dependencyCount: 160 Package: PROcess Version: 1.70.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: c60cb9523fbcb9965a016b5f34bfed79 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: RELEASE_3_14 git_last_commit: e1fcd63 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PROcess_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PROcess_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PROcess_1.70.0.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: 2.22.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: 6a00d6a1124c5bfacfcca5f98cb9b37d 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 Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/procoil/ https://github.com/UBod/procoil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/procoil git_branch: RELEASE_3_14 git_last_commit: 30afcdf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/procoil_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/procoil_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/procoil_2.22.0.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: 30 Package: proDA Version: 1.8.0 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 License: GPL-3 MD5sum: 8bc9e96cb08538dca43e8ff0035dad73 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: RELEASE_3_14 git_last_commit: 933d1ff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/proDA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proDA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proDA_1.8.0.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: 27 Package: proFIA Version: 1.20.0 Depends: R (>= 2.5.0), xcms Imports: stats, graphics, utils, grDevices, methods, pracma, Biobase, minpack.lm, BiocParallel, missForest, ropls Suggests: BiocGenerics, plasFIA, knitr, License: CeCILL MD5sum: 679eef379954044138eca734a3dafead NeedsCompilation: yes Title: Preprocessing of FIA-HRMS data Description: Flow Injection Analysis coupled to High-Resolution Mass Spectrometry is a promising approach for high-throughput metabolomics. FIA- HRMS data, however, cannot be pre-processed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. Here we present the proFIA package, which implements a new methodology to pre-process FIA-HRMS raw data (netCDF, mzData, mzXML, and mzML) including noise modelling and injection peak reconstruction, and generate the peak table. The workflow includes noise modelling, band detection and filtering then signal matching and missing value imputation. The peak table can then be exported as a .tsv file for further analysis. Visualisations to assess the quality of the data and of the signal made are easely produced. biocViews: MassSpectrometry, Metabolomics, Lipidomics, Preprocessing, PeakDetection, Proteomics Author: Alexis Delabriere and Etienne Thevenot. Maintainer: Alexis Delabriere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proFIA git_branch: RELEASE_3_14 git_last_commit: b9efdcd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/proFIA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proFIA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proFIA_1.20.0.tgz vignettes: vignettes/proFIA/inst/doc/proFIA-vignette.html vignetteTitles: processing FIA-HRMS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proFIA/inst/doc/proFIA-vignette.R dependsOnMe: plasFIA dependencyCount: 105 Package: profileplyr Version: 1.10.2 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: 2e56c68b52df4fc0146fbc00f52d5be0 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: RELEASE_3_14 git_last_commit: b86cda4 git_last_commit_date: 2021-12-27 Date/Publication: 2021-12-28 source.ver: src/contrib/profileplyr_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/profileplyr_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.1/profileplyr_1.10.2.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 dependencyCount: 187 Package: profileScoreDist Version: 1.22.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE MD5sum: 5d13d88a052c88fa9233f38de35e4bf1 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: RELEASE_3_14 git_last_commit: 215fd42 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/profileScoreDist_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/profileScoreDist_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/profileScoreDist_1.22.0.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: progeny Version: 1.16.0 Depends: R (>= 3.6.0) Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra Suggests: airway, biomaRt, BiocFileCache, broom, Seurat, SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl, pheatmap, tibble, rmarkdown, testthat (>= 2.1.0) License: Apache License (== 2.0) | file LICENSE MD5sum: 80ac2cc8bd79dedea9e0cd333f16fe1a NeedsCompilation: no Title: Pathway RespOnsive GENes for activity inference from gene expression Description: This package provides a function to infer pathway activity from gene expression using PROGENy. It contains the linear model we inferred in the publication "Perturbation-response genes reveal signaling footprints in cancer gene expression". 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 git_url: https://git.bioconductor.org/packages/progeny git_branch: RELEASE_3_14 git_last_commit: 766eb72 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/progeny_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/progeny_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/progeny_1.16.0.tgz vignettes: vignettes/progeny/inst/doc/progenyBulk.html, vignettes/progeny/inst/doc/ProgenySingleCell.html vignetteTitles: PROGENy pathway signatures: Application to Bulk transcriptomics, Applying PROGENy on single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/progeny/inst/doc/progenyBulk.R, vignettes/progeny/inst/doc/ProgenySingleCell.R importsMe: easier suggestsMe: mistyR dependencyCount: 49 Package: projectR Version: 1.10.0 Imports: methods, cluster, stats, limma, CoGAPS, NMF, ROCR, ggalluvial, RColorBrewer, dplyr, reshape2, viridis, scales, ggplot2 Suggests: BiocStyle, gridExtra, grid, testthat, devtools, knitr, rmarkdown, ComplexHeatmap License: GPL (==2) Archs: i386, x64 MD5sum: ab8997c8011e85e523ebfb7ab9459358 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, 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: RELEASE_3_14 git_last_commit: 36c5ad2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/projectR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/projectR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/projectR_1.10.0.tgz vignettes: vignettes/projectR/inst/doc/projectR.pdf vignetteTitles: projectR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/projectR/inst/doc/projectR.R dependencyCount: 102 Package: pRoloc Version: 1.34.0 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 (>= 1.15.3), dplyr, akima, fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals, reshape, magick License: GPL-2 MD5sum: 0d312657cdbb4195aaccdd5b75529659 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: RELEASE_3_14 git_last_commit: 5c4ca0d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pRoloc_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pRoloc_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pRoloc_1.34.0.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: pRolocGUI, proteomics suggestsMe: MSnbase, pRolocdata, RforProteomics dependencyCount: 208 Package: pRolocGUI Version: 2.4.0 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, colourpicker, shinyhelper, shinyWidgets, shinyjs, colorspace, shinydashboard, stats, grDevices, grid, BiocGenerics Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 0f3612d7d7bedd30122aacd5bcaf2451 NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise organelle (spatial) proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Lisa Breckels [aut], Thomas Naake [aut], Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: http://ComputationalProteomicsUnit.github.io/pRolocGUI/ VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/ComputationalProteomicsUnit/pRolocGUI/issues git_url: https://git.bioconductor.org/packages/pRolocGUI git_branch: RELEASE_3_14 git_last_commit: f76cfed git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pRolocGUI_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pRolocGUI_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pRolocGUI_2.4.0.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: 221 Package: PROMISE Version: 1.46.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: b41b7f275fe5bd43acd3437052a08fd2 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: RELEASE_3_14 git_last_commit: 5898cf2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PROMISE_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PROMISE_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PROMISE_1.46.0.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: 1.26.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq2,DSS,knitr License: GPL MD5sum: 5ebedacac7b867452f06dd8ee71b22b0 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: RELEASE_3_14 git_last_commit: bc04d64 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PROPER_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PROPER_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PROPER_1.26.0.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 dependencyCount: 11 Package: PROPS Version: 1.16.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: cd7b7a5deb027efd19d53d0007c1b683 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: RELEASE_3_14 git_last_commit: 8c39648 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PROPS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PROPS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PROPS_1.16.0.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: 75 Package: Prostar Version: 1.26.4 Depends: R (>= 4.1.0) Imports: DAPAR (>= 1.26.1), DAPARdata (>= 1.24.0), rhandsontable, data.table, shinyjs, DT, shiny, shinyBS, shinyAce, highcharter, htmlwidgets, webshot, R.utils, shinythemes, XML,later, rclipboard, shinycssloaders, future, promises, colourpicker, BiocManager, shinyjqui,shinyTree, shinyWidgets, sass, tibble Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: e0539aebce8be8c86fc5cc02fa709573 NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for DAPAR. biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing, ImmunoOncology, R.utils, GO, GUI Author: Samuel Wieczorek [cre,aut], Thomas Burger [aut], Enora Fremy [aut] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ BugReports: https://github.com/samWieczorek/Prostar/issues git_url: https://git.bioconductor.org/packages/Prostar git_branch: RELEASE_3_14 git_last_commit: 8bb9825 git_last_commit_date: 2022-01-21 Date/Publication: 2022-01-23 source.ver: src/contrib/Prostar_1.26.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/Prostar_1.26.4.zip mac.binary.ver: bin/macosx/contrib/4.1/Prostar_1.26.4.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: 328 Package: proteinProfiles Version: 1.34.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: e7fead04fb92f174fb3479af0d13bd09 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: RELEASE_3_14 git_last_commit: db57848 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/proteinProfiles_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/proteinProfiles_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/proteinProfiles_1.34.0.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: 1.0.0 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: 8c4360d1321d4c6b832850dbb5277bcb 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 (mutant) 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 git_url: https://git.bioconductor.org/packages/ProteoDisco git_branch: RELEASE_3_14 git_last_commit: efdf16d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ProteoDisco_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ProteoDisco_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ProteoDisco_1.0.0.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: 105 Package: ProteomicsAnnotationHubData Version: 1.24.0 Depends: AnnotationHub (>= 2.1.45), AnnotationHubData, Imports: mzR (>= 2.3.2), MSnbase, Biostrings, GenomeInfoDb, utils, Biobase, BiocManager, RCurl Suggests: knitr, BiocStyle, rmarkdown, testthat License: Artistic-2.0 MD5sum: 21550d12b08b83fe4062c326d915e67b NeedsCompilation: no Title: Transform public proteomics data resources into Bioconductor Data Structures Description: These recipes convert a variety and a growing number of public proteomics data sets into easily-used standard Bioconductor data structures. biocViews: DataImport, Proteomics Author: Gatto Laurent [aut, cre], Sonali Arora [aut] Maintainer: Laurent Gatto URL: https://github.com/lgatto/ProteomicsAnnotationHubData VignetteBuilder: knitr BugReports: https://github.com/lgatto/ProteomicsAnnotationHubData/issues git_url: https://git.bioconductor.org/packages/ProteomicsAnnotationHubData git_branch: RELEASE_3_14 git_last_commit: b8e3a6d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ProteomicsAnnotationHubData_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ProteomicsAnnotationHubData_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ProteomicsAnnotationHubData_1.24.0.tgz vignettes: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.html vignetteTitles: Proteomics Data in Annotation Hub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.R dependencyCount: 169 Package: ProteoMM Version: 1.12.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: 91b08a70ce60fbb2190677ee7b77d975 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: RELEASE_3_14 git_last_commit: 603969f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ProteoMM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ProteoMM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ProteoMM_1.12.0.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 dependencyCount: 92 Package: ProtGenerics Version: 1.26.0 Depends: methods Suggests: testthat License: Artistic-2.0 MD5sum: f9db451330c2037829afdfab536bedf5 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: RELEASE_3_14 git_last_commit: 2033289 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ProtGenerics_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ProtGenerics_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ProtGenerics_1.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, Spectra, tofsims, topdownr importsMe: ensembldb, matter, MsBackendMassbank, MsFeatures, MSGFplus, MSnID, mzID, mzR, QFeatures, xcms dependencyCount: 1 Package: PSEA Version: 1.28.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: bcfeee963cbe1a5118b49793a9625266 NeedsCompilation: no Title: Population-Specific Expression Analysis. Description: Deconvolution of gene expression data by Population-Specific Expression Analysis (PSEA). biocViews: Software Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/PSEA git_branch: RELEASE_3_14 git_last_commit: 677d1d9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PSEA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PSEA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PSEA_1.28.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.pdf, vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Deconvolution of RNA mixtures in Nature Methods paper, PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.R, vignettes/PSEA/inst/doc/PSEA.R dependencyCount: 8 Package: psichomics Version: 1.20.2 Depends: R (>= 4.0), shiny (>= 1.7.0), shinyBS Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster, colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR, fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices, highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma, pairsD3, plyr, purrr, Rcpp (>= 0.12.14), recount, Rfast, R.utils, reshape2, shinyjs, stringr, stats, SummarizedExperiment, survival, tools, utils, XML, xtable, methods LinkingTo: Rcpp Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car, rstudioapi, spelling License: MIT + file LICENSE MD5sum: ee3388c3c8336589bbbe308dc490028b NeedsCompilation: yes Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, ImmunoOncology, Visualization, MultipleComparison, GeneExpression, DifferentialExpression Author: Nuno Saraiva-Agostinho [aut, cre] (), 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 git_url: https://git.bioconductor.org/packages/psichomics git_branch: RELEASE_3_14 git_last_commit: fcc6503 git_last_commit_date: 2022-03-13 Date/Publication: 2022-03-15 source.ver: src/contrib/psichomics_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/psichomics_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.1/psichomics_1.20.2.tgz vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/custom_data.html, vignettes/psichomics/inst/doc/GUI_tutorial.html vignetteTitles: Preparing an Alternative Splicing Annotation for psichomics, Case study: command-line interface (CLI) tutorial, Loading user-provided data, Case study: visual interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R, vignettes/psichomics/inst/doc/CLI_tutorial.R, vignettes/psichomics/inst/doc/custom_data.R, vignettes/psichomics/inst/doc/GUI_tutorial.R dependencyCount: 203 Package: PSICQUIC Version: 1.32.0 Depends: R (>= 3.2.0), methods, IRanges, biomaRt (>= 2.34.1), BiocGenerics, httr, plyr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: 0a978b51ded747aab9023600ccf4318d NeedsCompilation: no Title: Proteomics Standard Initiative Common QUery InterfaCe Description: PSICQUIC is a project within the HUPO Proteomics Standard Initiative (HUPO-PSI). It standardises programmatic access to molecular interaction databases. biocViews: DataImport, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon git_url: https://git.bioconductor.org/packages/PSICQUIC git_branch: RELEASE_3_14 git_last_commit: 6595a61 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-31 source.ver: src/contrib/PSICQUIC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PSICQUIC_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PSICQUIC_1.32.0.tgz vignettes: vignettes/PSICQUIC/inst/doc/PSICQUIC.pdf vignetteTitles: PSICQUIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSICQUIC/inst/doc/PSICQUIC.R dependencyCount: 72 Package: psygenet2r Version: 1.26.0 Depends: R (>= 3.4) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO, Biobase, labeling, GO.db Suggests: testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 6c7ccc7c5f30d0ba99c667771e2cd1ad 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: RELEASE_3_14 git_last_commit: 79ad2d2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/psygenet2r_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/psygenet2r_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/psygenet2r_1.26.0.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: 104 Package: ptairMS Version: 1.2.0 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: 9fd43b36c95b3d3be013098a328ccba0 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 single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and quantification, feature alignment, missing value imputation and feature annotation. Applications to exhaled air and cell culture in headspace are described in the vignettes and examples. This package was used for data analysis of Gassin Delyle study on adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS), and permit to identfy four potentiel biomarquers of the infection. biocViews: Software, MassSpectrometry, Preprocessing, Metabolomics, PeakDetection, Alignment Author: camille Roquencourt [aut, cre] Maintainer: camille Roquencourt VignetteBuilder: knitr BugReports: https://github.com/camilleroquencourt/ptairMS/issues git_url: https://git.bioconductor.org/packages/ptairMS git_branch: RELEASE_3_14 git_last_commit: 0f1883b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ptairMS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ptairMS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ptairMS_1.2.0.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: 182 Package: PubScore Version: 1.6.0 Depends: R (>= 4.0.0) Imports: ggplot2, igraph, ggrepel,rentrez, progress, graphics, dplyr, utils, methods, intergraph, network, sna Suggests: FCBF, plotly, SummarizedExperiment, SingleCellExperiment, knitr, rmarkdown, testthat (>= 2.1.0), BiocManager, biomaRt License: MIT + file LICENSE Archs: i386, x64 MD5sum: af75bb615276c02d878c76468ab622d1 NeedsCompilation: no Title: Automatic calculation of literature relevance of genes Description: Calculates the importance score for a given gene. The importance score is the total counts of articles in the pubmed database that are a result for that gene AND each term of a list. biocViews: GeneSetEnrichment, GeneExpression, SystemsBiology, Genetics, Epigenetics, BiomedicalInformatics, Visualization, SingleCell Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PubScore git_branch: RELEASE_3_14 git_last_commit: 9794604 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PubScore_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PubScore_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PubScore_1.6.0.tgz vignettes: vignettes/PubScore/inst/doc/PubScore_vignette.html vignetteTitles: FCBF : Fast Correlation Based Filter for Feature Selection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PubScore/inst/doc/PubScore_vignette.R dependencyCount: 64 Package: pulsedSilac Version: 1.8.0 Depends: R (>= 3.6.0) Imports: robustbase, methods, R.utils, taRifx, S4Vectors, SummarizedExperiment, ggplot2, ggridges, stats, utils, UpSetR, cowplot, grid, MuMIn Suggests: testthat (>= 2.1.0), knitr, rmarkdown, gridExtra License: GPL-3 MD5sum: ce058776a4e7ade7ceb960b7ef0ece0d NeedsCompilation: no Title: Analysis of pulsed-SILAC quantitative proteomics data Description: This package provides several tools for pulsed-SILAC data analysis. Functions are provided to organize the data, calculate isotope ratios, isotope fractions, model protein turnover, compare turnover models, estimate cell growth and estimate isotope recycling. Several visualization tools are also included to do basic data exploration, quality control, condition comparison, individual model inspection and model comparison. biocViews: Proteomics Author: Marc Pagès-Gallego, Tobias B. Dansen Maintainer: Marc Pagès-Gallego VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pulsedSilac git_branch: RELEASE_3_14 git_last_commit: 69698be git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pulsedSilac_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pulsedSilac_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pulsedSilac_1.8.0.tgz vignettes: vignettes/pulsedSilac/inst/doc/pulsedsilac.html vignetteTitles: Pulsed-SILAC data analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pulsedSilac/inst/doc/pulsedsilac.R dependencyCount: 68 Package: puma Version: 3.36.0 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: b4447c82c64c3591a452a0ded6fc4bc4 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: RELEASE_3_14 git_last_commit: 661a7da git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/puma_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/puma_3.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/puma_3.36.0.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: 55 Package: PureCN Version: 2.0.2 Depends: R (>= 3.5.0), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, GenomeInfoDb, GenomicFeatures, Rsamtools, Biobase, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, methods, mclust, rhdf5, Matrix Suggests: BiocParallel, BiocStyle, PSCBS, R.utils, TxDb.Hsapiens.UCSC.hg19.knownGene, copynumber, covr, knitr, optparse, org.Hs.eg.db, jsonlite, markdown, rmarkdown, testthat Enhances: genomicsdb (>= 0.0.3) License: Artistic-2.0 Archs: i386, x64 MD5sum: 004d32d83ca29ba8ff1feac7fe4f5150 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: RELEASE_3_14 git_last_commit: bbf93a7 git_last_commit_date: 2022-03-01 Date/Publication: 2022-03-03 source.ver: src/contrib/PureCN_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/PureCN_2.0.2.zip mac.binary.ver: bin/macosx/contrib/4.1/PureCN_2.0.2.tgz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf, vignettes/PureCN/inst/doc/Quick.html vignetteTitles: Overview of the PureCN R package, Best practices,, quick start and command line usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R, vignettes/PureCN/inst/doc/Quick.R dependencyCount: 120 Package: pvac Version: 1.42.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: eeeaf00ad1e555cdade3ddd03e9a5375 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: RELEASE_3_14 git_last_commit: 9c15a4d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pvac_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pvac_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pvac_1.42.0.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: 1.34.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: 02d57b9292330d623449d9455e5e0d2c 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: RELEASE_3_14 git_last_commit: f673aeb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pvca_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pvca_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pvca_1.34.0.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: proBatch, ExpressionNormalizationWorkflow, statVisual dependencyCount: 70 Package: Pviz Version: 1.28.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 Archs: i386, x64 MD5sum: 2457dbf896b310dfb15c342bf8547054 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: RELEASE_3_14 git_last_commit: 18cff4d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Pviz_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Pviz_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Pviz_1.28.0.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: 142 Package: PWMEnrich Version: 4.30.0 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: a8f0feeacd9ff5d2bfe90f2ab596a00b 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: RELEASE_3_14 git_last_commit: 0cfd045 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/PWMEnrich_4.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/PWMEnrich_4.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/PWMEnrich_4.30.0.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: 23 Package: pwOmics Version: 1.26.0 Depends: R (>= 3.2) Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots, Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub, GenomicRanges, graph, grDevices, stats, utils Suggests: ebdbNet, longitudinal, Mfuzz License: GPL (>= 2) MD5sum: f74887f81b6c10d30e9afb09210f21c0 NeedsCompilation: no Title: Pathway-based data integration of omics data Description: pwOmics performs pathway-based level-specific data comparison of matching omics data sets based on pre-analysed user-specified lists of differential genes/transcripts and phosphoproteins. A separate downstream analysis of phosphoproteomic data including pathway identification, transcription factor identification and target gene identification is opposed to the upstream analysis starting with gene or transcript information as basis for identification of upstream transcription factors and potential proteomic regulators. The cross-platform comparative analysis allows for comprehensive analysis of single time point experiments and time-series experiments by providing static and dynamic analysis tools for data integration. In addition, it provides functions to identify individual signaling axes based on data integration. biocViews: SystemsBiology, Transcription, GeneTarget, GeneSignaling Author: Astrid Wachter Maintainer: Maren Sitte git_url: https://git.bioconductor.org/packages/pwOmics git_branch: RELEASE_3_14 git_last_commit: b5d245a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pwOmics_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pwOmics_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pwOmics_1.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 115 Package: pwrEWAS Version: 1.8.0 Depends: shinyBS, foreach Imports: doParallel, abind, truncnorm, CpGassoc, shiny, ggplot2, parallel, shinyWidgets, BiocManager, doSNOW, limma, genefilter, stats, grDevices, methods, utils, graphics, pwrEWAS.data Suggests: knitr, RUnit, BiocGenerics, rmarkdown License: Artistic-2.0 MD5sum: 54ed8a287ca9d2ec515a6f7d72b7582c NeedsCompilation: no Title: A user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS) Description: pwrEWAS is a user-friendly tool to assists researchers in the design and planning of EWAS to help circumvent under- and overpowered studies. biocViews: DNAMethylation, Microarray, DifferentialMethylation, TissueMicroarray Author: Stefan Graw Maintainer: Stefan Graw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pwrEWAS git_branch: RELEASE_3_14 git_last_commit: 5f58e55 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/pwrEWAS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/pwrEWAS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/pwrEWAS_1.8.0.tgz vignettes: vignettes/pwrEWAS/inst/doc/pwrEWAS.pdf vignetteTitles: pwrEWAS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwrEWAS/inst/doc/pwrEWAS.R dependencyCount: 122 Package: qckitfastq Version: 1.10.0 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: 2ed21e3840f0d376ec0530f904546152 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: RELEASE_3_14 git_last_commit: d824d8b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/qckitfastq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qckitfastq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qckitfastq_1.10.0.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: 52 Package: qcmetrics Version: 1.32.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle, rmarkdown License: GPL-2 MD5sum: 5f963782532dbbed8caa67c0b3076fb5 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: RELEASE_3_14 git_last_commit: 797b50c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/qcmetrics_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qcmetrics_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qcmetrics_1.32.0.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: 23 Package: QDNAseq Version: 1.30.0 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: 669a507701794f069409d05226318b15 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: RELEASE_3_14 git_last_commit: 9a1739a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/QDNAseq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QDNAseq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QDNAseq_1.30.0.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, HiCcompare dependencyCount: 48 Package: QFeatures Version: 1.4.0 Depends: R (>= 4.0), MultiAssayExperiment Imports: methods, stats, utils, S4Vectors, IRanges, SummarizedExperiment, BiocGenerics, ProtGenerics (>= 1.19.3), AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.1.2), igraph, plotly Suggests: SingleCellExperiment, HDF5Array, msdata, ggplot2, gplots, dplyr, limma, magrittr, DT, shiny, shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute, norm, ComplexHeatmap License: Artistic-2.0 MD5sum: 2efbbffae2bc7e22fac89bcfe56801ea 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: RELEASE_3_14 git_last_commit: 7a02fd7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/QFeatures_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QFeatures_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QFeatures_1.4.0.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 dependencyCount: 87 Package: qpcrNorm Version: 1.52.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) Archs: i386, x64 MD5sum: 3d03197726d16254a88f320516b7dd72 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: RELEASE_3_14 git_last_commit: 3c4c33e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/qpcrNorm_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qpcrNorm_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qpcrNorm_1.52.0.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: 13 Package: qpgraph Version: 2.28.2 Depends: R (>= 3.5) Imports: methods, parallel, Matrix (>= 1.0), 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: 3d3b779b157c102e67146168b15742b0 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: RELEASE_3_14 git_last_commit: b8a47b2 git_last_commit_date: 2022-04-05 Date/Publication: 2022-04-07 source.ver: src/contrib/qpgraph_2.28.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/qpgraph_2.28.2.zip mac.binary.ver: bin/macosx/contrib/4.1/qpgraph_2.28.2.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, simPATHy, topologyGSA dependencyCount: 102 Package: qPLEXanalyzer Version: 1.12.1 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, testthat, UniProt.ws, vdiffr License: GPL-2 MD5sum: 90212b1b36d3187a516c5e1b2c299db3 NeedsCompilation: no Title: Tools for qPLEX-RIME data analysis Description: Tools for quantitative proteomics data analysis generated from qPLEX-RIME method. 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: RELEASE_3_14 git_last_commit: 999e117 git_last_commit_date: 2022-03-16 Date/Publication: 2022-03-17 source.ver: src/contrib/qPLEXanalyzer_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/qPLEXanalyzer_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/qPLEXanalyzer_1.12.1.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: 103 Package: qrqc Version: 1.48.0 Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, testthat Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats LinkingTo: Rhtslib (>= 1.15.3) License: GPL (>=2) MD5sum: 2d76260e4d2ae531f32b2e8aed676146 NeedsCompilation: yes Title: Quick Read Quality Control Description: Quickly scans reads and gathers statistics on base and quality frequencies, read length, k-mers by position, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected. biocViews: Sequencing, QualityControl, DataImport, Preprocessing, Visualization Author: Vince Buffalo Maintainer: Vince Buffalo URL: http://github.com/vsbuffalo/qrqc SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/qrqc git_branch: RELEASE_3_14 git_last_commit: 351c639 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/qrqc_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qrqc_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qrqc_1.48.0.tgz vignettes: vignettes/qrqc/inst/doc/qrqc.pdf vignetteTitles: Using the qrqc package to gather information about sequence qualities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qrqc/inst/doc/qrqc.R dependencyCount: 157 Package: qsea Version: 1.20.0 Depends: R (>= 3.5) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, GenomeInfoDb, BiocGenerics, grDevices, zoo, BiocParallel Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown, BiocManager, MASS License: GPL (>=2) MD5sum: dbe409d81123d6662e6193cb61dda584 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, Lukas Chavez, Ralf Herwig Maintainer: Matthias Lienhard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsea git_branch: RELEASE_3_14 git_last_commit: ef9f9f7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/qsea_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qsea_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qsea_1.20.0.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: 50 Package: qsmooth Version: 1.10.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics, Hmisc Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: CC BY 4.0 Archs: i386, x64 MD5sum: 1108f581a59e6ccabc2edf215c2b2730 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: RELEASE_3_14 git_last_commit: c5c5e94 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/qsmooth_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qsmooth_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qsmooth_1.10.0.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: 118 Package: QSutils Version: 1.12.0 Depends: R (>= 3.5), Biostrings, BiocGenerics,methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: file LICENSE MD5sum: a8c9090a7905dbf9de0a88cace64bc9f 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: RELEASE_3_14 git_last_commit: 3942c07 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/QSutils_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QSutils_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QSutils_1.12.0.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 dependencyCount: 27 Package: Qtlizer Version: 1.8.1 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 82839cd28f6dcfd47c6b077e2200b125 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: RELEASE_3_14 git_last_commit: c8c8226 git_last_commit_date: 2022-02-28 Date/Publication: 2022-03-01 source.ver: src/contrib/Qtlizer_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/Qtlizer_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/Qtlizer_1.8.1.tgz vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html vignetteTitles: Qtlizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R dependencyCount: 25 Package: quantiseqr Version: 1.2.0 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 Archs: i386, x64 MD5sum: 491fd0556b0cf1dd6b530833db4ba950 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: RELEASE_3_14 git_last_commit: 6a0a48c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/quantiseqr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/quantiseqr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/quantiseqr_1.2.0.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: 65 Package: quantro Version: 1.28.0 Depends: R (>= 4.0) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: rmarkdown, knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: 3ca2f3153c822b647e14e3cd506959c6 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: RELEASE_3_14 git_last_commit: 109e745 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/quantro_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/quantro_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/quantro_1.28.0.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: qsmooth dependencyCount: 153 Package: quantsmooth Version: 1.60.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: ff9b35a5b28b149dac2afd748d45fa0e 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: RELEASE_3_14 git_last_commit: c5d7cb4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/quantsmooth_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/quantsmooth_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/quantsmooth_1.60.0.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 dependsOnMe: beadarraySNP importsMe: GWASTools, SIM suggestsMe: PREDA dependencyCount: 11 Package: QuartPAC Version: 1.26.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 13550edc255fac25cdbfb28136cac103 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: RELEASE_3_14 git_last_commit: c5955a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/QuartPAC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QuartPAC_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QuartPAC_1.26.0.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: 42 Package: QuasR Version: 1.34.0 Depends: R (>= 4.1), parallel, GenomicRanges, Rbowtie Imports: methods, grDevices, graphics, utils, stats, tools, BiocGenerics, S4Vectors, IRanges, Biobase, Biostrings, BSgenome, Rsamtools, GenomicFeatures, ShortRead, BiocParallel, GenomeInfoDb, rtracklayer, GenomicFiles, AnnotationDbi LinkingTo: Rhtslib Suggests: Gviz, BiocStyle, GenomicAlignments, Rhisat2, knitr, rmarkdown, covr, testthat License: GPL-2 MD5sum: 24a11245817c97a67c2b1c93c12aa945 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. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology Author: Anita Lerch [aut], Charlotte Soneson [aut] (), Dimos Gaidatzis [aut], Michael Stadler [aut, cre] () Maintainer: Michael Stadler SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuasR git_branch: RELEASE_3_14 git_last_commit: d22d4ad git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/QuasR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QuasR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QuasR_1.34.0.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: 105 Package: QuaternaryProd Version: 1.28.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) MD5sum: b3141c4fea1dbac10a8060d892f57811 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: RELEASE_3_14 git_last_commit: 2814921 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/QuaternaryProd_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QuaternaryProd_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QuaternaryProd_1.28.0.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: 23 Package: QUBIC Version: 1.22.0 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: 45343b17d3c8ff6652333e00b1a5bab0 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: RELEASE_3_14 git_last_commit: d547224 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/QUBIC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/QUBIC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/QUBIC_1.22.0.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: 2.28.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans, fftw License: GPL (>= 2) MD5sum: 1dbf6706172b9aa2ab0163d73c5ce94c 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: RELEASE_3_14 git_last_commit: 6203e48 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/qusage_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qusage_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qusage_2.28.0.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: NanoTube, SigCheck dependencyCount: 17 Package: qvalue Version: 2.26.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL Archs: x64 MD5sum: eba1a1d2e0139382b950687e09ad68c7 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: RELEASE_3_14 git_last_commit: 6d7410d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/qvalue_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/qvalue_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/qvalue_2.26.0.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 importsMe: Anaquin, anota, clusterProfiler, derfinder, DOSE, edge, epihet, erccdashboard, EventPointer, FindIT2, fishpond, metaseqR2, methylKit, MOMA, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, RiboDiPA, RNAsense, Rnits, SDAMS, sights, signatureSearch, subSeq, trigger, webbioc, IHWpaper, AEenrich, armada, cancerGI, DGEobj.utils, fdrDiscreteNull, glmmSeq, groupedSurv, HDMT, jaccard, jackstraw, NBPSeq, SeqFeatR, ssizeRNA suggestsMe: biobroom, LBE, maanova, PREDA, RnBeads, SummarizedBenchmark, swfdr, RNAinteractMAPK, BootstrapQTL, CpGassoc, dartR, easylabel, familiar, matR, mutoss, Rediscover, seqgendiff, wrMisc dependencyCount: 44 Package: R3CPET Version: 1.26.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), ggbio LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) Archs: i386, x64 MD5sum: 00c3bfb50dfe0d146e50f86b490b7d3e 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: RELEASE_3_14 git_last_commit: bbe68cd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/R3CPET_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/R3CPET_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/R3CPET_1.26.0.tgz 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: 157 Package: r3Cseq Version: 1.40.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, GenomeInfoDb, IRanges, Biostrings, data.table, sqldf, RColorBrewer Suggests: BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5.masked License: GPL-3 Archs: i386, x64 MD5sum: c4e96fde58c588d59c0e43c66b11c19f 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: RELEASE_3_14 git_last_commit: 4ade9b7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/r3Cseq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/r3Cseq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/r3Cseq_1.40.0.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: 94 Package: R453Plus1Toolbox Version: 1.44.0 Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), 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: 6ee7403a662de8435f00f6e5ab1188c2 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: RELEASE_3_14 git_last_commit: e86d2df git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/R453Plus1Toolbox_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/R453Plus1Toolbox_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/R453Plus1Toolbox_1.44.0.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: 106 Package: R4RNA Version: 1.22.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: c37956292259fa44b139cd7a1e6a339e 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: RELEASE_3_14 git_last_commit: a797ad8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/R4RNA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/R4RNA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/R4RNA_1.22.0.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 suggestsMe: rfaRm dependencyCount: 18 Package: RadioGx Version: 1.4.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, 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 Archs: i386, x64 MD5sum: 66fb5701cd7381ff7dc13a2c7b7a69b5 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], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RadioGx git_branch: RELEASE_3_14 git_last_commit: b51a819 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RadioGx_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RadioGx_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RadioGx_1.4.0.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: 135 Package: RaggedExperiment Version: 1.18.0 Depends: R (>= 3.6.0), GenomicRanges (>= 1.37.17) Imports: BiocGenerics, GenomeInfoDb, IRanges, Matrix, MatrixGenerics, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, MultiAssayExperiment License: Artistic-2.0 MD5sum: 9745951a87beeaa4e5b25bda73581f01 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. biocViews: Infrastructure, DataRepresentation Author: Martin Morgan [aut, cre], Marcel Ramos [aut] Maintainer: Martin Morgan VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues git_url: https://git.bioconductor.org/packages/RaggedExperiment git_branch: RELEASE_3_14 git_last_commit: c6fdb32 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RaggedExperiment_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RaggedExperiment_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RaggedExperiment_1.18.0.tgz vignettes: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html vignetteTitles: RaggedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R dependsOnMe: CNVRanger, compartmap importsMe: cBioPortalData, omicsPrint, RTCGAToolbox, TCGAutils suggestsMe: maftools, MultiAssayExperiment, MultiDataSet, curatedTCGAData, SingleCellMultiModal dependencyCount: 25 Package: rain Version: 1.28.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 61f06dc82b1c543f3b45e606efb30aa2 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: RELEASE_3_14 git_last_commit: 2451fb4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rain_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rain_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rain_1.28.0.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: rama Version: 1.68.0 Depends: R(>= 2.5.0) License: GPL (>= 2) MD5sum: 820266c74abdf831193ed4c0c21a12a6 NeedsCompilation: yes Title: Robust Analysis of MicroArrays Description: Robust estimation of cDNA microarray intensities with replicates. The package uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/rama git_branch: RELEASE_3_14 git_last_commit: ec6ec60 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rama_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rama_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rama_1.68.0.tgz vignettes: vignettes/rama/inst/doc/rama.pdf vignetteTitles: rama Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rama/inst/doc/rama.R dependsOnMe: bridge dependencyCount: 0 Package: ramr Version: 1.2.0 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: 5561701a12ff50eced60d875ea36efcd 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: RELEASE_3_14 git_last_commit: 5594008 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ramr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ramr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ramr_1.2.0.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: 68 Package: ramwas Version: 1.18.0 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: 80bf87847ae3a6aa07b8e3f6d878a014 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: RELEASE_3_14 git_last_commit: 9b57366 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ramwas_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ramwas_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ramwas_1.18.0.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: 100 Package: RandomWalkRestartMH Version: 1.14.0 Depends: R(>= 3.5.0) Imports: igraph, Matrix, dnet, methods Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 2) MD5sum: 2f58adc0e82917ab98e431ecd49d9f73 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 git_url: https://git.bioconductor.org/packages/RandomWalkRestartMH git_branch: RELEASE_3_14 git_last_commit: a2bfc3c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RandomWalkRestartMH_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RandomWalkRestartMH_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RandomWalkRestartMH_1.14.0.tgz vignettes: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH.html vignetteTitles: Random Walk with Restart on Multiplex and Heterogeneous Network hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH.R importsMe: netOmics dependencyCount: 54 Package: randPack Version: 1.40.0 Depends: methods Imports: Biobase License: Artistic 2.0 Archs: i386, x64 MD5sum: c2f07e2b1c1364e1b9c858a433ec40b3 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: RELEASE_3_14 git_last_commit: 4ded249 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/randPack_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/randPack_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/randPack_1.40.0.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: 1.6.0 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: 73b5fff0907f489efd60adfed139c2b7 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: RELEASE_3_14 git_last_commit: bf164f2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/randRotation_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/randRotation_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/randRotation_1.6.0.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: 3.20.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: d30692d9462afff7e47790baeafc3785 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: RELEASE_3_14 git_last_commit: 6696662 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RankProd_3.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RankProd_3.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RankProd_3.20.0.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: POMA, synlet, INCATome dependencyCount: 6 Package: RareVariantVis Version: 2.22.0 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: f67f2cadf9299b4c712154c003251948 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: RELEASE_3_14 git_last_commit: 60c034b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RareVariantVis_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RareVariantVis_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RareVariantVis_2.22.0.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: 130 Package: rawrr Version: 1.2.0 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: fee72b928a088cf5607dfec7c2f31cd0 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 (Kockmann T. et al. (2020) ). 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 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: RELEASE_3_14 git_last_commit: 6744b0f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rawrr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rawrr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rawrr_1.2.0.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: 1.62.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: cbaf1cc7b98efe8c587e31194f18acf8 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: RELEASE_3_14 git_last_commit: 33d7077 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RbcBook1_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RbcBook1_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RbcBook1_1.62.0.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: 1.2.0 Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach, grDevices, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 78edb51897d769fb6ec9e8e70168b1bb 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: RELEASE_3_14 git_last_commit: 551f02b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rbec_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rbec_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rbec_1.2.0.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: 95 Package: RBGL Version: 1.70.0 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: cef31518006ab15a800977ba1dfc4a56 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 , Li Long , R. Gentleman Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/RBGL git_branch: RELEASE_3_14 git_last_commit: 9cfd5fd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RBGL_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RBGL_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RBGL_1.70.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.pdf vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, fgga, pkgDepTools, archeofrag, PerfMeas, QuACN, RSeed, SubpathwayLNCE importsMe: alpine, BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, clipper, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace, GAPGOM, GeneAnswers, GOSim, GOstats, MIGSA, NCIgraph, OrganismDbi, pkgDepTools, predictionet, RpsiXML, Streamer, VariantFiltering, BiDAG, eff2, gRbase, HEMDAG, netgwas, pcalg, rags2ridges, RANKS, SID, wiseR suggestsMe: DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools, yeastExpData, gRc, maGUI dependencyCount: 8 Package: RBioinf Version: 1.54.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 MD5sum: faaaa86eb166dee3b5d378b884464a12 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: RELEASE_3_14 git_last_commit: e5acd0f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RBioinf_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RBioinf_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RBioinf_1.54.0.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: 2.34.0 Depends: R (>= 4.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph License: GPL (>= 2) MD5sum: 71f696900c8f0f63fc27a9e98a48e2ed 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: RELEASE_3_14 git_last_commit: 4dc794d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rBiopaxParser_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rBiopaxParser_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rBiopaxParser_2.34.0.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: RBM Version: 1.26.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) Archs: i386, x64 MD5sum: 80f168ba6cf5d8d5d99ab4259249d692 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: RELEASE_3_14 git_last_commit: 5e37925 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RBM_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RBM_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RBM_1.26.0.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: 7 Package: Rbowtie Version: 1.34.0 Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 30a5ba37aad189544b221e14d5a1637c 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, Anita Lerch, Michael B Stadler 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: RELEASE_3_14 git_last_commit: bd322b4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rbowtie_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rbowtie_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rbowtie_1.34.0.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: MACPET, multicrispr suggestsMe: eisaR dependencyCount: 0 Package: Rbowtie2 Version: 2.0.0 Depends: R (>= 4.1.0) Imports: magrittr, Rsamtools Suggests: knitr, testthat (>= 3.0.0), rmarkdown License: GPL (>= 3) Archs: i386, x64 MD5sum: 7bc47a0722aef62a0d453b4688a7c869 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: RELEASE_3_14 git_last_commit: 67ee493 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rbowtie2_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rbowtie2_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rbowtie2_2.0.0.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: esATAC, UMI4Cats dependencyCount: 30 Package: rbsurv Version: 2.52.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 9686f69dbba41a3228e87212a498ff0e 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: RELEASE_3_14 git_last_commit: cf3df1b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rbsurv_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rbsurv_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rbsurv_2.52.0.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: Rcade Version: 1.36.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Rsamtools, baySeq Imports: utils, grDevices, stats, graphics, rgl, plotrix, S4Vectors (>= 0.23.19), IRanges, GenomeInfoDb, GenomicAlignments Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: 2032fb0c2ed16549314c2e2f0ba5173e NeedsCompilation: no Title: R-based analysis of ChIP-seq And Differential Expression - a tool for integrating a count-based ChIP-seq analysis with differential expression summary data Description: Rcade (which stands for "R-based analysis of ChIP-seq And Differential Expression") is a tool for integrating ChIP-seq data with differential expression summary data, through a Bayesian framework. A key application is in identifing the genes targeted by a transcription factor of interest - that is, we collect genes that are associated with a ChIP-seq peak, and differential expression under some perturbation related to that TF. biocViews: DifferentialExpression, GeneExpression, Transcription, ChIPSeq, Sequencing, Genetics Author: Jonathan Cairns Maintainer: Jonathan Cairns git_url: https://git.bioconductor.org/packages/Rcade git_branch: RELEASE_3_14 git_last_commit: 8803391 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rcade_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rcade_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rcade_1.36.0.tgz vignettes: vignettes/Rcade/inst/doc/Rcade.pdf vignetteTitles: Rcade Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcade/inst/doc/Rcade.R dependencyCount: 63 Package: RCAS Version: 1.20.0 Depends: R (>= 3.3.0), plotly (>= 4.5.2), DT (>= 0.2), data.table, Imports: GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer, GenomicFeatures, rmarkdown (>= 0.9.5), genomation (>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, plotrix, pbapply, RSQLite, proxy, pheatmap, ggplot2, cowplot, ggseqlogo, utils, ranger, gprofiler2 Suggests: testthat, covr License: Artistic-2.0 Archs: x64 MD5sum: 27e61c612e81e74b004a3dea3e745f5f 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: RELEASE_3_14 git_last_commit: 1d023c7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RCAS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCAS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCAS_1.20.0.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 dependencyCount: 155 Package: RCASPAR Version: 1.40.0 License: GPL (>=3) Archs: x64 MD5sum: 34a9339f438c9a5155b77b21945519b7 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: RELEASE_3_14 git_last_commit: 9cf9219 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RCASPAR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCASPAR_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCASPAR_1.40.0.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: 2.16.0 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 MD5sum: 1131fd12828217a51d1fdaafc4ac72fc 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: RELEASE_3_14 git_last_commit: 9cb3b16 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rcellminer_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rcellminer_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rcellminer_2.16.0.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: 1.24.0 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: fd4f8dfe9de5876a2a5bfda0c3b72daf 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: RELEASE_3_14 git_last_commit: ee2a9d3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rCGH_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rCGH_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rCGH_1.24.0.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 suggestsMe: excluderanges dependencyCount: 140 Package: RcisTarget Version: 1.14.0 Depends: R (>= 3.5.0) Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, feather, graphics, GenomeInfoDb, GenomicRanges, arrow (>= 2.0.0), dplyr, tibble, GSEABase, methods, R.utils, stats, SummarizedExperiment, utils Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, foreach, gplots, rtracklayer, igraph, knitr, RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat, visNetwork Enhances: doMC, doRNG, zoo License: GPL-3 MD5sum: 9d1fb49551dcade063c5b2b767b933d2 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: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr BugReports: https://github.com/aertslab/RcisTarget/issues git_url: https://git.bioconductor.org/packages/RcisTarget git_branch: RELEASE_3_14 git_last_commit: 46ebc61 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RcisTarget_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RcisTarget_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RcisTarget_1.14.0.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 regiions, 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: 101 Package: RCM Version: 1.10.0 Depends: R (>= 3.6.0) Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, VGAM, ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, stats, grDevices, graphics, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 43dc0e6f3551945aeb0c62893d558865 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. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization Author: Stijn Hawinkel Maintainer: Joris Meys 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: RELEASE_3_14 git_last_commit: a753919 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RCM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCM_1.10.0.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: 92 Package: Rcpi Version: 1.30.0 Depends: R (>= 3.0.2) Imports: stats, utils, methods, RCurl, rjson, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR, rcdk (>= 3.3.8) Suggests: knitr, rmarkdown, RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 | file LICENSE MD5sum: b189954e2b56d7070d514879bdaa80ba NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: Rcpi offers a molecular informatics toolkit with a comprehensive 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: RELEASE_3_14 git_last_commit: d21a4c1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rcpi_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rcpi_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rcpi_1.30.0.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: 101 Package: RCSL Version: 1.2.0 Depends: R (>= 4.1) Imports: RcppAnnoy, igraph, NbClust, Rtsne, ggplot2, methods, pracma, umap, grDevices, graphics, stats Suggests: knitr, rmarkdown, mclust, RcppAnnoy License: GPL-3 MD5sum: 16a90ade20c7367fdf34845786bc9ad1 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: RELEASE_3_14 git_last_commit: 40bea04 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RCSL_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCSL_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCSL_1.2.0.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: 57 Package: Rcwl Version: 1.10.0 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: 7cd1445d83582880140cb413717cb7df 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: RELEASE_3_14 git_last_commit: 36acaa0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rcwl_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/Rcwl_1.10.0.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 dependencyCount: 116 Package: RcwlPipelines Version: 1.10.0 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: rappdirs, methods, utils, git2r, httr, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: de9d0e1a08727907706ab86a8724c4f5 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: RELEASE_3_14 git_last_commit: 02f40ac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RcwlPipelines_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/RcwlPipelines_1.10.0.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 dependencyCount: 131 Package: RCy3 Version: 2.14.2 Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, stats, graph, fs, uuid, uchardet, glue, RCurl, base64url, base64enc, IRkernel, IRdisplay, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, igraph, grDevices License: MIT + file LICENSE MD5sum: c84923d5e3cac785d161856d584ff81e 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: RELEASE_3_14 git_last_commit: e8a1789 git_last_commit_date: 2022-03-01 Date/Publication: 2022-03-13 source.ver: src/contrib/RCy3_2.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCy3_2.14.2.zip mac.binary.ver: bin/macosx/contrib/4.1/RCy3_2.14.2.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/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, 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/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, fedup, MOGAMUN, NCIgraph, regutools, TimiRGeN, transomics2cytoscape, lilikoi, netgsa, ScriptMapR suggestsMe: graphite, netDx, rScudo, sparsebnUtils dependencyCount: 46 Package: RCyjs Version: 2.16.0 Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0) Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: d52fe6982c77b31eaa94036973c07da8 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: RELEASE_3_14 git_last_commit: b8e3abf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RCyjs_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RCyjs_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RCyjs_2.16.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.html vignetteTitles: "RCyjs: programmatic access to the web browser-based network viewer,, cytoscape.js" hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R dependencyCount: 17 Package: rDGIdb Version: 1.20.0 Imports: jsonlite,httr,methods,graphics Suggests: BiocStyle,knitr,testthat License: MIT + file LICENSE MD5sum: 5574a3f9fd695ffe91660f9f6cd8903c 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: RELEASE_3_14 git_last_commit: 043cfd6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rDGIdb_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rDGIdb_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rDGIdb_1.20.0.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: 1.54.0 Depends: R (>= 2.0.0), Rcpp LinkingTo: Rcpp Suggests: RUnit License: GPL-2 MD5sum: 8d4380b811fc4cedf72e3a5f9422b00a 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: RELEASE_3_14 git_last_commit: a0c52f0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rdisop_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rdisop_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rdisop_1.54.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE importsMe: CorMID, enviGCMS, HiResTEC, InterpretMSSpectrum, MetaDBparse suggestsMe: adductomicsR, MSnbase, RforProteomics dependencyCount: 3 Package: RDRToolbox Version: 1.44.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 2bcb79bdb2b603d3242e1772c3ac39c0 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: RELEASE_3_14 git_last_commit: b6c59be git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RDRToolbox_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RDRToolbox_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RDRToolbox_1.44.0.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: 25 Package: ReactomeContentService4R Version: 1.2.0 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: 67ce22e6bd934e88cd936d7cace4e4b4 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: RELEASE_3_14 git_last_commit: d1410f3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ReactomeContentService4R_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReactomeContentService4R_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReactomeContentService4R_1.2.0.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: 1.2.0 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) MD5sum: 700991bcbbf0dc0d2b38502d13dd9ceb 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: RELEASE_3_14 git_last_commit: a5491ca git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ReactomeGraph4R_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReactomeGraph4R_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReactomeGraph4R_1.2.0.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: 69 Package: ReactomeGSA Version: 1.8.0 Imports: jsonlite, httr, progress, ggplot2, methods, gplots, RColorBrewer, dplyr, tidyr Suggests: testthat, knitr, rmarkdown, ReactomeGSA.data, Biobase, devtools Enhances: limma, edgeR, Seurat (>= 3.0), scater License: MIT + file LICENSE Archs: i386, x64 MD5sum: a08b0d6defdff506d11cd09f7b27ac38 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: RELEASE_3_14 git_last_commit: 51c41bd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ReactomeGSA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReactomeGSA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReactomeGSA_1.8.0.tgz vignettes: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.html, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.html vignetteTitles: Analysing single-cell RNAseq data, Using the ReactomeGSA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.R, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.R dependsOnMe: ReactomeGSA.data dependencyCount: 60 Package: ReactomePA Version: 1.38.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2, ggraph, reactome.db, igraph, graphite Suggests: BiocStyle, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: b3ef5b0f74dc2c9a16b94edb6a73ada1 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. 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: RELEASE_3_14 git_last_commit: b0aae92 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ReactomePA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReactomePA_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReactomePA_1.38.0.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, epihet, miRspongeR, multiSight, Pigengene, scTensor, ExpHunterSuite suggestsMe: ChIPseeker, CINdex, clusterProfiler, cola, scGPS dependencyCount: 129 Package: ReadqPCR Version: 1.40.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 MD5sum: 45f2c9eb8cb7dba3e80a99b825cd677f 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: RELEASE_3_14 git_last_commit: 0fb5fe7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ReadqPCR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReadqPCR_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReadqPCR_1.40.0.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: 1.12.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 4728367076c3af7d0c026bebb34c3fad 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: RELEASE_3_14 git_last_commit: 3d8fc42 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/REBET_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/REBET_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/REBET_1.12.0.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: 1.4.0 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: c913dd3cfa21ac2aeb10964fa7537a2f 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: RELEASE_3_14 git_last_commit: 8c4ac33 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rebook_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rebook_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rebook_1.4.0.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.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows suggestsMe: SingleRBook dependencyCount: 40 Package: receptLoss Version: 1.6.0 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: 699abeb03de31c3d4272af08239437a4 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: RELEASE_3_14 git_last_commit: a303916 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/receptLoss_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/receptLoss_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/receptLoss_1.6.0.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: 61 Package: reconsi Version: 1.6.0 Imports: phyloseq, KernSmooth, reshape2, ggplot2, stats, methods, graphics, grDevices, matrixStats Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 8c91c221eafe82e629f53141ce796e54 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 Maintainer: Joris Meys VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues git_url: https://git.bioconductor.org/packages/reconsi git_branch: RELEASE_3_14 git_last_commit: 353b8ef git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/reconsi_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/reconsi_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/reconsi_1.6.0.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: 79 Package: recount Version: 1.20.0 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, 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 Archs: i386, x64 MD5sum: 882ca0854f07376b27cd75e82cd7524a NeedsCompilation: no Title: Explore and download data from the recount project Description: Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), 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/ git_url: https://git.bioconductor.org/packages/recount git_branch: RELEASE_3_14 git_last_commit: aac8fa9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/recount_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/recount_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/recount_1.20.0.tgz vignettes: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html vignetteTitles: recount quick start guide, Basic DESeq2 results exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R importsMe: psichomics, RNAAgeCalc, recountWorkflow suggestsMe: dasper, ODER, recount3 dependencyCount: 161 Package: recount3 Version: 1.4.0 Depends: SummarizedExperiment Imports: BiocFileCache, methods, rtracklayer, S4Vectors, utils, RCurl, data.table, R.utils, Matrix, GenomicRanges, sessioninfo, tools Suggests: BiocStyle, covr, knitcitations, knitr, RefManageR, rmarkdown, testthat, pryr, interactiveDisplayBase, recount License: Artistic-2.0 MD5sum: f4c8f13cd66832aa48e7f1e890c16c8c 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: RELEASE_3_14 git_last_commit: e4612a2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/recount3_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/recount3_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/recount3_1.4.0.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: 89 Package: recountmethylation Version: 1.4.5 Depends: R (>= 4.1) Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl, R.utils, BiocFileCache, IlluminaHumanMethylation450kmanifest Suggests: knitr, testthat, ggplot2, gridExtra, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub License: Artistic-2.0 MD5sum: ba0aa9f4fa32e7b873f6c2c324f25974 NeedsCompilation: no Title: Access and analyze DNA methylation database compilations Description: Access cross-study compilations of DNA methylation array databases. Database files can be downloaded and accessed using provided functions. Background about database file types (HDF5 and HDF5-SummarizedExperiment), SummarizedExperiment classes, and examples for data handling, validation, and analyses, can be found in the package vignettes. Note the disclaimer on package load, and consult the main manuscript for further info. biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray, ExperimentHub Author: Sean K Maden [cre, aut] (), Reid F Thompson [aut] (), Kasper D Hansen [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: RELEASE_3_14 git_last_commit: 95d24f4 git_last_commit_date: 2022-03-12 Date/Publication: 2022-03-13 source.ver: src/contrib/recountmethylation_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/recountmethylation_1.4.5.zip mac.binary.ver: bin/macosx/contrib/4.1/recountmethylation_1.4.5.tgz vignettes: 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: 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/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: 143 Package: recoup Version: 1.22.0 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, utils Suggests: grid, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager, BSgenome, RMySQL License: GPL (>= 3) MD5sum: a89bd3db9009541c11aaf38b17a4ab8d 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: RELEASE_3_14 git_last_commit: 3eb0470 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/recoup_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/recoup_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/recoup_1.22.0.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: 121 Package: RedeR Version: 1.42.0 Depends: R (>= 3.5), methods Imports: igraph Suggests: pvclust, BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 27bd9083e3a9134cdf8068fd6dfd41e1 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 modular structures, nested networks and multiple levels of hierarchical associations. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Mauro Castro, Xin Wang, Florian Markowetz Maintainer: Mauro Castro URL: http://genomebiology.com/2012/13/4/R29 SystemRequirements: Java Runtime Environment (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedeR git_branch: RELEASE_3_14 git_last_commit: 1bd8c6a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RedeR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RedeR_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RedeR_1.42.0.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: hierarchical network representation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R dependsOnMe: Fletcher2013b, dc3net importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf dependencyCount: 11 Package: REDseq Version: 1.40.0 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: df2ee195246a757c7724294e8c6c644a 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: RELEASE_3_14 git_last_commit: a5a0e6d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/REDseq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/REDseq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/REDseq_1.40.0.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: 124 Package: RefPlus Version: 1.64.0 Depends: R (>= 2.8.0), Biobase (>= 2.1.0), affy (>= 1.20.0), affyPLM (>= 1.18.0), preprocessCore (>= 1.4.0) Suggests: affydata License: GPL (>= 2) MD5sum: 3557e85f27c4857c27a1443525bd9997 NeedsCompilation: no Title: A function set for the Extrapolation Strategy (RMA+) and Extrapolation Averaging (RMA++) methods. Description: The package contains functions for pre-processing Affymetrix data using the RMA+ and the RMA++ methods. biocViews: Microarray, OneChannel, Preprocessing Author: Kai-Ming Chang , Chris Harbron , Marie C South Maintainer: Kai-Ming Chang git_url: https://git.bioconductor.org/packages/RefPlus git_branch: RELEASE_3_14 git_last_commit: 9930f72 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RefPlus_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RefPlus_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RefPlus_1.64.0.tgz vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf vignetteTitles: RefPlus Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefPlus/inst/doc/RefPlus.R dependencyCount: 26 Package: RegEnrich Version: 1.4.0 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 Suggests: GEOquery, rmarkdown, knitr, BiocManager, testthat License: GPL (>= 2) MD5sum: 5cbe760aeea1fec4a31414fdd9dbb5dd 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: RELEASE_3_14 git_last_commit: 1dcf069 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RegEnrich_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RegEnrich_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RegEnrich_1.4.0.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: 147 Package: regioneR Version: 1.26.1 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: 75bf9df182931d69272831dd528a1292 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: RELEASE_3_14 git_last_commit: ef1a9c9 git_last_commit_date: 2022-01-12 Date/Publication: 2022-01-13 source.ver: src/contrib/regioneR_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/regioneR_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.1/regioneR_1.26.1.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 importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots, karyoploteR, RIPAT, RLSeq, UMI4Cats suggestsMe: CNVRanger, MitoHEAR dependencyCount: 49 Package: regionReport Version: 1.28.1 Depends: R(>= 3.2) Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.25.3), DEFormats, DESeq2, GenomeInfoDb, GenomicRanges, knitr (>= 1.6), knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment, utils Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot (>= 1.28.1), 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: 4c3ee8f6adb4c9efce180774f3ff01d5 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: RELEASE_3_14 git_last_commit: df8f531 git_last_commit_date: 2021-11-22 Date/Publication: 2021-11-23 source.ver: src/contrib/regionReport_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/regionReport_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.1/regionReport_1.28.1.tgz vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/regionReport.html vignetteTitles: Example report using bumphunter results, Introduction to regionReport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R, vignettes/regionReport/inst/doc/regionReport.R importsMe: recountWorkflow suggestsMe: recount dependencyCount: 171 Package: regsplice Version: 1.20.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: d6b5b094271c4c209ca1ac27f9a2d077 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: RELEASE_3_14 git_last_commit: 87d7136 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/regsplice_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/regsplice_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/regsplice_1.20.0.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: 39 Package: regutools Version: 1.6.0 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: 106f017a1ebcd6f87cc0fd0f6b4f9b16 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: RELEASE_3_14 git_last_commit: 3795415 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/regutools_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/regutools_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/regutools_1.6.0.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: 170 Package: REMP Version: 1.18.0 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: e36ac9c28f2b2b4ef4f79f7f6165f231 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: RELEASE_3_14 git_last_commit: 3164bd7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/REMP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/REMP_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/REMP_1.18.0.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: 207 Package: Repitools Version: 1.40.0 Depends: R (>= 3.0.0), 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, Ringo, Rsolnp, cluster Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) MD5sum: 48f475f55c85c539c8eed6280c8bd6ff 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: RELEASE_3_14 git_last_commit: 6f54c77 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Repitools_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Repitools_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Repitools_1.40.0.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: 116 Package: ReportingTools Version: 2.34.0 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: e2375eb69130505b274ab04163efeb29 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, knitr git_url: https://git.bioconductor.org/packages/ReportingTools git_branch: RELEASE_3_14 git_last_commit: fb5aef0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ReportingTools_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ReportingTools_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ReportingTools_2.34.0.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, vignettes/ReportingTools/inst/doc/knitr.html vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny, Knitr and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R, vignettes/ReportingTools/inst/doc/knitr.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R dependsOnMe: rnaseqGene importsMe: affycoretools suggestsMe: cpvSNP, EnrichmentBrowser, GSEABase, npGSEA dependencyCount: 173 Package: RepViz Version: 1.10.0 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: 1f39495d8338d6d2b7f843fffa12cc57 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: RELEASE_3_14 git_last_commit: dd8d297 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RepViz_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RepViz_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RepViz_1.10.0.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: ReQON Version: 1.40.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: 96395866b5fafe9005b0e46a3de56f5b NeedsCompilation: no Title: Recalibrating Quality Of Nucleotides Description: Algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format. biocViews: Sequencing, HighThroughputSequencing, Preprocessing, QualityControl Author: Christopher Cabanski, Keary Cavin, Chris Bizon Maintainer: Christopher Cabanski SystemRequirements: Java version >= 1.6 git_url: https://git.bioconductor.org/packages/ReQON git_branch: RELEASE_3_14 git_last_commit: 120ea6a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ReQON_1.40.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/ReQON_1.40.0.tgz vignettes: vignettes/ReQON/inst/doc/ReQON.pdf vignetteTitles: ReQON Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReQON/inst/doc/ReQON.R dependencyCount: 31 Package: ResidualMatrix Version: 1.4.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: be25e0053ad57148766c93f16766ee92 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: RELEASE_3_14 git_last_commit: 6394364 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ResidualMatrix_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ResidualMatrix_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ResidualMatrix_1.4.0.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: BiocSingular, scran dependencyCount: 15 Package: restfulSE Version: 1.16.0 Depends: R (>= 3.6), SummarizedExperiment,DelayedArray Imports: utils, stats, methods, S4Vectors, Biobase,reshape2, AnnotationDbi, DBI, GO.db, rhdf5client, dplyr (>= 0.7.1), magrittr, bigrquery, ExperimentHub, AnnotationHub, rlang Suggests: knitr, testthat, Rtsne, org.Mm.eg.db, org.Hs.eg.db, BiocStyle, restfulSEData, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 78a9ce5c2586533626b414570103ecc3 NeedsCompilation: no Title: Access matrix-like HDF5 server content or BigQuery content through a SummarizedExperiment interface Description: This package provides functions and classes to interface with remote data stores by operating on SummarizedExperiment-like objects. biocViews: Infrastructure, SingleCell, Transcriptomics, Sequencing, Coverage Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut] Maintainer: Shweta Gopaulakrishnan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/restfulSE git_branch: RELEASE_3_14 git_last_commit: 61a70ed git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/restfulSE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/restfulSE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/restfulSE_1.16.0.tgz vignettes: vignettes/restfulSE/inst/doc/restfulSE.pdf vignetteTitles: restfulSE -- experiments with SE interface to remote HDF5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/restfulSE/inst/doc/restfulSE.R dependsOnMe: tenXplore suggestsMe: BiocOncoTK, BiocSklearn dependencyCount: 109 Package: rexposome Version: 1.16.0 Depends: R (>= 3.5), Biobase Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize, corrplot, ggplot2, reshape2, pryr, S4Vectors, imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools, scales, lme4, grDevices, graphics, ggrepel, mice Suggests: mclust, flexmix, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 5a744fbb1a2ec69a73704c9aa49f92fc 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: RELEASE_3_14 git_last_commit: 42df3c5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rexposome_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rexposome_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rexposome_1.16.0.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: 148 Package: rfaRm Version: 1.6.0 Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils, rvest, xml2, IRanges, S4Vectors Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics License: GPL-3 MD5sum: 2ea761026e980ed4567fe4ce5838c883 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: RELEASE_3_14 git_last_commit: fe8fcda git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rfaRm_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rfaRm_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rfaRm_1.6.0.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: 47 Package: Rfastp Version: 1.4.0 Imports: Rcpp, rjson, ggplot2, reshape2 LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: f5825fdd06636670636af9776b39a5f0 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: RELEASE_3_14 git_last_commit: 81990f1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rfastp_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rfastp_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rfastp_1.4.0.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: 47 Package: rfPred Version: 1.32.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) MD5sum: 4047a3fba61a55d338b2b169355fa605 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 be connected on the Internet or 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 URL: http://www.sbim.fr/rfPred git_url: https://git.bioconductor.org/packages/rfPred git_branch: RELEASE_3_14 git_last_commit: 14d2e8a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rfPred_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/rfPred_1.32.0.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: 30 Package: rGADEM Version: 2.42.0 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: 0fcdcf1815c0cc540dae5f7f0c15153c 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: RELEASE_3_14 git_last_commit: 2cbd75d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rGADEM_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rGADEM_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rGADEM_2.42.0.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: 46 Package: RGalaxy Version: 1.38.0 Depends: XML, methods, tools, optparse Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, AnnotationDbi, knitr, formatR, Rserve, markdown Enhances: RSclient License: Artistic-2.0 MD5sum: 5d7b71662ebb0dbf875db547d17d7929 NeedsCompilation: no Title: Make an R function available in the Galaxy web platform Description: Given an R function and its manual page, make the documented function available in Galaxy. biocViews: Infrastructure Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/RGalaxy git_branch: RELEASE_3_14 git_last_commit: c552dca git_last_commit_date: 2021-10-26 Date/Publication: 2021-12-19 source.ver: src/contrib/RGalaxy_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RGalaxy_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RGalaxy_1.38.0.tgz vignettes: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html vignetteTitles: Introduction to RGalaxy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R dependencyCount: 36 Package: rGenomeTracks Version: 1.0.0 Depends: R (>= 4.1.0), Imports: imager, reticulate, methods, rGenomeTracksData Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: d0715d3742098509c5c6121a94328f29 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: RELEASE_3_14 git_last_commit: a067af3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rGenomeTracks_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rGenomeTracks_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rGenomeTracks_1.0.0.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: 103 Package: Rgin Version: 1.14.0 Depends: R (>= 3.5) LinkingTo: RcppEigen (>= 0.3.3.5.0) Suggests: knitr, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: 69845879d8891ae40cfd806235b883b5 NeedsCompilation: yes Title: gin in R Description: C++ implementation of SConES. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre], Dominik Gerhard Grimm [aut], Chloe-Agathe Azencott [aut] Maintainer: Hector Climente-Gonzalez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rgin git_branch: RELEASE_3_14 git_last_commit: c34f8cb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rgin_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rgin_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rgin_1.14.0.tgz vignettes: vignettes/Rgin/inst/doc/Rgin-UsingCppLibraries.html vignetteTitles: Using Rgin C++ libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 10 Package: RGMQL Version: 1.14.1 Depends: R(>= 3.4.2), RGMQLlib Imports: httr, rJava, GenomicRanges, rtracklayer, data.table, utils, plyr, xml2, methods, S4Vectors, dplyr, stats, glue, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 0c4c968a69dbf3bd21e0e81c33efe6d5 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: RELEASE_3_14 git_last_commit: bfab815 git_last_commit_date: 2021-11-18 Date/Publication: 2021-11-18 source.ver: src/contrib/RGMQL_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/RGMQL_1.14.1.zip 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: 74 Package: RGraph2js Version: 1.22.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 MD5sum: 6b8e6286895be0870b76c1b1579baf44 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: RELEASE_3_14 git_last_commit: bdf7fe6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RGraph2js_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RGraph2js_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RGraph2js_1.22.0.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: 2.38.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL MD5sum: 90c21d5fc0fc081ee37969c9f861f1a3 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: RELEASE_3_14 git_last_commit: 004de09 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rgraphviz_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rgraphviz_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rgraphviz_2.38.0.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, flowCL, MineICA, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE, maEndToEnd, dlsem, gridGraphviz, GUIProfiler, hasseDiagram importsMe: apComplex, biocGraph, BiocOncoTK, bnem, chimeraviz, CytoML, dce, DEGraph, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph, MIGSA, mirIntegrator, mnem, OncoSimulR, ontoProc, paircompviz, pathview, Pigengene, qpgraph, SplicingGraphs, trackViewer, TRONCO, abn, BiDAG, bnpa, ceg, CePa, classGraph, cogmapr, dnet, gRain, gRbase, hmma, hpoPlot, ontologyPlot, SEMgraph, stablespec, wiseR suggestsMe: a4, altcdfenvs, annotate, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, MLP, NCIgraph, OmnipathR, pkgDepTools, RBGL, RBioinf, rBiopaxParser, RpsiXML, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, ViSEAGO, vtpnet, NCIgraphData, SNAData, arulesViz, BayesNetBP, bnclassify, bnlearn, bnstruct, bsub, ChoR, CodeDepends, gbutils, GeneNet, gRc, HEMDAG, iTOP, kpcalg, kst, lava, loon, maGUI, MCDA, msSurv, multiplex, ParallelPC, pcalg, psych, relations, rEMM, rPref, RSeed, SCCI, sisal, SourceSet, textplot, tm, topologyGSA, unifDAG, zenplots dependencyCount: 9 Package: rGREAT Version: 1.26.0 Depends: R (>= 3.1.2), GenomicRanges, IRanges, methods Imports: rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats Suggests: testthat (>= 0.3), knitr, circlize (>= 0.4.8), rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: b9be8aea193b8e8d6fb5d7bb2a598d4e NeedsCompilation: no Title: Client for GREAT Analysis Description: This package makes GREAT (Genomic Regions Enrichment of Annotations Tool) analysis automatic by constructing a HTTP POST request according to user's input and automatically retrieving results from GREAT web server. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation, Coverage Author: Zuguang Gu 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: RELEASE_3_14 git_last_commit: 28aedeb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rGREAT_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rGREAT_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rGREAT_1.26.0.tgz vignettes: vignettes/rGREAT/inst/doc/rGREAT.html vignetteTitles: Analyze with GREAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rGREAT/inst/doc/rGREAT.R suggestsMe: TADCompare dependencyCount: 21 Package: RGSEA Version: 1.28.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 8c269b34cffb9e37d079ee6144df5e43 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: RELEASE_3_14 git_last_commit: 370fd16 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RGSEA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RGSEA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RGSEA_1.28.0.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: rgsepd Version: 1.26.0 Depends: R (>= 4.0.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, hash, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 546453d2437cb6130f33f90fd99dd632 NeedsCompilation: no Title: Gene Set Enrichment / Projection Displays Description: R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group. biocViews: ImmunoOncology, Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rgsepd git_branch: RELEASE_3_14 git_last_commit: 02c7033 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rgsepd_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rgsepd_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rgsepd_1.26.0.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R dependencyCount: 128 Package: rhdf5 Version: 2.38.1 Depends: R (>= 4.0.0), methods Imports: Rhdf5lib (>= 1.13.4), rhdf5filters LinkingTo: Rhdf5lib Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, microbenchmark, dplyr, ggplot2, mockery License: Artistic-2.0 Archs: i386, x64 MD5sum: 61d2ae532ec5a74da6ed457df5bd49b2 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: RELEASE_3_14 git_last_commit: 1a5587e git_last_commit_date: 2022-03-09 Date/Publication: 2022-03-10 source.ver: src/contrib/rhdf5_2.38.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/rhdf5_2.38.1.zip mac.binary.ver: bin/macosx/contrib/4.1/rhdf5_2.38.1.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, LoomExperiment importsMe: BayesSpace, BgeeCall, biomformat, bnbc, bsseq, CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper, diffHic, DropletUtils, epigraHMM, EventPointer, FRASER, GenomicScores, gep2pep, h5vc, HiCcompare, IONiseR, MOFA2, NxtIRFcore, phantasus, ptairMS, PureCN, recountmethylation, ribor, scCB2, scone, signatureSearch, slinky, trackViewer, MafH5.gnomAD.v3.1.1.GRCh38, DmelSGI, MethylSeqData, ptairData, signatureSearchData, BigDataStatMeth, bioRad, file2meco, NEONiso, ondisc, rDataPipeline, smapr suggestsMe: edgeR, rhdf5filters, slalom, Spectra, SummarizedExperiment, tximport, zellkonverter, antaresProcessing, antaresRead, antaresViz, conos, digitalDLSorteR, hadron, io, MplusAutomation, neonstore, neonUtilities, rbiom, SignacX dependencyCount: 3 Package: rhdf5client Version: 1.16.0 Depends: R (>= 3.6), methods, DelayedArray Imports: S4Vectors, httr, R6, rjson, utils Suggests: knitr, testthat, BiocStyle, DT, rmarkdown License: Artistic-2.0 MD5sum: fcaf1aed6e0cdae61d6e960ec227c416 NeedsCompilation: yes Title: Access HDF5 content from h5serv Description: Provides functionality for reading data from h5serv server from within R. biocViews: DataImport, Software Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], Vincent Carey [cre, aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: RELEASE_3_14 git_last_commit: 6dc9197 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rhdf5client_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rhdf5client_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rhdf5client_1.16.0.tgz vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html vignetteTitles: HSDSArray DelayedArray backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R importsMe: restfulSE suggestsMe: BiocOncoTK, HumanTranscriptomeCompendium dependencyCount: 25 Package: rhdf5filters Version: 1.6.0 LinkingTo: Rhdf5lib Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), rhdf5 (>= 2.34.0) License: BSD_2_clause + file LICENSE MD5sum: b1acd6704a1dc3f9fea3548733951342 NeedsCompilation: yes Title: HDF5 Compression Filters Description: Provides a collection of compression filters for use with HDF5 datasets. 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: RELEASE_3_14 git_last_commit: 5f7f3a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rhdf5filters_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rhdf5filters_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rhdf5filters_1.6.0.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: 1.16.0 Depends: R (>= 4.0.0) Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery License: Artistic-2.0 MD5sum: 33ad25b8e54ef1a0acca3056c0ff8707 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: RELEASE_3_14 git_last_commit: 534c497 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rhdf5lib_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rhdf5lib_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rhdf5lib_1.16.0.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: CytoML, DropletUtils, epigraHMM, HDF5Array, mbkmeans, mzR, ncdfFlow, rhdf5, rhdf5filters, BigDataStatMeth, ondisc dependencyCount: 0 Package: Rhisat2 Version: 1.10.0 Depends: R (>= 3.6) Imports: GenomicFeatures, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 526c1160483af60123867b158dbd33ba 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: RELEASE_3_14 git_last_commit: c16857e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rhisat2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rhisat2_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rhisat2_1.10.0.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 suggestsMe: eisaR, QuasR dependencyCount: 99 Package: Rhtslib Version: 1.26.0 Imports: zlibbioc LinkingTo: zlibbioc Suggests: knitr, rmarkdown, BiocStyle License: LGPL (>= 2) Archs: i386, x64 MD5sum: 719fa3ad818c96b9a054c617b9e97dc1 NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.7 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], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer 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: RELEASE_3_14 git_last_commit: f5b20e9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rhtslib_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rhtslib_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rhtslib_1.26.0.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: ArrayExpressHTS, bamsignals, BitSeq, csaw, deepSNV, DiffBind, diffHic, epialleleR, FLAMES, h5vc, maftools, methylKit, mitoClone2, podkat, qrqc, QuasR, Rfastp, Rsamtools, scPipe, seqbias, ShortRead, TransView, VariantAnnotation, jackalope dependencyCount: 1 Package: RiboCrypt Version: 1.0.0 Depends: R (>= 3.6.0), ORFik (>= 1.13.12) Imports: BiocGenerics, BiocParallel, Biostrings, data.table, dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, IRanges, plotly, rlang Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE Archs: i386, x64 MD5sum: ef9ebc3cf7404f7a5bd62317aa1b3a93 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], Haakon Tjeldnes [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: RELEASE_3_14 git_last_commit: c07b93f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RiboCrypt_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RiboCrypt_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RiboCrypt_1.0.0.tgz vignettes: vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.html vignetteTitles: RiboCrypt_overview.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.R dependencyCount: 152 Package: RiboDiPA Version: 1.2.0 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 LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL (>= 3) Archs: i386, x64 MD5sum: 13ea3d71df8902b824574ec0bfec6df3 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: RELEASE_3_14 git_last_commit: 0b22a63 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RiboDiPA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RiboDiPA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RiboDiPA_1.2.0.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: 163 Package: RiboProfiling Version: 1.24.0 Depends: R (>= 3.2.2), Biostrings Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape2, GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments, ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment License: GPL-3 Archs: x64 MD5sum: 272215ea0193a23b30e09a0a229622a3 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: RELEASE_3_14 git_last_commit: 225f775 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RiboProfiling_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RiboProfiling_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RiboProfiling_1.24.0.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: 157 Package: ribor Version: 1.6.0 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: i386, x64 MD5sum: b5148cb12431481e0b88e76775011cd2 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: RELEASE_3_14 git_last_commit: fa3edd7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ribor_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ribor_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ribor_1.6.0.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: 53 Package: riboSeqR Version: 1.28.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, baySeq, GenomeInfoDb, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: 172ff636ba2093c33f1b178c155cd922 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 Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/riboSeqR git_branch: RELEASE_3_14 git_last_commit: 8caafa9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/riboSeqR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/riboSeqR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/riboSeqR_1.28.0.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: 38 Package: ribosomeProfilingQC Version: 1.6.1 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 Suggests: RUnit, BiocStyle, knitr, BSgenome.Drerio.UCSC.danRer10, edgeR, limma, testthat, rmarkdown License: GPL (>=3) + file LICENSE Archs: i386, x64 MD5sum: f7cd5ef7fffa6b4757175bdd8f3c1d35 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: RELEASE_3_14 git_last_commit: 7ab254d git_last_commit_date: 2021-11-10 Date/Publication: 2021-11-11 source.ver: src/contrib/ribosomeProfilingQC_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ribosomeProfilingQC_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ribosomeProfilingQC_1.6.1.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: 137 Package: RImmPort Version: 1.22.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: 10de63aab41d112e3ef68952eeebc451 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: RELEASE_3_14 git_last_commit: 5cca829 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RImmPort_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RImmPort_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RImmPort_1.22.0.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: 43 Package: Ringo Version: 1.58.0 Depends: methods, Biobase (>= 1.14.1), RColorBrewer, limma, Matrix, grid, lattice Imports: BiocGenerics (>= 0.1.11), genefilter, limma, vsn, stats4 Suggests: rtracklayer (>= 1.3.1), mclust, topGO (>= 1.15.0) License: Artistic-2.0 MD5sum: e1aeb43596449e7430c87ec8fbd473e7 NeedsCompilation: yes Title: R Investigation of ChIP-chip Oligoarrays Description: The package Ringo facilitates the primary analysis of ChIP-chip data. The main functionalities of the package are data read-in, quality assessment, data visualisation and identification of genomic regions showing enrichment in ChIP-chip. The package has functions to deal with two-color oligonucleotide microarrays from NimbleGen used in ChIP-chip projects, but also contains more general functions for ChIP-chip data analysis, given that the data is supplied as RGList (raw) or ExpressionSet (pre- processed). The package employs functions from various other packages of the Bioconductor project and provides additional ChIP-chip-specific and NimbleGen-specific functionalities. biocViews: Microarray,TwoChannel,DataImport,QualityControl,Preprocessing Author: Joern Toedling, Oleg Sklyar, Tammo Krueger, Matt Ritchie, Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/Ringo git_branch: RELEASE_3_14 git_last_commit: 0d4f926 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Ringo_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Ringo_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Ringo_1.58.0.tgz vignettes: vignettes/Ringo/inst/doc/Ringo.pdf vignetteTitles: R Investigation of NimbleGen Oligoarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Ringo/inst/doc/Ringo.R dependsOnMe: SimBindProfiles, ccTutorial importsMe: Repitools dependencyCount: 82 Package: RIPAT Version: 1.4.0 Depends: R (>= 4.0) Imports: biomaRt (>= 2.38.0), GenomicRanges (>= 1.34.0), ggplot2 (>= 3.1.0), grDevices (>= 3.5.3), IRanges (>= 2.16.0), karyoploteR (>= 1.6.3), openxlsx (>= 4.1.4), plyr (>= 1.8.4), regioneR (>= 1.12.0), rtracklayer (>= 1.42.2), stats (>= 3.5.3), stringr (>= 1.3.1), utils (>= 3.5.3) Suggests: knitr (>= 1.28) License: Artistic-2.0 MD5sum: 60b40cd166449c074c7ad491b5359a17 NeedsCompilation: no Title: Retroviral Integration Pattern Analysis Tool (RIPAT) Description: RIPAT is developed as an R package for retroviral integration sites annotation and distribution analysis. RIPAT needs local alignment results from BLAST and BLAT. Specific input format is depicted in RIPAT manual. RIPAT provides RV integration pattern analysis result as forms of R objects, excel file with multiple sheets and plots. biocViews: Annotation Author: Min-Jeong Baek [aut, cre] Maintainer: Min-Jeong Baek URL: https://github.com/bioinfo16/RIPAT/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RIPAT git_branch: RELEASE_3_14 git_last_commit: 8b46270 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RIPAT_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RIPAT_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RIPAT_1.4.0.tgz vignettes: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.html vignetteTitles: RIPAT : Retroviral Integration Pattern Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.R dependencyCount: 148 Package: Risa Version: 1.36.0 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 MD5sum: b506c9687b0ea08c3876fadd9f209270 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/Risa git_branch: RELEASE_3_14 git_last_commit: e0c196c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Risa_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Risa_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Risa_1.36.0.tgz vignettes: vignettes/Risa/inst/doc/Risa.pdf vignetteTitles: Risa: converts experimental metadata from ISA-tab into Bioconductor data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Risa/inst/doc/Risa.R suggestsMe: mtbls2 dependencyCount: 99 Package: RITAN Version: 1.18.0 Depends: R (>= 3.4), Imports: graphics, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, linkcomm, dynamicTreeCut, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata Suggests: rmarkdown License: file LICENSE MD5sum: 393aeec39258d8efe08de0ef78435c42 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 Author: Michael Zimmermann Maintainer: Michael Zimmermann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: RELEASE_3_14 git_last_commit: e2ef1ac git_last_commit_date: 2021-10-26 Date/Publication: 2021-11-14 source.ver: src/contrib/RITAN_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RITAN_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RITAN_1.18.0.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: 114 Package: RIVER Version: 1.18.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) Archs: i386, x64 MD5sum: 62df9bbb8e05cc103def2c9501c0f16d 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: RELEASE_3_14 git_last_commit: 1375380 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RIVER_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RIVER_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RIVER_1.18.0.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: 50 Package: RJMCMCNucleosomes Version: 1.18.0 Depends: R (>= 3.4), 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: 88e9b136bc524e75fb858f6226c4d4f1 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: RELEASE_3_14 git_last_commit: 0d6b941 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RJMCMCNucleosomes_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RJMCMCNucleosomes_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RJMCMCNucleosomes_1.18.0.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: 50 Package: RLassoCox Version: 1.2.0 Depends: R (>= 4.1), glmnet Imports: Matrix, igraph, survival, stats Suggests: knitr License: Artistic-2.0 MD5sum: ccba75943d88f6868778d8c185452ce5 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: RELEASE_3_14 git_last_commit: 34c7cf4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RLassoCox_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RLassoCox_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RLassoCox_1.2.0.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: 20 Package: RLMM Version: 1.56.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 4cfd47d4a8be33239308bc579b2f1b61 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: RELEASE_3_14 git_last_commit: 9d64e14 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RLMM_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RLMM_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RLMM_1.56.0.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: RLSeq Version: 1.0.0 Depends: R (>= 4.1.0) Imports: dplyr, ggplot2, RColorBrewer, grid, regioneR, valr, caretEnsemble, GenomicFeatures, rtracklayer, GenomicRanges, GenomeInfoDb, ComplexHeatmap, AnnotationHub, VennDiagram, callr, circlize, ggplotify, ggprism, methods, stats, RLHub, aws.s3, pheatmap Suggests: AnnotationDbi, BiocStyle, covr, lintr, rcmdcheck, DT, httr, jsonlite, kableExtra, kernlab, knitr, magick, MASS, org.Hs.eg.db, R.utils, randomForest, readr, rmarkdown, rpart, testthat (>= 3.0.0), tibble, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, futile.logger License: MIT + file LICENSE MD5sum: 9367e2c5d8ccc2d3b413b4561202416b NeedsCompilation: no Title: RLSeq: An analysis package for R-loop mapping data Description: RLSeq is a toolkit for analyzing and evaluating R-loop mapping datasets. RLSeq serves two primary purposes: (1) to facilitate the evaluation of dataset quality, and (2) to enable R-loop analysis in the context of publicly-available data sets from RLBase. The package is intended to provide a simple pipeline, called with the `RLSeq()` function, which performs all main analyses. Individual functions are also accessible and provide custom analysis capabilities. Finally an HTML report is generated with `report()`. biocViews: Sequencing, Coverage, Epigenetics, Transcriptomics, Classification Author: Henry Miller [aut, cre, cph] (), Daniel Montemayor [ctb] (), Simon Levy [ctb] (), Anna Vines [ctb] (), Alexander Bishop [ths, cph] () Maintainer: Henry Miller URL: https://github.com/Bishop-Laboratory/RLSeq, https://bishop-laboratory.github.io/RLSeq/ VignetteBuilder: knitr BugReports: https://github.com/Bishop-Laboratory/RLSeq/issues git_url: https://git.bioconductor.org/packages/RLSeq git_branch: RELEASE_3_14 git_last_commit: 191326e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RLSeq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RLSeq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RLSeq_1.0.0.tgz vignettes: vignettes/RLSeq/inst/doc/RLSeq.html vignetteTitles: Analyzing R-loop Data with RLSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RLSeq/inst/doc/RLSeq.R dependencyCount: 199 Package: Rmagpie Version: 1.50.0 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) Archs: i386, x64 MD5sum: 8fe8206c3f61b34895c1e5068b307773 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: RELEASE_3_14 git_last_commit: e8a3cac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rmagpie_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rmagpie_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rmagpie_1.50.0.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: 3.4.0 Depends: Rcpp Imports: XML,rjson,S4Vectors,digest, rcdk,yaml,mzR,methods,Biobase,MSnbase,httr, enviPat,assertthat,logger,RCurl Suggests: BiocStyle,gplots,RMassBankData, xcms (>= 1.37.1), CAMERA, RUnit, knitr License: Artistic-2.0 MD5sum: 10246efe4b68d6f3f611387d00f75790 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, with contributions from Tobias Schulze Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RMassBank git_branch: RELEASE_3_14 git_last_commit: 78bf126 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RMassBank_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RMassBank_3.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RMassBank_3.4.0.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.html, vignettes/RMassBank/inst/doc/RMassBankNonstandard.html, vignettes/RMassBank/inst/doc/RMassBankXCMS.html vignetteTitles: RMassBank: The workflow by example, RMassBank: Non-standard usage, RMassBank for XCMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R, vignettes/RMassBank/inst/doc/RMassBankXCMS.R suggestsMe: RMassBankData dependencyCount: 98 Package: rmelting Version: 1.10.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.5-0) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 MD5sum: e8593043a108659e336b6400ffa23352 NeedsCompilation: no Title: R Interface to MELTING 5 Description: R interface to the MELTING 5 program (https://www.ebi.ac.uk/biomodels-static/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: RELEASE_3_14 git_last_commit: 2fef10f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rmelting_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rmelting_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rmelting_1.10.0.tgz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: Rmmquant Version: 1.12.0 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, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 26664dbe0181336da2ed36683d650d9f 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: RELEASE_3_14 git_last_commit: 9d2dc01 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rmmquant_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rmmquant_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rmmquant_1.12.0.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: 169 Package: rmspc Version: 1.0.0 Imports: processx, BiocManager, rtracklayer, stats, tools, methods, GenomicRanges, stringr Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 681ffb7b006f5ad327f6cc594da908e8 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 5.0 VignetteBuilder: knitr BugReports: https://github.com/Genometric/MSPC/issues git_url: https://git.bioconductor.org/packages/rmspc git_branch: RELEASE_3_14 git_last_commit: 76cce83 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rmspc_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rmspc_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rmspc_1.0.0.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: 52 Package: RNAAgeCalc Version: 1.6.0 Depends: R (>= 3.6) Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: eea881829ac13ad978e584bf6ec9ab2a NeedsCompilation: no Title: A multi-tissue transcriptional age calculator Description: It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data. biocViews: RNASeq,GeneExpression Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/RNAAgeCalc VignetteBuilder: knitr BugReports: https://github.com/reese3928/RNAAgeCalc/issues git_url: https://git.bioconductor.org/packages/RNAAgeCalc git_branch: RELEASE_3_14 git_last_commit: ee56a68 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNAAgeCalc_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAAgeCalc_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAAgeCalc_1.6.0.tgz vignettes: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html vignetteTitles: RNAAgeCalc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R dependencyCount: 164 Package: RNAdecay Version: 1.14.0 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr, scales Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 MD5sum: 61e0819812326bcc2daa48e4d53867e3 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: RELEASE_3_14 git_last_commit: 1a580bf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNAdecay_1.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/RNAdecay_1.14.0.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: 1.4.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, BiocGenerics, GenomeInfoDb, bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 0c72f828d045d978e6b792e6d5ab435b 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: RELEASE_3_14 git_last_commit: 635036a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rnaEditr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rnaEditr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rnaEditr_1.4.0.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: 129 Package: RNAinteract Version: 1.42.0 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 Archs: i386, x64 MD5sum: 7741d1cb3d15691b7e5de1419bd83433 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/RNAinteract git_branch: RELEASE_3_14 git_last_commit: 5d8fc4b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNAinteract_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAinteract_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAinteract_1.42.0.tgz vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf vignetteTitles: RNAinteract hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAinteract/inst/doc/RNAinteract.R dependsOnMe: RNAinteractMAPK dependencyCount: 106 Package: RNAmodR Version: 1.8.0 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, 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: 0d37ce5e38c006aab9471fbbd4aecf57 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: RELEASE_3_14 git_last_commit: 33b6760 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNAmodR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAmodR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR_1.8.0.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: 151 Package: RNAmodR.AlkAnilineSeq Version: 1.8.0 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: 63a2f22b359044b15d98ec4192864b7f 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: RELEASE_3_14 git_last_commit: 710b408 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAmodR.AlkAnilineSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR.AlkAnilineSeq_1.8.0.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: 152 Package: RNAmodR.ML Version: 1.8.0 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: aad00db6527db392c8bcbb3ed5fb5119 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: RELEASE_3_14 git_last_commit: 406eeec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNAmodR.ML_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAmodR.ML_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR.ML_1.8.0.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: 154 Package: RNAmodR.RiboMethSeq Version: 1.8.0 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: 8ea4755997429e810738cad25db663d8 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: RELEASE_3_14 git_last_commit: c592de7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNAmodR.RiboMethSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAmodR.RiboMethSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAmodR.RiboMethSeq_1.8.0.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: 152 Package: RNAsense Version: 1.8.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 0a9353f10dc244c97c8f3267053da8e6 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: RELEASE_3_14 git_last_commit: 9f8a50b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNAsense_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNAsense_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNAsense_1.8.0.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: 63 Package: rnaseqcomp Version: 1.24.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 6013fa2cb3d7ccd9abce478dfb8427cf 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: RELEASE_3_14 git_last_commit: fc207bc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rnaseqcomp_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rnaseqcomp_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rnaseqcomp_1.24.0.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 suggestsMe: SummarizedBenchmark dependencyCount: 2 Package: RNASeqPower Version: 1.34.0 License: LGPL (>=2) MD5sum: ae68069ea03255e3ad9cb9cb8c4ca2a0 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: RELEASE_3_14 git_last_commit: c334060 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNASeqPower_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RNASeqPower_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RNASeqPower_1.34.0.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: RNASeqR Version: 1.12.0 Depends: R(>= 3.5.0), ggplot2, pathview, edgeR, methods Imports: Rsamtools, tools, reticulate, ballgown, gridExtra, rafalib, FactoMineR, factoextra, corrplot, PerformanceAnalytics, reshape2, DESeq2, systemPipeR, systemPipeRdata, clusterProfiler, org.Hs.eg.db, org.Sc.sgd.db, stringr, pheatmap, grDevices, graphics, stats, utils, DOSE, Biostrings, parallel Suggests: knitr, rmarkdown, png, grid, RNASeqRData License: Artistic-2.0 MD5sum: 91a7567052462c3e8fc86212e72d59ff NeedsCompilation: no Title: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow Description: This R package is designed for case-control RNA-Seq analysis (two-group). There are six steps: "RNASeqRParam S4 Object Creation", "Environment Setup", "Quality Assessment", "Reads Alignment & Quantification", "Gene-level Differential Analyses" and "Functional Analyses". Each step corresponds to a function in this package. After running functions in order, a basic RNASeq analysis would be done easily. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, GeneExpression, GeneSetEnrichment, Alignment, QualityControl, DifferentialExpression, FunctionalPrediction, ExperimentalDesign, GO, KEGG, Visualization, Normalization, Pathways, Clustering, ImmunoOncology Author: Kuan-Hao Chao Maintainer: Kuan-Hao Chao URL: https://github.com/HowardChao/RNASeqR SystemRequirements: RNASeqR only support Linux and macOS. Window is not supported. Python2 is highly recommended. If your machine is Python3, make sure '2to3' command is available. VignetteBuilder: knitr BugReports: https://github.com/HowardChao/RNASeqR/issues git_url: https://git.bioconductor.org/packages/RNASeqR git_branch: RELEASE_3_14 git_last_commit: 91e973c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RNASeqR_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/RNASeqR_1.12.0.tgz vignettes: vignettes/RNASeqR/inst/doc/RNASeqR.html vignetteTitles: RNA-Seq analysis based on one independent variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqR/inst/doc/RNASeqR.R dependencyCount: 242 Package: RnaSeqSampleSize Version: 2.4.1 Depends: R (>= 4.0.0), RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices, graphics, stats, utils,Rcpp (>= 0.11.2) LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: de0c12e611d91ff22f96c1a472cb68f8 NeedsCompilation: yes Title: RnaSeqSampleSize Description: RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao Developer [aut, cre], Chung-I Li Developer [aut], Yan Guo Developer [aut], Quanhu Sheng Developer [aut], Yu Shyr Developer [aut] Maintainer: Shilin Zhao Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize git_branch: RELEASE_3_14 git_last_commit: 7759ebe git_last_commit_date: 2022-02-09 Date/Publication: 2022-02-10 source.ver: src/contrib/RnaSeqSampleSize_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/RnaSeqSampleSize_2.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/RnaSeqSampleSize_2.4.1.tgz vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R dependencyCount: 80 Package: RnBeads Version: 2.12.2 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RefFreeEWAS, RnBeads.hg19, RnBeads.mm9, 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, GLAD, IlluminaHumanMethylation450kanno.ilmn12.hg19, scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit, MethylSeekR, sesame License: GPL-3 MD5sum: 4260588117d42b8a12b8cdfd5b4c5c9e 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: RELEASE_3_14 git_last_commit: 17eba00 git_last_commit_date: 2021-11-05 Date/Publication: 2021-11-05 source.ver: src/contrib/RnBeads_2.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/RnBeads_2.12.2.zip mac.binary.ver: bin/macosx/contrib/4.1/RnBeads_2.12.2.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: 169 Package: Rnits Version: 1.28.0 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: d39d61f95027a71eca6827a89228c0ab 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: RELEASE_3_14 git_last_commit: fc51552 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rnits_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rnits_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rnits_1.28.0.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: 55 Package: roar Version: 1.30.0 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: 9f03570378fe29c3fd9548bfc98b419d 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: RELEASE_3_14 git_last_commit: b16c785 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/roar_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/roar_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/roar_1.30.0.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: 44 Package: ROC Version: 1.70.0 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: rmarkdown, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: f32038c938fe15029f44cdf6a62d7b54 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: RELEASE_3_14 git_last_commit: 44fd639 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ROC_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROC_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ROC_1.70.0.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, rMisbeta suggestsMe: genefilter dependencyCount: 13 Package: ROCpAI Version: 1.6.0 Depends: boot, SummarizedExperiment, fission, knitr, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 9e76d55954af3b81297f6bc2512bed03 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: RELEASE_3_14 git_last_commit: 6f8089b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ROCpAI_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROCpAI_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ROCpAI_1.6.0.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: 36 Package: rols Version: 2.22.1 Depends: methods Imports: httr, progress, jsonlite, utils, Biobase, BiocGenerics (>= 0.23.1) Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat, lubridate, DT, rmarkdown, License: GPL-2 MD5sum: 5e62aef4dd290a1b2465412efb59fe05 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.com/rols/ VignetteBuilder: knitr BugReports: https://github.com/lgatto/rols/issues git_url: https://git.bioconductor.org/packages/rols git_branch: RELEASE_3_14 git_last_commit: c90505b git_last_commit_date: 2022-03-12 Date/Publication: 2022-03-13 source.ver: src/contrib/rols_2.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/rols_2.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/rols_2.22.1.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 dependsOnMe: proteomics importsMe: spatialHeatmap, struct suggestsMe: MSnbase, RforProteomics dependencyCount: 27 Package: ROntoTools Version: 2.22.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 4accff5c5c7a4ce3766033eb00bed53a 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: Calin Voichita git_url: https://git.bioconductor.org/packages/ROntoTools git_branch: RELEASE_3_14 git_last_commit: 9600abd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ROntoTools_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROntoTools_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ROntoTools_2.22.0.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 dependencyCount: 34 Package: ropls Version: 1.26.4 Depends: Biobase Imports: graphics, grDevices, methods, MultiDataSet, stats Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, rmarkdown, testthat License: CeCILL Archs: i386, x64 MD5sum: a5b8742f576f78665add577c263941a5 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 Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ropls git_branch: RELEASE_3_14 git_last_commit: c47268e git_last_commit_date: 2022-01-10 Date/Publication: 2022-01-11 source.ver: src/contrib/ropls_1.26.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/ropls_1.26.4.zip mac.binary.ver: bin/macosx/contrib/4.1/ropls_1.26.4.tgz vignettes: vignettes/ropls/inst/doc/ropls-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R dependsOnMe: biosigner importsMe: ASICS, lipidr, MultiBaC, proFIA, MetabolomicsBasics suggestsMe: autonomics, ptairMS, structToolbox dependencyCount: 61 Package: ROSeq Version: 1.6.0 Depends: R (>= 4.0) Imports: pbmcapply, edgeR, limma Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics License: GPL-3 MD5sum: cf6f37879f296b69a752a66b3d60c43e 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: RELEASE_3_14 git_last_commit: c65bb15 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ROSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ROSeq_1.6.0.tgz vignettes: vignettes/ROSeq/inst/doc/ROSeq.html vignetteTitles: ROSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROSeq/inst/doc/ROSeq.R dependencyCount: 13 Package: ROTS Version: 1.22.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) MD5sum: 532bad3d081bcf6fbdd207c381a28f88 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: RELEASE_3_14 git_last_commit: a53ec77 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ROTS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ROTS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ROTS_1.22.0.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 suggestsMe: wrProteo dependencyCount: 7 Package: RPA Version: 1.50.0 Depends: R (>= 3.1.1), affy, BiocGenerics, BiocStyle, methods, rmarkdown Imports: phyloseq Suggests: affydata, knitr, parallel License: BSD_2_clause + file LICENSE MD5sum: f043311c90420dd54612be4971682f3e 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: RELEASE_3_14 git_last_commit: 1306d67 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RPA_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RPA_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RPA_1.50.0.tgz vignettes: vignettes/RPA/inst/doc/RPA.html vignetteTitles: RPA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs dependencyCount: 98 Package: RProtoBufLib Version: 2.6.0 Suggests: knitr, rmarkdown License: BSD_3_clause MD5sum: 732d1b8f314e17b0b2555f5fad9c9183 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: RELEASE_3_14 git_last_commit: 394836f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RProtoBufLib_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RProtoBufLib_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RProtoBufLib_2.6.0.tgz vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html vignetteTitles: Using RProtoBufLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R importsMe: cytolib, flowWorkspace linksToMe: cytolib, CytoML, flowCore, flowWorkspace dependencyCount: 0 Package: RpsiXML Version: 2.36.0 Depends: methods, XML (>= 2.4.0), utils Imports: annotate (>= 1.21.0), graph (>= 1.21.0), Biobase, RBGL (>= 1.17.0), hypergraph (>= 1.15.2), AnnotationDbi Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db, Rgraphviz, ppiStats, ScISI, testthat License: LGPL-3 MD5sum: 4357dcb3924c751fc23fa7d53c358b25 NeedsCompilation: no Title: R interface to PSI-MI 2.5 files Description: Queries, data structure and interface to visualization of interaction datasets. This package inplements the PSI-MI 2.5 standard and supports up to now 8 databases. Further databases supporting PSI-MI 2.5 standard will be added continuously. biocViews: Infrastructure, Proteomics Author: Jitao David Zhang [aut, cre, ctb] (), Stefan Wiemann [ctb], Marc Carlson [ctb], Tony Chiang [ctb] Maintainer: Jitao David Zhang URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/RpsiXML git_branch: RELEASE_3_14 git_last_commit: cc895e5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RpsiXML_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RpsiXML_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RpsiXML_2.36.0.tgz vignettes: vignettes/RpsiXML/inst/doc/RpsiXML.pdf, vignettes/RpsiXML/inst/doc/RpsiXMLApp.pdf vignetteTitles: Reading PSI-25 XML files, Application Examples of RpsiXML package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RpsiXML/inst/doc/RpsiXML.R, vignettes/RpsiXML/inst/doc/RpsiXMLApp.R dependencyCount: 52 Package: rpx Version: 2.2.2 Depends: methods Imports: BiocFileCache, jsonlite, xml2, RCurl, curl, utils Suggests: Biostrings, BiocStyle, testthat, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: cc777ad58af347f7938fce0132b6419a 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: RELEASE_3_14 git_last_commit: b168ddf git_last_commit_date: 2022-01-24 Date/Publication: 2022-01-25 source.ver: src/contrib/rpx_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/rpx_2.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/rpx_2.2.2.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 dependsOnMe: proteomics suggestsMe: MSnbase, RforProteomics dependencyCount: 50 Package: Rqc Version: 1.28.0 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: 46f7d3f1914f6defe9d6e9f16a15ebb8 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: RELEASE_3_14 git_last_commit: 2f449e1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rqc_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rqc_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rqc_1.28.0.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: 164 Package: rqt Version: 1.20.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: i386, x64 MD5sum: d6d1cf9ce0117359a3079ec6542981e1 NeedsCompilation: no Title: rqt: utilities for gene-level meta-analysis Description: Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, . 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 git_url: https://git.bioconductor.org/packages/rqt git_branch: RELEASE_3_14 git_last_commit: 26d52cc git_last_commit_date: 2021-10-26 Date/Publication: 2021-12-19 source.ver: src/contrib/rqt_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rqt_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rqt_1.20.0.tgz vignettes: vignettes/rqt/inst/doc/rqt-vignette.html vignetteTitles: Tutorial for rqt package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R dependencyCount: 137 Package: rqubic Version: 1.40.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 MD5sum: 6875d81c4dbed21710e9f082e71aeddc 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: RELEASE_3_14 git_last_commit: aceee0b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rqubic_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rqubic_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rqubic_1.40.0.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: 1.28.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: 6feb77506a4a93cabad718995561244d NeedsCompilation: no Title: Interface to the RDP Classifier Description: Seamlessly interfaces RDP classifier (version 2.9). biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome, ImmunoOncology Author: Michael Hahsler, Anurag Nagar Maintainer: Michael Hahsler SystemRequirements: Java git_url: https://git.bioconductor.org/packages/rRDP git_branch: RELEASE_3_14 git_last_commit: c788ea6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rRDP_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rRDP_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rRDP_1.28.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.pdf vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R dependsOnMe: rRDPData dependencyCount: 18 Package: RRHO Version: 1.34.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 Archs: i386, x64 MD5sum: dd494fbdaa31e90d82c3ca50e7f7d297 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: RELEASE_3_14 git_last_commit: eeffbc3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RRHO_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RRHO_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RRHO_1.34.0.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: 1.6.0 Imports: GOSemSim, AnnotationDbi, GO.db, pheatmap, ggplot2, ggrepel, treemap, tm, wordcloud, shiny, grDevices, grid, stats, methods 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.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db License: GPL-3 MD5sum: d9a81e336d48168cee7839e457f2641a 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] 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: RELEASE_3_14 git_last_commit: 46ae2cf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rrvgo_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rrvgo_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rrvgo_1.6.0.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 dependencyCount: 101 Package: Rsamtools Version: 2.10.0 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 (>= 1.17.7), 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 License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: d53af0a206a0e7e56b4e56bb752d7758 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, Hervé Pagès, Valerie Obenchain, Nathaniel Hayden Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Rsamtools SystemRequirements: GNU make 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: RELEASE_3_14 git_last_commit: b19738e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rsamtools_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rsamtools_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rsamtools_2.10.0.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf vignetteTitles: An introduction to Rsamtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R dependsOnMe: ArrayExpressHTS, BitSeq, CODEX, contiBAIT, CoverageView, esATAC, exomeCopy, FRASER, GenomicAlignments, GenomicFiles, girafe, gmapR, HelloRanges, IntEREst, MEDIPS, MMDiff2, podkat, r3Cseq, Rcade, RepViz, ReQON, rfPred, RiboDiPA, SCOPE, SGSeq, ShortRead, SICtools, SNPhood, spiky, systemPipeR, TarSeqQC, TEQC, VariantAnnotation, wavClusteR, leeBamViews, TBX20BamSubset, sequencing, csawBook, Brundle importsMe: AllelicImbalance, alpine, AneuFinder, annmap, AnnotationHubData, APAlyzer, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, ATACseqQC, atena, BadRegionFinder, bambu, BBCAnalyzer, biovizBase, biscuiteer, breakpointR, BRGenomics, BSgenome, CAGEr, casper, cellbaseR, CexoR, cfDNAPro, ChIC, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChIPQC, ChromSCape, chromstaR, chromVAR, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2, consensusDE, CopyNumberPlots, CopywriteR, CrispRVariants, csaw, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEXSeq, DiffBind, diffHic, easyRNASeq, EDASeq, ensembldb, epigenomix, epigraHMM, eudysbiome, FilterFFPE, FLAMES, FunChIP, gcapc, GeneGeneInteR, genomation, GenomicAlignments, GenomicInteractions, GenVisR, ggbio, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz, h5vc, HTSeqGenie, icetea, IMAS, INSPEcT, karyoploteR, ldblock, MACPET, MADSEQ, MDTS, metagene, metagene2, metaseqR2, methylKit, MMAPPR2, mosaics, motifmatchr, msgbsR, NADfinder, NanoMethViz, nearBynding, nucleR, ORFik, panelcn.mops, PICS, plyranges, pram, profileplyr, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox, ramwas, Rbowtie2, recoup, Repitools, RiboProfiling, riboSeqR, ribosomeProfilingQC, RNAmodR, RNASeqR, Rqc, rtracklayer, scruff, segmentSeq, seqsetvis, SimFFPE, sitadela, soGGi, SplicingGraphs, srnadiff, strandCheckR, TCseq, TFutils, tracktables, trackViewer, transcriptR, TRESS, tRNAscanImport, TSRchitect, TVTB, UMI4Cats, uncoverappLib, VariantFiltering, VariantTools, VaSP, VCFArray, VplotR, chipseqDBData, LungCancerLines, MMAPPR2data, systemPipeRdata, BinQuasi, ExomeDepth, hoardeR, intePareto, kibior, MAAPER, NIPTeR, noisyr, PlasmaMutationDetector, pulseTD, RAPIDR, Signac, spp, umiAnalyzer, VALERIE suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, Chicago, epivizrChart, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gwascat, IRanges, MungeSumstats, omicsPrint, RNAmodR.ML, SeqArray, seqbias, SigFuge, similaRpeak, Streamer, GeuvadisTranscriptExpr, NanoporeRNASeq, parathyroidSE, chipseqDB, polyRAD, seqmagick dependencyCount: 28 Package: rsbml Version: 2.52.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 MD5sum: c3905e11077cebbc29c7a6c33d4ef8d0 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: RELEASE_3_14 git_last_commit: 0f2f1fd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rsbml_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rsbml_2.52.0.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: 1.10.0 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: b1aa64be1c8020af392dd7af7ddd2f26 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: RELEASE_3_14 git_last_commit: c44ecd1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rScudo_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rScudo_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rScudo_1.10.0.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: 31 Package: rsemmed Version: 1.4.0 Depends: R (>= 4.0), igraph Imports: methods, magrittr, stringr, dplyr Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 442ff7e0ad620abfcc85ff7a307cadd5 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: RELEASE_3_14 git_last_commit: 5f0dd6f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rsemmed_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rsemmed_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rsemmed_1.4.0.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: 30 Package: RSeqAn Version: 1.14.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: da186fe1025ed6db3e317ae048086e3f 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: RELEASE_3_14 git_last_commit: 0bf114c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RSeqAn_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RSeqAn_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RSeqAn_1.14.0.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: 2.8.2 Imports: grDevices, stats, utils, Matrix License: GPL (>=3) Archs: i386, x64 MD5sum: 27b95e89661563e1b7df82b61b02b8dc 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: RELEASE_3_14 git_last_commit: 9fde88e git_last_commit_date: 2022-03-16 Date/Publication: 2022-03-17 source.ver: src/contrib/Rsubread_2.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rsubread_2.8.2.zip mac.binary.ver: bin/macosx/contrib/4.1/Rsubread_2.8.2.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, scruff suggestsMe: autonomics, icetea, NxtIRFcore, scPipe, singleCellTK, tidybulk dependencyCount: 8 Package: RSVSim Version: 1.34.0 Depends: R (>= 3.0.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer License: LGPL-3 MD5sum: c53db838dc4d4da7b0de3a8b9a82a2c2 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: RELEASE_3_14 git_last_commit: f133a25 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RSVSim_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RSVSim_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RSVSim_1.34.0.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: 44 Package: rSWeeP Version: 1.6.0 Depends: R (>= 4.0) Imports: pracma, stats Suggests: Biostrings, methods, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: a85e2cd77da4b357ac99018e66ae4c07 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: RELEASE_3_14 git_last_commit: 5b84f77 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rSWeeP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rSWeeP_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rSWeeP_1.6.0.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: 1.46.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: 69c4c7c0cf831b48f72ce9afa612cc0a 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: RELEASE_3_14 git_last_commit: 40e84c0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RTCA_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTCA_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTCA_1.46.0.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: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R dependencyCount: 8 Package: RTCGA Version: 1.24.0 Depends: R (>= 3.3.0) Imports: XML, assertthat, stringi, rvest, data.table, xml2, dplyr, purrr, survival, survminer, ggplot2, ggthemes, viridis, knitr, scales Suggests: devtools, testthat, pander, rmarkdown, Biobase, GenomicRanges, IRanges, S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations, RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation, RTCGA.CNV, RTCGA.PANCAN12, magrittr, tidyr License: GPL-2 Archs: i386, x64 MD5sum: 33b9026b513ee3ce614295e41a43a5a3 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 Author: Marcin Kosinski , Przemyslaw Biecek 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: RELEASE_3_14 git_last_commit: 25fa349 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RTCGA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTCGA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTCGA_1.24.0.tgz vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html vignetteTitles: Integrating TCGA Data - RTCGA Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RTCGA.clinical, RTCGA.CNV, RTCGA.methylation, RTCGA.miRNASeq, RTCGA.mRNA, RTCGA.mutations, RTCGA.PANCAN12, RTCGA.rnaseq, RTCGA.RPPA dependencyCount: 127 Package: RTCGAToolbox Version: 2.24.0 Depends: R (>= 3.5.0) Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges, GenomeInfoDb, httr, limma, methods, RaggedExperiment, RCircos, RCurl, RJSONIO, S4Vectors (>= 0.23.10), stats, stringr, SummarizedExperiment, survival, TCGAutils (>= 1.9.4), XML Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: file LICENSE MD5sum: f6cd40a0e8f1a1f8fbdd2f3b2c6b9f36 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: RELEASE_3_14 git_last_commit: b1359f0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RTCGAToolbox_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTCGAToolbox_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTCGAToolbox_2.24.0.tgz vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html vignetteTitles: RTCGAToolbox Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R importsMe: cBioPortalData, TCGAWorkflow suggestsMe: TCGAutils dependencyCount: 114 Package: RTN Version: 2.18.0 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: af78cde30aa9afb4f332f95deb6d4ada 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: RELEASE_3_14 git_last_commit: cf102aa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RTN_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTN_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTN_2.18.0.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: 121 Package: RTNduals Version: 1.18.0 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: 55685bd9ae58b882697c1a3e8ca70ce9 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: RELEASE_3_14 git_last_commit: 04c447b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RTNduals_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTNduals_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTNduals_1.18.0.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: 122 Package: RTNsurvival Version: 1.18.0 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: 2b2aed49c2b890dc886baf55a1b84d98 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: RELEASE_3_14 git_last_commit: 08175b3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RTNsurvival_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTNsurvival_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RTNsurvival_1.18.0.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: 126 Package: RTopper Version: 1.40.1 Depends: R (>= 2.12.0), Biobase Imports: limma, multtest Suggests: org.Hs.eg.db, KEGGREST, GO.db License: GPL (>= 3) + file LICENSE MD5sum: 1ec98af29b13cc326829fae674a061b1 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: RELEASE_3_14 git_last_commit: 9e3009b git_last_commit_date: 2022-01-26 Date/Publication: 2022-01-27 source.ver: src/contrib/RTopper_1.40.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/RTopper_1.40.1.zip mac.binary.ver: bin/macosx/contrib/4.1/RTopper_1.40.1.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: 16 Package: Rtpca Version: 1.4.0 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: cc40e81869bddc7a875fa801576091a3 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: RELEASE_3_14 git_last_commit: 5502202 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rtpca_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rtpca_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rtpca_1.4.0.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: 1.54.0 Depends: R (>= 3.3), 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, RCurl (>= 1.4-2), 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: 0832febad60331a01ecff98837977a31 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: RELEASE_3_14 git_last_commit: 04cdd75 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rtracklayer_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rtracklayer_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rtracklayer_1.54.0.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: BRGenomics, BSgenome, CAGEfightR, CoverageView, CSSQ, cummeRbund, ExCluster, geneXtendeR, GenomicFiles, groHMM, HelloRanges, MethylSeekR, ORFhunteR, r3Cseq, StructuralVariantAnnotation, svaNUMT, svaRetro, EatonEtAlChIPseq, liftOver, sequencing, csawBook, OSCA.intro, HiCfeat importsMe: AnnotationHubData, annotatr, APAlyzer, ASpediaFI, ATACseqQC, ballgown, BgeeCall, BindingSiteFinder, biscuiteer, BiSeq, branchpointer, BSgenome, CAGEr, casper, CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap, ChromSCape, chromswitch, circRNAprofiler, cliProfiler, CNEr, coMET, compartmap, consensusSeekeR, contiBAIT, conumee, customProDB, dasper, DeepBlueR, derfinder, DEScan2, diffHic, diffloop, diffUTR, DMCFB, DMCHMM, dmrseq, ELMER, enhancerHomologSearch, enrichTF, ensembldb, epidecodeR, epigraHMM, erma, esATAC, fcScan, FindIT2, genbankr, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, genotypeeval, ggbio, gmapR, gmoviz, GOTHiC, GreyListChIP, Gviz, hiAnnotator, HiTC, HTSeqGenie, icetea, igvR, INSPEcT, IsoformSwitchAnalyzeR, karyoploteR, m6Aboost, MACPET, MADSEQ, maser, MEDIPS, metagene, metagene2, metaseqR2, methrix, methylKit, motifbreakR, MotifDb, multicrispr, MungeSumstats, NADfinder, nearBynding, normr, NxtIRFcore, ODER, OMICsPCA, ORFik, PAST, periodicDNA, plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea, QuasR, RCAS, recount, recount3, recoup, regioneR, REMP, Repitools, RGMQL, RiboProfiling, ribosomeProfilingQC, RIPAT, RLSeq, rmspc, RNAmodR, roar, scanMiRApp, scPipe, scruff, seqCAT, seqsetvis, sevenC, SGSeq, shinyepico, SigsPack, sitadela, soGGi, srnadiff, TFBSTools, trackViewer, transcriptR, TRESS, tRNAscanImport, TSRchitect, txcutr, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr, GenomicState, chipenrich.data, DMRcatedata, geneLenDataBase, NxtIRFdata, spatialLIBD, systemPipeRdata, SingscoreAMLMutations, crispRdesignR, GALLO, kibior, PlasmaMutationDetector, utr.annotation, valr suggestsMe: alpine, AnnotationHub, autonomics, BiocFileCache, biovizBase, bsseq, cicero, CINdex, CrispRVariants, DAMEfinder, eisaR, epistack, epivizrChart, epivizrData, geneXtendeR, GenomicAlignments, GenomicDistributions, GenomicRanges, goseq, gwascat, InPAS, interactiveDisplay, megadepth, methylumi, miRBaseConverter, MutationalPatterns, OrganismDbi, Pi, PICS, PING, pipeFrame, plotgardener, pqsfinder, ProteoDisco, R453Plus1Toolbox, RcisTarget, rGADEM, Ringo, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, signeR, similaRpeak, SynExtend, systemPipeR, TAPseq, TCGAutils, triplex, tRNAdbImport, TVTB, EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3, FDb.FANTOM4.promoters.hg19, GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr, chipseqDB, gkmSVM, LDheatmap, RTIGER, Seurat, Signac dependencyCount: 43 Package: Rtreemix Version: 1.56.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL MD5sum: ad8c8c0ee1a416da6a8dea582395d629 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: RELEASE_3_14 git_last_commit: 2d80dc8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Rtreemix_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Rtreemix_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Rtreemix_1.56.0.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: 72 Package: rTRM Version: 1.32.0 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: 9ce856c97670c9d0c022d11ffbb6c463 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: RELEASE_3_14 git_last_commit: 29f92ac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rTRM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rTRM_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rTRM_1.32.0.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: 1.32.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 1d7b1ea1de2ff08079986423c75ab1f6 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: RELEASE_3_14 git_last_commit: 8765141 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rTRMui_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rTRMui_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rTRMui_1.32.0.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: 97 Package: runibic Version: 1.16.0 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: i386, x64 MD5sum: d788faee08d89e448b4df58d7d1db3a3 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: RELEASE_3_14 git_last_commit: 4507cce git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/runibic_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/runibic_1.16.0.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: 83 Package: RUVcorr Version: 1.26.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2, graphics Suggests: knitr, hgu133a2.db, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 865738629421e2bcf9b3ce812b6f519b 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: RELEASE_3_14 git_last_commit: 303fb49 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RUVcorr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RUVcorr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RUVcorr_1.26.0.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: 35 Package: RUVnormalize Version: 1.28.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: 09e5e303fe9714d7112e69fc59bdfd1e 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: RELEASE_3_14 git_last_commit: ee3dd4d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RUVnormalize_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RUVnormalize_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RUVnormalize_1.28.0.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: 1.28.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: da50366f283ed322fcbbbb4af2310d6b 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: RELEASE_3_14 git_last_commit: 8d91a95 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RUVSeq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RUVSeq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RUVSeq_1.28.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf 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: rnaseqGene importsMe: consensusDE, ribosomeProfilingQC, scone suggestsMe: DEScan2 dependencyCount: 111 Package: RVS Version: 1.16.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 Archs: i386, x64 MD5sum: 0ecc4e78ff4b3e8e63adbed987c1fa61 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: Thomas Sherman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: RELEASE_3_14 git_last_commit: 235dff2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/RVS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/RVS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/RVS_1.16.0.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: 36 Package: rWikiPathways Version: 1.14.0 Imports: httr, utils, XML, rjson, data.table, tidyr, RCurl Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 77cc5663685e77e6e26eeced92a4167b 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: RELEASE_3_14 git_last_commit: b83547a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/rWikiPathways_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/rWikiPathways_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/rWikiPathways_1.14.0.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, multiSight, TimiRGeN, RVA suggestsMe: TRONCO dependencyCount: 36 Package: S4Vectors Version: 0.32.4 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 License: Artistic-2.0 MD5sum: 561e5f88b7c9301dda70f6fe278f4a53 NeedsCompilation: yes Title: Foundation of vector-like and list-like containers in Bioconductor Description: The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). biocViews: Infrastructure, DataRepresentation Author: H. Pagès, M. Lawrence and P. Aboyoun Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/S4Vectors BugReports: https://github.com/Bioconductor/S4Vectors/issues git_url: https://git.bioconductor.org/packages/S4Vectors git_branch: RELEASE_3_14 git_last_commit: 6703ee8 git_last_commit_date: 2022-03-23 Date/Publication: 2022-03-24 source.ver: src/contrib/S4Vectors_0.32.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/S4Vectors_0.32.4.zip mac.binary.ver: bin/macosx/contrib/4.1/S4Vectors_0.32.4.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf, vignettes/S4Vectors/inst/doc/S4QuickOverview.pdf, vignettes/S4Vectors/inst/doc/S4VectorsOverview.pdf vignetteTitles: Rle Tips and Tricks, A quick overview of the S4 class system, An Overview of the S4Vectors package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R, vignettes/S4Vectors/inst/doc/S4QuickOverview.R, vignettes/S4Vectors/inst/doc/S4VectorsOverview.R dependsOnMe: 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pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, 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scTensor, scTGIF, scTreeViz, scuttle, sechm, segmenter, SeqArray, seqCAT, seqsetvis, SeqSQC, SeqVarTools, sesame, SEtools, sevenbridges, sevenC, SGSeq, ShortRead, SingleCellExperiment, singleCellTK, SingleR, singscore, sitadela, skewr, slingshot, SMITE, SNPhood, soGGi, SomaticSignatures, Spaniel, spatialDE, SpatialExperiment, spatialHeatmap, spatzie, spicyR, spiky, splatter, SplicingGraphs, SPLINTER, sRACIPE, srnadiff, STAN, strandCheckR, struct, StructuralVariantAnnotation, SummarizedExperiment, svaNUMT, svaRetro, SynExtend, systemPipeR, TAPseq, TarSeqQC, TBSignatureProfiler, TCGAbiolinks, TCGAutils, TFBSTools, TFHAZ, tidySingleCellExperiment, tidySummarizedExperiment, TileDBArray, TnT, ToxicoGx, trackViewer, tradeSeq, TrajectoryUtils, transcriptR, TransView, Trendy, tricycle, tRNA, tRNAdbImport, tRNAscanImport, TSCAN, tscR, TSRchitect, TVTB, twoddpcr, txcutr, tximeta, Ularcirc, UMI4Cats, universalmotif, VanillaICE, VariantAnnotation, VariantFiltering, VaSP, VCFArray, velociraptor, VplotR, wavClusteR, weitrix, wiggleplotr, xcms, XNAString, XVector, yamss, zellkonverter, 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.v3.1.1.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, celldex, chipenrich.data, chipseqDBData, curatedMetagenomicData, curatedTCGAData, DropletTestFiles, HighlyReplicatedRNASeq, HMP16SData, HMP2Data, imcdatasets, leeBamViews, MetaGxPancreas, MethylSeqData, MouseGastrulationData, MouseThymusAgeing, pd.atdschip.tiling, scpdata, scRNAseq, SimBenchData, SingleCellMultiModal, SomaticCancerAlterations, spatialLIBD, tuberculosis, GeoMxWorkflows, ActiveDriverWGS, BinQuasi, crispRdesignR, digitalDLSorteR, DR.SC, driveR, genBaRcode, geno2proteo, hoardeR, imcExperiment, LoopRig, microbial, NIPTeR, oncoPredict, PlasmaMutationDetector, pulseTD, restfulr, SC.MEB, SCRIP, Signac, SNPassoc, toxpiR suggestsMe: BiocGenerics, conclus, dearseq, epivizrChart, globalSeq, GWASTools, GWENA, hca, maftools, martini, MicrobiotaProcess, MungeSumstats, RTCGA, TFEA.ChIP, TFutils, tidybulk, traviz, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, curatedAdipoChIP, curatedAdipoRNA, ObMiTi, cancerTiming, GeoTcgaData, gkmSVM, polyRAD, rliger, Seurat, updog, valr linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments, GenomicRanges, HDF5Array, IRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 6 Package: safe Version: 3.34.0 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: 1fe1abb068ecb8264004d23971ee499f 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: RELEASE_3_14 git_last_commit: 527fe92 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/safe_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/safe_3.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/safe_3.34.0.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 dependsOnMe: PCGSE importsMe: EGSEA, EnrichmentBrowser dependencyCount: 46 Package: sagenhaft Version: 1.64.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, stats, utils License: GPL (>= 2) MD5sum: 3ce60ec153c4563c4edfce6941eea021 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: RELEASE_3_14 git_last_commit: 408505c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sagenhaft_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sagenhaft_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sagenhaft_1.64.0.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: 1.8.1 Depends: R (>= 3.5.0), gdsfmt (>= 1.20.0), SeqArray (>= 1.31.8), Rcpp Imports: methods, stats, utils, RcppParallel, SPAtest (>= 3.0.0) LinkingTo: Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0) Suggests: parallel, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics, SNPRelate License: GPL-3 MD5sum: 2f33ad35f227490c300e9933dafb4c7d 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 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 original SAIGE R package (v0.29.4.4 for single variant tests, Zhou et al. 2018). SAIGEgds also implements some of the SPAtest functions in C to speed up the calculation of Saddlepoint approximation. Benchmarks show that SAIGEgds is 5 to 6 times faster than the original SAIGE R package. biocViews: Software, Genetics, StatisticalMethod 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: RELEASE_3_14 git_last_commit: 13e4275 git_last_commit_date: 2022-03-29 Date/Publication: 2022-03-31 source.ver: src/contrib/SAIGEgds_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/SAIGEgds_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SAIGEgds_1.8.1.tgz vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html vignetteTitles: SAIGEgds Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R dependencyCount: 26 Package: sampleClassifier Version: 1.18.0 Depends: R (>= 4.0), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 Archs: i386, x64 MD5sum: 72b38f3e26092838571e4ec32f87fbda 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: RELEASE_3_14 git_last_commit: 4a223a7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sampleClassifier_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sampleClassifier_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sampleClassifier_1.18.0.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: 95 Package: SamSPECTRAL Version: 1.48.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) MD5sum: 909b5f72f976415ba24faba437cc1161 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: RELEASE_3_14 git_last_commit: d76f549 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SamSPECTRAL_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SamSPECTRAL_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SamSPECTRAL_1.48.0.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: 1.4.0 Depends: R (>= 4.0.0), stringr, ape, Biostrings, DECIPHER, parallel, reshape2, phangorn, 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: e3f3caf744c03c5c65ed44efa42c91ed 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: RELEASE_3_14 git_last_commit: 789ab3e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sangeranalyseR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sangeranalyseR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sangeranalyseR_1.4.0.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: 132 Package: sangerseqR Version: 1.30.1 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: 1085c768acd0472f9abd8d2f74e0c219 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: RELEASE_3_14 git_last_commit: 6f346af git_last_commit_date: 2022-03-22 Date/Publication: 2022-03-24 source.ver: src/contrib/sangerseqR_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/sangerseqR_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.1/sangerseqR_1.30.1.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf vignetteTitles: sangerseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.R dependsOnMe: sangeranalyseR suggestsMe: CrispRVariants, bold dependencyCount: 46 Package: SANTA Version: 2.30.0 Depends: R (>= 4.1), igraph Imports: graphics, Matrix, methods, stats Suggests: BiocGenerics, BioNet, DLBCL, formatR, knitr, msm, org.Sc.sgd.db, markdown, rmarkdown, RUnit License: GPL (>= 2) MD5sum: 8117957fefeb64a981331977a72acf05 NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex Cornish [cre, aut] Maintainer: Alex Cornish VignetteBuilder: knitr BugReports: https://github.com/alexjcornish/SANTA git_url: https://git.bioconductor.org/packages/SANTA git_branch: RELEASE_3_14 git_last_commit: 968bc92 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SANTA_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SANTA_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SANTA_2.30.0.tgz vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html vignetteTitles: Introduction to SANTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R dependencyCount: 11 Package: sarks Version: 1.6.0 Depends: R (>= 4.0) Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom Suggests: RUnit, BiocGenerics, ggplot2 License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: 24568db9a7ad1ba2a1010d78f9e515b7 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: RELEASE_3_14 git_last_commit: 16bb3c7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sarks_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sarks_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sarks_1.6.0.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: 21 Package: satuRn Version: 1.2.0 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: 650a1b56d2005366ce16f933748ee11e 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: RELEASE_3_14 git_last_commit: 2ac9d68 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/satuRn_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/satuRn_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/satuRn_1.2.0.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 dependencyCount: 67 Package: savR Version: 1.32.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 MD5sum: 3efa088ab230fb79eb40050e6231b66a NeedsCompilation: no Title: Parse and analyze Illumina SAV files Description: Parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots. biocViews: Sequencing Author: R. Brent Calder Maintainer: R. Brent Calder URL: https://github.com/bcalder/savR BugReports: https://github.com/bcalder/savR/issues git_url: https://git.bioconductor.org/packages/savR git_branch: RELEASE_3_14 git_last_commit: 55b6249 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/savR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/savR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/savR_1.32.0.tgz vignettes: vignettes/savR/inst/doc/savR.pdf vignetteTitles: Using savR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/savR/inst/doc/savR.R dependencyCount: 46 Package: SBGNview Version: 1.8.0 Depends: R (>= 3.6), pathview, SBGNview.data Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph, rmarkdown, knitr, SummarizedExperiment, AnnotationDbi, httr, KEGGREST, bookdown Suggests: testthat, gage License: AGPL-3 Archs: i386, x64 MD5sum: 36d2154dc37cf178f6abead205c074fb 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: RELEASE_3_14 git_last_commit: 1a402c7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SBGNview_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SBGNview_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SBGNview_1.8.0.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: 84 Package: SBMLR Version: 1.90.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: 2b7a9d5d691b13fe30e0a917e2a055ee 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: RELEASE_3_14 git_last_commit: faed667 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SBMLR_1.90.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SBMLR_1.90.0.zip 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: 1.22.0 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 License: GPL-3 MD5sum: 6fcee18b932a6cf29b91eea3ecb79000 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: RELEASE_3_14 git_last_commit: b96f6de git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SC3_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SC3_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SC3_1.22.0.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: 100 Package: Scale4C Version: 1.16.0 Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: 01193890d242d06733e09da863952eea 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: RELEASE_3_14 git_last_commit: 0233513 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Scale4C_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Scale4C_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Scale4C_1.16.0.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: 26 Package: ScaledMatrix Version: 1.2.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: 289ef8390a6046db37740fad9581049f 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: RELEASE_3_14 git_last_commit: d0573e1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ScaledMatrix_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ScaledMatrix_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ScaledMatrix_1.2.0.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: 15 Package: scAlign Version: 1.8.0 Depends: R (>= 3.6), SingleCellExperiment (>= 1.4), Seurat (>= 2.3.4), tensorflow, purrr, irlba, Rtsne, ggplot2, methods, utils, FNN Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 5698fe8c391c228dcfc20b419849781e NeedsCompilation: no Title: An alignment and integration method for single cell genomics Description: An unsupervised deep learning method for data alignment, integration and estimation of per-cell differences in -omic data (e.g. gene expression) across datasets (conditions, tissues, species). See Johansen and Quon (2019) for more details. biocViews: SingleCell, Transcriptomics, DimensionReduction, NeuralNetwork Author: Nelson Johansen [aut, cre], Gerald Quon [aut] Maintainer: Nelson Johansen URL: https://github.com/quon-titative-biology/scAlign SystemRequirements: python (< 3.7), tensorflow VignetteBuilder: knitr BugReports: https://github.com/quon-titative-biology/scAlign/issues git_url: https://git.bioconductor.org/packages/scAlign git_branch: RELEASE_3_14 git_last_commit: 9693380 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scAlign_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scAlign_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scAlign_1.8.0.tgz vignettes: vignettes/scAlign/inst/doc/scAlign.pdf vignetteTitles: alignment_tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAlign/inst/doc/scAlign.R dependencyCount: 167 Package: SCAN.UPC Version: 2.36.0 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 Archs: i386, x64 MD5sum: ad76c8fba68bb723711afd12745dc129 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: RELEASE_3_14 git_last_commit: 5a49e61 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SCAN.UPC_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCAN.UPC_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCAN.UPC_2.36.0.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: 112 Package: scanMiR Version: 1.0.0 Depends: R (>= 4.0) Imports: Biostrings, GenomicRanges, IRanges, data.table, BiocParallel, methods, GenomeInfoDb, S4Vectors, ggplot2, stats, stringi, utils, graphics, grid, gridExtra, seqLogo Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: df52c02d96fe11ae7c6eb1ab0d5ffdc0 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 [aut] (), Michael Soutschek [aut], Fridolin Gross [cre, aut] Maintainer: Fridolin Gross VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiR git_branch: RELEASE_3_14 git_last_commit: 91f9c75 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scanMiR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scanMiR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scanMiR_1.0.0.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: FALSE Rfiles: vignettes/scanMiR/inst/doc/Kdmodels.R, vignettes/scanMiR/inst/doc/scanning.R importsMe: scanMiRApp, scanMiRData dependencyCount: 63 Package: scanMiRApp Version: 1.0.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationFilter, AnnotationHub, BiocParallel, Biostrings, data.table, digest, DT, ensembldb, fst, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, htmlwidgets, IRanges, Matrix, methods, plotly, rintrojs, rtracklayer, S4Vectors, scanMiR, scanMiRData, shiny, shinycssloaders, shinydashboard, stats, utils, waiter Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), shinytest, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-3 MD5sum: 413aac0b622bedafc4e7f6063aee3be9 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 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: RELEASE_3_14 git_last_commit: 71c1334 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scanMiRApp_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scanMiRApp_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scanMiRApp_1.0.0.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: 149 Package: scAnnotatR Version: 1.0.0 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: 81365789f2d5ac77067b779a4212df96 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: RELEASE_3_14 git_last_commit: c896512 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scAnnotatR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scAnnotatR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scAnnotatR_1.0.0.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: 200 Package: SCANVIS Version: 1.8.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: 0ccd31b00b44497e8a102d0ad830612c 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: RELEASE_3_14 git_last_commit: d49015e git_last_commit_date: 2021-10-26 Date/Publication: 2021-11-04 source.ver: src/contrib/SCANVIS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCANVIS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCANVIS_1.8.0.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: 45 Package: SCArray Version: 1.2.1 Depends: R (>= 3.5.0), gdsfmt (>= 1.27.4), methods, DelayedArray (>= 0.16.0) Imports: BiocGenerics, S4Vectors, IRanges, utils, SummarizedExperiment, SingleCellExperiment, DelayedMatrixStats Suggests: Matrix, scater, uwot, RUnit, knitr, markdown, rmarkdown License: GPL-3 MD5sum: d795328e767d3f63109a10bdbafbeefd NeedsCompilation: yes Title: Large-scale single-cell RNA-seq data manipulation with GDS files Description: Provides large-scale single-cell RNA-seq 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: RELEASE_3_14 git_last_commit: ffcf19d git_last_commit_date: 2022-01-24 Date/Publication: 2022-01-25 source.ver: src/contrib/SCArray_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCArray_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SCArray_1.2.1.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 dependencyCount: 30 Package: SCATE Version: 1.4.0 Depends: parallel, preprocessCore, splines, splines2, xgboost, SCATEData, Rtsne, mclust Imports: utils, stats, GenomicAlignments, GenomicRanges Suggests: rmarkdown, ggplot2, knitr License: MIT + file LICENSE MD5sum: 553d6318f46ca3b6224a6c3e48851b21 NeedsCompilation: no Title: SCATE: Single-cell ATAC-seq Signal Extraction and Enhancement Description: SCATE is a software tool for extracting and enhancing the sparse and discrete Single-cell ATAC-seq Signal. Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. SCATE was developed to adaptively integrate information from co-activated CREs, similar cells, and publicly available regulome data and substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample. biocViews: ExperimentHub, ExperimentData, Genome, SequencingData, SingleCellData, SNPData Author: Zhicheng Ji [aut], Weiqiang Zhou [aut], Wenpin Hou [cre, aut] (), Hongkai Ji [aut] Maintainer: Wenpin Hou VignetteBuilder: knitr BugReports: https://github.com/Winnie09/SCATE/issues git_url: https://git.bioconductor.org/packages/SCATE git_branch: RELEASE_3_14 git_last_commit: bbec10a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SCATE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCATE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCATE_1.4.0.tgz vignettes: vignettes/SCATE/inst/doc/SCATE.html vignetteTitles: 1. SCATE package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCATE/inst/doc/SCATE.R dependencyCount: 116 Package: scater Version: 1.22.0 Depends: SingleCellExperiment, scuttle, ggplot2 Imports: stats, utils, methods, grid, gridExtra, Matrix, BiocGenerics, S4Vectors, SummarizedExperiment, DelayedArray, DelayedMatrixStats, beachmat, BiocNeighbors, BiocSingular, BiocParallel, rlang, ggbeeswarm, viridis, Rtsne, RColorBrewer, ggrepel Suggests: BiocStyle, biomaRt, cowplot, destiny, knitr, scRNAseq, robustbase, rmarkdown, uwot, NMF, testthat, pheatmap, snifter, Biobase License: GPL-3 MD5sum: f771935d64c0df01f81d4e3c627bf5bb 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] 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: RELEASE_3_14 git_last_commit: ea2c95c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scater_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scater_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scater_1.22.0.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.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: airpart, BayesSpace, CATALYST, celda, CelliD, CellMixS, ChromSCape, conclus, distinct, FLAMES, IRISFGM, mia, miaViz, muscat, peco, pipeComp, scDblFinder, scPipe, scTreeViz, singleCellTK, Spaniel, splatter, tricycle, VAExprs, spatialLIBD, SC.MEB, ZetaSuite suggestsMe: batchelor, bluster, CellaRepertorium, CellTrails, Cepo, CiteFuse, dittoSeq, ExperimentSubset, fcoex, ggspavis, Glimma, InteractiveComplexHeatmap, iSEE, iSEEu, M3Drop, MAST, mbkmeans, miloR, miQC, monocle, mumosa, Nebulosa, netDx, SC3, SCArray, scds, schex, scHOT, scMerge, scone, scp, scran, scRepertoire, SingleR, slalom, snifter, spatialHeatmap, SummarizedBenchmark, tidySingleCellExperiment, traviz, velociraptor, waddR, curatedMetagenomicData, DuoClustering2018, HCAData, muscData, SingleCellMultiModal, TabulaMurisData, tuberculosis, simpleSingleCell, SingleRBook, bcTSNE dependencyCount: 82 Package: scatterHatch Version: 1.0.0 Depends: R (>= 4.1) Imports: grid, ggplot2, plyr, spatstat.geom, stats, grDevices Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 7a6002a4d0d4f76eb591a6d255307d2f 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: RELEASE_3_14 git_last_commit: 83acfaf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scatterHatch_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scatterHatch_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scatterHatch_1.0.0.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: 45 Package: scBFA Version: 1.8.0 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 Archs: i386, x64 MD5sum: b996242723f8b4afc128fcf035f0b3b7 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: RELEASE_3_14 git_last_commit: 90a43b4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scBFA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scBFA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scBFA_1.8.0.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: 192 Package: SCBN Version: 1.12.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown License: GPL-2 MD5sum: bc64c8a1262debc52d3e508575d65424 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: RELEASE_3_14 git_last_commit: 4707cd7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SCBN_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCBN_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCBN_1.12.0.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 dependencyCount: 1 Package: scCB2 Version: 1.4.0 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: d79488ccf4ca93488a0c8834e82ca272 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: RELEASE_3_14 git_last_commit: f8be9e6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scCB2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scCB2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scCB2_1.4.0.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: 182 Package: scClassifR Version: 1.2.0 Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats, e1071, ape, kernlab, utils Suggests: knitr, rmarkdown, scRNAseq, testthat License: MIT + file LICENSE MD5sum: 28c53a271241896486f9e1db55ba55bc 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. scClassifR 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 VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/scClassifR git_branch: RELEASE_3_14 git_last_commit: e9e304f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scClassifR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scClassifR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scClassifR_1.2.0.tgz vignettes: vignettes/scClassifR/inst/doc/classifying-cells.html, vignettes/scClassifR/inst/doc/training-basic-model.html, vignettes/scClassifR/inst/doc/training-child-model.html vignetteTitles: 1. Introduction to scClassifR, 2. Training basic model, 3. Training child model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scClassifR/inst/doc/classifying-cells.R, vignettes/scClassifR/inst/doc/training-basic-model.R, vignettes/scClassifR/inst/doc/training-child-model.R dependencyCount: 181 Package: scClassify Version: 1.6.0 Depends: R (>= 4.0) Imports: S4Vectors, limma, ggraph, igraph, methods, cluster, minpack.lm, mixtools, BiocParallel, proxy, proxyC, Matrix, ggplot2, hopach, diptest, mgcv, stats, graphics, statmod, Cepo Suggests: knitr, rmarkdown, BiocStyle, pkgdown License: GPL-3 MD5sum: 3b10efb802b0c15a9463034c67553ebd NeedsCompilation: no Title: scClassify: single-cell Hierarchical Classification Description: scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references. biocViews: SingleCell, GeneExpression, Classification Author: Yingxin Lin Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scClassify/issues git_url: https://git.bioconductor.org/packages/scClassify git_branch: RELEASE_3_14 git_last_commit: cf2735b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scClassify_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scClassify_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scClassify_1.6.0.tgz vignettes: vignettes/scClassify/inst/doc/pretrainedModel.html, vignettes/scClassify/inst/doc/scClassify.html vignetteTitles: pretrainedModel, scClassify hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scClassify/inst/doc/pretrainedModel.R, vignettes/scClassify/inst/doc/scClassify.R dependencyCount: 133 Package: scDataviz Version: 1.4.0 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: 7632608d2981747452576158680a3722 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: RELEASE_3_14 git_last_commit: ef96ca1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scDataviz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scDataviz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scDataviz_1.4.0.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: 165 Package: scDblFinder Version: 1.8.0 Depends: R (>= 4.0) Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors, BiocSingular, S4Vectors, SummarizedExperiment, SingleCellExperiment, scran, scater, scuttle, bluster, methods, DelayedArray, xgboost, stats, utils, MASS Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, circlize, ComplexHeatmap, ggplot2, dplyr, MASS, viridisLite, mbkmeans License: GPL-3 MD5sum: c187da33a96da60c33e90da8464ce31d NeedsCompilation: no Title: scDblFinder Description: The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell RNA sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, and the new fast and comprehensive scDblFinder method. biocViews: Preprocessing, SingleCell, RNASeq Author: Pierre-Luc Germain [cre, aut] (), Aaron Lun [ctb] Maintainer: Pierre-Luc Germain URL: https://github.com/plger/scDblFinder VignetteBuilder: knitr BugReports: https://github.com/plger/scDblFinder/issues git_url: https://git.bioconductor.org/packages/scDblFinder git_branch: RELEASE_3_14 git_last_commit: bd1331d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scDblFinder_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scDblFinder_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scDblFinder_1.8.0.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/scDblFinder.html vignetteTitles: 4_computeDoubletDensity, 3_findDoubletClusters, 1_introduction, 5_recoverDoublets, 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/scDblFinder.R dependsOnMe: OSCA.advanced importsMe: singleCellTK dependencyCount: 97 Package: scDD Version: 1.18.0 Depends: R (>= 3.4) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 MD5sum: 02ed08f59d375bf5d745bff37e60a851 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: RELEASE_3_14 git_last_commit: b01c368 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scDD_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scDD_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scDD_1.18.0.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: 123 Package: scde Version: 2.22.0 Depends: R (>= 3.0.0), flexmix Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db, rmarkdown License: GPL-2 MD5sum: 466449f23604948adaef7b4b67afd70d 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] Maintainer: Jean Fan 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: RELEASE_3_14 git_last_commit: 527bac9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scde_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scde_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scde_2.22.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pagoda2 dependencyCount: 45 Package: scds Version: 1.10.0 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 MD5sum: 6a1a61339d91295f7c84adaf86bdc543 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: RELEASE_3_14 git_last_commit: ebea67a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scds_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scds_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scds_1.10.0.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: 50 Package: SCFA Version: 1.4.0 Depends: R (>= 4.0) Imports: matrixStats, keras, tensorflow, BiocParallel, igraph, Matrix, cluster, clusterCrit, psych, glmnet, RhpcBLASctl, stats, utils, methods, survival Suggests: knitr, rmarkdown License: LGPL MD5sum: bc7fa31370195b5235f54ba85c6a114c 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: RELEASE_3_14 git_last_commit: c96b3e2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SCFA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCFA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCFA_1.4.0.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: 66 Package: scFeatureFilter Version: 1.14.0 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: 69e4982380c319784c63591781c5c810 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: RELEASE_3_14 git_last_commit: 906e52d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scFeatureFilter_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scFeatureFilter_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scFeatureFilter_1.14.0.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: 42 Package: scGPS Version: 1.8.0 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 Archs: i386, x64 MD5sum: 9b10bd21abba9bcdc62094ecb83041af 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: RELEASE_3_14 git_last_commit: 505c489 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scGPS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scGPS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scGPS_1.8.0.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: 139 Package: schex Version: 1.8.0 Depends: SingleCellExperiment (>= 1.7.4), Seurat, ggplot2 (>= 3.2.1), shiny Imports: hexbin, stats, methods, cluster, dplyr, entropy, ggforce, scales, grid, concaveman Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr, TENxPBMCData, scater, shinydashboard, iSEE, igraph, scran License: GPL-3 MD5sum: 8a64f77cdac0ec73b6882dbddd0e1c42 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 and SeuratObject. 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 Author: Saskia Freytag 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: RELEASE_3_14 git_last_commit: 7259f37 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/schex_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/schex_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/schex_1.8.0.tgz vignettes: vignettes/schex/inst/doc/multi_modal_schex.html, vignettes/schex/inst/doc/picking_the_right_resolution.html, vignettes/schex/inst/doc/Seurat_schex.html, vignettes/schex/inst/doc/shiny_schex.html, vignettes/schex/inst/doc/using_schex.html vignetteTitles: multi_modal_schex, picking_the_right_resolution, Seurat_schex, shiny_schhex, using_schex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/schex/inst/doc/multi_modal_schex.R, vignettes/schex/inst/doc/picking_the_right_resolution.R, vignettes/schex/inst/doc/Seurat_schex.R, vignettes/schex/inst/doc/shiny_schex.R, vignettes/schex/inst/doc/using_schex.R importsMe: scTensor, scTGIF suggestsMe: fcoex dependencyCount: 173 Package: scHOT Version: 1.6.0 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: 6c4b442624340e163c754f8a67d93238 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: RELEASE_3_14 git_last_commit: daee27c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scHOT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scHOT_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scHOT_1.6.0.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: 74 Package: scMAGeCK Version: 1.6.0 Imports: Seurat, ggplot2, stats, utils Suggests: knitr, rmarkdown License: BSD_2_clause MD5sum: 3b611c2b3fcd30e0a72b3fc251d03a49 NeedsCompilation: yes Title: Identify genes associated with multiple expression phenotypes in single-cell CRISPR screening data Description: scMAGeCK is a computational model to identify genes associated with multiple expression phenotypes from CRISPR screening coupled with single-cell RNA sequencing data (CROP-seq) biocViews: CRISPR, SingleCell, RNASeq, PooledScreens, Transcriptomics, GeneExpression, Regression Author: Wei Li, Xiaolong Cheng, Lin Yang Maintainer: Xiaolong Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scMAGeCK git_branch: RELEASE_3_14 git_last_commit: 194b6e1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scMAGeCK_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scMAGeCK_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scMAGeCK_1.6.0.tgz vignettes: vignettes/scMAGeCK/inst/doc/scMAGeCK.html vignetteTitles: scMAGeCK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMAGeCK/inst/doc/scMAGeCK.R dependencyCount: 143 Package: scmap Version: 1.16.0 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 License: GPL-3 Archs: i386, x64 MD5sum: 563891a0e4aee602dee6159ad82038ea 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: RELEASE_3_14 git_last_commit: f913ff6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scmap_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scmap_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scmap_1.16.0.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: 72 Package: scMerge Version: 1.10.0 Depends: R (>= 3.6.0) Imports: BiocParallel, BiocSingular, cluster, DelayedArray, DelayedMatrixStats, distr, igraph, M3Drop (>= 1.9.4), parallel, pdist, proxy, ruv, S4Vectors (>= 0.23.19), SingleCellExperiment (>= 1.7.3), SummarizedExperiment Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales, scater, testthat, badger License: GPL-3 MD5sum: 293c020b72bbdb5a1eb22531846e4c97 NeedsCompilation: no Title: scMerge: Merging multiple batches of scRNA-seq data Description: Like all gene expression data, single-cell RNA-seq (scRNA-Seq) 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 scRNA-Seq data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of scRNA-Seq 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: RELEASE_3_14 git_last_commit: 8a5a638 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scMerge_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scMerge_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scMerge_1.10.0.tgz vignettes: vignettes/scMerge/inst/doc/scMerge.html vignetteTitles: scMerge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMerge/inst/doc/scMerge.R importsMe: singleCellTK suggestsMe: Cepo dependencyCount: 135 Package: scmeth Version: 1.14.0 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: d59379bd2c0effd3f815cee16e0466ec 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: RELEASE_3_14 git_last_commit: cb59e2c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scmeth_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scmeth_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scmeth_1.14.0.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: 164 Package: SCnorm Version: 1.16.0 Depends: R (>= 3.4.0), Imports: SingleCellExperiment, SummarizedExperiment, stats, methods, graphics, grDevices, parallel, quantreg, cluster, moments, data.table, BiocParallel, S4Vectors, ggplot2, forcats, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL (>= 2) MD5sum: f5cd0b9d9382bdd8cdbca7c61007a50a 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: RELEASE_3_14 git_last_commit: d9a9166 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SCnorm_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCnorm_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCnorm_1.16.0.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: 71 Package: scone Version: 1.18.0 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, BatchJobs, splatter, scater, kableExtra, mclust, TENxPBMCData License: Artistic-2.0 Archs: i386, x64 MD5sum: ff730c054cdc7b7d4af31708eb3b7861 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: RELEASE_3_14 git_last_commit: 80329bd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scone_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scone_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scone_1.18.0.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: 145 Package: Sconify Version: 1.14.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 1f3209408040c285bf45cbc6a3b65ab0 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: RELEASE_3_14 git_last_commit: 63a1bff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Sconify_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Sconify_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Sconify_1.14.0.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: 67 Package: SCOPE Version: 1.6.0 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: af5df50265d6c77966406cc5d3748208 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: RELEASE_3_14 git_last_commit: 02dfee8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SCOPE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SCOPE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SCOPE_1.6.0.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: 72 Package: scoreInvHap Version: 1.16.0 Depends: R (>= 3.6.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: 7d55701199ecb5e2f7790d2b55de7123 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: RELEASE_3_14 git_last_commit: d6e7b5f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scoreInvHap_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scoreInvHap_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scoreInvHap_1.16.0.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: 101 Package: scp Version: 1.4.0 Depends: R (>= 4.0), QFeatures (>= 1.3.5) Imports: methods, stats, utils, SingleCellExperiment, SummarizedExperiment, MultiAssayExperiment, MsCoreUtils, matrixStats, S4Vectors, dplyr, magrittr, rlang Suggests: testthat, knitr, BiocStyle, rmarkdown, patchwork, ggplot2, impute, scater, sva, preprocessCore, vsn, uwot License: Artistic-2.0 MD5sum: 123124a7bd5af87564b2e29051a81690 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 (SCP) data. The package is an extension to the 'QFeatures' package designed for SCP applications. 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: RELEASE_3_14 git_last_commit: e3ef0ca git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scp_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scp_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scp_1.4.0.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/scp.html vignetteTitles: Advanced usage of `scp`, QFeatures in a nutshell, Load data using readSCP, 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/scp.R suggestsMe: scpdata dependencyCount: 89 Package: scPCA Version: 1.8.0 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: 28b53a09773f1edd4f5e9617825713e7 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: RELEASE_3_14 git_last_commit: 2c73a80 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scPCA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scPCA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scPCA_1.8.0.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.advanced, OSCA.workflows dependencyCount: 68 Package: scPipe Version: 1.16.1 Depends: R (>= 3.4), ggplot2, methods, SingleCellExperiment Imports: Rhtslib, biomaRt, GGally, MASS, mclust, Rcpp (>= 0.11.3), reshape, BiocGenerics, robustbase, scales, utils, stats, S4Vectors, SummarizedExperiment, AnnotationDbi, org.Hs.eg.db, org.Mm.eg.db, stringr, rtracklayer, hash, dplyr, GenomicRanges, magrittr, glue (>= 1.3.0), rlang, scater (>= 1.11.0) LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc, testthat Suggests: Rsubread, knitr, rmarkdown, testthat License: GPL (>= 2) MD5sum: a27a1ae1e442778b4b2244df51ed86dd NeedsCompilation: yes Title: pipeline for single cell RNA-seq data analysis Description: A preprocessing pipeline for single cell RNA-seq data that starts from the fastq files and produces a gene 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 Author: Luyi Tian Maintainer: Luyi Tian 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: RELEASE_3_14 git_last_commit: d7b9a87 git_last_commit_date: 2022-03-08 Date/Publication: 2022-03-10 source.ver: src/contrib/scPipe_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/scPipe_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.1/scPipe_1.16.1.tgz vignettes: vignettes/scPipe/inst/doc/scPipe_tutorial.html vignetteTitles: scPipe: flexible data preprocessing pipeline for 3' end scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scPipe/inst/doc/scPipe_tutorial.R dependencyCount: 154 Package: scran Version: 1.22.1 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, monocle, Biobase, pheatmap, scater License: GPL-3 MD5sum: 8195a851d0102ddaddbd7866dd5579be 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: RELEASE_3_14 git_last_commit: db835e3 git_last_commit_date: 2021-11-12 Date/Publication: 2021-11-14 source.ver: src/contrib/scran_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/scran_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/scran_1.22.1.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.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: BASiCS, BayesSpace, celda, ChromSCape, CiteFuse, conclus, Dino, IRISFGM, msImpute, mumosa, pipeComp, scDblFinder, scDD, scTreeViz, SingleCellSignalR, singleCellTK, Spaniel, SC.MEB suggestsMe: batchelor, bluster, CellTrails, clusterExperiment, destiny, dittoSeq, ExperimentSubset, fcoex, ggspavis, Glimma, glmGamPoi, iSEEu, miloR, Nebulosa, PCAtools, schex, scone, scuttle, SingleR, snifter, spatialHeatmap, splatter, tidySingleCellExperiment, transformGamPoi, TSCAN, velociraptor, HCAData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell, SingleRBook dependencyCount: 57 Package: scReClassify Version: 1.0.0 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: 9c4987240d8885e5034dfe6d868a2002 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: RELEASE_3_14 git_last_commit: 6c385d5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scReClassify_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scReClassify_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scReClassify_1.0.0.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: 31 Package: scRecover Version: 1.10.0 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), Rmagic (>= 1.3.0), BiocParallel (>= 1.12.0) Suggests: knitr, rmarkdown, SingleCellExperiment, testthat License: GPL MD5sum: ccdd015cc66b9ee962923cecb7436039 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: RELEASE_3_14 git_last_commit: 2bb4216 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scRecover_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scRecover_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scRecover_1.10.0.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: 80 Package: scRepertoire Version: 1.4.0 Depends: ggplot2, R (>= 4.0) Imports: stringdist, dplyr, reshape2, ggalluvial, stringr, vegan, powerTCR, SummarizedExperiment, plyr, parallel, doParallel, methods, utils, rlang, igraph, SeuratObject Suggests: knitr, rmarkdown, BiocStyle, scater, circlize, scales, Seurat License: Apache License 2.0 Archs: i386, x64 MD5sum: 17bba436e8c46b3e335ade10115f1a95 NeedsCompilation: no Title: A toolkit for single-cell immune receptor profiling Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Author: Nick Borcherding [aut, cre] Maintainer: Nick Borcherding VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scRepertoire git_branch: RELEASE_3_14 git_last_commit: 6140f15 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scRepertoire_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scRepertoire_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scRepertoire_1.4.0.tgz vignettes: vignettes/scRepertoire/inst/doc/vignette.html vignetteTitles: Using scRepertoire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRepertoire/inst/doc/vignette.R dependencyCount: 88 Package: scruff Version: 1.12.1 Depends: R (>= 4.0) Imports: data.table, GenomicAlignments, GenomicFeatures, GenomicRanges, Rsamtools, ShortRead, parallel, plyr, BiocGenerics, BiocParallel, S4Vectors, AnnotationDbi, Biostrings, methods, ggplot2, ggthemes, scales, GenomeInfoDb, stringdist, ggbio, rtracklayer, SingleCellExperiment, SummarizedExperiment, Rsubread Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 750156ae4cf3f643aec52714db4a9da3 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: RELEASE_3_14 git_last_commit: ecade1b git_last_commit_date: 2021-11-12 Date/Publication: 2021-11-14 source.ver: src/contrib/scruff_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/scruff_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/scruff_1.12.1.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: 159 Package: scry Version: 1.6.0 Depends: R (>= 4.0), stats, methods Imports: DelayedArray, glmpca (>= 0.2.0), HDF5Array, Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular Suggests: markdown, BiocGenerics, covr, DuoClustering2018, ggplot2, knitr, rmarkdown, TENxPBMCData, testthat License: Artistic-2.0 MD5sum: 42a7d46cdfa17c94ab769febe93ab5da 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: RELEASE_3_14 git_last_commit: e83654b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scry_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scry_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scry_1.6.0.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: 46 Package: scShapes Version: 1.0.0 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: b7ccddb179711559c280dd64996663fc 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: RELEASE_3_14 git_last_commit: b8dd5db git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scShapes_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scShapes_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scShapes_1.0.0.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: 32 Package: scTensor Version: 2.4.1 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 License: Artistic-2.0 MD5sum: 31b009acf7e8f2629bdfd00b2472cd6a 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: RELEASE_3_14 git_last_commit: e0462fa git_last_commit_date: 2021-12-16 Date/Publication: 2021-12-16 source.ver: src/contrib/scTensor_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/scTensor_2.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/scTensor_2.4.1.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: 277 Package: scTGIF Version: 1.8.0 Depends: R (>= 3.6.0) Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly, tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr, S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor, methods, scales, msigdbr, schex, tibble, ggplot2, igraph Suggests: testthat License: Artistic-2.0 MD5sum: ee8a8f183c422b3458ac3a878ebf6e9a 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: RELEASE_3_14 git_last_commit: d5fbf5a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scTGIF_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scTGIF_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scTGIF_1.8.0.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: 209 Package: scTHI Version: 1.6.0 Depends: R (>= 4.0) Imports: BiocParallel, Rtsne, grDevices, graphics, stats Suggests: scTHI.data, knitr, rmarkdown License: GPL-2 MD5sum: 1c845ad6fa3c6b73f5b3665e01cb74f5 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: RELEASE_3_14 git_last_commit: 90cf888 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scTHI_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scTHI_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scTHI_1.6.0.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: 15 Package: scTreeViz Version: 1.0.0 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: 16c73264fdb6d0ce65ef70025ff40710 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: RELEASE_3_14 git_last_commit: 7eb447c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scTreeViz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scTreeViz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scTreeViz_1.0.0.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: 236 Package: scuttle Version: 1.4.0 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: 5b7a6f86294b56dd6aabe9cde03ea857 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: RELEASE_3_14 git_last_commit: b335263 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/scuttle_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/scuttle_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/scuttle_1.4.0.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.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: BASiCS, batchelor, DropletUtils, FLAMES, imcRtools, mia, mumosa, muscat, scDblFinder, velociraptor suggestsMe: bluster, SingleR, snifter, splatter, TSCAN, HCAData, MouseThymusAgeing, SingleRBook linksToMe: DropletUtils, scran dependencyCount: 38 Package: SDAMS Version: 1.14.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL MD5sum: 7af73ec87f7ec503f83bd17c76cfe539 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: RELEASE_3_14 git_last_commit: 0d8fb49 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SDAMS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SDAMS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SDAMS_1.14.0.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: 62 Package: sechm Version: 1.2.0 Depends: R (>= 4.1) Imports: S4Vectors, SummarizedExperiment, seriation, ComplexHeatmap, circlize, methods, randomcoloR, stats, grid, grDevices Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 148b03bb1a1bb88fd9afd9a55deafe2c 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: RELEASE_3_14 git_last_commit: dbe2ebf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sechm_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sechm_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sechm_1.2.0.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 dependencyCount: 67 Package: segmenter Version: 1.0.0 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: e47f98ae10b59d3f804a9011571a08ea 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: RELEASE_3_14 git_last_commit: 45aa76e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/segmenter_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/segmenter_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/segmenter_1.0.0.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: 169 Package: segmentSeq Version: 2.28.0 Depends: R (>= 3.0.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel, GenomicRanges, ShortRead, stats Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 791c60cb9794dfec848e475f6a4bd0ed 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 Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/segmentSeq git_branch: RELEASE_3_14 git_last_commit: bc3e0b2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/segmentSeq_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/segmentSeq_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/segmentSeq_2.28.0.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: 50 Package: selectKSigs Version: 1.6.0 Depends: R(>= 3.6) Imports: HiLDA, magrittr, gtools, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr License: GPL-3 MD5sum: b3af0a9766e9a04f881d802a973f7be3 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: RELEASE_3_14 git_last_commit: c3189d8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/selectKSigs_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/selectKSigs_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/selectKSigs_1.6.0.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: 125 Package: SELEX Version: 1.26.0 Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0) Imports: stats, utils License: GPL (>=2) MD5sum: f3d76194a5148509a851ee70eebc1fc0 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: RELEASE_3_14 git_last_commit: cd751a9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SELEX_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SELEX_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SELEX_1.26.0.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: 19 Package: SemDist Version: 1.28.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: 53980dcadb6def5148342915d7acadfd 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: RELEASE_3_14 git_last_commit: 53871da git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SemDist_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SemDist_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SemDist_1.28.0.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: 1.18.0 Depends: R (>= 3.0.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: 37967e166325d2c0d8011ffb712fb336 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: RELEASE_3_14 git_last_commit: 9cca670 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/semisup_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/semisup_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/semisup_1.18.0.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: SEPIRA Version: 1.14.0 Depends: R (>= 3.5.0) Imports: limma (>= 3.32.5), corpcor (>= 1.6.9), parallel (>= 3.3.1), stats Suggests: knitr, rmarkdown, testthat, igraph License: GPL-3 Archs: i386, x64 MD5sum: 9121b3790d7abe4446d90392f2eafbff NeedsCompilation: no Title: Systems EPigenomics Inference of Regulatory Activity Description: SEPIRA (Systems EPigenomics Inference of Regulatory Activity) is an algorithm that infers sample-specific transcription factor activity from the genome-wide expression or DNA methylation profile of the sample. biocViews: GeneExpression, Transcription, GeneRegulation, GeneTarget, NetworkInference, Network, Software Author: Yuting Chen [aut, cre], Andrew Teschendorff [aut] Maintainer: Yuting Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SEPIRA git_branch: RELEASE_3_14 git_last_commit: 6e70be9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SEPIRA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SEPIRA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SEPIRA_1.14.0.tgz vignettes: vignettes/SEPIRA/inst/doc/SEPIRA.html vignetteTitles: Introduction to `SEPIRA` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEPIRA/inst/doc/SEPIRA.R dependencyCount: 8 Package: seq2pathway Version: 1.26.0 Depends: R (>= 3.6.2) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: a38585e9baa07a0c2431f4b46000e1aa 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: RELEASE_3_14 git_last_commit: 2352a4a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/seq2pathway_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seq2pathway_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seq2pathway_1.26.0.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: 127 Package: SeqArray Version: 1.34.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.23.5) 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: ed091a84e3c600d651f41aa145ae82d8 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: http://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SeqArray/issues git_url: https://git.bioconductor.org/packages/SeqArray git_branch: RELEASE_3_14 git_last_commit: b2f6a92 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SeqArray_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqArray_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqArray_1.34.0.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: SAIGEgds, SeqVarTools importsMe: GDSArray, GENESIS, VariantExperiment, GMMAT, MAGEE suggestsMe: DelayedDataFrame, HIBAG, VCFArray dependencyCount: 21 Package: seqbias Version: 1.42.0 Depends: R (>= 3.0.2), GenomicRanges (>= 0.1.0), Biostrings (>= 2.15.0), methods LinkingTo: Rhtslib (>= 1.15.3) Suggests: Rsamtools, ggplot2 License: LGPL-3 MD5sum: 19a01de5a8bfb8319e972eb3bced66a4 NeedsCompilation: yes Title: Estimation of per-position bias in high-throughput sequencing data Description: This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence. biocViews: Sequencing Author: Daniel Jones Maintainer: Daniel Jones SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/seqbias git_branch: RELEASE_3_14 git_last_commit: e8ef6fa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/seqbias_1.42.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/seqbias_1.42.0.tgz vignettes: vignettes/seqbias/inst/doc/overview.pdf vignetteTitles: Assessing and Adjusting for Technical Bias in High Throughput Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqbias/inst/doc/overview.R dependsOnMe: ReQON dependencyCount: 20 Package: seqCAT Version: 1.16.1 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: afe6f23e8ce3f07e2f931968f19e6748 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: RELEASE_3_14 git_last_commit: 2a3dc7e git_last_commit_date: 2022-02-14 Date/Publication: 2022-02-15 source.ver: src/contrib/seqCAT_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqCAT_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.1/seqCAT_1.16.1.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: 114 Package: seqCNA Version: 1.40.0 Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT (>= 0.3.4), seqCNA.annot (>= 0.99), methods License: GPL-3 MD5sum: 52f1be8e6b892c428682edd642b30d61 NeedsCompilation: yes Title: Copy number analysis of high-throughput sequencing cancer data Description: Copy number analysis of high-throughput sequencing cancer data with fast summarization, extensive filtering and improved normalization biocViews: CopyNumberVariation, Genetics, Sequencing Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena SystemRequirements: samtools git_url: https://git.bioconductor.org/packages/seqCNA git_branch: RELEASE_3_14 git_last_commit: d200c4c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/seqCNA_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqCNA_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seqCNA_1.40.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R dependencyCount: 26 Package: seqcombo Version: 1.16.1 Depends: R (>= 3.4.0) Imports: Biostrings, cowplot, dplyr, ggplot2, grid, igraph, magrittr, methods, utils, yulab.utils Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble License: Artistic-2.0 MD5sum: 39a3e01b8e327501690e15f91622a673 NeedsCompilation: no Title: Visualization Tool for Sequence Recombination and Reassortment Description: Provides useful functions for visualizing sequence recombination and 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: RELEASE_3_14 git_last_commit: d1e889d git_last_commit_date: 2022-03-17 Date/Publication: 2022-03-20 source.ver: src/contrib/seqcombo_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqcombo_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.1/seqcombo_1.16.1.tgz vignettes: vignettes/seqcombo/inst/doc/reassortment.html vignetteTitles: Reassortment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/reassortment.R dependencyCount: 57 Package: SeqGate Version: 1.4.0 Depends: S4Vectors, SummarizedExperiment, GenomicRanges Imports: stats, methods, BiocManager Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown License: GPL (>= 2.0) Archs: i386, x64 MD5sum: a322df25d60bbebedc64da8090e5b361 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: RELEASE_3_14 git_last_commit: df8da0d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SeqGate_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqGate_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqGate_1.4.0.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: 26 Package: SeqGSEA Version: 1.34.0 Depends: Biobase, doParallel, DESeq2 Imports: methods, biomaRt Suggests: GenomicRanges License: GPL (>= 3) MD5sum: 167b355cf9ee29885554f5e977ae5638 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: RELEASE_3_14 git_last_commit: 055cbb6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SeqGSEA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqGSEA_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqGSEA_1.34.0.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: 113 Package: seqLogo Version: 1.60.0 Depends: methods, grid Imports: stats4, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: LGPL (>= 2) Archs: i386, x64 MD5sum: 25c7614f4a3b61b23b4532e4cd7ced83 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: RELEASE_3_14 git_last_commit: 4115c8e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/seqLogo_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqLogo_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seqLogo_1.60.0.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: igvR, IntEREst, PWMEnrich, rGADEM, riboSeqR, scanMiR, SPLINTER, TFBSTools suggestsMe: BCRANK, DiffLogo, MAGAR, motifcounter, MotifDb, universalmotif, phangorn dependencyCount: 4 Package: seqPattern Version: 1.26.0 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: a34ad790ca84b23f88c3086ef1322f13 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: RELEASE_3_14 git_last_commit: 08e4e4a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/seqPattern_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqPattern_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seqPattern_1.26.0.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 dependencyCount: 21 Package: seqsetvis Version: 1.14.4 Depends: R (>= 3.6), ggplot2 Imports: data.table, eulerr, GenomeInfoDb, GenomicAlignments, GenomicRanges, ggplotify, grDevices, grid, IRanges, limma, methods, pbapply, pbmcapply, png, RColorBrewer, Rsamtools, rtracklayer, S4Vectors, stats, UpSetR Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, covr, cowplot, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: b3970759a13568aab3d51f07e6b6e30b 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: RELEASE_3_14 git_last_commit: 6b8d904 git_last_commit_date: 2022-04-05 Date/Publication: 2022-04-07 source.ver: src/contrib/seqsetvis_1.14.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqsetvis_1.14.4.zip mac.binary.ver: bin/macosx/contrib/4.1/seqsetvis_1.14.4.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: 90 Package: SeqSQC Version: 1.16.0 Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2) Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges, methods, rbokeh, RColorBrewer, reshape2, rmarkdown, S4Vectors, stats, utils Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: 77ac2581d9aa186429b52d62b77c6e70 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: RELEASE_3_14 git_last_commit: 82e840a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SeqSQC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqSQC_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqSQC_1.16.0.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: 139 Package: seqTools Version: 1.28.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: dfc4766dfdcbf65bfe548510c06975ee 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: RELEASE_3_14 git_last_commit: c3b1669 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/seqTools_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/seqTools_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/seqTools_1.28.0.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: 1.32.0 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: 847feec215c6d1450e2ff30b861fe31a 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: RELEASE_3_14 git_last_commit: 311c07d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SeqVarTools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SeqVarTools_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SeqVarTools_1.32.0.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, VariantExperiment, GMMAT, MAGEE dependencyCount: 72 Package: sesame Version: 1.12.9 Depends: R (>= 4.1), sesameData, methods Imports: BiocParallel, grDevices, utils, stringr, tibble, illuminaio, MASS, GenomicRanges, IRanges, grid, preprocessCore, S4Vectors, randomForest, wheatmap, ggplot2, graphics, KernSmooth, parallel, matrixStats, DNAcopy, stats, SummarizedExperiment, e1071, fgsea, ggrepel, reshape2 Suggests: scales, knitr, rmarkdown, testthat, dplyr, tidyr, BiocStyle, IlluminaHumanMethylation450kmanifest, minfi, FlowSorted.CordBloodNorway.450k, FlowSorted.Blood.450k, HDF5Array License: MIT + file LICENSE MD5sum: 3a4a496baffa106f3355394c81d06ecf 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 more accurate detection calling, intelligenet inference of ethnicity, sex and advanced quality control routines. biocViews: DNAMethylation, MethylationArray, Preprocessing, QualityControl Author: Wanding Zhou [aut, cre], Hui Shen [aut], Timothy Triche [ctb], Bret Barnes [ctb] 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: RELEASE_3_14 git_last_commit: 88cfdf2 git_last_commit_date: 2022-02-06 Date/Publication: 2022-02-08 source.ver: src/contrib/sesame_1.12.9.tar.gz win.binary.ver: bin/windows/contrib/4.1/sesame_1.12.9.zip mac.binary.ver: bin/macosx/contrib/4.1/sesame_1.12.9.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/other.html, vignettes/sesame/inst/doc/QC.html, vignettes/sesame/inst/doc/sesame.html vignetteTitles: "4. Data Inference", "6. knowYourCG", 3. Modeling, 2. Non-human Array, 5. Other Features, 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/other.R, vignettes/sesame/inst/doc/QC.R, vignettes/sesame/inst/doc/sesame.R importsMe: TCGAbiolinksGUI suggestsMe: MethReg, RnBeads, TCGAbiolinks, sesameData dependencyCount: 143 Package: SEtools Version: 1.8.0 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, data.table, seriation, ComplexHeatmap, circlize, methods, BiocParallel, randomcoloR, edgeR, openxlsx, sva, stats, DESeq2, Matrix, grid Suggests: BiocStyle, knitr, rmarkdown, ggplot2, pheatmap License: GPL MD5sum: 51c560559fc1a590d91667b6dcb6ddea NeedsCompilation: no Title: SEtools: tools for working with SummarizedExperiment Description: This includes a set of tools for working with the SummarizedExperiment class, including merging, melting, aggregation and plotting functions. In particular, SEtools offers a simple interface for plotting complex heatmaps from SE objects. biocViews: GeneExpression, Visualization 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: RELEASE_3_14 git_last_commit: 30a597c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SEtools_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SEtools_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SEtools_1.8.0.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: 121 Package: sevenbridges Version: 1.24.0 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 Archs: i386, x64 MD5sum: 0b0e3969ed12b62ea2ca69a8489fafd4 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: Soner Koc [aut, cre], Nan Xiao [aut], Tengfei Yin [aut], Dusan Randjelovic [ctb], Emile Young [ctb], Seven Bridges Genomics [cph, fnd] Maintainer: Soner Koc 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: RELEASE_3_14 git_last_commit: 9e0d83f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sevenbridges_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sevenbridges_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sevenbridges_1.24.0.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: 26 Package: sevenC Version: 1.14.0 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: 051397dd6f287ac6aa042fa471ef0a3e 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: RELEASE_3_14 git_last_commit: df326ea git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sevenC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sevenC_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sevenC_1.14.0.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: 74 Package: SGSeq Version: 1.28.0 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: 11aad89860c57ef8aa5a921a41a1fa7c 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: RELEASE_3_14 git_last_commit: 9468314 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SGSeq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SGSeq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SGSeq_1.28.0.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 dependencyCount: 98 Package: SharedObject Version: 1.8.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 015f1a42130d955ce058b206a2e49bb1 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] 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: RELEASE_3_14 git_last_commit: 98f4a95 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SharedObject_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SharedObject_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SharedObject_1.8.0.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: shinyepico Version: 1.2.0 Depends: R (>= 4.0.0) Imports: DT (>= 0.15.0), data.table (>= 1.13.0), doParallel (>= 1.0.0), dplyr (>= 1.0.0), 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 (>= 0.4.0), 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 (>= 1.1.0), zip (>= 2.1.0) Suggests: knitr (>= 1.30.0), mCSEA (>= 1.10.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, testthat, minfiData License: AGPL-3 + file LICENSE MD5sum: f6c970ecc4f913c465d2436835835f78 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: RELEASE_3_14 git_last_commit: 513ba64 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/shinyepico_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/shinyepico_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/shinyepico_1.2.0.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: 204 Package: shinyMethyl Version: 1.30.0 Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.13.2), minfi (>= 1.18.2), IlluminaHumanMethylation450kmanifest, matrixStats, R (>= 3.0.0) Imports: RColorBrewer Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr License: Artistic-2.0 MD5sum: cb729922d172bb4a62e3df40f818b9c1 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 Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/shinyMethyl git_branch: RELEASE_3_14 git_last_commit: a6b9628 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/shinyMethyl_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/shinyMethyl_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/shinyMethyl_1.30.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf 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: 156 Package: ShortRead Version: 1.52.0 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), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib, zlibbioc Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 MD5sum: 798355d1af39f3ba320f517069ec8cb2 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: Martin Morgan, Michael Lawrence, Simon Anders Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/ShortRead git_branch: RELEASE_3_14 git_last_commit: 4d7304d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ShortRead_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ShortRead_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ShortRead_1.52.0.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.pdf 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, SimRAD, STRMPS importsMe: amplican, ArrayExpressHTS, basecallQC, BEAT, CellBarcode, chipseq, ChIPseqR, ChIPsim, dada2, easyRNASeq, FastqCleaner, GOTHiC, icetea, IONiseR, MACPET, nucleR, QuasR, R453Plus1Toolbox, RSVSim, scruff, UMI4Cats, systemPipeRdata, genBaRcode suggestsMe: BiocParallel, CSAR, GenomicAlignments, PING, Repitools, Rsamtools, S4Vectors, HiCDataLymphoblast, yeastRNASeq dependencyCount: 43 Package: SIAMCAT Version: 1.14.0 Depends: R (>= 3.6.0), mlr, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, ParamHelpers, pROC, PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo, progress, corrplot Suggests: BiocStyle, optparse, testthat, knitr, rmarkdown, tidyverse, ggpubr License: GPL-3 Archs: i386, x64 MD5sum: c82fb1a1c5b639f3585e2a5c361963b6 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: RELEASE_3_14 git_last_commit: 596ef06 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SIAMCAT_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIAMCAT_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIAMCAT_1.14.0.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: 100 Package: SICtools Version: 1.24.0 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: ef414084d64dd09fdd7262f5a3f4bd42 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: RELEASE_3_14 git_last_commit: 0b80b21 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SICtools_1.24.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/SICtools_1.24.0.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: 40 Package: SigCheck Version: 2.26.0 Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: f202553cd51b56c405a1548a02f6e38f 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: RELEASE_3_14 git_last_commit: ac7a8fa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SigCheck_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SigCheck_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SigCheck_2.26.0.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: 122 Package: sigFeature Version: 1.12.0 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: 69618fdb7683d3bd684ae9e5e253292f 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: RELEASE_3_14 git_last_commit: 4ad448d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sigFeature_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sigFeature_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sigFeature_1.12.0.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: 62 Package: SigFuge Version: 1.32.0 Depends: R (>= 3.1.1), 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 Archs: i386, x64 MD5sum: f7017ec4683da2731fdede88e8cc41b1 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: RELEASE_3_14 git_last_commit: 56f0ccf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SigFuge_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SigFuge_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SigFuge_1.32.0.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: 55 Package: siggenes Version: 1.68.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: f9629d7071c0d78f3b970198cc9375dd 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: RELEASE_3_14 git_last_commit: a29bf02 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/siggenes_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/siggenes_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/siggenes_1.68.0.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: DAPAR, minfi, trio, XDE, DeSousa2013, INCATome suggestsMe: GCSscore, logicFS dependencyCount: 16 Package: sights Version: 1.20.0 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: 7ac8a5d058b3c9e611efbaa9edd197c7 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: RELEASE_3_14 git_last_commit: 0b87c08 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sights_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sights_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sights_1.20.0.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: 45 Package: signatureSearch Version: 1.8.2 Depends: R(>= 3.6.0), Rcpp, SummarizedExperiment 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, tools LinkingTo: Rcpp Suggests: knitr, testthat, rmarkdown, BiocStyle, org.Hs.eg.db, signatureSearchData, DT License: Artistic-2.0 MD5sum: 66c729f89fcb736cce5a617c98ae24f0 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 [cre, aut], Thomas Girke [aut] Maintainer: Yuzhu Duan 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: RELEASE_3_14 git_last_commit: 5b73dc1 git_last_commit_date: 2021-12-06 Date/Publication: 2021-12-07 source.ver: src/contrib/signatureSearch_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/signatureSearch_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.1/signatureSearch_1.8.2.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 dependencyCount: 175 Package: signeR Version: 1.20.0 Depends: VariantAnnotation, NMF Imports: BiocGenerics, Biostrings, class, graphics, grDevices, GenomeInfoDb, GenomicRanges, IRanges, nloptr, methods, stats, utils, PMCMRplus LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100) Suggests: knitr, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: e53dedfacc2ebfd2db8f39048b0bcbee 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 variaton (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, Israel Tojal da Silva Maintainer: Renan Valieris URL: https://github.com/rvalieris/signeR SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signeR git_branch: RELEASE_3_14 git_last_commit: 4e3b399 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/signeR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/signeR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/signeR_1.20.0.tgz vignettes: vignettes/signeR/inst/doc/signeR-vignette.html vignetteTitles: signeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R dependencyCount: 151 Package: sigPathway Version: 1.62.0 Depends: R (>= 2.10) Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>= 1.3.12) License: GPL-2 MD5sum: 35d9e8abaec9ea31009b5f33636f27a6 NeedsCompilation: yes Title: Pathway Analysis Description: Conducts pathway analysis by calculating the NT_k and NE_k statistics as described in Tian et al. (2005) biocViews: DifferentialExpression, MultipleComparison Author: Weil Lai (optimized R and C code), Lu Tian and Peter Park (algorithm development and initial R code) Maintainer: Weil Lai URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102, http://www.chip.org/~ppark/Supplements/PNAS05.html git_url: https://git.bioconductor.org/packages/sigPathway git_branch: RELEASE_3_14 git_last_commit: cd1f56e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sigPathway_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sigPathway_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sigPathway_1.62.0.tgz vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf vignetteTitles: sigPathway hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigPathway/inst/doc/sigPathway-vignette.R dependsOnMe: tRanslatome dependencyCount: 0 Package: SigsPack Version: 1.8.0 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: dc8b6bca8c75c5849abbab8387f22314 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: RELEASE_3_14 git_last_commit: 1f0ea5f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SigsPack_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SigsPack_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SigsPack_1.8.0.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: 99 Package: sigsquared Version: 1.26.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 Archs: i386, x64 MD5sum: 37695c75f9fac3b9e005319f1899fa52 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: RELEASE_3_14 git_last_commit: 9ef5a53 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sigsquared_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sigsquared_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sigsquared_1.26.0.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: 1.64.0 Depends: R (>= 3.5), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: c051410b2eada2a5fc0ae6d80ad80bdd 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: RELEASE_3_14 git_last_commit: 8ae32c9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SIM_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIM_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIM_1.64.0.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: 58 Package: SIMAT Version: 1.26.0 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: 139ba55a7156e3d1704bd2bd995d4459 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: RELEASE_3_14 git_last_commit: 03f478f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SIMAT_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIMAT_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIMAT_1.26.0.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: 51 Package: SimBindProfiles Version: 1.32.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: df4346c4d83678a12f02be3fa4541fdc NeedsCompilation: no Title: Similar Binding Profiles Description: SimBindProfiles identifies common and unique binding regions in genome tiling array data. This package does not rely on peak calling, but directly compares binding profiles processed on the same array platform. It implements a simple threshold approach, thus allowing retrieval of commonly and differentially bound regions between datasets as well as events of compensation and increased binding. biocViews: Microarray, Software Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell Maintainer: Bettina Fischer git_url: https://git.bioconductor.org/packages/SimBindProfiles git_branch: RELEASE_3_14 git_last_commit: 8b823ef git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SimBindProfiles_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SimBindProfiles_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SimBindProfiles_1.32.0.tgz vignettes: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.pdf vignetteTitles: SimBindProfiles: Similar Binding Profiles,, identifies common and unique regions in array genome tiling array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.R dependencyCount: 84 Package: SIMD Version: 1.12.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 MD5sum: bf76ada4a49a4e809c943a78a68e3ec8 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: RELEASE_3_14 git_last_commit: 3f95560 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SIMD_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIMD_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIMD_1.12.0.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: 1.6.0 Depends: Biostrings Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges, Rsamtools, parallel, graphics, stats, utils, methods Suggests: BiocStyle License: LGPL-3 Archs: i386, x64 MD5sum: a1dad26f9b9b72ddeeb3c227d7dfca0a 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: RELEASE_3_14 git_last_commit: 903dd41 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SimFFPE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SimFFPE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SimFFPE_1.6.0.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: 51 Package: similaRpeak Version: 1.26.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: d0f26996a2d469becf783cd977dbb6c4 NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which assign a level of similarity between ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschenes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes 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: RELEASE_3_14 git_last_commit: 9b96ea4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/similaRpeak_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/similaRpeak_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/similaRpeak_1.26.0.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 suggestsMe: metagene dependencyCount: 2 Package: SIMLR Version: 1.20.0 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: 0201cc71732514fa54570d047946e44f 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 [cre, aut] (), Bo Wang [aut], Luca De Sano [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: RELEASE_3_14 git_last_commit: 7cfd473 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SIMLR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SIMLR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SIMLR_1.20.0.tgz vignettes: vignettes/SIMLR/inst/doc/vignette.pdf vignetteTitles: Single-cell Interpretation via Multi-kernel LeaRning (\Biocpkg{SIMLR}) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/vignette.R importsMe: SingleCellSignalR dependencyCount: 14 Package: simplifyEnrichment Version: 1.4.0 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) 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 License: MIT + file LICENSE Archs: i386, x64 MD5sum: 508a0deee9dfc206a4f57697adb7d877 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 provideds functionalities 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: RELEASE_3_14 git_last_commit: 549d5f1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/simplifyEnrichment_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/simplifyEnrichment_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/simplifyEnrichment_1.4.0.tgz vignettes: vignettes/simplifyEnrichment/inst/doc/interactive.html, vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html, vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.html vignetteTitles: 3. A Shiny app to interactively visualize clustering results, 1. Simplify Functional Enrichment Results, 2. 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, scITD dependencyCount: 76 Package: sincell Version: 1.26.0 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) MD5sum: a04c31bb8204f290b8435fc91ac90111 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: RELEASE_3_14 git_last_commit: 821f81c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sincell_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sincell_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sincell_1.26.0.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 importsMe: ctgGEM dependencyCount: 63 Package: SingleCellExperiment Version: 1.16.0 Depends: SummarizedExperiment Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq, Rtsne License: GPL-3 MD5sum: 934e33067b6a88b5c210f52d067b0e7e 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] Maintainer: Davide Risso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellExperiment git_branch: RELEASE_3_14 git_last_commit: bb27609 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SingleCellExperiment_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SingleCellExperiment_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SingleCellExperiment_1.16.0.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: BASiCS, batchelor, BayesSpace, CATALYST, CellBench, CelliD, CellTrails, clusterExperiment, cydar, cytomapper, DropletUtils, ExperimentSubset, iSEE, LoomExperiment, MAST, mia, mumosa, NeuCA, POWSC, scAlign, scAnnotatR, scater, scClassifR, scGPS, schex, scPipe, scran, scuttle, singleCellTK, SpatialExperiment, splatter, switchde, tidySingleCellExperiment, TrajectoryUtils, TreeSummarizedExperiment, tricycle, TSCAN, zinbwave, HCAData, imcdatasets, MouseGastrulationData, MouseThymusAgeing, muscData, scATAC.Explorer, scRNAseq, TENxBrainData, TENxPBMCData, TMExplorer, OSCA.intro, DIscBIO, imcExperiment importsMe: ADImpute, aggregateBioVar, airpart, bayNorm, BEARscc, BUSseq, ccfindR, celda, CellMixS, Cepo, ChromSCape, CiteFuse, clustifyr, CoGAPS, conclus, condiments, corral, destiny, Dino, distinct, dittoSeq, escape, fcoex, FEAST, fishpond, FLAMES, ggspavis, GSVA, HIPPO, ILoReg, imcRtools, infercnv, IRISFGM, iSEEu, LineagePulse, mbkmeans, MetaNeighbor, miloR, miQC, muscat, Nebulosa, netSmooth, NewWave, peco, phemd, pipeComp, SC3, SCArray, scBFA, scCB2, scDblFinder, scDD, scds, scHOT, scmap, scMerge, SCnorm, scone, scp, scReClassify, scruff, scry, scTensor, scTGIF, scTreeViz, slalom, slingshot, Spaniel, SPsimSeq, tradeSeq, traviz, treekoR, VAExprs, velociraptor, waddR, zellkonverter, scpdata, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, digitalDLSorteR, SC.MEB, SCRIP suggestsMe: CellaRepertorium, DEsingle, EWCE, FCBF, genomicInstability, hca, HDF5Array, InteractiveComplexHeatmap, M3Drop, mistyR, MOFA2, ontoProc, phenopath, progeny, PubScore, QFeatures, scFeatureFilter, scPCA, scRecover, SingleR, dorothea, DuoClustering2018, GSE103322, microbiomeDataSets, TabulaMurisData, simpleSingleCell, Canek, clustree, dyngen, Seurat, singleCellHaystack dependencyCount: 25 Package: SingleCellSignalR Version: 1.6.0 Depends: R (>= 4.0) Imports: BiocManager, circlize, limma, igraph, gplots, grDevices, edgeR, SIMLR, data.table, pheatmap, stats, Rtsne, graphics, stringr, foreach, multtest, scran, utils, Suggests: knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 3a75c75c26a8cebcef4321af68c27e82 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 [aut], Jacques Colinge [cre, aut] Maintainer: Jacques Colinge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellSignalR git_branch: RELEASE_3_14 git_last_commit: dd12f95 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SingleCellSignalR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SingleCellSignalR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SingleCellSignalR_1.6.0.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 suggestsMe: tidySingleCellExperiment, scDiffCom dependencyCount: 95 Package: singleCellTK Version: 2.4.0 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, DelayedArray, Biobase Imports: ape, batchelor, BiocParallel, celldex, colourpicker, colorspace, cowplot, cluster, ComplexHeatmap, data.table, DelayedMatrixStats, DESeq2, dplyr, DT, ExperimentHub, fields, ggplot2, ggplotify, ggrepel, ggtree, gridExtra, GSVA (>= 1.26.0), GSVAdata, igraph, KernSmooth, limma, MAST, Matrix, matrixStats, methods, msigdbr, multtest, plotly, RColorBrewer, ROCR, Rtsne, S4Vectors, scater, scMerge (>= 1.2.0), scran, Seurat (>= 3.1.3), shiny, shinyjs, SingleR, sva, reshape2, AnnotationDbi, shinyalert, circlize, enrichR, celda, shinycssloaders, DropletUtils, scds (>= 1.2.0), reticulate (>= 1.14), tools, tximport, fishpond, withr, GSEABase, R.utils, zinbwave, scRNAseq (>= 2.0.2), TENxPBMCData, yaml, rmarkdown, magrittr, scDblFinder, metap, VAM (>= 0.5.3), tibble, rlang, stats Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, xtable, spelling, org.Mm.eg.db, stringr, kableExtra, shinythemes, shinyBS, shinyjqui, shinyWidgets, shinyFiles, BiocGenerics License: MIT + file LICENSE MD5sum: a6d0ac53cc587f5a469cde5596e62379 NeedsCompilation: no Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data Description: Run common single cell analysis in the R console or directly through your browser. Includes many functions for import, quality control, normalization, batch correction, clustering, differential expression, and visualization.. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering, ImmunoOncology Author: Yichen Wang [aut, cre] (), Irzam Sarfraz [aut], Rui Hong [aut], Yusuke Koga [aut], Salam Alabdullatif [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] (), Joshua David Campbell [aut] Maintainer: Yichen Wang 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: RELEASE_3_14 git_last_commit: 91f98fc git_last_commit_date: 2021-10-27 Date/Publication: 2021-10-27 source.ver: src/contrib/singleCellTK_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/singleCellTK_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/singleCellTK_2.4.0.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: 361 Package: SingleMoleculeFootprinting Version: 1.2.0 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 Archs: i386, x64 MD5sum: 26bb4c6f7e3656541a8dffe248b0075b 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: RELEASE_3_14 git_last_commit: a45923a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SingleMoleculeFootprinting_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SingleMoleculeFootprinting_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SingleMoleculeFootprinting_1.2.0.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: 108 Package: SingleR Version: 1.8.1 Depends: SummarizedExperiment Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats, BiocNeighbors, BiocParallel, BiocSingular, stats, utils, Rcpp, beachmat, parallel LinkingTo: Rcpp, beachmat Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics, SingleCellExperiment, scuttle, scater, scran, scRNAseq, ggplot2, pheatmap, grDevices, gridExtra, viridis, celldex License: GPL-3 + file LICENSE MD5sum: 73a881b154d99eabe554f215fefc1fe0 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++11 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/SingleR git_branch: RELEASE_3_14 git_last_commit: f284296 git_last_commit_date: 2022-01-26 Date/Publication: 2022-01-27 source.ver: src/contrib/SingleR_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/SingleR_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SingleR_1.8.1.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.advanced, OSCA.basic, OSCA.multisample, OSCA.workflows importsMe: singleCellTK suggestsMe: tidySingleCellExperiment, SingleRBook, tidyseurat dependencyCount: 43 Package: singscore Version: 1.14.0 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: hexbin, knitr, rmarkdown, testthat License: GPL-3 MD5sum: abaebdd4666b6462710917d552713498 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: Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb] (), Dharmesh D. Bhuva [aut, cre] () Maintainer: Dharmesh D. Bhuva 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: RELEASE_3_14 git_last_commit: 5a179ba git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/singscore_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/singscore_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/singscore_1.14.0.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: vissE, msigdb dependencyCount: 114 Package: SISPA Version: 1.24.0 Depends: R (>= 3.5),genefilter,GSVA,changepoint Imports: data.table, plyr, ggplot2 Suggests: knitr License: GPL-2 MD5sum: 1f865f71097be13c79a45472d6b2b0d9 NeedsCompilation: no Title: SISPA: Method for Sample Integrated Set Profile Analysis Description: Sample Integrated Set Profile Analysis (SISPA) is a method designed to define sample groups with similar gene set enrichment profiles. biocViews: GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SISPA git_branch: RELEASE_3_14 git_last_commit: 9c31cd6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SISPA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SISPA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SISPA_1.24.0.tgz vignettes: vignettes/SISPA/inst/doc/SISPA.html vignetteTitles: SISPA:Method for Sample Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SISPA/inst/doc/SISPA.R dependencyCount: 108 Package: sitadela Version: 1.2.0 Depends: R (>= 4.1.0) Imports: Biobase, BiocGenerics, biomaRt, Biostrings, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, methods, parallel, Rsamtools, RSQLite, rtracklayer, S4Vectors, tools, utils Suggests: BSgenome, knitr, rmarkdown, RMySQL, RUnit License: Artistic-2.0 MD5sum: e5208be5adaddd4cf70932219794b32b 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: RELEASE_3_14 git_last_commit: 2fa94c2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sitadela_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sitadela_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sitadela_1.2.0.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: 96 Package: sitePath Version: 1.10.2 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: i386, x64 MD5sum: 4a309b98d23dad1428da16a0053fa796 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: RELEASE_3_14 git_last_commit: 7a842eb git_last_commit_date: 2022-01-26 Date/Publication: 2022-01-27 source.ver: src/contrib/sitePath_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/sitePath_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.1/sitePath_1.10.2.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: 66 Package: sizepower Version: 1.64.0 Depends: stats License: LGPL MD5sum: 458538c46cd4f0ad75e005bad5800bad 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: RELEASE_3_14 git_last_commit: 05b1745 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sizepower_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sizepower_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sizepower_1.64.0.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: skewr Version: 1.26.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 MD5sum: b681feeaf8eabc50a0c91405940bbebd 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: RELEASE_3_14 git_last_commit: f037f2a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/skewr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/skewr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/skewr_1.26.0.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: 173 Package: slalom Version: 1.16.0 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: 93cb976ea6448c0dded3a9ef4677b15e 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: RELEASE_3_14 git_last_commit: 140fa92 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/slalom_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/slalom_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/slalom_1.16.0.tgz 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: 85 Package: SLGI Version: 1.54.0 Depends: R (>= 2.10), ScISI, lattice Imports: AnnotationDbi, Biobase, GO.db, ScISI, graphics, lattice, methods, stats, BiocGenerics Suggests: GO.db, org.Sc.sgd.db License: Artistic-2.0 Archs: i386, x64 MD5sum: 575782af0cacd03fa62fb1891a126aab NeedsCompilation: no Title: Synthetic Lethal Genetic Interaction Description: A variety of data files and functions for the analysis of genetic interactions biocViews: GraphAndNetwork, Proteomics, Genetics, Network Author: Nolwenn LeMeur, Zhen Jiang, Ting-Yuan Liu, Jess Mar and Robert Gentleman Maintainer: Nolwenn Le Meur git_url: https://git.bioconductor.org/packages/SLGI git_branch: RELEASE_3_14 git_last_commit: b4f5841 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SLGI_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SLGI_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SLGI_1.54.0.tgz vignettes: vignettes/SLGI/inst/doc/SLGI.pdf vignetteTitles: SLGI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLGI/inst/doc/SLGI.R dependencyCount: 49 Package: slingshot Version: 2.2.1 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: 7696110392b206bc69312c548749ebce 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: RELEASE_3_14 git_last_commit: b93c716 git_last_commit_date: 2022-04-07 Date/Publication: 2022-04-10 source.ver: src/contrib/slingshot_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/slingshot_2.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/slingshot_2.2.1.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 dependsOnMe: OSCA.advanced importsMe: condiments, tradeSeq, traviz dependencyCount: 32 Package: slinky Version: 1.12.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, curl, dplyr, foreach, httr, stats, utils, methods, readr, rhdf5, jsonlite, tidyr Suggests: GeoDE, doParallel, testthat, knitr, rmarkdown, ggplot2, Rtsne, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: acc2bf6ecddcd1bbf8fe9523a51559d3 NeedsCompilation: no Title: Putting the fun in LINCS L1000 data analysis Description: Wrappers to query the L1000 metadata available via the clue.io REST API as well as helpers for dealing with LINCS gctx files, extracting data sets of interest, converting to SummarizedExperiment objects, and some facilities for performing streamlined differential expression analysis of these data sets. biocViews: DataImport, ThirdPartyClient, GeneExpression, DifferentialExpression, GeneSetEnrichment, PatternLogic Author: Eric Kort [aut, cre] Maintainer: Eric Kort VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/slinky git_branch: RELEASE_3_14 git_last_commit: b7c2f83 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/slinky_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/slinky_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/slinky_1.12.0.tgz vignettes: vignettes/slinky/inst/doc/LINCS-analysis.html vignetteTitles: "LINCS analysis with slinky" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/slinky/inst/doc/LINCS-analysis.R dependencyCount: 68 Package: SLqPCR Version: 1.60.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: ac0d5382ba5f48a7ff87e6f02f3a6959 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: RELEASE_3_14 git_last_commit: 35169d7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SLqPCR_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SLqPCR_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SLqPCR_1.60.0.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: 1.10.0 Depends: R (>= 3.6.0), RcppAlgos Imports: magrittr (>= 1.5), dplyr, stats, tidyr, utils, Rcpp (>= 1.0.0) LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 3a154416d5adec6c771b203044bb18d9 NeedsCompilation: yes Title: Statistical Modelling of AP-MS Data (SMAD) Description: Assigning probability scores to prey proteins 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: RELEASE_3_14 git_last_commit: 29082a8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SMAD_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SMAD_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SMAD_1.10.0.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: 27 Package: SMAP Version: 1.58.0 Depends: R (>= 2.10), methods License: GPL-2 MD5sum: 3c9bc89a397ccfe3dceae3cac482f58d NeedsCompilation: yes Title: A Segmental Maximum A Posteriori Approach to Array-CGH Copy Number Profiling Description: Functions and classes for DNA copy number profiling of array-CGH data biocViews: Microarray, TwoChannel, CopyNumberVariation Author: Robin Andersson Maintainer: Robin Andersson git_url: https://git.bioconductor.org/packages/SMAP git_branch: RELEASE_3_14 git_last_commit: 0a35c4a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SMAP_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SMAP_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SMAP_1.58.0.tgz vignettes: vignettes/SMAP/inst/doc/SMAP.pdf vignetteTitles: SMAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMAP/inst/doc/SMAP.R dependencyCount: 1 Package: SMITE Version: 1.22.0 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) MD5sum: 70e3798351cbe896ff7e425671b8dcb7 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 git_url: https://git.bioconductor.org/packages/SMITE git_branch: RELEASE_3_14 git_last_commit: 6979357 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SMITE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SMITE_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SMITE_1.22.0.tgz vignettes: vignettes/SMITE/inst/doc/SMITE.pdf vignetteTitles: SMITE Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMITE/inst/doc/SMITE.R dependencyCount: 144 Package: SNAGEE Version: 1.34.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: cfc06746de749083a3482deb50e81eff 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: RELEASE_3_14 git_last_commit: 85e8c57 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SNAGEE_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SNAGEE_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SNAGEE_1.34.0.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: snapCGH Version: 1.64.0 Depends: R (>= 3.5.0) Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL Archs: i386, x64 MD5sum: 528dd34215922a72b3a70733775b4e8a NeedsCompilation: yes Title: Segmentation, normalisation and processing of aCGH data Description: Methods for segmenting, normalising and processing aCGH data; including plotting functions for visualising raw and segmented data for individual and multiple arrays. biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas Hardcastle, Natalie P. Thorne Maintainer: John Marioni git_url: https://git.bioconductor.org/packages/snapCGH git_branch: RELEASE_3_14 git_last_commit: dc77b07 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/snapCGH_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snapCGH_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snapCGH_1.64.0.tgz vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf vignetteTitles: Segmentation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snapCGH/inst/doc/snapCGHguide.R importsMe: ADaCGH2 suggestsMe: beadarraySNP dependencyCount: 95 Package: snapcount Version: 1.6.0 Depends: R (>= 4.0.0) Imports: R6, httr, rlang, purrr, jsonlite, assertthat, data.table, Matrix, magrittr, methods, stringr, stats, IRanges, GenomicRanges, SummarizedExperiment Suggests: BiocManager, bit64, covr, knitcitations, knitr (>= 1.6), devtools, BiocStyle (>= 2.5.19), rmarkdown (>= 0.9.5), testthat (>= 2.1.0) License: MIT + file LICENSE Archs: i386, x64 MD5sum: 9665c00bc121bb1271d3faf05eedce88 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: RELEASE_3_14 git_last_commit: 77b28ef git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/snapcount_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snapcount_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snapcount_1.6.0.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: 41 Package: snifter Version: 1.4.0 Depends: R (>= 4.0.0) Imports: basilisk, reticulate, irlba, stats, assertthat Suggests: knitr, rmarkdown, scRNAseq, BiocStyle, scater, scran, scuttle, ggplot2, testthat (>= 3.0.0) License: GPL-3 MD5sum: ce4e4e70671f220429c6170257837d42 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 VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/snifter/issues git_url: https://git.bioconductor.org/packages/snifter git_branch: RELEASE_3_14 git_last_commit: 548182d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/snifter_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snifter_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snifter_1.4.0.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 dependsOnMe: OSCA.advanced suggestsMe: scater dependencyCount: 25 Package: snm Version: 1.42.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 1eab6d95f2488a15f0c8bd9e210c42a4 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: RELEASE_3_14 git_last_commit: 81aacc6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/snm_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snm_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snm_1.42.0.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: edge, ExpressionNormalizationWorkflow dependencyCount: 51 Package: SNPediaR Version: 1.20.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 8dd7e6e5c64ffbe14a440de9a47bbe4d 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: RELEASE_3_14 git_last_commit: 2a5466d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SNPediaR_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/SNPediaR_1.20.0.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: 1.24.0 Depends: R (>= 3.1), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb, 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) Archs: i386, x64 MD5sum: b39a4a2386f6c03501cf039e31518266 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: christian.arnold@embl.de git_url: https://git.bioconductor.org/packages/SNPhood git_branch: RELEASE_3_14 git_last_commit: a479ac9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SNPhood_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SNPhood_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SNPhood_1.24.0.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: 127 Package: SNPRelate Version: 1.28.0 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: a502f02f9388281296dac6985dedf153 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: http://github.com/zhengxwen/SNPRelate VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SNPRelate/issues git_url: https://git.bioconductor.org/packages/SNPRelate git_branch: RELEASE_3_14 git_last_commit: 8fcd837 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SNPRelate_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SNPRelate_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SNPRelate_1.28.0.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: SeqSQC importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray dependencyCount: 2 Package: snpStats Version: 1.44.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 MD5sum: 0709552859a606782eab56bf13d20a7f 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: RELEASE_3_14 git_last_commit: 72392da git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/snpStats_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/snpStats_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/snpStats_1.44.0.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, snpStatsWriter importsMe: DExMA, GeneGeneInteR, gwascat, ldblock, martini, RVS, scoreInvHap, GenomicTools, GenomicTools.fileHandler, GWASbyCluster, LDheatmap, PhenotypeSimulator, snpEnrichment, TriadSim suggestsMe: crlmm, GenomicFiles, GWASTools, omicRexposome, omicsPrint, VariantAnnotation, adjclust, coloc, dartR, genio, pegas dependencyCount: 12 Package: soGGi Version: 1.26.0 Depends: R (>= 3.2.0), 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: 191cdfe65869469a83ddfb4d900aed17 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: RELEASE_3_14 git_last_commit: 15900b3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/soGGi_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/soGGi_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/soGGi_1.26.0.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: 85 Package: sojourner Version: 1.8.0 Imports: ggplot2,dplyr,reshape2,gridExtra,EBImage,MASS,R.matlab,Rcpp,fitdistrplus,mclust,minpack.lm,mixtools,mltools,nls2,plyr,sampSurf,scales,shiny,shinyjs,sp,truncnorm,utils,stats,pixmap,rlang,graphics,grDevices,grid,compiler,lattice Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: c068eff75f48d917b3caafae181ce29b NeedsCompilation: no Title: Statistical analysis of single molecule trajectories Description: Single molecule tracking has evolved as a novel new approach complementing genomic sequencing, it reports live biophysical properties of molecules being investigated besides properties relating their coding sequence; here we provided "sojourner" package, to address statistical and bioinformatic needs related to the analysis and comprehension of high throughput single molecule tracking data. biocViews: Technology, WorkflowStep Author: Sheng Liu [aut], Sun Jay Yoo [aut], Xiao Na Tang [aut], Young Soo Sung [aut], Carl Wu [aut], Anand Ranjan [ctb], Vu Nguyen [ctb], Sojourner Developer [cre] Maintainer: Sojourner Developer URL: https://github.com/sheng-liu/sojourner VignetteBuilder: knitr BugReports: https://github.com/sheng-liu/sojourner/issues git_url: https://git.bioconductor.org/packages/sojourner git_branch: RELEASE_3_14 git_last_commit: 64477c4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sojourner_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sojourner_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sojourner_1.8.0.tgz vignettes: vignettes/sojourner/inst/doc/sojourner-vignette.html vignetteTitles: Sojourner: an R package for statistical analysis of single molecule trajectories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sojourner/inst/doc/sojourner-vignette.R dependencyCount: 109 Package: SomaticSignatures Version: 2.30.0 Depends: R (>= 3.1.0), 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 Archs: i386, x64 MD5sum: 4de3143f647cdd20dea74e155f343a24 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: RELEASE_3_14 git_last_commit: 03f7ad7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SomaticSignatures_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SomaticSignatures_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SomaticSignatures_2.30.0.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: 165 Package: SOMNiBUS Version: 1.2.0 Depends: R (>= 4.1.0) Imports: graphics, Matrix, mgcv, stats, VGAM Suggests: BiocStyle, covr, devtools, dplyr, knitr, magick, rmarkdown, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: c265c96239896e108577c8934a9b95db 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] 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: RELEASE_3_14 git_last_commit: b95856b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SOMNiBUS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SOMNiBUS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SOMNiBUS_1.2.0.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: 13 Package: SpacePAC Version: 1.32.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 9b09d89962f60c09007095760967ad23 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: RELEASE_3_14 git_last_commit: ca99b60 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SpacePAC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpacePAC_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpacePAC_1.32.0.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: 30 Package: Spaniel Version: 1.8.0 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: 7ac8dfa729d94f9d0dc47bf9f19bb118 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: RELEASE_3_14 git_last_commit: 4a69e26 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Spaniel_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Spaniel_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Spaniel_1.8.0.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: 194 Package: sparrow Version: 1.0.3 Depends: R (>= 4.0) Imports: babelgene (>= 21.4), BiocGenerics, BiocParallel, BiocSet, checkmate, circlize, ComplexHeatmap (>= 2.0), data.table (>= 1.10.4), DelayedMatrixStats, edgeR (>= 3.18.1), ggplot2 (>= 2.2.0), graphics, grDevices, GSEABase, irlba, limma, Matrix, methods, plotly (>= 4.9.0), stats, utils, viridis Suggests: AnnotationDbi, BiasedUrn, Biobase (>= 2.24.0), BiocStyle, DESeq2, dplyr, dtplyr, fgsea, GSVA, GO.db, goseq, hexbin, magrittr, matrixStats, msigdbr (>= 7.4.1), KernSmooth, knitr, PANTHER.db (>= 1.0.3), R.utils, reactome.db, rmarkdown, SummarizedExperiment, statmod, stringr, testthat, webshot License: MIT + file LICENSE MD5sum: 9b294290cd945ff314d69bd5038f7aa0 NeedsCompilation: no Title: Take command of set enrichment analyses through a unified interface Description: Provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package. biocViews: GeneSetEnrichment, Pathways Author: Steve Lianoglou [aut, cre] (), Arkadiusz Gladki [ctb], Denali Therapeutics [fnd] (2018+), Genentech [fnd] (2014 - 2017) Maintainer: Steve Lianoglou URL: https://github.com/lianos/sparrow VignetteBuilder: knitr BugReports: https://github.com/lianos/sparrow/issues git_url: https://git.bioconductor.org/packages/sparrow git_branch: RELEASE_3_14 git_last_commit: 7cde579 git_last_commit_date: 2022-03-23 Date/Publication: 2022-03-24 source.ver: src/contrib/sparrow_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/sparrow_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.1/sparrow_1.0.3.tgz vignettes: vignettes/sparrow/inst/doc/sparrow.html vignetteTitles: Performing gene set enrichment analyses with sparrow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparrow/inst/doc/sparrow.R suggestsMe: gCrisprTools dependencyCount: 130 Package: sparseDOSSA Version: 1.18.0 Imports: stats, utils, optparse, MASS, tmvtnorm (>= 1.4.10), MCMCpack Suggests: knitr, BiocStyle, BiocGenerics, rmarkdown License: MIT + file LICENSE MD5sum: 10244be854a52a9d5f138d73137082ee NeedsCompilation: no Title: Sparse Data Observations for Simulating Synthetic Abundance Description: The package is to provide a model based Bayesian method to characterize and simulate microbiome data. sparseDOSSA's model captures the marginal distribution of each microbial feature as a truncated, zero-inflated log-normal distribution, with parameters distributed as a parent log-normal distribution. The model can be effectively fit to reference microbial datasets in order to parameterize their microbes and communities, or to simulate synthetic datasets of similar population structure. Most importantly, it allows users to include both known feature-feature and feature-metadata correlation structures and thus provides a gold standard to enable benchmarking of statistical methods for metagenomic data analysis. biocViews: ImmunoOncology, Bayesian, Microbiome, Metagenomics, Software Author: Boyu Ren, Emma Schwager, Timothy Tickle, Curtis Huttenhower Maintainer: Boyu Ren, Emma Schwager , George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparseDOSSA git_branch: RELEASE_3_14 git_last_commit: 19015de git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sparseDOSSA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sparseDOSSA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sparseDOSSA_1.18.0.tgz vignettes: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.html vignetteTitles: Sparse Data Observations for the Simulation of Synthetic Abundances (sparseDOSSA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.R dependencyCount: 23 Package: sparseMatrixStats Version: 1.6.0 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: 60fe7fc52f466333f0b62433df52c42c 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 VignetteBuilder: knitr BugReports: https://github.com/const-ae/sparseMatrixStats/issues git_url: https://git.bioconductor.org/packages/sparseMatrixStats git_branch: RELEASE_3_14 git_last_commit: 78627a8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sparseMatrixStats_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sparseMatrixStats_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sparseMatrixStats_1.6.0.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, GSVA, adjclust suggestsMe: MatrixGenerics, scPCA dependencyCount: 11 Package: sparsenetgls Version: 1.12.0 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: 6e3363b702b3bb8e38b35c9d9f417090 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: RELEASE_3_14 git_last_commit: 9e5aa8d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sparsenetgls_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sparsenetgls_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sparsenetgls_1.12.0.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: 22 Package: SparseSignatures Version: 2.4.0 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: 56e04e6efdb1e351f2eceb7496067645 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 [cre, aut] (), Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [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: RELEASE_3_14 git_last_commit: 54424a2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SparseSignatures_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SparseSignatures_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SparseSignatures_2.4.0.tgz vignettes: vignettes/SparseSignatures/inst/doc/vignette.pdf vignetteTitles: SparseSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SparseSignatures/inst/doc/vignette.R dependencyCount: 96 Package: SpatialCPie Version: 1.10.0 Depends: R (>= 3.6) Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), digest (>= 0.6.21), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>= 0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>= 3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>= 3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2), shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0), shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment (>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect (>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>= 0.1.0) Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20), rmarkdown (>= 1.10), testthat (>= 2.0.0) License: MIT + file LICENSE MD5sum: 0170fb166c0cab2eea0ec37d7b5eeefa 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: RELEASE_3_14 git_last_commit: 6b6ce6f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SpatialCPie_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpatialCPie_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpatialCPie_1.10.0.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: 107 Package: spatialDE Version: 1.0.0 Depends: R (>= 4.1) Imports: reticulate, basilisk, checkmate, stats, SpatialExperiment, methods, SummarizedExperiment, Matrix, S4Vectors, ggplot2, ggrepel, scales, gridExtra Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: i386, x64 MD5sum: 93572fc52954c1b6a79aea32de5cd920 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, cre] (), Milan Malfait [aut] (), Lambda Moses [aut] () Maintainer: Davide Corso 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: RELEASE_3_14 git_last_commit: bb66d91 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/spatialDE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spatialDE_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spatialDE_1.0.0.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: 122 Package: SpatialDecon Version: 1.4.3 Depends: R (>= 4.0.0) Imports: grDevices, stats, utils, graphics, SeuratObject, Biobase, GeomxTools, repmis, methods, Matrix Suggests: testthat, knitr, rmarkdown, qpdf License: MIT + file LICENSE MD5sum: 0249bfd07731f8ad3299be44692e768b 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 spatially-resolved gene expression data", Danaher (2020). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data. biocViews: ImmunoOncology, FeatureExtraction, GeneExpression, Transcriptomics Author: Nicole Ortogero [cre], Patrick Danaher [aut], Maddy Griswold [aut] Maintainer: Nicole Ortogero VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues git_url: https://git.bioconductor.org/packages/SpatialDecon git_branch: RELEASE_3_14 git_last_commit: 72c2c83 git_last_commit_date: 2021-11-02 Date/Publication: 2021-11-03 source.ver: src/contrib/SpatialDecon_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpatialDecon_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.1/SpatialDecon_1.4.3.tgz vignettes: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_ST.html, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html vignetteTitles: Use of SpatialDecon in a large GeoMx dataset with GeomxTools, Use of SpatialDecon in a Spatial Transcriptomics dataset, 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_ST.R, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R dependencyCount: 122 Package: SpatialExperiment Version: 1.4.0 Depends: methods, SingleCellExperiment Imports: BiocFileCache, DropletUtils, rjson, magick, grDevices, S4Vectors, SummarizedExperiment, BiocGenerics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix License: GPL-3 MD5sum: d5f297a8ae30f18258f6555f389193ad NeedsCompilation: no Title: S4 Class for Spatial Experiments handling Description: Defines S4 classes for storing data for spatial experiments. Main examples are reported by using seqFISH and 10x-Visium Spatial Gene Expression data. This includes specialized methods for storing, retrieving spatial coordinates, 10x dedicated parameters and their handling. biocViews: DataRepresentation, DataImport, ImmunoOncology, DataRepresentation, Infrastructure, SingleCell, GeneExpression Author: Dario Righelli [aut, cre], Davide Risso [aut], Helena L. Crowell [aut], Lukas M. Weber [aut] Maintainer: Dario Righelli VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpatialExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialExperiment git_branch: RELEASE_3_14 git_last_commit: fd11626 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SpatialExperiment_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpatialExperiment_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpatialExperiment_1.4.0.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: ExperimentSubset, imcRtools, MouseGastrulationData, spatialLIBD, STexampleData, TENxVisiumData importsMe: ggspavis, spatialDE, SingleCellMultiModal suggestsMe: mistyR dependencyCount: 94 Package: spatialHeatmap Version: 2.0.0 Depends: R (>= 3.5.0) Imports: av, BiocFileCache, data.table, DESeq2, distinct, edgeR, WGCNA, flashClust, htmlwidgets, genefilter, ggplot2, ggdendro, grImport, grid, gridExtra, gplots, igraph, HDF5Array, limma, methods, magick, rsvg, shiny, dynamicTreeCut, grDevices, graphics, ggplotify, parallel, plotly, rols, rappdirs, reshape2, stats, SummarizedExperiment, shinydashboard, S4Vectors, utils, visNetwork, UpSetR, xml2, yaml Suggests: knitr, rmarkdown, BiocStyle, BiocSingular, RUnit, BiocGenerics, ExpressionAtlas, DT, Biobase, GEOquery, shinyWidgets, shinyjs, htmltools, shinyBS, sortable, scater, scran License: Artistic-2.0 MD5sum: 8c011ba413b3f27df94192cb2ca882b2 NeedsCompilation: no Title: spatialHeatmap Description: The spatialHeatmap package provides functionalities for visualizing cell-, tissue- and organ-specific data of biological assays by coloring the corresponding spatial features defined in anatomical images according to a numeric color key. biocViews: Visualization, Microarray, Sequencing, GeneExpression, DataRepresentation, Network, Clustering, GraphAndNetwork, CellBasedAssays, ATACSeq, DNASeq, TissueMicroarray, SingleCell, CellBiology, GeneTarget Author: Jianhai Zhang [aut, trl, cre], Jordan Hayes [aut], Le Zhang [aut], Bing Yang [aut], Wolf Frommer [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: RELEASE_3_14 git_last_commit: 4bdc542 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/spatialHeatmap_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spatialHeatmap_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spatialHeatmap_2.0.0.tgz vignettes: vignettes/spatialHeatmap/inst/doc/spatialHeatmap.html vignetteTitles: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Network Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spatialHeatmap/inst/doc/spatialHeatmap.R dependencyCount: 203 Package: spatzie Version: 1.0.1 Depends: R (>= 4.1) Imports: BiocGenerics, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicInteractions, GenomicRanges, ggplot2, IRanges, 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: 28494ef4021669b0032ca0dea665ba3a 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: RELEASE_3_14 git_last_commit: cb4b61e git_last_commit_date: 2022-02-02 Date/Publication: 2022-02-03 source.ver: src/contrib/spatzie_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/spatzie_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/spatzie_1.0.1.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: 169 Package: specL Version: 1.28.0 Depends: R (>= 3.6), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.5), RSQLite (>= 1.1), seqinr (>= 3.3) Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown, RUnit (>= 0.4) License: GPL-3 MD5sum: 68d47ad2d4d9987134cd6a02e1101728 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: RELEASE_3_14 git_last_commit: 876f521 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/specL_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/specL_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/specL_1.28.0.tgz vignettes: vignettes/specL/inst/doc/specL.pdf, vignettes/specL/inst/doc/report.html vignetteTitles: Introduction to specL, Automatic Workflow 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: 29 Package: SpeCond Version: 1.48.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: f1d1d70fe81b97cc8f8889e89e3ebd02 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: RELEASE_3_14 git_last_commit: bf010d0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SpeCond_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpeCond_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpeCond_1.48.0.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: 48 Package: Spectra Version: 1.4.3 Depends: R (>= 4.0.0), S4Vectors, BiocParallel, ProtGenerics (>= 1.25.1) Imports: methods, IRanges, MsCoreUtils (>= 1.3.3), graphics, grDevices, stats, tools, utils, fs, BiocGenerics 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), magrittr License: Artistic-2.0 MD5sum: 46802978ce997d30088d5272016ecf8b 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] () 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: RELEASE_3_14 git_last_commit: 4e99629 git_last_commit_date: 2022-02-25 Date/Publication: 2022-02-27 source.ver: src/contrib/Spectra_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/Spectra_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.1/Spectra_1.4.3.tgz vignettes: vignettes/Spectra/inst/doc/Spectra.html vignetteTitles: Description and usage of Spectra object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Spectra/inst/doc/Spectra.R dependsOnMe: MsBackendMassbank, MsBackendMgf, MsBackendRawFileReader suggestsMe: xcms dependencyCount: 25 Package: SpectralTAD Version: 1.10.0 Depends: R (>= 3.6) Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel, magrittr, HiCcompare, GenomicRanges Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr License: MIT + file LICENSE MD5sum: 954d1e0f19b179009e78f59da1958fc3 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: Kellen Cresswell , John Stansfield , Mikhail Dozmorov Maintainer: Kellen Cresswell 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: RELEASE_3_14 git_last_commit: c041eff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SpectralTAD_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpectralTAD_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpectralTAD_1.10.0.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: 100 Package: SPEM Version: 1.34.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: dac0ef7161a79b884fe726715276e21d 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: RELEASE_3_14 git_last_commit: 53fd404 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SPEM_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPEM_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SPEM_1.34.0.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: 2.46.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: ae4dcaa015bb9d96d029991afdb7fca4 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: RELEASE_3_14 git_last_commit: 9f45604 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SPIA_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPIA_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SPIA_2.46.0.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: spicyR Version: 1.6.0 Depends: R (>= 4.1) Imports: ggplot2, concaveman, BiocParallel, spatstat.core, spatstat.geom, lmerTest, BiocGenerics, S4Vectors, lme4, methods, mgcv, pheatmap, rlang, grDevices, IRanges, stats, data.table, dplyr, tidyr, scam Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 6705a7038ddb5fa7131f087b63a2657a NeedsCompilation: no Title: Spatial analysis of in situ cytometry data Description: spicyR provides a series of functions to aid in the analysis of both immunofluorescence and mass cytometry imaging data as well as other assays that can deeply phenotype individual cells and their spatial location. biocViews: SingleCell, CellBasedAssays Author: Nicolas Canete [aut], Ellis Patrick [aut, cre] Maintainer: Ellis Patrick VignetteBuilder: knitr BugReports: https://github.com/ellispatrick/spicyR/issues git_url: https://git.bioconductor.org/packages/spicyR git_branch: RELEASE_3_14 git_last_commit: ef4f5a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/spicyR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spicyR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spicyR_1.6.0.tgz vignettes: vignettes/spicyR/inst/doc/segmentedCells.html, vignettes/spicyR/inst/doc/spicy.html vignetteTitles: "Introduction to SegmentedCells", "Introduction to spicy" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spicyR/inst/doc/segmentedCells.R, vignettes/spicyR/inst/doc/spicy.R importsMe: lisaClust dependencyCount: 108 Package: SpidermiR Version: 1.24.0 Depends: R (>= 3.0.0) Imports: httr, igraph, utils, stats, miRNAtap, miRNAtap.db, AnnotationDbi, org.Hs.eg.db, ggplot2, gridExtra, gplots, grDevices, lattice, latticeExtra, TCGAbiolinks, gdata, MAGeCKFlute Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2 License: GPL (>= 3) MD5sum: e870f059cc376898350453dab4d67d51 NeedsCompilation: no Title: SpidermiR: An R/Bioconductor package for integrative network analysis with miRNA data Description: The aims of SpidermiR are : i) facilitate the network open-access data retrieval from GeneMania data, ii) prepare the data using the appropriate gene nomenclature, iii) integration of miRNA data in a specific network, iv) provide different standard analyses and v) allow the user to visualize the results. In more detail, the package provides multiple methods for query, prepare and download network data (GeneMania), and the integration with validated and predicted miRNA data (mirWalk, miRTarBase, miRandola, Miranda, PicTar and TargetScan). Furthermore, we also present a statistical test to identify pharmaco-mir relationships using the gene-drug interactions derived by DGIdb and MATADOR database. biocViews: GeneRegulation, miRNA, Network Author: Claudia Cava, Antonio Colaprico, Alex Graudenzi, Gloria Bertoli, Tiago C. Silva, Catharina Olsen, Houtan Noushmehr, Gianluca Bontempi, Giancarlo Mauri, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/SpidermiR VignetteBuilder: knitr BugReports: https://github.com/claudiacava/SpidermiR/issues git_url: https://git.bioconductor.org/packages/SpidermiR git_branch: RELEASE_3_14 git_last_commit: 2316683 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SpidermiR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SpidermiR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SpidermiR_1.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: StarBioTrek dependencyCount: 172 Package: spikeLI Version: 2.54.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: 590417c1e0730d547428d4c655e83edc 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: RELEASE_3_14 git_last_commit: 8509a10 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/spikeLI_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spikeLI_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spikeLI_2.54.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spiky Version: 1.0.0 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, equatiomatic, universalmotif, kebabs, ComplexHeatmap, rmarkdown, markdown, knitr, devtools, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg38.masked, BiocManager License: GPL-2 MD5sum: b6198261ca78c58b36102704a1a23b4d 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], Jordan Veldboom [ctb], 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: RELEASE_3_14 git_last_commit: 1b25b68 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/spiky_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spiky_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spiky_1.0.0.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: 82 Package: spkTools Version: 1.50.0 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: fae7839b7ede29a87bdacd64f6368754 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: RELEASE_3_14 git_last_commit: c3f97ea git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/spkTools_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spkTools_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spkTools_1.50.0.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: 1.18.2 Depends: R (>= 4.0), SingleCellExperiment Imports: BiocGenerics, BiocParallel, checkmate (>= 2.0.0), edgeR, fitdistrplus, ggplot2, locfit, matrixStats, methods, scales, scater (>= 1.15.16), stats, SummarizedExperiment, utils, crayon, S4Vectors, grDevices Suggests: BiocStyle, covr, cowplot, magick, knitr, limSolve, lme4, progress, pscl, testthat, preprocessCore, rmarkdown, scDD, scran, mfa, phenopath, BASiCS (>= 1.7.10), zinbwave, SparseDC, BiocManager, spelling, igraph, scuttle, BiocSingular, VariantAnnotation, Biostrings, GenomeInfoDb, GenomicRanges, IRanges License: GPL-3 + file LICENSE MD5sum: 15e356956f13566d2637c0db1f8d2d67 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] (), Belinda Phipson [aut] (), Christina Azodi [ctb] (), Alicia Oshlack [aut] () Maintainer: Luke Zappia URL: https://github.com/Oshlack/splatter VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues git_url: https://git.bioconductor.org/packages/splatter git_branch: RELEASE_3_14 git_last_commit: 67d8f16 git_last_commit_date: 2022-01-10 Date/Publication: 2022-01-11 source.ver: src/contrib/splatter_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/splatter_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.1/splatter_1.18.2.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: bcTSNE, digitalDLSorteR, SCRIP suggestsMe: NewWave, scone, scPCA, SummarizedBenchmark dependencyCount: 90 Package: SplicingFactory Version: 1.2.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, methods, stats Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr License: GPL-3 + file LICENSE MD5sum: dfb8ce79040fa6d9926439f9535f15ec 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/SU-CompBio/SplicingFactory VignetteBuilder: knitr BugReports: https://github.com/SU-CompBio/SplicingFactory/issues git_url: https://git.bioconductor.org/packages/SplicingFactory git_branch: RELEASE_3_14 git_last_commit: 906d50d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SplicingFactory_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SplicingFactory_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SplicingFactory_1.2.0.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: 25 Package: SplicingGraphs Version: 1.34.0 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), GenomicFeatures, Rsamtools, GenomicAlignments, graph, Rgraphviz Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 8d154339b3ffb5c6f44803c526478248 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: RELEASE_3_14 git_last_commit: 25cc726 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SplicingGraphs_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SplicingGraphs_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SplicingGraphs_1.34.0.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: 99 Package: splineTimeR Version: 1.22.0 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: 16271e4cf134403fb8d221e9a3e699f0 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: RELEASE_3_14 git_last_commit: 5bd9ea9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/splineTimeR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/splineTimeR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/splineTimeR_1.22.0.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: 63 Package: SPLINTER Version: 1.20.0 Depends: R (>= 3.6.0), grDevices, stats Imports: graphics, ggplot2, seqLogo, Biostrings, biomaRt, GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz, IRanges, S4Vectors, GenomeInfoDb, utils, plyr,stringr, methods, BSgenome.Mmusculus.UCSC.mm9, googleVis Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 5d035d666f6cfb87816d055e24081a44 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: RELEASE_3_14 git_last_commit: d208881 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/SPLINTER_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPLINTER_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SPLINTER_1.20.0.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: 146 Package: splots Version: 1.60.0 Imports: grid, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI License: LGPL MD5sum: ea70552a218617bfed730c4e6d46c9fe NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: This package is provided to support legacy code and reverse dependencies, but it should not be used as a dependency 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 generic ggplot2 graphics functionality. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splots git_branch: RELEASE_3_14 git_last_commit: 7ac826e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/splots_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/splots_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/splots_1.60.0.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: SPONGE Version: 1.16.1 Depends: R (>= 3.4) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, Suggests: testthat, knitr, rmarkdown, visNetwork, ggplot2, ggrepel, gridExtra, digest, doParallel, bigmemory License: GPL (>=3) MD5sum: b1cf85c0d8f3ee471ad2ec78ba7a6a57 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. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression Author: Markus List, Markus Hoffmann, Azim Dehghani Amirabad, Dennis Kostka, Marcel H. Schulz Maintainer: Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPONGE git_branch: RELEASE_3_14 git_last_commit: 0e56a73 git_last_commit_date: 2022-04-07 Date/Publication: 2022-04-10 source.ver: src/contrib/SPONGE_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPONGE_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.1/SPONGE_1.16.1.tgz vignettes: vignettes/SPONGE/inst/doc/SPONGE.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R dependencyCount: 39 Package: spqn Version: 1.6.0 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: f4d6d2488bcf3f6626f7f1166bcaaa5c 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: RELEASE_3_14 git_last_commit: ff710cb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/spqn_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/spqn_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/spqn_1.6.0.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: 58 Package: SPsimSeq Version: 1.4.0 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: 95bfba555a3b5a6e8bb9b37e0c1b8a89 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: RELEASE_3_14 git_last_commit: abaa3d8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SPsimSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SPsimSeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SPsimSeq_1.4.0.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 dependencyCount: 134 Package: SQLDataFrame Version: 1.8.0 Depends: R (>= 3.6), dplyr (>= 0.8.0.1), dbplyr (>= 1.4.0), S4Vectors Imports: DBI, lazyeval, methods, tools, stats, BiocGenerics, RSQLite, tibble Suggests: RMySQL, bigrquery, testthat, knitr, rmarkdown, DelayedArray License: GPL-3 MD5sum: db651bfded69e42e7d7098ec8782f916 NeedsCompilation: no Title: Representation of SQL database in DataFrame metaphor Description: SQLDataFrame is developed to lazily represent and efficiently analyze SQL-based tables in _R_. SQLDataFrame supports common and familiar 'DataFrame' operations such as '[' subsetting, rbind, cbind, etc.. The internal implementation is based on the widely adopted dplyr grammar and SQL commands. In-memory datasets or plain text files (.txt, .csv, etc.) could also be easily converted into SQLDataFrames objects (which generates a new database on-disk). biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre] (), 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: RELEASE_3_14 git_last_commit: ed5bde5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SQLDataFrame_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SQLDataFrame_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SQLDataFrame_1.8.0.tgz vignettes: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.html, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.html vignetteTitles: SQLDataFrame Internal Implementation, SQLDataFrame: Lazy representation of SQL database in DataFrame metaphor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.R, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.R dependencyCount: 41 Package: SQUADD Version: 1.44.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: 70fbe514bffd39d512fce3372add0e6f NeedsCompilation: no Title: Add-on of the SQUAD Software Description: This package SQUADD is a SQUAD add-on. It permits to generate SQUAD simulation matrix, prediction Heat-Map and Correlation Circle from PCA analysis. biocViews: GraphAndNetwork, Network, Visualization Author: Martial Sankar, supervised by Christian Hardtke and Ioannis Xenarios Maintainer: Martial Sankar URL: http://www.unil.ch/dbmv/page21142_en.html git_url: https://git.bioconductor.org/packages/SQUADD git_branch: RELEASE_3_14 git_last_commit: 5ff3274 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SQUADD_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SQUADD_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SQUADD_1.44.0.tgz vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf, vignettes/SQUADD/inst/doc/SQUADD.pdf vignetteTitles: SQUADD ERK exemple, SQUADD HOW-TO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R, vignettes/SQUADD/inst/doc/SQUADD.R dependencyCount: 6 Package: sRACIPE Version: 1.10.0 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: 55812972fe3915085e8283587ef6bc97 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: RELEASE_3_14 git_last_commit: 9aec069 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sRACIPE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sRACIPE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sRACIPE_1.10.0.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: 84 Package: SRAdb Version: 1.56.0 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: e928379404ca6b1b00356e8ef03fe193 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 URL: http://gbnci.abcc.ncifcrf.gov/sra/ BugReports: https://github.com/seandavi/SRAdb/issues/new git_url: https://git.bioconductor.org/packages/SRAdb git_branch: RELEASE_3_14 git_last_commit: 519051c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SRAdb_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SRAdb_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SRAdb_1.56.0.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: 64 Package: srnadiff Version: 1.14.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, devtools, S4Vectors, GenomeInfoDb, rtracklayer, SummarizedExperiment, IRanges, GenomicRanges, DESeq2, edgeR, baySeq, Rsamtools, GenomicFeatures, GenomicAlignments, grDevices, Gviz, BiocParallel, BiocStyle, BiocManager LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocManager, BiocStyle License: GPL-3 MD5sum: f7f0d2f11dce0d17ced8ffa3873bc05d 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: RELEASE_3_14 git_last_commit: bcc5a9d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/srnadiff_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/srnadiff_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/srnadiff_1.14.0.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: 194 Package: sscore Version: 1.66.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: 64989d85816134251d4526a8352919d2 NeedsCompilation: no Title: S-Score Algorithm for Affymetrix Oligonucleotide Microarrays Description: This package contains an implementation of the S-Score algorithm as described by Zhang et al (2002). biocViews: DifferentialExpression Author: Richard Kennedy , based on C++ code from Li Zhang and Borland Delphi code from Robnet Kerns . Maintainer: Richard Kennedy URL: http://home.att.net/~richard-kennedy/professional.html git_url: https://git.bioconductor.org/packages/sscore git_branch: RELEASE_3_14 git_last_commit: 1a5a32e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sscore_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sscore_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sscore_1.66.0.tgz vignettes: vignettes/sscore/inst/doc/sscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sscore/inst/doc/sscore.R dependencyCount: 12 Package: sscu Version: 2.24.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: f28274939e15c4baf3ad156270d42c9c 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: RELEASE_3_14 git_last_commit: 16126f9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sscu_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sscu_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sscu_2.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 26 Package: sSeq Version: 1.32.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: 9147d1c7565b11954477422f55a68bde 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: RELEASE_3_14 git_last_commit: c0d3c30 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sSeq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sSeq_1.32.0.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: 1.68.0 Depends: gdata, xtable License: LGPL Archs: i386, x64 MD5sum: 3e59edfdf63cdfc1815e587e434cfc8c 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: RELEASE_3_14 git_last_commit: b53deec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ssize_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ssize_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ssize_1.68.0.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: ssPATHS Version: 1.8.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 78b00f14a5cfd52bd62f8e31f4e34cdc 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: RELEASE_3_14 git_last_commit: bbdb432 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ssPATHS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ssPATHS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ssPATHS_1.8.0.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: 110 Package: ssrch Version: 1.10.0 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 45a1a2a2a0970515cdb51a233d43f245 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: RELEASE_3_14 git_last_commit: 7ef02a8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ssrch_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ssrch_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ssrch_1.10.0.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: 40 Package: ssviz Version: 1.28.0 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer,stats Suggests: knitr License: GPL-2 MD5sum: cdc8053f8886b418da885043426442c3 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: RELEASE_3_14 git_last_commit: b66c36f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ssviz_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ssviz_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ssviz_1.28.0.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: 64 Package: stageR Version: 1.16.0 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: 19e8ecb861ee71f4c0e212a39eee82fe 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: RELEASE_3_14 git_last_commit: 816d3b2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/stageR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/stageR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/stageR_1.16.0.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: 25 Package: STAN Version: 2.22.0 Depends: methods, poilog, parallel Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp Suggests: BiocStyle, gplots, knitr License: GPL (>= 2) MD5sum: 62cfe9dd3c263649d9e76a32950dac89 NeedsCompilation: yes Title: The Genomic STate ANnotation Package Description: Genome segmentation with hidden Markov models has become a useful tool to annotate genomic elements, such as promoters and enhancers. STAN (genomic STate ANnotation) implements (bidirectional) hidden Markov models (HMMs) using a variety of different probability distributions, which can model a wide range of current genomic data (e.g. continuous, discrete, binary). STAN de novo learns and annotates the genome into a given number of 'genomic states'. The 'genomic states' may for instance reflect distinct genome-associated protein complexes (e.g. 'transcription states') or describe recurring patterns of chromatin features (referred to as 'chromatin states'). Unlike other tools, STAN also allows for the integration of strand-specific (e.g. RNA) and non-strand-specific data (e.g. ChIP). biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing, ChIPSeq, RNASeq, ChipOnChip, Transcription, ImmunoOncology Author: Benedikt Zacher, Julia Ertl, Rafael Campos-Martin, Julien Gagneur, Achim Tresch Maintainer: Rafael Campos-Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STAN git_branch: RELEASE_3_14 git_last_commit: 3fab9c3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/STAN_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/STAN_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/STAN_2.22.0.tgz vignettes: vignettes/STAN/inst/doc/STAN-knitr.pdf vignetteTitles: The genomic STate ANnotation package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN-knitr.R dependencyCount: 145 Package: staRank Version: 1.36.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: 36be75429872345c8643f381b15a46da NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/staRank git_branch: RELEASE_3_14 git_last_commit: 4f35afa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/staRank_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/staRank_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/staRank_1.36.0.tgz vignettes: vignettes/staRank/inst/doc/staRank.pdf vignetteTitles: Using staRank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/staRank/inst/doc/staRank.R dependencyCount: 91 Package: StarBioTrek Version: 1.20.0 Depends: R (>= 3.3) Imports: SpidermiR, graphite, AnnotationDbi, e1071, ROCR, MLmetrics, grDevices, igraph, reshape2, ggplot2 Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, qgraph, png, grid License: GPL (>= 3) MD5sum: e934dee6f0466cd815b8cb7909aa5d6a NeedsCompilation: no Title: StarBioTrek Description: This tool StarBioTrek presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network data. biocViews: GeneRegulation, Network, Pathways, KEGG Author: Claudia Cava, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/StarBioTrek VignetteBuilder: knitr BugReports: https://github.com/claudiacava/StarBioTrek/issues git_url: https://git.bioconductor.org/packages/StarBioTrek git_branch: RELEASE_3_14 git_last_commit: b3fd611 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/StarBioTrek_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/StarBioTrek_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/StarBioTrek_1.20.0.tgz vignettes: vignettes/StarBioTrek/inst/doc/StarBioTrek.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StarBioTrek/inst/doc/StarBioTrek.R dependencyCount: 182 Package: STATegRa Version: 1.30.0 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: 81fa719a88692ac94edd47c29b490a96 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: RELEASE_3_14 git_last_commit: 9124807 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/STATegRa_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/STATegRa_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/STATegRa_1.30.0.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: 59 Package: statTarget Version: 1.24.0 Depends: R (>= 3.6.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute Suggests: testthat, BiocStyle, knitr, rmarkdown, gWidgets2,gWidgets2RGtk2,RGtk2 License: LGPL (>= 3) Archs: i386, x64 MD5sum: 29ed79d93128e632f6377e04039ac217 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, QC-RLSC, ComBat, DifferentialExpression, BatchEffect, Visualization, MultipleComparison,Preprocessing, GUI, Software Author: Hemi Luan Maintainer: Hemi Luan URL: https://stattarget.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/statTarget git_branch: RELEASE_3_14 git_last_commit: dffbb02 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/statTarget_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/statTarget_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/statTarget_1.24.0.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: 30 Package: stepNorm Version: 1.66.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: 2d0a5614ddbb7a2c18ef9da93b36feb9 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: RELEASE_3_14 git_last_commit: 1417ae5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/stepNorm_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/stepNorm_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/stepNorm_1.66.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: strandCheckR Version: 1.12.0 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: fc510049ee5cd3d550c51b50438bd985 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: RELEASE_3_14 git_last_commit: 4979e4f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/strandCheckR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/strandCheckR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/strandCheckR_1.12.0.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: 126 Package: Streamer Version: 1.40.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 MD5sum: fda5191064527c87458dd9f4000a0d2f 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: RELEASE_3_14 git_last_commit: 43fe994 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Streamer_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Streamer_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Streamer_1.40.0.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 importsMe: plethy dependencyCount: 10 Package: STRINGdb Version: 2.6.5 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: ada9fa2f9b9e85025600edb9c4fc49a4 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: RELEASE_3_14 git_last_commit: 9232960 git_last_commit_date: 2022-03-12 Date/Publication: 2022-03-17 source.ver: src/contrib/STRINGdb_2.6.5.tar.gz win.binary.ver: bin/windows/contrib/4.1/STRINGdb_2.6.5.zip mac.binary.ver: bin/macosx/contrib/4.1/STRINGdb_2.6.5.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: IMMAN, pwOmics, RITAN, XINA suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN, protti dependencyCount: 40 Package: STROMA4 Version: 1.18.0 Depends: R (>= 3.4), Biobase, BiocParallel, cluster, matrixStats, stats, graphics, utils Suggests: breastCancerMAINZ License: GPL-3 MD5sum: 7b568b2218196bb7030e8a1baebf25d3 NeedsCompilation: no Title: Assign Properties to TNBC Patients Description: This package estimates four stromal properties identified in TNBC patients in each patient of a gene expression datasets. These stromal property assignments can be combined to subtype patients. These four stromal properties were identified in Triple negative breast cancer (TNBC) patients and represent the presence of different cells in the stroma: T-cells (T), B-cells (B), stromal infiltrating epithelial cells (E), and desmoplasia (D). Additionally this package can also be used to estimate generative properties for the Lehmann subtypes, an alternative TNBC subtyping scheme (PMID: 21633166). biocViews: ImmunoOncology, GeneExpression, BiomedicalInformatics, Classification, Microarray, RNASeq, Software Author: Sadiq Saleh [aut, cre], Michael Hallett [aut] Maintainer: Sadiq Saleh git_url: https://git.bioconductor.org/packages/STROMA4 git_branch: RELEASE_3_14 git_last_commit: c4891a5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/STROMA4_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/STROMA4_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/STROMA4_1.18.0.tgz vignettes: vignettes/STROMA4/inst/doc/STROMA4-vignette.pdf vignetteTitles: Using the STROMA4 package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STROMA4/inst/doc/STROMA4-vignette.R dependencyCount: 17 Package: struct Version: 1.6.0 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: 3fd3a5461aa01f1a12a252144dba25c6 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: RELEASE_3_14 git_last_commit: 65817c3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/struct_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/struct_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/struct_1.6.0.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: 55 Package: Structstrings Version: 1.10.0 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 Archs: i386, x64 MD5sum: 9e1fe67cd274fe8c92ac317024f041d3 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: RELEASE_3_14 git_last_commit: 3ddb25f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Structstrings_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Structstrings_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Structstrings_1.10.0.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: 22 Package: structToolbox Version: 1.6.1 Depends: R (>= 4.0), struct (>= 1.5.1) Imports: ggplot2, ggthemes, grid, gridExtra, methods, scales, sp, stats, utils Suggests: agricolae, BiocFileCache, BiocStyle, car, covr, cowplot, e1071, emmeans, ggdendro, knitr, magick, nlme, openxlsx, pls, pmp, reshape2, ropls, rmarkdown, Rtsne, testthat, rappdirs License: GPL-3 MD5sum: e85a71932403483e573fa9e1afc91c17 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/structToolbox git_branch: RELEASE_3_14 git_last_commit: 29455a0 git_last_commit_date: 2022-02-04 Date/Publication: 2022-02-06 source.ver: src/contrib/structToolbox_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/structToolbox_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/structToolbox_1.6.1.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: 80 Package: StructuralVariantAnnotation Version: 1.10.1 Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R (>= 4.1.0) Imports: assertthat, Biostrings, 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: 26f73195a77f3d941b3a0ec22a139269 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: RELEASE_3_14 git_last_commit: 5267bc3 git_last_commit_date: 2021-12-22 Date/Publication: 2021-12-23 source.ver: src/contrib/StructuralVariantAnnotation_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/StructuralVariantAnnotation_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.1/StructuralVariantAnnotation_1.10.1.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 dependencyCount: 98 Package: SubCellBarCode Version: 1.10.0 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: d26179fd4c6fad43a0073284adc44e0c 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: RELEASE_3_14 git_last_commit: 578a4ae git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SubCellBarCode_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SubCellBarCode_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SubCellBarCode_1.10.0.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: 123 Package: subSeq Version: 1.24.0 Depends: R (>= 3.2) Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99), digest, Biobase Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr License: MIT + file LICENSE Archs: i386, x64 MD5sum: debeba1ffd1f6f1d917b2b67ff498e55 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: RELEASE_3_14 git_last_commit: 6609184 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/subSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/subSeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/subSeq_1.24.0.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: 54 Package: SummarizedBenchmark Version: 2.12.0 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) MD5sum: c9049d7f2454a08c911f3ec15eeacc78 NeedsCompilation: no 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 git_url: https://git.bioconductor.org/packages/SummarizedBenchmark git_branch: RELEASE_3_14 git_last_commit: 78e1a22 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SummarizedBenchmark_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SummarizedBenchmark_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SummarizedBenchmark_2.12.0.tgz vignettes: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.html, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.html, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.html vignetteTitles: Case Study: Benchmarking non-R Methods, Case Study: Single-Cell RNA-Seq Simulation, Feature: Error Handling, Feature: Iterative Benchmarking, Feature: Parallelization, SummarizedBenchmark: Class Details, SummarizedBenchmark: Full Case Study, SummarizedBenchmark: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.R, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.R, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.R suggestsMe: benchmarkfdrData2019 dependencyCount: 77 Package: SummarizedExperiment Version: 1.24.0 Depends: R (>= 4.0.0), methods, MatrixGenerics (>= 1.1.3), GenomicRanges (>= 1.41.5), Biobase Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), DelayedArray (>= 0.15.10) Suggests: HDF5Array (>= 1.7.5), annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, jsonlite, rhdf5, airway, BiocStyle, knitr, rmarkdown, RUnit, testthat, digest License: Artistic-2.0 MD5sum: 35c25511fec17486001ef2ef673e6899 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, Valerie Obenchain, Jim Hester, Hervé Pagès Maintainer: Bioconductor Package Maintainer 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: RELEASE_3_14 git_last_commit: d37f193 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SummarizedExperiment_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SummarizedExperiment_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SummarizedExperiment_1.24.0.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, AllelicImbalance, ASpediaFI, atena, bambu, BDMMAcorrect, BiocSklearn, BioPlex, BiSeq, bnbc, BrainSABER, bsseq, CAGEfightR, celaref, clusterExperiment, compartmap, CoreGx, coseq, csaw, CSSQ, DaMiRseq, deco, deepSNV, DeMixT, DESeq2, DEXSeq, DiffBind, diffcoexp, diffHic, divergence, DMCFB, DMCHMM, ENmix, EnrichmentBrowser, epigenomix, evaluomeR, EventPointer, exomePeak2, ExperimentSubset, FEAST, FRASER, GenomicAlignments, GenomicFiles, GenomicSuperSignature, GRmetrics, GSEABenchmarkeR, HelloRanges, hipathia, IgGeneUsage, InteractionSet, IntEREst, iSEE, isomiRs, ivygapSE, lefser, lipidr, LoomExperiment, made4, MatrixQCvis, MBASED, methrix, MetNet, mia, miaSim, miaViz, minfi, miRmine, moanin, mpra, MultiAssayExperiment, NADfinder, NBAMSeq, NewWave, OUTRIDER, padma, pairkat, PDATK, PhIPData, profileplyr, recount, recount3, RegEnrich, REMP, ROCpAI, rqt, runibic, Scale4C, scAnnotatR, scClassifR, scGPS, scone, scTreeViz, SDAMS, SeqGate, SGSeq, signatureSearch, SingleCellExperiment, singleCellTK, SingleR, soGGi, spqn, ssPATHS, stageR, SummarizedBenchmark, survtype, tidySummarizedExperiment, TimeSeriesExperiment, TissueEnrich, TNBC.CMS, UMI4Cats, VanillaICE, VariantAnnotation, VariantExperiment, velociraptor, weitrix, yamss, zinbwave, airway, benchmarkfdrData2019, bodymapRat, celldex, curatedAdipoChIP, curatedAdipoRNA, curatedMetagenomicData, DREAM4, fission, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HighlyReplicatedRNASeq, HMP16SData, MetaGxOvarian, MetaGxPancreas, MethylSeqData, microbiomeDataSets, microRNAome, MouseGastrulationData, MouseThymusAgeing, ObMiTi, parathyroidSE, restfulSEData, sampleClassifierData, spatialDmelxsim, spqnData, timecoursedata, tuberculosis, DRomics, ordinalbayes importsMe: ADAM, ADImpute, aggregateBioVar, airpart, ALDEx2, alpine, AlpsNMR, animalcules, anota2seq, APAlyzer, apeglm, appreci8R, ASICS, AUCell, autonomics, awst, barcodetrackR, BASiCS, batchelor, BayesSpace, bayNorm, BBCAnalyzer, benchdamic, bigPint, BiocOncoTK, BioNERO, biotmle, biovizBase, biscuiteer, BiSeq, blacksheepr, BloodGen3Module, BRGenomics, BUMHMM, BUScorrect, BUSseq, CAGEr, CATALYST, cBioPortalData, ccfindR, celda, CelliD, CellMixS, CellTrails, censcyt, Cepo, CeTF, ChIPpeakAnno, ChromSCape, chromVAR, CiteFuse, clustifyr, cmapR, CNVfilteR, CNVRanger, CoGAPS, combi, conclus, condiments, consensusDE, CopyNumberPlots, corral, countsimQC, crossmeta, cydar, CyTOFpower, cytoKernel, cytomapper, DAMEfinder, dasper, debCAM, debrowser, DEComplexDisease, decompTumor2Sig, DEFormats, DEGreport, deltaCaptureC, DEP, DEScan2, destiny, DEWSeq, diffcyt, diffUTR, Dino, DiscoRhythm, distinct, dittoSeq, DMRcate, DominoEffect, doppelgangR, doseR, DropletUtils, Dune, easyRNASeq, eisaR, ELMER, ensemblVEP, epialleleR, epigraHMM, epivizrData, erma, EWCE, FCBF, fcScan, FindIT2, fishpond, FLAMES, GARS, gCrisprTools, GeneTonic, GenomicDataCommons, genomicInstability, getDEE2, ggbio, ggspavis, Glimma, glmGamPoi, glmSparseNet, GreyListChIP, gscreend, GSVA, gwasurvivr, GWENA, HTSeqGenie, HumanTranscriptomeCompendium, hummingbird, iasva, icetea, ideal, ILoReg, imcRtools, infercnv, INSPEcT, InterMineR, iSEEu, iteremoval, LACE, LineagePulse, lionessR, MADSEQ, MAI, marr, MAST, mbkmeans, MBQN, mCSEA, MEAL, MEAT, MEB, metabolomicsWorkbenchR, MetaNeighbor, metaseqR2, MethReg, MethylAid, methylscaper, methylumi, MicrobiotaProcess, midasHLA, miloR, MinimumDistance, miRSM, missMethyl, MLSeq, monaLisa, MoonlightR, motifbreakR, motifmatchr, MPRAnalyze, MsFeatures, msgbsR, MSPrep, msqrob2, MultiDataSet, multiOmicsViz, mumosa, muscat, musicatk, MWASTools, NanoMethViz, Nebulosa, netSmooth, NormalyzerDE, NxtIRFcore, oligoClasses, omicRexposome, OmicsLonDA, omicsPrint, oncomix, ORFik, OVESEG, PAIRADISE, pcaExplorer, peco, PharmacoGx, phemd, phenopath, PhosR, pipeComp, pmp, POWSC, proActiv, proDA, psichomics, pulsedSilac, PureCN, QFeatures, qsmooth, quantiseqr, R453Plus1Toolbox, RadioGx, RaggedExperiment, RareVariantVis, RcisTarget, receptLoss, regionReport, regsplice, rgsepd, Rmmquant, RNAAgeCalc, RNAsense, roar, rScudo, RTCGAToolbox, RTN, satuRn, SBGNview, SC3, SCArray, scater, scBFA, scCB2, scDblFinder, scDD, scds, scHOT, scmap, scMerge, scmeth, SCnorm, scoreInvHap, scp, scPipe, scran, scReClassify, scRepertoire, scruff, scry, scTensor, scTGIF, scuttle, sechm, segmenter, seqCAT, sesame, SEtools, sigFeature, SigsPack, singscore, slalom, slingshot, slinky, snapcount, SNPhood, Spaniel, SpatialCPie, spatialDE, SpatialExperiment, spatialHeatmap, spatzie, splatter, SplicingFactory, srnadiff, struct, StructuralVariantAnnotation, supersigs, switchde, systemPipeR, systemPipeTools, TBSignatureProfiler, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TCseq, tenXplore, tidybulk, tidySingleCellExperiment, TOAST, tomoda, ToxicoGx, tradeSeq, TrajectoryUtils, transformGamPoi, TraRe, traviz, TreeSummarizedExperiment, Trendy, tricycle, TSCAN, tscR, TSRchitect, TTMap, TVTB, tximeta, VAExprs, VariantFiltering, vidger, wpm, xcms, zellkonverter, zFPKM, BloodCancerMultiOmics2017, brgedata, CLLmethylation, COSMIC.67, curatedTCGAData, easierData, emtdata, FieldEffectCrc, GSE13015, HMP2Data, IHWpaper, MetaGxBreast, scRNAseq, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TCGAWorkflowData, ExpHunterSuite, fluentGenomics, SingscoreAMLMutations, TCGAWorkflow, BinQuasi, digitalDLSorteR, HeritSeq, imcExperiment, microbial, PlasmaMutationDetector, pulseTD, RNAseqQC, SC.MEB, SCRIP suggestsMe: AnnotationHub, biobroom, BiocPkgTools, cageminer, dcanr, dce, dearseq, decoupleR, DelayedArray, easier, edgeR, EnMCB, epivizr, epivizrChart, esetVis, fobitools, GENIE3, GenomicRanges, globalSeq, gsean, hca, HDF5Array, HPiP, Informeasure, InteractiveComplexHeatmap, interactiveDisplay, MatrixGenerics, mistyR, MOFA2, MSnbase, ODER, pathwayPCA, philr, podkat, PubScore, RiboProfiling, S4Vectors, scFeatureFilter, semisup, sparrow, svaNUMT, svaRetro, systemPipeShiny, TFutils, biotmleData, curatedAdipoArray, curatedTBData, dorothea, DuoClustering2018, GSE103322, RforProteomics, SBGNview.data, tissueTreg, CAGEWorkflow, Canek, clustree, conos, dyngen, Platypus, polyRAD, RaceID, seqgendiff, Seurat, Signac, singleCellHaystack dependencyCount: 24 Package: Summix Version: 2.0.0 Depends: R (>= 4.1) Imports: nloptr, methods Suggests: rmarkdown, markdown, knitr License: MIT + file LICENSE MD5sum: e97c8f183df7fd9c958010898f921f29 NeedsCompilation: no Title: Summix: A method to estimate and adjust for population structure in genetic summary data Description: This package contains the Summix method for estimating and adjusting for ancestry in genetic summary allele frequency data. The function summix estimates reference ancestry proportions using a mixture model. The adjAF function produces ancestry adjusted allele frequencies for an observed sample with ancestry proportions matching a target person or sample. biocViews: StatisticalMethod, WholeGenome, Genetics Author: Audrey Hendricks [cre], 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: RELEASE_3_14 git_last_commit: 3b053e1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Summix_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Summix_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Summix_2.0.0.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: 38 Package: supersigs Version: 1.2.0 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: 0507fc454891d5452642af6a6d4d95b3 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: RELEASE_3_14 git_last_commit: 74453a0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/supersigs_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/supersigs_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/supersigs_1.2.0.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: 103 Package: supraHex Version: 1.32.0 Depends: R (>= 3.6), hexbin Imports: ape, MASS, grDevices, graphics, stats, readr, tibble, tidyr, dplyr, stringr, purrr, magrittr, igraph, methods License: GPL-2 MD5sum: 51d38b730b662d1bf810dcb265a70cfb 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: RELEASE_3_14 git_last_commit: 944166c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/supraHex_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/supraHex_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/supraHex_1.32.0.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 dependsOnMe: dnet importsMe: Pi suggestsMe: OmnipathR, TCGAbiolinks dependencyCount: 48 Package: surfaltr Version: 1.0.0 Depends: R (>= 4.0) Imports: dplyr (>= 1.0.6), biomaRt (>= 2.46.0), protr (>= 1.6-2), seqinr (>= 4.2-5), ggplot2 (>= 3.3.2), utils (>= 2.10.1), stringr (>= 1.4.0), Biostrings (>= 2.58.0),readr (>= 1.4.0), httr (>= 1.4.2), testthat(>= 3.0.0), xml2(>= 1.3.2), msa (>= 1.22.0), methods (>= 4.0.3) Suggests: knitr, rmarkdown, devtools, kableExtra License: MIT + file LICENSE MD5sum: ac0ba552bc77ca28a143bac00067a724 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: RELEASE_3_14 git_last_commit: 25e74d9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/surfaltr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/surfaltr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/surfaltr_1.0.0.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: survcomp Version: 1.44.1 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: 3ebc8faf7ee9cd75f783f95d7dd3aade 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: RELEASE_3_14 git_last_commit: 5d966f3 git_last_commit_date: 2021-12-14 Date/Publication: 2021-12-16 source.ver: src/contrib/survcomp_1.44.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/survcomp_1.44.1.zip mac.binary.ver: bin/macosx/contrib/4.1/survcomp_1.44.1.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, pencal, plsRcox, SIGN suggestsMe: glmSparseNet, GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 35 Package: survtype Version: 1.10.0 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr, rmarkdown License: Artistic-2.0 MD5sum: 5f02603bff4c3635af7504cce1e1e857 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: RELEASE_3_14 git_last_commit: 8acb698 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/survtype_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/survtype_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/survtype_1.10.0.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: 143 Package: Sushi Version: 1.32.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: a33a5e39fb71c43d7e77d8a48887ce2d NeedsCompilation: no Title: Tools for visualizing genomics data Description: Flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures biocViews: DataRepresentation, Visualization, Genetics, Sequencing, Infrastructure, HiC Author: Douglas H Phanstiel Maintainer: Douglas H Phanstiel git_url: https://git.bioconductor.org/packages/Sushi git_branch: RELEASE_3_14 git_last_commit: 55042b9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-12-14 source.ver: src/contrib/Sushi_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Sushi_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Sushi_1.32.0.tgz vignettes: vignettes/Sushi/inst/doc/Sushi.pdf vignetteTitles: Sushi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sushi/inst/doc/Sushi.R importsMe: ChromSCape, diffloop, Ularcirc, VaSP dependencyCount: 74 Package: sva Version: 3.42.0 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: 5b0dd7fc19954658e0492110f3185c4a 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: RELEASE_3_14 git_last_commit: 54c843c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/sva_3.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/sva_3.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/sva_3.42.0.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: SCAN.UPC, rnaseqGene, leapp, SmartSVA importsMe: ASSIGN, ballgown, BatchQC, BioNERO, bnbc, bnem, crossmeta, CytoTree, DaMiRseq, debrowser, DExMA, doppelgangR, edge, KnowSeq, MSPrep, omicRexposome, PAA, proBatch, PROPS, qsmooth, SEtools, singleCellTK, trigger, DeSousa2013, ExpressionNormalizationWorkflow, cate, cinaR, DGEobj.utils, dSVA, oncoPredict, scITD, seqgendiff suggestsMe: Harman, iasva, MAGeCKFlute, randRotation, RnBeads, scp, SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk, curatedBladderData, curatedCRCData, curatedOvarianData, curatedTBData, FieldEffectCrc, CAGEWorkflow, SuperLearner dependencyCount: 68 Package: svaNUMT Version: 1.0.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, StructuralVariantAnnotation, BiocGenerics, R (>= 4.0) Imports: assertthat, Biostrings, stringr, dplyr, methods, rlang, GenomeInfoDb, S4Vectors, GenomicFeatures Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, plyranges, circlize, tictoc, IRanges, SummarizedExperiment, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 0fc9cf31f2a6e2b3fa7a8ce90b7b2e46 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: RELEASE_3_14 git_last_commit: 2fc3188 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/svaNUMT_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/svaNUMT_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/svaNUMT_1.0.0.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: 99 Package: svaRetro Version: 1.0.0 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: 1fc33b06908255369724d2c07a1bd662 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: RELEASE_3_14 git_last_commit: c7b9142 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/svaRetro_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/svaRetro_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/svaRetro_1.0.0.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: 99 Package: SWATH2stats Version: 1.24.0 Depends: R(>= 2.10.0) Imports: data.table, reshape2, ggplot2, stats, grDevices, graphics, utils, biomaRt, methods Suggests: testthat, knitr, rmarkdown Enhances: MSstats, PECA, aLFQ License: GPL-3 MD5sum: f7d3e7be222225c75862bb94e9634bb8 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: RELEASE_3_14 git_last_commit: aa96ce6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SWATH2stats_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SWATH2stats_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SWATH2stats_1.24.0.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: 91 Package: SwathXtend Version: 2.16.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 Archs: i386, x64 MD5sum: b5b322994ef39958fabfbd3eb3054f63 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: RELEASE_3_14 git_last_commit: cbf2476 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SwathXtend_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SwathXtend_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SwathXtend_2.16.0.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: 1.20.0 Depends: R (>= 3.4) Imports: methods, splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) MD5sum: 3f29e572c794b734dc0007c7ec942def 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: RELEASE_3_14 git_last_commit: 537e91c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/swfdr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/swfdr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/swfdr_1.20.0.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: 1.30.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 MD5sum: c55900f5c96aa9319bccf85bd4e96ee7 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: RELEASE_3_14 git_last_commit: 1b69b16 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/switchBox_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/switchBox_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/switchBox_1.30.0.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: 1.20.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: ab4a82b2b7596c6af63defa20d687d59 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: RELEASE_3_14 git_last_commit: 5e5b678 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/switchde_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/switchde_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/switchde_1.20.0.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: 60 Package: synapsis Version: 1.0.0 Depends: R (>= 4.1) Imports: EBImage, stats, utils, graphics Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2, tidyverse, BiocStyle License: MIT + file LICENSE MD5sum: 82c6c892b3375fc5f953071a28e25d3e 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: RELEASE_3_14 git_last_commit: 1728ef1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/synapsis_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/synapsis_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/synapsis_1.0.0.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: 25 Package: synergyfinder Version: 3.2.10 Depends: R (>= 4.0.0) Imports: drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0), dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>= 0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>= 4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5), methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>= 0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>= 1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>= 0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>= 4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>= 1.4-3), metR (>= 0.9.1) Suggests: knitr, rmarkdown License: Mozilla Public License 2.0 MD5sum: 72a3610753df0fe4329a863f5c426a97 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 (https://synergyfinderplus.org/) for users who prefer graphical user interface. biocViews: Software, StatisticalMethod Author: Shuyu Zheng [aut, cre], Jing Tang [aut] Maintainer: Shuyu Zheng URL: https://synergyfinderplus.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synergyfinder git_branch: RELEASE_3_14 git_last_commit: fc446e0 git_last_commit_date: 2022-03-30 Date/Publication: 2022-03-31 source.ver: src/contrib/synergyfinder_3.2.10.tar.gz win.binary.ver: bin/windows/contrib/4.1/synergyfinder_3.2.10.zip mac.binary.ver: bin/macosx/contrib/4.1/synergyfinder_3.2.10.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: 190 Package: SynExtend Version: 1.6.0 Depends: R (>= 4.1.0), DECIPHER (>= 2.20.0) Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats Suggests: BiocStyle, knitr, rtracklayer, igraph, markdown, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 7e3c56f705a74908b1a531cd36adbadf NeedsCompilation: no 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] (), Adelle Fernando [ctb], Erik Wright [aut] Maintainer: Nicholas Cooley VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SynExtend git_branch: RELEASE_3_14 git_last_commit: 8d9293e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SynExtend_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SynExtend_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SynExtend_1.6.0.tgz vignettes: vignettes/SynExtend/inst/doc/UsingSynExtend.pdf vignetteTitles: UsingSynExtend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynExtend/inst/doc/UsingSynExtend.R dependencyCount: 35 Package: synlet Version: 1.24.0 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: 617583fa1a2e8a4887e85cdc41d134fe 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 Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synlet git_branch: RELEASE_3_14 git_last_commit: 6701054 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/synlet_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/synlet_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/synlet_1.24.0.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: 85 Package: SynMut Version: 1.10.0 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: 53dfdd0ac81950083ef628a589d9d059 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: RELEASE_3_14 git_last_commit: 9913ce4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/SynMut_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/SynMut_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/SynMut_1.10.0.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: 30 Package: systemPipeR Version: 2.0.8 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 (>= 1.31.3), VariantAnnotation (>= 1.25.11) License: Artistic-2.0 MD5sum: 3a08f97fb3437b42c961434497a95ce1 NeedsCompilation: no Title: systemPipeR: NGS workflow and report generation environment Description: R package for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. Efficient handling of complex sample sets and experimental designs is facilitated by a consistently implemented sample annotation infrastructure. Instructions for using systemPipeR are given in the Overview Vignette (HTML). The remaining Vignettes, linked below, are workflow templates for common NGS use cases. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology, ReportWriting, Workflow Author: Thomas Girke Maintainer: Thomas Girke URL: https://systempipe.org/ 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: RELEASE_3_14 git_last_commit: 8db93ad git_last_commit_date: 2022-04-11 Date/Publication: 2022-04-12 source.ver: src/contrib/systemPipeR_2.0.8.tar.gz win.binary.ver: bin/windows/contrib/4.1/systemPipeR_2.0.8.zip mac.binary.ver: bin/macosx/contrib/4.1/systemPipeR_2.0.8.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR: Workflows collection, systemPipeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R, vignettes/systemPipeR/inst/doc/systemPipeR.R importsMe: DiffBind, RNASeqR suggestsMe: systemPipeShiny, systemPipeTools, systemPipeRdata dependencyCount: 80 Package: systemPipeShiny Version: 1.4.0 Depends: R (>= 4.0.0), shiny (>= 1.5.0), spsUtil (>= 0.2.0), spsComps (>= 0.3.1), drawer 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, systemPipeRdata, rhandsontable, zip, callr, pushbar, fs, readr, R.utils, DESeq2, SummarizedExperiment, glmpca, pheatmap, grid, ape, Rtsne, UpSetR, tidyr, esquisse (>= 1.0.2), cicerone License: GPL (>= 3) MD5sum: e3509280a1db4339b9fd730d23894d17 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: 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: RELEASE_3_14 git_last_commit: 4747056 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/systemPipeShiny_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/systemPipeShiny_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/systemPipeShiny_1.4.0.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: 121 Package: systemPipeTools Version: 1.2.0 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: ad702d44079f8172f7ab870e7232bc4d 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: RELEASE_3_14 git_last_commit: 1997851 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/systemPipeTools_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/systemPipeTools_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/systemPipeTools_1.2.0.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: 132 Package: TADCompare Version: 1.4.0 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, rGREAT, SpectralTAD License: MIT + file LICENSE MD5sum: ba0a45239adba58bcbd157ec4c40585b 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: Kellen Cresswell , Mikhail Dozmorov Maintainer: Kellen Cresswell 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: RELEASE_3_14 git_last_commit: 8b7f9ac git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TADCompare_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TADCompare_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TADCompare_1.4.0.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: 152 Package: tanggle Version: 1.0.0 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: 5c8512cf5eaa826c73b9e794a1d236fe 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: RELEASE_3_14 git_last_commit: b54fd2e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tanggle_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tanggle_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tanggle_1.0.0.tgz vignettes: vignettes/tanggle/inst/doc/tanggle_vignette.html vignetteTitles: ***tanggle***: Visualization of phylogenetic networks in a *ggplot2* framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tanggle/inst/doc/tanggle_vignette.R dependencyCount: 63 Package: TAPseq Version: 1.6.0 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: a3800a8db0096873197209a9c024d871 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: RELEASE_3_14 git_last_commit: ea1cda0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TAPseq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TAPseq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TAPseq_1.6.0.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: 99 Package: target Version: 1.8.0 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: 2b8bf6ba7eb8fb9aec80007f66da169f 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: RELEASE_3_14 git_last_commit: a29b0fe git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/target_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/target_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/target_1.8.0.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: 47 Package: TargetDecoy Version: 1.0.0 Depends: R (>= 4.1) Imports: ggplot2, ggpubr, methods, mzID, mzR, stats Suggests: BiocStyle, knitr, msdata, sessioninfo, rmarkdown, gridExtra, testthat (>= 3.0.0), covr License: Artistic-2.0 MD5sum: 86b385d98460b2a9f0f047f0c632a664 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://github.com/statOmics/TargetDecoy VignetteBuilder: knitr BugReports: https://github.com/statOmics/TargetDecoy/issues git_url: https://git.bioconductor.org/packages/TargetDecoy git_branch: RELEASE_3_14 git_last_commit: 368866a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TargetDecoy_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TargetDecoy_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TargetDecoy_1.0.0.tgz vignettes: vignettes/TargetDecoy/inst/doc/TargetDecoy.html vignetteTitles: Introduction to TargetDecoy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetDecoy/inst/doc/TargetDecoy.R dependencyCount: 110 Package: TargetScore Version: 1.32.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 58df9482694da8fc8ec8e3936c514d26 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: RELEASE_3_14 git_last_commit: 1b24f38 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TargetScore_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TargetScore_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TargetScore_1.32.0.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: 1.50.1 Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat Suggests: TargetSearchData, BiocStyle, knitr, tinytest License: GPL (>= 2) MD5sum: 3a3f005a8bfadccc6ae16111007f921d NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data Description: This packages provides a targeted pre-processing method for GC-MS data. biocViews: MassSpectrometry, Preprocessing, DecisionTree, ImmunoOncology Author: Alvaro Cuadros-Inostroza , Jan Lisec, Henning Redestig, Matt Hannah 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: RELEASE_3_14 git_last_commit: c3a63ea git_last_commit_date: 2022-01-31 Date/Publication: 2022-02-01 source.ver: src/contrib/TargetSearch_1.50.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/TargetSearch_1.50.1.zip mac.binary.ver: bin/macosx/contrib/4.1/TargetSearch_1.50.1.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction extra, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R, vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R dependencyCount: 8 Package: TarSeqQC Version: 1.24.0 Depends: R (>= 3.5.1), methods, GenomicRanges, Rsamtools (>= 1.9.2), ggplot2, plyr, openxlsx Imports: grDevices, stats, utils, S4Vectors, IRanges, BiocGenerics, reshape2, GenomeInfoDb, BiocParallel, Biostrings, cowplot, graphics, GenomicAlignments, Hmisc Suggests: BiocManager, RUnit License: GPL (>=2) MD5sum: 0565982a1fc5e152bec53ab249a7adb0 NeedsCompilation: no Title: TARgeted SEQuencing Experiment Quality Control Description: The package allows the representation of targeted experiment in R. This is based on current packages and incorporates functions to do a quality control over this kind of experiments and a fast exploration of the sequenced regions. An xlsx file is generated as output. biocViews: Software, Sequencing, TargetedResequencing, QualityControl, Visualization, Coverage, Alignment, DataImport Author: Gabriela A. Merino, Cristobal Fresno, Yanina Murua, Andrea S. Llera and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar git_url: https://git.bioconductor.org/packages/TarSeqQC git_branch: RELEASE_3_14 git_last_commit: 1a26d02 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TarSeqQC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TarSeqQC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TarSeqQC_1.24.0.tgz vignettes: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.pdf vignetteTitles: TarSeqQC: Targeted Sequencing Experiment Quality Control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.R dependencyCount: 102 Package: TBSignatureProfiler Version: 1.6.0 Depends: R (>= 4.1) Imports: ASSIGN (>= 1.23.1), GSVA, singscore, methods, ComplexHeatmap, RColorBrewer, ggplot2, S4Vectors, reshape2, ROCit, DESeq2, DT, edgeR, gdata, SummarizedExperiment, magrittr, stats, rlang, BiocParallel, BiocGenerics Suggests: testthat, spelling, lintr, covr, knitr, rmarkdown, BiocStyle, shiny, circlize, caret, dplyr, plyr, impute, sva, glmnet, randomForest, MASS, class, e1071, pROC, HGNChelper License: MIT + file LICENSE MD5sum: f0ced58e565fa5df60bf0abf048d1331 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. biocViews: GeneExpression, DifferentialExpression Author: David Jenkins [aut], Aubrey Odom [aut, cre], Xutao Wang [aut], Yue Zhao [aut], Christian Love [aut], W. Evan Johnson [aut] Maintainer: Aubrey Odom URL: https://github.com/compbiomed/TBSignatureProfiler https://compbiomed.github.io/TBSignatureProfiler-docs/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/TBSignatureProfiler/issues git_url: https://git.bioconductor.org/packages/TBSignatureProfiler git_branch: RELEASE_3_14 git_last_commit: fe6bf5e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TBSignatureProfiler_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TBSignatureProfiler_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TBSignatureProfiler_1.6.0.tgz vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.html vignetteTitles: "Introduction to the TBSignatureProfiler" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.R dependencyCount: 159 Package: TCC Version: 1.34.0 Depends: R (>= 3.0), methods, DESeq2, edgeR, baySeq, ROC Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: d336f8be41579aa601b43ed6b9b55c94 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: RELEASE_3_14 git_last_commit: f3e5d27 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TCC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCC_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TCC_1.34.0.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, ExpHunterSuite dependencyCount: 105 Package: TCGAbiolinks Version: 2.22.4 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, readr, tools, tidyr, purrr, xml2, httr (>= 1.2.1) Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, dnet, Biobase, affy, testthat, sesame, pathview, clusterProfiler, ComplexHeatmap, circlize, ConsensusClusterPlus, igraph, supraHex, limma, edgeR, sva, EDASeq, survminer, genefilter, gridExtra, survival, doParallel, parallel, ggrepel (>= 0.6.3), scales, grid License: GPL (>= 3) MD5sum: aba45477e900a891c93d9040d863f964 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: RELEASE_3_14 git_last_commit: 7d9a8aa3 git_last_commit_date: 2022-01-21 Date/Publication: 2022-01-23 source.ver: src/contrib/TCGAbiolinks_2.22.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCGAbiolinks_2.22.4.zip mac.binary.ver: bin/macosx/contrib/4.1/TCGAbiolinks_2.22.4.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/gui.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.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", "9. Graphical User Interface (GUI)", "1. Introduction", "5. Mutation data", "2. Searching GDC database", 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/gui.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/subtypes.R dependsOnMe: MAFDash importsMe: ELMER, MoonlightR, musicatk, SpidermiR, TCGAbiolinksGUI, SingscoreAMLMutations, TCGAWorkflow suggestsMe: Rediscover dependencyCount: 113 Package: TCGAbiolinksGUI Version: 1.20.0 Depends: R (>= 3.3.1), shinydashboard (>= 0.5.3), TCGAbiolinksGUI.data Imports: shiny (>= 0.14.1), downloader (>= 0.4), grid, DT, plotly, readr, maftools, stringr (>= 1.1.0), SummarizedExperiment, ggrepel, data.table, caret, shinyFiles (>= 0.6.2), ggplot2 (>= 2.1.0), pathview, ELMER (>= 2.0.0), clusterProfiler, parallel, TCGAbiolinks (>= 2.5.5), shinyjs (>= 0.7), colourpicker, sesame, shinyBS (>= 0.61) Suggests: testthat, dplyr, knitr, roxygen2, devtools, rvest, xml2, BiocStyle, animation, rmarkdown, pander License: GPL (>= 3) Archs: i386, x64 MD5sum: 7ebd2b9771d6e42445efe8ddc941a881 NeedsCompilation: no Title: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data" Description: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data. A demo version of GUI is found in https://tcgabiolinksgui.shinyapps.io/tcgabiolinks/" biocViews: Genetics, GUI, DNAMethylation, StatisticalMethod, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Sequencing, Pathways, Network, DNASeq Author: Tiago Chedraoui Silva , Antonio Colaprico , Catharina Olsen , Michele Ceccarelli, Gianluca Bontempi , Benjamin P. Berman , Houtan Noushmehr Maintainer: Tiago C. Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TCGAbiolinksGUI git_branch: RELEASE_3_14 git_last_commit: 94f9190 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TCGAbiolinksGUI_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCGAbiolinksGUI_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TCGAbiolinksGUI_1.20.0.tgz vignettes: vignettes/TCGAbiolinksGUI/inst/doc/analysis.html, vignettes/TCGAbiolinksGUI/inst/doc/Cases.html, vignettes/TCGAbiolinksGUI/inst/doc/data.html, vignettes/TCGAbiolinksGUI/inst/doc/index.html, vignettes/TCGAbiolinksGUI/inst/doc/integrative.html vignetteTitles: "3. Analysis menu", "5. Cases study", "2. Data menu", "1. Introduction", "4. Integrative analysis menu" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinksGUI/inst/doc/data.R, vignettes/TCGAbiolinksGUI/inst/doc/index.R dependencyCount: 301 Package: TCGAutils Version: 1.14.1 Depends: R (>= 4.0.0) Imports: AnnotationDbi, BiocGenerics, GenomeInfoDb, GenomicFeatures, GenomicRanges, GenomicDataCommons, IRanges, methods, MultiAssayExperiment, RaggedExperiment (>= 1.5.7), rvest, S4Vectors, stats, stringr, SummarizedExperiment, utils, xml2 Suggests: BiocFileCache, BiocStyle, curatedTCGAData, ComplexHeatmap, devtools, dplyr, 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: a246361848a4cfec8d10bf3856f63f15 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. biocViews: Software, WorkflowStep, Preprocessing 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: RELEASE_3_14 git_last_commit: 94577cb git_last_commit_date: 2022-04-04 Date/Publication: 2022-04-05 source.ver: src/contrib/TCGAutils_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCGAutils_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.1/TCGAutils_1.14.1.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, RTCGAToolbox suggestsMe: CNVRanger, dce, glmSparseNet, curatedTCGAData dependencyCount: 107 Package: TCseq Version: 1.18.0 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: a5f7c518ca7d5d1efea83bd6bfa095e2 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 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: RELEASE_3_14 git_last_commit: e2f916f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TCseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TCseq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TCseq_1.18.0.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: 79 Package: TDARACNE Version: 1.44.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: 8b6930680f39376f1e3873369f8e094a NeedsCompilation: no Title: Network reverse engineering from time course data. Description: To infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data. biocViews: Microarray, TimeCourse Author: Zoppoli P.,Morganella S., Ceccarelli M. Maintainer: Zoppoli Pietro git_url: https://git.bioconductor.org/packages/TDARACNE git_branch: RELEASE_3_14 git_last_commit: 2f4e461 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TDARACNE_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TDARACNE_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TDARACNE_1.44.0.tgz vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf vignetteTitles: TDARACNE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDARACNE/inst/doc/TDARACNE.R dependencyCount: 13 Package: tenXplore Version: 1.16.0 Depends: R (>= 3.4), 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 License: Artistic-2.0 MD5sum: 05345fe761d168c7b5e407558cb27978 NeedsCompilation: no Title: ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics Description: Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics. biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent, Transcriptomics, SingleCell Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tenXplore git_branch: RELEASE_3_14 git_last_commit: 7ef77de git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tenXplore_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tenXplore_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tenXplore_1.16.0.tgz vignettes: vignettes/tenXplore/inst/doc/tenXplore.html vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3 million neurons hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R dependencyCount: 118 Package: TEQC Version: 4.16.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: da11f78e56130ec50d684b5dc87c121d 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: RELEASE_3_14 git_last_commit: 5854c95 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TEQC_4.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TEQC_4.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TEQC_4.16.0.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: 31 Package: ternarynet Version: 1.38.0 Depends: R (>= 4.0) Imports: utils, igraph, methods, graphics, stats, BiocParallel Suggests: testthat Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: f4d8d99a7032d91364cc8c580dc311a8 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: RELEASE_3_14 git_last_commit: a463985 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ternarynet_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ternarynet_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ternarynet_1.38.0.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: 19 Package: TFARM Version: 1.16.0 Depends: R (>= 3.4) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: 1468adbf75ccc7cdf9170468e315d026 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: RELEASE_3_14 git_last_commit: da33167 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TFARM_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TFARM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TFARM_1.16.0.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: 64 Package: TFBSTools Version: 1.32.0 Depends: R (>= 3.2.2) Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), 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: 668957d41cea76909fd1abe824fc76e4 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: RELEASE_3_14 git_last_commit: 2355056 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TFBSTools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TFBSTools_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TFBSTools_1.32.0.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: chromVAR, enrichTF, esATAC, MatrixRider, monaLisa, motifmatchr, primirTSS, spatzie suggestsMe: enhancerHomologSearch, MAGAR, MethReg, pageRank, universalmotif, JASPAR2018, JASPAR2020, CAGEWorkflow, Signac dependencyCount: 122 Package: TFEA.ChIP Version: 1.14.0 Depends: R (>= 3.3) Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices, dplyr, stats, utils, R.utils, methods, org.Hs.eg.db Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2, GSEABase, DESeq2, BiocGenerics, ggrepel, rcompanion, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 7a389ab22c64bdfe6fb70129df69c122 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: RELEASE_3_14 git_last_commit: 42c4ce4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TFEA.ChIP_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TFEA.ChIP_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TFEA.ChIP_1.14.0.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: 100 Package: TFHAZ Version: 1.16.0 Depends: R(>= 3.4) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 9511c4a1391b09937dd74dfb7744c02b 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, Marco Masseroli Maintainer: Alberto Marchesi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFHAZ git_branch: RELEASE_3_14 git_last_commit: 30d2da0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TFHAZ_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TFHAZ_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TFHAZ_1.16.0.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: 17 Package: TFutils Version: 1.14.0 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 Archs: i386, x64 MD5sum: bbce1e63abdff3e677236d83c5d75922 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: RELEASE_3_14 git_last_commit: 3c4cda1 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TFutils_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TFutils_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TFutils_1.14.0.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: 107 Package: tidybulk Version: 1.6.1 Depends: R (>= 4.1.0) Imports: tibble, readr, dplyr, magrittr, tidyr, stringi, stringr, rlang, purrr, tidyselect, preprocessCore, stats, parallel, utils, lifecycle, scales, SummarizedExperiment, GenomicRanges, methods 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, S4Vectors, ggplot2, widyr, clusterProfiler, msigdbr, DESeq2, broom, survival, boot, betareg, tidyHeatmap, pasilla, ggrepel, devtools, functional, survminer, tidySummarizedExperiment, markdown, uwot License: GPL-3 MD5sum: 1f36d23f809ca86eca4d1b5fd012fa34 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: RELEASE_3_14 git_last_commit: 13706d8 git_last_commit_date: 2021-10-28 Date/Publication: 2021-10-31 source.ver: src/contrib/tidybulk_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/tidybulk_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/tidybulk_1.6.1.tgz vignettes: vignettes/tidybulk/inst/doc/comparison_with_base_R.html, vignettes/tidybulk/inst/doc/introduction.html, vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.html, vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.html vignetteTitles: Comparison with base R, Overview of the tidybulk package, Manuscript code - differential feature abundance, Manuscript code - transcriptional signature identification hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidybulk/inst/doc/comparison_with_base_R.R, vignettes/tidybulk/inst/doc/introduction.R, vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.R, vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.R dependencyCount: 66 Package: tidySingleCellExperiment Version: 1.4.0 Depends: R (>= 4.0.0), SingleCellExperiment Imports: SummarizedExperiment, dplyr, tibble, tidyr, ggplot2, plotly, magrittr, rlang, purrr, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, pillar, stringr, cli, fansi Suggests: BiocStyle, testthat, knitr, markdown, SingleCellSignalR, SingleR, scater, scran, tidyHeatmap, igraph, GGally, Matrix, uwot, celldex, dittoSeq, EnsDb.Hsapiens.v86 License: GPL-3 Archs: i386, x64 MD5sum: 08e6ed77371413af3417b3071e91cbd2 NeedsCompilation: no Title: Brings SingleCellExperiment to the Tidyverse Description: tidySingleCellExperiment is an adapter that abstracts the 'SingleCellExperiment' container in the form of tibble and allows the data manipulation, plotting and nesting using 'tidyverse' biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, 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: RELEASE_3_14 git_last_commit: e143961 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tidySingleCellExperiment_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tidySingleCellExperiment_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tidySingleCellExperiment_1.4.0.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 dependencyCount: 83 Package: tidySummarizedExperiment Version: 1.4.1 Depends: R (>= 4.0.0), SummarizedExperiment Imports: tibble (>= 3.0.4), dplyr, magrittr, tidyr, ggplot2, rlang, purrr, lifecycle, methods, plotly, utils, S4Vectors, tidyselect, ellipsis, pillar, stringr, cli, fansi Suggests: BiocStyle, testthat, knitr, markdown License: GPL-3 MD5sum: 6238615733c837fe8f0378fbc1318d8a NeedsCompilation: no Title: Brings SummarizedExperiment to the Tidyverse Description: tidySummarizedExperiment is an adapter that abstracts the 'SummarizedExperiment' container in the form of tibble and allows the data manipulation, plotting and nesting using 'tidyverse' 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: RELEASE_3_14 git_last_commit: d6496b2 git_last_commit_date: 2021-10-27 Date/Publication: 2021-10-27 source.ver: src/contrib/tidySummarizedExperiment_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/tidySummarizedExperiment_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.1/tidySummarizedExperiment_1.4.1.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 suggestsMe: tidybulk dependencyCount: 82 Package: tigre Version: 1.48.0 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: 7043effb25195defb2e8f0ca8c161c8e 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: RELEASE_3_14 git_last_commit: bb256f7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tigre_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tigre_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tigre_1.48.0.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: 52 Package: TileDBArray Version: 1.4.0 Depends: DelayedArray (>= 0.15.16) Imports: methods, Rcpp, tiledb, S4Vectors LinkingTo: Rcpp Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat License: MIT + file LICENSE MD5sum: 70813a765c2f493028ae9fa2cbf4b809 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: RELEASE_3_14 git_last_commit: fa2ccec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TileDBArray_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TileDBArray_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TileDBArray_1.4.0.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: 23 Package: tilingArray Version: 1.72.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 MD5sum: b2de6efcef37b7e834a9fbe0e102e4ef 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: RELEASE_3_14 git_last_commit: b7c6272 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tilingArray_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tilingArray_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tilingArray_1.72.0.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, snapCGH dependencyCount: 86 Package: timecourse Version: 1.66.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 3d0382f25c0650b9814a6e53891a0b0d 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: RELEASE_3_14 git_last_commit: a54fe59 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/timecourse_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/timecourse_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/timecourse_1.66.0.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: 10 Package: timeOmics Version: 1.6.0 Depends: mixOmics, R (>= 4.0) Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr, ggrepel, propr, lmtest, plyr Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse, igraph, gplots License: GPL-3 MD5sum: 0ea9f516c23b3f1022b02ca3f101d199 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: RELEASE_3_14 git_last_commit: 6d94bd4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/timeOmics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/timeOmics_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/timeOmics_1.6.0.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 suggestsMe: netOmics dependencyCount: 72 Package: timescape Version: 1.18.0 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: i386, x64 MD5sum: f71b67194ac3de33911b42e74ea9159a 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: RELEASE_3_14 git_last_commit: 79de8cc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/timescape_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/timescape_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/timescape_1.18.0.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: 33 Package: TimeSeriesExperiment Version: 1.12.0 Depends: R (>= 4.1), S4Vectors (>= 0.19.23), SummarizedExperiment (>= 1.11.6) Imports: dynamicTreeCut, dplyr, edgeR, DESeq2, ggplot2 (>= 3.0.0), graphics, Hmisc, limma, methods, magrittr, proxy, stats, tibble, tidyr, vegan, viridis, utils Suggests: Biobase, BiocFileCache (>= 1.5.8), circlize, ComplexHeatmap, GO.db, grDevices, grid, knitr, org.Mm.eg.db, org.Hs.eg.db, MASS, RColorBrewer, rmarkdown, UpSetR, License: MIT + file LICENSE Archs: i386, x64 MD5sum: 3d80350179d3cf02a0b1aafc83c291ad NeedsCompilation: no Title: Analysis for short time-series data Description: TimeSeriesExperiment is a visualization and analysis toolbox for short time course data. The package includes dimensionality reduction, clustering, two-sample differential expression testing and gene ranking techniques. Additionally, it also provides methods for retrieving enriched pathways. biocViews: TimeCourse, Sequencing, RNASeq, Microbiome, GeneExpression, ImmunoOncology, Transcription, Normalization, DifferentialExpression, PrincipalComponent, Clustering, Visualization, Pathways Author: Lan Huong Nguyen [cre, aut] () Maintainer: Lan Huong Nguyen URL: https://github.com/nlhuong/TimeSeriesExperiment VignetteBuilder: knitr BugReports: https://github.com/nlhuong/TimeSeriesExperiment/issues git_url: https://git.bioconductor.org/packages/TimeSeriesExperiment git_branch: RELEASE_3_14 git_last_commit: 858bcd5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TimeSeriesExperiment_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TimeSeriesExperiment_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TimeSeriesExperiment_1.12.0.tgz vignettes: vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.html vignetteTitles: Gene expression time course data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.R dependencyCount: 130 Package: TimiRGeN Version: 1.4.0 Depends: R (>= 4.0), Mfuzz, MultiAssayExperiment Imports: biomaRt, clusterProfiler, dplyr (>= 0.8.4), FreqProf, gtools (>= 3.8.1), gplots, ggdendro, gghighlight, ggplot2, graphics, grDevices, igraph (>= 1.2.4.2), RCy3, readxl, reshape2, rWikiPathways, scales, stats, tidyr (>= 1.0.2), stringr (>= 1.4.0) Suggests: BiocManager, kableExtra, knitr (>= 1.27), org.Hs.eg.db, org.Mm.eg.db, testthat, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: cb48fdc7747f62a2b3090e7585631932 NeedsCompilation: no Title: Time sensitive microRNA-mRNA integration, analysis and network generation tool Description: TimiRGeN (Time Incorporated miR-mRNA Generation of Networks) is a novel R package which functionally analyses and integrates time course miRNA-mRNA differential expression data. This tool can generate small networks within R or export results into cytoscape or pathvisio for more detailed network construction and hypothesis generation. This tool is created for researchers that wish to dive deep into time series multi-omic datasets. TimiRGeN goes further than many other tools in terms of data reduction. Here, potentially hundreds-of-thousands of potential miRNA-mRNA interactions can be whittled down into a handful of high confidence miRNA-mRNA interactions affecting a signalling pathway, across a time course. biocViews: Clustering, miRNA, Network, Pathways, Software, TimeCourse, Visualization Author: Krutik Patel [aut, cre] Maintainer: Krutik Patel URL: https://github.com/Krutik6/TimiRGeN/ VignetteBuilder: knitr BugReports: https://github.com/Krutik6/TimiRGeN/issues git_url: https://git.bioconductor.org/packages/TimiRGeN git_branch: RELEASE_3_14 git_last_commit: ba148ba git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TimiRGeN_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TimiRGeN_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TimiRGeN_1.4.0.tgz vignettes: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.html vignetteTitles: TimiRGeN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.R dependencyCount: 188 Package: TIN Version: 1.26.0 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 MD5sum: 2faa45eabb7e89eb750355a703022cfb 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: RELEASE_3_14 git_last_commit: 4919bf7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TIN_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TIN_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TIN_1.26.0.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: 129 Package: TissueEnrich Version: 1.14.0 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 MD5sum: 3d6ffe030efe1582305224a7eab46854 NeedsCompilation: no Title: Tissue-specific gene enrichment analysis Description: The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments. biocViews: GeneSetEnrichment, GeneExpression, Sequencing Author: Ashish Jain [aut, cre], Geetu Tuteja [aut] Maintainer: Ashish Jain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TissueEnrich git_branch: RELEASE_3_14 git_last_commit: a844a65 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TissueEnrich_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TissueEnrich_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TissueEnrich_1.14.0.tgz vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html vignetteTitles: TissueEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R dependencyCount: 88 Package: TitanCNA Version: 1.32.0 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: 87b90e944e3ffd6a3e32bfb83d33150f 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: RELEASE_3_14 git_last_commit: 5fd6845 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TitanCNA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TitanCNA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TitanCNA_1.32.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R dependencyCount: 102 Package: tkWidgets Version: 1.72.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: 6291d5791d3aaedb0317c89a08616c36 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: RELEASE_3_14 git_last_commit: e63c5b2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tkWidgets_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tkWidgets_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tkWidgets_1.72.0.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: 1.2.0 Depends: R (>= 4.0) Imports: scales, stats, utils, ggplot2, data.table, purrr, dplyr, VariantAnnotation, GenomicRanges, MatrixGenerics Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: cec67fe98997cce9870ee00aece1c06f 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: RELEASE_3_14 git_last_commit: 628ddd6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tLOH_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tLOH_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tLOH_1.2.0.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: 113 Package: TMixClust Version: 1.16.0 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: 26a090b7fa0c5bc82db3278db9c38f83 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: RELEASE_3_14 git_last_commit: e525cfd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TMixClust_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TMixClust_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TMixClust_1.16.0.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: 29 Package: TNBC.CMS Version: 1.10.0 Depends: R (>= 3.6.0), e1071, quadprog, SummarizedExperiment Imports: GSVA (>= 1.26.0), pheatmap, grDevices, RColorBrewer, pracma, GGally, R.utils, forestplot, ggplot2, ggpubr, survival, grid, stats, methods Suggests: knitr License: GPL-3 MD5sum: a4e79ea96fe736f7923227b804633597 NeedsCompilation: no Title: TNBC.CMS: Prediction of TNBC Consensus Molecular Subtypes Description: This package implements a machine learning-based classifier for the assignment of consensus molecular subtypes to TNBC samples. It also provides functions to summarize genomic and clinical characteristics. biocViews: Classification, Clustering, GeneExpression, GenePrediction, SupportVectorMachine Author: Doyeong Yu, Jihyun Kim, In Hae Park, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TNBC.CMS git_branch: RELEASE_3_14 git_last_commit: dd4cd1f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TNBC.CMS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TNBC.CMS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TNBC.CMS_1.10.0.tgz vignettes: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.pdf vignetteTitles: TNBC.CMS.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.R dependencyCount: 175 Package: TnT Version: 1.16.0 Depends: R (>= 3.4), GenomicRanges Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite, data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat License: AGPL-3 Archs: i386, x64 MD5sum: a10fea0b39c6acb3739c4cb588b577a1 NeedsCompilation: no Title: Interactive Visualization for Genomic Features Description: A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data. biocViews: Infrastructure, Visualization Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking [aut] Maintainer: Jialin Ma URL: https://github.com/Marlin-Na/TnT VignetteBuilder: knitr BugReports: https://github.com/Marlin-Na/TnT/issues git_url: https://git.bioconductor.org/packages/TnT git_branch: RELEASE_3_14 git_last_commit: df1530f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TnT_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TnT_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TnT_1.16.0.tgz vignettes: vignettes/TnT/inst/doc/introduction.html vignetteTitles: Introduction to TnT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TnT/inst/doc/introduction.R dependencyCount: 35 Package: TOAST Version: 1.8.3 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: ed829d9a6ffa3f13b3c724161ca6e343 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: RELEASE_3_14 git_last_commit: 80a747f git_last_commit_date: 2022-03-24 Date/Publication: 2022-03-27 source.ver: src/contrib/TOAST_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/TOAST_1.8.3.zip mac.binary.ver: bin/macosx/contrib/4.1/TOAST_1.8.3.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 dependencyCount: 85 Package: tofsims Version: 1.22.0 Depends: R (>= 3.3.0), methods, utils, ProtGenerics Imports: Rcpp (>= 0.11.2), ALS, alsace, signal, KernSmooth, graphics, grDevices, stats LinkingTo: Rcpp, RcppArmadillo Suggests: EBImage, knitr, rmarkdown, testthat, tofsimsData, BiocParallel, RColorBrewer Enhances: parallel License: GPL-3 MD5sum: 00e8995856baea92b1a3bea9d5c126e0 NeedsCompilation: yes Title: Import, process and analysis of Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging data Description: This packages offers a pipeline for import, processing and analysis of ToF-SIMS 2D image data. Import of Iontof and Ulvac-Phi raw or preprocessed data is supported. For rawdata, mass calibration, peak picking and peak integration exist. General funcionality includes data binning, scaling, image subsetting and visualization. A range of multivariate tools common in the ToF-SIMS community are implemented (PCA, MCR, MAF, MNF). An interface to the bioconductor image processing package EBImage offers image segmentation functionality. biocViews: ImmunoOncology, Infrastructure, DataImport, MassSpectrometry, ImagingMassSpectrometry, Proteomics, Metabolomics Author: Lorenz Gerber, Viet Mai Hoang Maintainer: Lorenz Gerber URL: https://github.com/lorenzgerber/tofsims VignetteBuilder: knitr BugReports: https://github.com/lorenzgerber/tofsims/issues git_url: https://git.bioconductor.org/packages/tofsims git_branch: RELEASE_3_14 git_last_commit: 6cfbd33 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tofsims_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tofsims_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tofsims_1.22.0.tgz vignettes: vignettes/tofsims/inst/doc/workflow.html vignetteTitles: Workflow with the `tofsims` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tofsims/inst/doc/workflow.R dependencyCount: 17 Package: tomoda Version: 1.4.0 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: 3ef6379b2a3613a5804146258d2fc6b2 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, 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: RELEASE_3_14 git_last_commit: e61a25f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tomoda_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tomoda_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tomoda_1.4.0.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: 75 Package: topconfects Version: 1.10.0 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: 2eb77fa2bee0c8470faa0e20b8e32d84 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: RELEASE_3_14 git_last_commit: 4896a44 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/topconfects_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/topconfects_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/topconfects_1.10.0.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: weitrix dependencyCount: 40 Package: topdownr Version: 1.16.0 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 (>= 1.2.10), MSnbase (>= 2.3.10), ggplot2 (>= 2.2.1), mzR (>= 2.11.4) Suggests: topdownrdata (>= 0.2), knitr, rmarkdown, ranger, testthat, BiocStyle, xml2 License: GPL (>= 3) MD5sum: b469ae2463fef4272c078825f64d5e7f 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: RELEASE_3_14 git_last_commit: 449221d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/topdownr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/topdownr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/topdownr_1.16.0.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: 84 Package: topGO Version: 2.46.0 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: 126e8d4ffbfe2c0e10772ed48bce2c50 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: RELEASE_3_14 git_last_commit: 2bfa9df git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/topGO_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/topGO_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.1/topGO_2.46.0.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, cellTree, EGSEA, ideal, moanin, tRanslatome, ccTutorial, maEndToEnd importsMe: BioMM, cellity, FoldGO, GOSim, OmaDB, pcaExplorer, psygenet2r, transcriptogramer, ViSEAGO, ExpHunterSuite suggestsMe: FGNet, geva, IntramiRExploreR, miRNAtap, Ringo dependencyCount: 51 Package: ToxicoGx Version: 1.4.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, 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 Archs: i386, x64 MD5sum: 642be1371d67b21115abab38a789fb1a 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], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToxicoGx git_branch: RELEASE_3_14 git_last_commit: 539e793 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ToxicoGx_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ToxicoGx_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ToxicoGx_1.4.0.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: 126 Package: TPP Version: 3.22.1 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: d61b1a3571446c31cd11af71e4c8179e 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: RELEASE_3_14 git_last_commit: a779f13 git_last_commit_date: 2021-11-03 Date/Publication: 2021-11-04 source.ver: src/contrib/TPP_3.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/TPP_3.22.1.zip mac.binary.ver: bin/macosx/contrib/4.1/TPP_3.22.1.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: 93 Package: TPP2D Version: 1.10.0 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 License: GPL-3 MD5sum: 9b722d55a27b1b2e84313e66a72f1ec3 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: RELEASE_3_14 git_last_commit: 2456623 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TPP2D_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TPP2D_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TPP2D_1.10.0.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: 65 Package: tracktables Version: 1.28.0 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: a11e30715fd40bd625fed9cfb8294808 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: RELEASE_3_14 git_last_commit: c70e614 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tracktables_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tracktables_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tracktables_1.28.0.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: 41 Package: trackViewer Version: 1.30.0 Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid, Rcpp Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, plotrix, Rgraphviz, InteractionSet, graph, utils, rhdf5 LinkingTo: Rcpp Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr, htmltools, rmarkdown License: GPL (>= 2) MD5sum: 50316e46c6e645c1b4a6bfd58cf529c7 NeedsCompilation: yes 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: RELEASE_3_14 git_last_commit: 033811a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/trackViewer_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/trackViewer_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/trackViewer_1.30.0.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: 150 Package: tradeSeq Version: 1.8.0 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 License: MIT + file LICENSE MD5sum: 1ce3cba0a1a2f58ee9e3f62e869d7301 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: RELEASE_3_14 git_last_commit: 0a323ec git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tradeSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tradeSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tradeSeq_1.8.0.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 dependsOnMe: OSCA.advanced dependencyCount: 75 Package: TrajectoryGeometry Version: 1.2.0 Depends: R (>= 4.1) Imports: pracma, rgl, ggplot2, stats, methods Suggests: dplyr, knitr, RColorBrewer, rmarkdown License: MIT + file LICENSE MD5sum: e2c1a8e68899cfc4ab11d12c8c938904 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: RELEASE_3_14 git_last_commit: d76c075 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TrajectoryGeometry_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TrajectoryGeometry_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TrajectoryGeometry_1.2.0.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: 53 Package: TrajectoryUtils Version: 1.2.0 Depends: SingleCellExperiment Imports: methods, stats, Matrix, igraph, S4Vectors, SummarizedExperiment Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats, BiocParallel, testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: ec5b57db6fbee2f715c5cab4380cc851 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: RELEASE_3_14 git_last_commit: 485b0bf git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TrajectoryUtils_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TrajectoryUtils_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TrajectoryUtils_1.2.0.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, tradeSeq dependencyCount: 29 Package: transcriptogramer Version: 1.16.0 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: 901d1104835f70487249594949f8efbd 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: RELEASE_3_14 git_last_commit: 2bd1dc6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/transcriptogramer_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/transcriptogramer_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/transcriptogramer_1.16.0.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: 105 Package: transcriptR Version: 1.22.0 Depends: methods, R (>= 3.3) 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 Archs: i386, x64 MD5sum: f10773424f5bad04ff1567a99f4994b4 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: RELEASE_3_14 git_last_commit: 6605278 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/transcriptR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/transcriptR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/transcriptR_1.22.0.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: 149 Package: transformGamPoi Version: 1.0.0 Imports: glmGamPoi, DelayedArray, Matrix, MatrixGenerics, SummarizedExperiment, HDF5Array, methods, utils Suggests: testthat, TENxPBMCData, scran, knitr, rmarkdown License: GPL-3 MD5sum: 3a67b1af075b9b80a1fa2ed0fcb158d3 NeedsCompilation: no 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: RELEASE_3_14 git_last_commit: 38f5cfa git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/transformGamPoi_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/transformGamPoi_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/transformGamPoi_1.0.0.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: 36 Package: transite Version: 1.12.1 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), ggseqlogo (>= 0.1), 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: 731ce321d745a29a8db48d52bbdc2821 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: RELEASE_3_14 git_last_commit: 98034c4 git_last_commit_date: 2021-11-13 Date/Publication: 2021-11-14 source.ver: src/contrib/transite_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/transite_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.1/transite_1.12.1.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: 60 Package: tRanslatome Version: 1.32.0 Depends: R (>= 2.15.0), methods, limma, sigPathway, anota, DESeq2, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: 11249dc10007789ec5e057e6fce5a338 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: RELEASE_3_14 git_last_commit: ad95360 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tRanslatome_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tRanslatome_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tRanslatome_1.32.0.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: 118 Package: transomics2cytoscape Version: 1.4.0 Imports: RCy3, KEGGREST, dplyr Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 00904f3c08cd80570381964761ed2d99 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: Java 11, Cytoscape 3.8.2, Cy3D >= 1.1.3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transomics2cytoscape git_branch: RELEASE_3_14 git_last_commit: 0112663 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/transomics2cytoscape_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/transomics2cytoscape_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/transomics2cytoscape_1.4.0.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: 63 Package: TransView Version: 1.38.0 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, zlibbioc, gplots LinkingTo: Rhtslib (>= 1.15.3) Suggests: RUnit, pasillaBamSubset, BiocManager License: GPL-3 MD5sum: c0bace8480b1e373aee778cefc39cb40 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: RELEASE_3_14 git_last_commit: 4828b3b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TransView_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TransView_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TransView_1.38.0.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: 21 Package: TraRe Version: 1.2.0 Depends: R (>= 4.1) Imports: hash, ggplot2, stats, methods, igraph, utils, glmnet, vbsr, grDevices, gplots, gtools, pvclust, R.utils, dqrng, SummarizedExperiment, BiocParallel, matrixStats Suggests: knitr, rmarkdown, BiocGenerics, RUnit, BiocStyle License: MIT + file LICENSE MD5sum: e44d6365bf9f6cb1b483852a568d1a6b NeedsCompilation: no Title: Transcriptional Rewiring Description: TraRe (Transcriptional Rewiring) is an R package which contains the necessary tools to carry out several functions. Identification of module-based gene regulatory networks (GRN); score-based classification of these modules via a rewiring test; visualization of rewired modules to analyze condition-based GRN deregulation and drop out genes recovering via cliques methodology. For each tool, an html report can be generated containing useful information about the generated GRN and statistical data about the performed tests. These tools have been developed considering sequenced data (RNA-Seq). biocViews: GeneRegulation, RNASeq, GraphAndNetwork, Bayesian, GeneTarget, Classification Author: Jesus De La Fuente Cedeño [aut, cre, cph] (), Mikel Hernaez [aut, cph, ths] (), Charles Blatti [aut, cph] () Maintainer: Jesus De La Fuente Cedeño URL: https://github.com/ubioinformat/TraRe/tree/master VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TraRe git_branch: RELEASE_3_14 git_last_commit: 4b2175f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TraRe_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TraRe_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TraRe_1.2.0.tgz vignettes: vignettes/TraRe/inst/doc/TraRe.html vignetteTitles: TraRe: Identification of conditions dependant Gene Regulatory Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TraRe/inst/doc/TraRe.R dependencyCount: 84 Package: traseR Version: 1.24.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL Archs: i386, x64 MD5sum: 83e280371d4c1437cee91ff9f2b4e307 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: RELEASE_3_14 git_last_commit: 5cb7974 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/traseR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/traseR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.1/traseR_1.24.0.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: 46 Package: Travel Version: 1.2.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, inline, parallel License: GPL-3 MD5sum: f67a36b5df7db2da6df4ad58c32fcbfa NeedsCompilation: yes Title: An utility to create an ALTREP object with a virtual pointer Description: Creates a virtual pointer for R's ALTREP object which does not have the data allocates in memory. The pointer is made by the file mapping of a virtual file so it behaves exactly the same as a regular pointer. All the requests to access the pointer will be sent to the underlying file system and eventually handled by a customized data-reading function. The main purpose of the package is to reduce the memory consumption when using R's vector to represent a large data. The use cases of the package include on-disk data representation, compressed vector(e.g. RLE) and etc. biocViews: Infrastructure Author: Jiefei Wang [aut, cre] Maintainer: Jiefei Wang URL: https://github.com/Jiefei-Wang/Travel SystemRequirements: C++11 Windows: Dokan Linux&Mac: fuse, pkg-config VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/Travel/issues git_url: https://git.bioconductor.org/packages/Travel git_branch: RELEASE_3_14 git_last_commit: 90ae232 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Travel_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Travel_1.2.0.zip vignettes: vignettes/Travel/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/Travel/inst/doc/vignette.R dependencyCount: 3 Package: traviz Version: 1.0.0 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: 2e26680ea086aeb17bc6c7d2a984ae1c 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: RELEASE_3_14 git_last_commit: 91d62fb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/traviz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/traviz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/traviz_1.0.0.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: 76 Package: TreeAndLeaf Version: 1.6.1 Depends: R(>= 4.0) Imports: RedeR(>= 1.40.4), igraph, ape Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr, geneplast, ggtree, ggplot2, dplyr, dendextend, RColorBrewer License: Artistic-2.0 Archs: i386, x64 MD5sum: 4c099f333822b9b04f2948d362560b5b 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: RELEASE_3_14 git_last_commit: 171d519 git_last_commit_date: 2021-10-27 Date/Publication: 2021-10-27 source.ver: src/contrib/TreeAndLeaf_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/TreeAndLeaf_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/TreeAndLeaf_1.6.1.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 dependencyCount: 17 Package: treeio Version: 1.18.1 Depends: R (>= 3.6.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, tibble, tidytree (>= 0.3.0), utils Suggests: Biostrings, ggplot2, ggtree, igraph, knitr, rmarkdown, phangorn, prettydoc, testthat, tidyr, vroom, xml2, yaml License: Artistic-2.0 MD5sum: 91c82ed0c2a4b8bdbe16a293e0f81260 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://yulab-smu.top/treedata-book/ (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: RELEASE_3_14 git_last_commit: a06b6b3 git_last_commit_date: 2021-11-12 Date/Publication: 2021-11-14 source.ver: src/contrib/treeio_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/treeio_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.1/treeio_1.18.1.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 suggestsMe: enrichplot, ggtreeExtra, rfaRm, idiogramFISH, nosoi dependencyCount: 36 Package: treekoR Version: 1.2.1 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: 075b212ee8d8f0d59989a205236c5bba 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: RELEASE_3_14 git_last_commit: 71e2a0a git_last_commit_date: 2022-02-10 Date/Publication: 2022-02-13 source.ver: src/contrib/treekoR_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/treekoR_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/treekoR_1.2.1.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: 232 Package: TreeSummarizedExperiment Version: 2.2.0 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) Archs: x64 MD5sum: 2d39c20eb5e2cf50173bac3b737b05b3 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: RELEASE_3_14 git_last_commit: 3fc3f90 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TreeSummarizedExperiment_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TreeSummarizedExperiment_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TreeSummarizedExperiment_2.2.0.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, mia, miaViz, curatedMetagenomicData, microbiomeDataSets suggestsMe: philr dependencyCount: 63 Package: trena Version: 1.16.0 Depends: R (>= 3.5.0), utils, glmnet (>= 2.0.3), MotifDb (>= 1.19.17) Imports: RSQLite, RMySQL, lassopv, randomForest, vbsr, xgboost, RPostgreSQL, methods, DBI, BSgenome, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, SNPlocs.Hsapiens.dbSNP150.GRCh38, org.Hs.eg.db, Biostrings, GenomicRanges, biomaRt, AnnotationDbi, WGCNA Suggests: RUnit, plyr, knitr, BiocGenerics, rmarkdown, formatR, markdown, BiocParallel, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Athaliana.TAIR.TAIR9 License: GPL-3 MD5sum: 4d6e73f9d66e50cc2fb0d52f6a51410e NeedsCompilation: no Title: Fit transcriptional regulatory networks using gene expression, priors, machine learning Description: Methods for reconstructing transcriptional regulatory networks, especially in species for which genome-wide TF binding site information is available. biocViews: Transcription, GeneRegulation, NetworkInference, FeatureExtraction, Regression, SystemsBiology, GeneExpression Author: Seth Ament , Paul Shannon , Matthew Richards Maintainer: Paul Shannon URL: https://pricelab.github.io/trena/ VignetteBuilder: knitr, rmarkdown, formatR, markdown BugReports: https://github.com/PriceLab/trena/issues git_url: https://git.bioconductor.org/packages/trena git_branch: RELEASE_3_14 git_last_commit: f742819 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/trena_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/trena_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/trena_1.16.0.tgz vignettes: vignettes/trena/inst/doc/caseStudyFour.html, vignettes/trena/inst/doc/caseStudyOne.html, vignettes/trena/inst/doc/caseStudyThree.html, vignettes/trena/inst/doc/caseStudyTwo.html, vignettes/trena/inst/doc/overview.html, vignettes/trena/inst/doc/simple.html, vignettes/trena/inst/doc/tiny.html, vignettes/trena/inst/doc/TReNA_Vignette.html vignetteTitles: "Case Study Four: a novel regulator of GATA2 in erythropoieis?", "Case Study One: reproduce known regulation of NFE2 by GATA1 in bulk RNA-seq", "Case Study Three: reproduce known regulation of NFE2 by GATA1 in bulk RNA-seq", "Case Study Two reproduces known regulation of NFE2 by GATA1 in erytrhop RNA-seq", "TRENA: computational prediction of gene regulation", "Explore output controls", "Tiny Vignette Example", A Brief Introduction to TReNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trena/inst/doc/overview.R, vignettes/trena/inst/doc/simple.R, vignettes/trena/inst/doc/tiny.R, vignettes/trena/inst/doc/TReNA_Vignette.R dependencyCount: 160 Package: Trendy Version: 1.16.0 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: 4ed6dea781d4e41992ed91991ada7fa7 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: RELEASE_3_14 git_last_commit: e897047 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Trendy_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Trendy_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Trendy_1.16.0.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: 81 Package: TRESS Version: 1.0.0 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: da1cc6546ed84a3a04cdc77f0182ade8 NeedsCompilation: no Title: Toolbox for mRNA epigenetics sequencing analysis Description: This package is devoted to analyzing MeRIP-seq data. Current functionality is for detection of transcriptome-wide m6A methylation regions. The method is based on hierarchical negative binomial models. biocViews: Epigenetics, RNASeq, PeakDetection Author: Zhenxing Guo [aut, cre], Hao Wu [ctb] Maintainer: Zhenxing Guo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TRESS git_branch: RELEASE_3_14 git_last_commit: 8a50b92 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TRESS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TRESS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TRESS_1.0.0.tgz vignettes: vignettes/TRESS/inst/doc/TRESS.html vignetteTitles: The TRESS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TRESS/inst/doc/TRESS.R dependencyCount: 96 Package: tricycle Version: 1.2.1 Depends: R (>= 4.0), SingleCellExperiment Imports: methods, circular, ggplot2, 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: 48d515aa1b90471fe2b4c3249f8c7101 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: RELEASE_3_14 git_last_commit: 7855270 git_last_commit_date: 2022-02-08 Date/Publication: 2022-02-10 source.ver: src/contrib/tricycle_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/tricycle_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/tricycle_1.2.1.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: 111 Package: trigger Version: 1.40.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 MD5sum: 38aad8e8168fcc15b4eb9ef1557b8732 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: RELEASE_3_14 git_last_commit: 5a73204 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/trigger_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/trigger_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/trigger_1.40.0.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: 96 Package: trio Version: 3.32.0 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: a71c592075ca1708f7f3196d92dc80a9 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: RELEASE_3_14 git_last_commit: aade2b9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/trio_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/trio_3.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/trio_3.32.0.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: 1.34.0 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: 94013e17d18e885762e733e625ddb0cb 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: RELEASE_3_14 git_last_commit: 85bd1f7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/triplex_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/triplex_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/triplex_1.34.0.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: 20 Package: tripr Version: 1.0.0 Depends: shiny (>= 1.6.0), shinyBS Imports: shinyjs, shinyFiles, plyr, data.table, DT, stringr, stringdist, plot3D, gridExtra, RColorBrewer, plotly, dplyr, pryr, 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 Enhances: parallel License: MIT + file LICENSE MD5sum: 5f1a581c0be90b78ff6d5f8aad534eac 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 [aut], Andreas Agathangelidis [aut], Raphael Sandaltzopoulos [aut], Pericles A. Mitkas [aut], Kostas Stamatopoulos [aut], Anastasia Chatzidimitriou [aut], Fotis E. Psomopoulos [aut], Iason Ofeidis [cre] Maintainer: Iason Ofeidis 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: RELEASE_3_14 git_last_commit: ba301e8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tripr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tripr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tripr_1.0.0.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: 117 Package: tRNA Version: 1.12.0 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: 91aee9028479d8eb4950936dd55ed287 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: RELEASE_3_14 git_last_commit: 4c8bf97 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tRNA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tRNA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tRNA_1.12.0.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: 55 Package: tRNAdbImport Version: 1.12.0 Depends: R (>= 3.5), GenomicRanges, Modstrings, Structstrings, tRNA Imports: Biostrings, BiocGenerics, stringr, xml2, S4Vectors, methods, httr, IRanges, utils Suggests: knitr, rmarkdown, testthat, httptest, BiocStyle, rtracklayer License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 30f0171397f840263f2781347d37154f 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: RELEASE_3_14 git_last_commit: dafb0a6 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/tRNAdbImport_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tRNAdbImport_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tRNAdbImport_1.12.0.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: 64 Package: tRNAscanImport Version: 1.14.0 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: 0abda612f29f57aeb69fee03e8d738e2 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: RELEASE_3_14 git_last_commit: 48d7a43 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tRNAscanImport_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tRNAscanImport_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tRNAscanImport_1.14.0.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: 79 Package: TRONCO Version: 2.26.0 Depends: R (>= 4.1.0), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, cgdsr, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways License: GPL-3 Archs: i386, x64 MD5sum: d948c65b23d84734dd3a7c6542a5742f 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, cre], Luca De Sano [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: RELEASE_3_14 git_last_commit: 1a274d4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TRONCO_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TRONCO_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TRONCO_2.26.0.tgz vignettes: vignettes/TRONCO/inst/doc/vignette.pdf vignetteTitles: An R Package for TRanslational ONCOlogy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TRONCO/inst/doc/vignette.R dependencyCount: 45 Package: TSCAN Version: 1.32.0 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: 8f80a0b77ba2140903ec3478e5bf7152 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: RELEASE_3_14 git_last_commit: 7108824 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TSCAN_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TSCAN_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TSCAN_1.32.0.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.advanced, OSCA.multisample importsMe: ctgGEM, FEAST, DIscBIO suggestsMe: condiments dependencyCount: 86 Package: tscR Version: 1.6.1 Depends: R (>= 4.1), dplyr Imports: gridExtra, methods, dtw, class, kmlShape, graphics, cluster, RColorBrewer, grDevices, knitr, rmarkdown, prettydoc, grid, ggplot2, latex2exp, stats, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors Suggests: testthat License: Artistic-2.0 MD5sum: ef8b55f0153db99dcad3879ff93ef528 NeedsCompilation: yes Title: A time series clustering package combining slope and Frechet distances Description: Clustering for time series data using slope distance and/or shape distance. biocViews: GeneExpression, Clustering, DNAMethylation, Microarray Author: Fernando Pérez-Sanz [aut, cre], Miriam Riquelme-Pérez [aut] Maintainer: Fernando Pérez-Sanz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tscR git_branch: RELEASE_3_14 git_last_commit: f2624fa git_last_commit_date: 2022-01-27 Date/Publication: 2022-01-30 source.ver: src/contrib/tscR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/tscR_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.1/tscR_1.6.1.tgz vignettes: vignettes/tscR/inst/doc/tscR.html vignetteTitles: tscR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tscR/inst/doc/tscR.R dependencyCount: 92 Package: tspair Version: 1.52.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 MD5sum: 3d8e8cd4dbfb1100d01a656cbba6147d NeedsCompilation: yes Title: Top Scoring Pairs for Microarray Classification Description: These functions calculate the pair of genes that show the maximum difference in ranking between two user specified groups. This "top scoring pair" maximizes the average of sensitivity and specificity over all rank based classifiers using a pair of genes in the data set. The advantage of classifying samples based on only the relative rank of a pair of genes is (a) the classifiers are much simpler and often more interpretable than more complicated classification schemes and (b) if arrays can be classified using only a pair of genes, PCR based tests could be used for classification of samples. See the references for the tspcalc() function for references regarding TSP classifiers. biocViews: Microarray Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/tspair git_branch: RELEASE_3_14 git_last_commit: 1ca02a8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tspair_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tspair_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tspair_1.52.0.tgz vignettes: vignettes/tspair/inst/doc/tsp.pdf vignetteTitles: tspTutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tspair/inst/doc/tsp.R dependencyCount: 6 Package: TSRchitect Version: 1.20.0 Depends: R (>= 3.5) Imports: AnnotationHub, BiocGenerics, BiocParallel, dplyr, GenomicAlignments, GenomeInfoDb, GenomicRanges, gtools, IRanges, methods, readxl, Rsamtools (>= 1.14.3), rtracklayer, S4Vectors, SummarizedExperiment, tools, utils Suggests: ENCODExplorer, ggplot2, knitr, rmarkdown License: GPL-3 MD5sum: 31dfab5565fe265589711de025421051 NeedsCompilation: no Title: Promoter identification from large-scale TSS profiling data Description: In recent years, large-scale transcriptional sequence data has yielded considerable insights into the nature of gene expression and regulation in eukaryotes. Techniques that identify the 5' end of mRNAs, most notably CAGE, have mapped the promoter landscape across a number of model organisms. Due to the variability of TSS distributions and the transcriptional noise present in datasets, precisely identifying the active promoter(s) for genes from these datasets is not straightforward. TSRchitect allows the user to efficiently identify the putative promoter (the transcription start region, or TSR) from a variety of TSS profiling data types, including both single-end (e.g. CAGE) as well as paired-end (RAMPAGE, PEAT, STRIPE-seq). In addition, (new with version 1.3.0) TSRchitect provides the ability to import aligned EST and cDNA data. Along with the coordiantes of identified TSRs, TSRchitect also calculates the width, abundance and two forms of the Shape Index, and handles biological replicates for expression profiling. Finally, TSRchitect imports annotation files, allowing the user to associate identified promoters with genes and other genomic features. Three detailed examples of TSRchitect's utility are provided in the User's Guide, included with this package. biocViews: Clustering, FunctionalGenomics, GeneExpression, GeneRegulation, GenomeAnnotation, Sequencing, Transcription Author: R. Taylor Raborn [aut, cre, cph] Volker P. Brendel [aut, cph] Krishnakumar Sridharan [ctb] Maintainer: R. Taylor Raborn URL: https://github.com/brendelgroup/tsrchitect VignetteBuilder: knitr BugReports: https://github.com/brendelgroup/tsrchitect/issues git_url: https://git.bioconductor.org/packages/TSRchitect git_branch: RELEASE_3_14 git_last_commit: 3f5e213 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TSRchitect_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TSRchitect_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TSRchitect_1.20.0.tgz vignettes: vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.pdf, vignettes/TSRchitect/inst/doc/TSRchitect.html, vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.html vignetteTitles: TSRchitect User's Guide, TSRchitect vignette, TSRchitect User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TSRchitect/inst/doc/TSRchitect.R dependencyCount: 118 Package: ttgsea Version: 1.2.1 Depends: keras Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table, purrr, DiagrammeR, stats Suggests: fgsea, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: 3a873b0f36e0e60d0f57e0eb78951258 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: RELEASE_3_14 git_last_commit: bdfd91e git_last_commit_date: 2021-12-14 Date/Publication: 2021-12-16 source.ver: src/contrib/ttgsea_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/ttgsea_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.1/ttgsea_1.2.1.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 dependencyCount: 124 Package: TTMap Version: 1.16.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: d8b7f9abff3b90ceb8e149c226786052 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: RELEASE_3_14 git_last_commit: c42a884 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TTMap_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TTMap_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TTMap_1.16.0.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: 44 Package: TurboNorm Version: 1.42.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata License: LGPL Archs: i386, x64 MD5sum: 4b3b6fb01bdaef9851dc5e4bd59ffc0a 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: RELEASE_3_14 git_last_commit: 8ebd6b0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TurboNorm_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TurboNorm_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TurboNorm_1.42.0.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: 17 Package: TVTB Version: 1.20.0 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 MD5sum: d35c73119dd010cf50ff1b4588ba10d8 NeedsCompilation: no Title: TVTB: The VCF Tool Box Description: The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE). biocViews: Software, Genetics, GeneticVariability, GenomicVariation, DataRepresentation, GUI, Genetics, DNASeq, WholeGenome, Visualization, MultipleComparison, DataImport, VariantAnnotation, Sequencing, Coverage, Alignment, SequenceMatching Author: Kevin Rue-Albrecht [aut, cre] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues git_url: https://git.bioconductor.org/packages/TVTB git_branch: RELEASE_3_14 git_last_commit: 5332bf5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TVTB_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/TVTB_1.20.0.tgz vignettes: vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/tSVE.html, vignettes/TVTB/inst/doc/VcfFilterRules.html vignetteTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF filter rules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TVTB/inst/doc/Introduction.R, vignettes/TVTB/inst/doc/tSVE.R, vignettes/TVTB/inst/doc/VcfFilterRules.R dependencyCount: 151 Package: tweeDEseq Version: 1.40.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: 6bb69bba2f9fcefd417e5fbf324c2aaf 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 family of distributions. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, Sequencing, RNASeq Author: Juan R Gonzalez and Mikel Esnaola (with contributions from Robert Castelo ) Maintainer: Juan R Gonzalez URL: http://www.creal.cat/jrgonzalez/software.htm git_url: https://git.bioconductor.org/packages/tweeDEseq git_branch: RELEASE_3_14 git_last_commit: ef3a739 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tweeDEseq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tweeDEseq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tweeDEseq_1.40.0.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: 22 Package: twilight Version: 1.70.0 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: dc461c3a5bfd32fcb3b96fce9c5859c1 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: RELEASE_3_14 git_last_commit: 2b82a43 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/twilight_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/twilight_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.1/twilight_1.70.0.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: 1.18.0 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: e9a191b92ba7663894cd54d8997bc9d6 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: RELEASE_3_14 git_last_commit: 1f50992 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/twoddpcr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/twoddpcr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/twoddpcr_1.18.0.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: 1.0.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, GenomicFeatures, 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: 348d69bc577a52cbc876fe07d7c4a970 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: RELEASE_3_14 git_last_commit: 21b6a51 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/txcutr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/txcutr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/txcutr_1.0.0.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: 96 Package: tximeta Version: 1.12.4 Imports: SummarizedExperiment, tximport, jsonlite, S4Vectors, IRanges, GenomicRanges, AnnotationDbi, GenomicFeatures, ensembldb, BiocFileCache, AnnotationHub, Biostrings, tibble, GenomeInfoDb, tools, utils, methods, Matrix Suggests: knitr, rmarkdown, testthat, tximportData, org.Dm.eg.db, DESeq2, edgeR, limma, devtools License: GPL-2 Archs: i386, x64 MD5sum: f0561894dbad7cdd72237b289e32dbdf NeedsCompilation: no Title: Transcript Quantification Import with Automatic Metadata Description: Transcript quantification import from Salmon and alevin 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/mikelove/tximeta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximeta git_branch: RELEASE_3_14 git_last_commit: 78d1a79 git_last_commit_date: 2021-12-20 Date/Publication: 2021-12-21 source.ver: src/contrib/tximeta_1.12.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/tximeta_1.12.4.zip mac.binary.ver: bin/macosx/contrib/4.1/tximeta_1.12.4.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: 122 Package: tximport Version: 1.22.0 Imports: utils, stats, methods Suggests: knitr, rmarkdown, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), limma, edgeR, DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats, Matrix, fishpond License: GPL (>=2) Archs: i386, x64 MD5sum: 570127c29d79d3d2b3e07910f72158f8 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/mikelove/tximport VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximport git_branch: RELEASE_3_14 git_last_commit: 335213b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/tximport_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/tximport_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.1/tximport_1.22.0.tgz vignettes: vignettes/tximport/inst/doc/tximport.html vignetteTitles: Importing transcript abundance datasets with tximport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R importsMe: alevinQC, BgeeCall, EventPointer, IsoformSwitchAnalyzeR, singleCellTK, tximeta suggestsMe: BANDITS, DESeq2, HumanTranscriptomeCompendium, SummarizedBenchmark, variancePartition dependencyCount: 3 Package: TypeInfo Version: 1.60.0 Depends: methods Suggests: Biobase License: BSD Archs: i386, x64 MD5sum: 77201e33c7a343af0e964679c0038189 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: RELEASE_3_14 git_last_commit: 5147eb5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/TypeInfo_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/TypeInfo_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/TypeInfo_1.60.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R dependencyCount: 1 Package: Ularcirc Version: 1.12.0 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, S4Vectors, shiny, shinydashboard, shinyFiles, shinyjs, Sushi, yaml Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr, org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: 1ba593da6d107a6bac661a94296e80ab 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: RELEASE_3_14 git_last_commit: 6609d72 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Ularcirc_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Ularcirc_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Ularcirc_1.12.0.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: 146 Package: UMI4Cats Version: 1.4.0 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: 7fa82646d4b58953f6848eff6098c990 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: RELEASE_3_14 git_last_commit: b355dcd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/UMI4Cats_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/UMI4Cats_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/UMI4Cats_1.4.0.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: 154 Package: uncoverappLib Version: 1.4.0 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, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi, BSgenome.Hsapiens.UCSC.hg19, processx, Rsamtools, GenomicRanges Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: fa75abcbee0f5a868d8a55ccf9423b89 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. 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: RELEASE_3_14 git_last_commit: d0b98b7 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/uncoverappLib_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/uncoverappLib_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/uncoverappLib_1.4.0.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: 181 Package: UNDO Version: 1.36.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: 512a571412dad10f3ee6432f3ac4af3f 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: RELEASE_3_14 git_last_commit: 3f2d112 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/UNDO_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/UNDO_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.1/UNDO_1.36.0.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: unifiedWMWqPCR Version: 1.30.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: 38aeb8026ffd92f8c902be5d774a554d NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR git_branch: RELEASE_3_14 git_last_commit: 396d01a git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/unifiedWMWqPCR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/unifiedWMWqPCR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/unifiedWMWqPCR_1.30.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: Using unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R dependencyCount: 21 Package: UniProt.ws Version: 2.34.0 Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi, BiocFileCache, rappdirs Suggests: RUnit, BiocStyle, knitr License: Artistic License 2.0 MD5sum: 3aef68eb5e2afac0809638739b484ef2 NeedsCompilation: no Title: R Interface to UniProt Web Services Description: A collection of functions for retrieving, processing and repackaging the UniProt web services. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson [aut], Csaba Ortutay [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: RELEASE_3_14 git_last_commit: d2d3855 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/UniProt.ws_2.34.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.1/UniProt.ws_2.34.0.tgz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.pdf 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 suggestsMe: cleaver, qPLEXanalyzer dependencyCount: 62 Package: Uniquorn Version: 2.14.0 Depends: R (>= 3.5) Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation Suggests: testthat, knitr, rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 08b3aa896f07743fe80d0a8affb15d02 NeedsCompilation: no Title: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint Description: This packages 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). The implemented method is optimized for the Next-generation whole exome and whole genome DNA-sequencing technology. RNA-seq data is very likely to work as well but hasn't been rigiously tested yet. Panel-seq will require manual adjustment of thresholds biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq Author: Raik Otto Maintainer: 'Raik Otto' VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Uniquorn git_branch: RELEASE_3_14 git_last_commit: 8cd3d60 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Uniquorn_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Uniquorn_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Uniquorn_2.14.0.tgz vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Uniquorn vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Uniquorn/inst/doc/Uniquorn.R dependencyCount: 106 Package: universalmotif Version: 1.12.4 Depends: R (>= 3.5.0) Imports: methods, stats, utils, MASS, ggplot2, yaml, IRanges, Rcpp, Biostrings, BiocGenerics, S4Vectors, rlang, grid 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: 3baabf63fb0d1119fd6cc6c97cb42b42 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: RELEASE_3_14 git_last_commit: 5135090 git_last_commit_date: 2022-02-24 Date/Publication: 2022-02-27 source.ver: src/contrib/universalmotif_1.12.4.tar.gz win.binary.ver: bin/windows/contrib/4.1/universalmotif_1.12.4.zip mac.binary.ver: bin/macosx/contrib/4.1/universalmotif_1.12.4.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: circRNAprofiler, memes suggestsMe: spiky dependencyCount: 53 Package: uSORT Version: 1.20.0 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: 52219b1541e964482fee57d151b76c11 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: RELEASE_3_14 git_last_commit: 8361250 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/uSORT_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/uSORT_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/uSORT_1.20.0.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: 103 Package: VAExprs Version: 1.0.1 Depends: keras, mclust Imports: SingleCellExperiment, SummarizedExperiment, tensorflow, scater, gradDescent, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats Suggests: SC3, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: cc773583e9e4489c377938a7be66dad7 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: RELEASE_3_14 git_last_commit: 9587441 git_last_commit_date: 2021-12-14 Date/Publication: 2021-12-16 source.ver: src/contrib/VAExprs_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/VAExprs_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.1/VAExprs_1.0.1.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 dependencyCount: 187 Package: VanillaICE Version: 1.56.3 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: 78057e06fcbb80b8d3196310c53b76df 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: person("Robert", "Scharpf", email="rscharpf@jhu.edu", role=c("aut", "cre")) Maintainer: R.B. Scharpf git_url: https://git.bioconductor.org/packages/VanillaICE git_branch: RELEASE_3_14 git_last_commit: 65bbda3 git_last_commit_date: 2021-12-05 Date/Publication: 2021-12-07 source.ver: src/contrib/VanillaICE_1.56.3.tar.gz win.binary.ver: bin/windows/contrib/4.1/VanillaICE_1.56.3.zip mac.binary.ver: bin/macosx/contrib/4.1/VanillaICE_1.56.3.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: 83 Package: VarCon Version: 1.2.0 Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1) Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles, ggplot2 Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: ef5373c317e26b3c95b389500677b5b4 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: RELEASE_3_14 git_last_commit: 1853e9f git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VarCon_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VarCon_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VarCon_1.2.0.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: 96 Package: variancePartition Version: 1.24.1 Depends: R (>= 4.0.0), ggplot2, limma, BiocParallel Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, Matrix, iterators, foreach, doParallel, gplots, RhpcBLASctl, progress, reshape2, aod, scales, Rdpack, rlang, lme4 (>= 1.1-10), grDevices, graphics, Biobase, methods, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, BiocGenerics, r2glmm, readr License: GPL-2 Archs: i386, x64 MD5sum: 922a5518ac302be34b1e416772190db5 NeedsCompilation: no Title: Quantify and interpret divers 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: RELEASE_3_14 git_last_commit: c2877be git_last_commit_date: 2022-04-11 Date/Publication: 2022-04-12 source.ver: src/contrib/variancePartition_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/variancePartition_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.1/variancePartition_1.24.1.tgz vignettes: vignettes/variancePartition/inst/doc/variancePartition.pdf, vignettes/variancePartition/inst/doc/additional_visualization.html, vignettes/variancePartition/inst/doc/dream.html, vignettes/variancePartition/inst/doc/FAQ.html, vignettes/variancePartition/inst/doc/theory_practice_random_effects.html vignetteTitles: 1) Tutorial on using variancePartition, 2) Additional visualizations, 4) dream: differential expression testing with repeated measures designs, 5) Frequently asked questions, 3) Theory and practice of random effects and REML 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/FAQ.R, vignettes/variancePartition/inst/doc/theory_practice_random_effects.R, vignettes/variancePartition/inst/doc/variancePartition.R importsMe: muscat dependencyCount: 102 Package: VariantAnnotation Version: 1.40.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), MatrixGenerics, GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5), Rsamtools (>= 1.99.0) 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 Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 MD5sum: 6f150cac9c1797e5b11500c9830bd570 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: Bioconductor Package Maintainer [aut, cre], Valerie Oberchain [aut], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb] Maintainer: Bioconductor Package Maintainer SystemRequirements: GNU make Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: RELEASE_3_14 git_last_commit: 50ead7c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VariantAnnotation_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VariantAnnotation_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VariantAnnotation_1.40.0.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: 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/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: CNVrd2, deepSNV, ensemblVEP, genotypeeval, 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 importsMe: AllelicImbalance, APAlyzer, appreci8R, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, CNVfilteR, CopyNumberPlots, customProDB, DAMEfinder, decompTumor2Sig, DominoEffect, epialleleR, fcScan, GA4GHclient, genbankr, GenomicFiles, GenVisR, ggbio, gmapR, gwascat, gwasurvivr, icetea, igvR, karyoploteR, ldblock, MADSEQ, MMAPPR2, motifbreakR, MungeSumstats, musicatk, MutationalPatterns, ProteoDisco, scoreInvHap, SigsPack, SNPhood, svaRetro, TitanCNA, tLOH, TVTB, Uniquorn, VCFArray, YAPSA, COSMIC.67, SNPassoc suggestsMe: AnnotationHub, BiocParallel, cellbaseR, CNVgears, CrispRVariants, GenomicDataCommons, GenomicRanges, GenomicScores, GWASTools, omicsPrint, podkat, RVS, SeqArray, splatter, supersigs, systemPipeR, trackViewer, trio, vtpnet, AshkenazimSonChr21, GeuvadisTranscriptExpr, deconstructSigs, ldsep, polyRAD, updog dependencyCount: 97 Package: VariantExperiment Version: 1.8.0 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, SeqVarTools, DelayedArray, Biostrings, IRanges Suggests: testthat, knitr, rmarkdown, markdown License: GPL-3 MD5sum: 527a22b0d2e9eb37ae7d0e4918f5b12a 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: RELEASE_3_14 git_last_commit: 22c0089 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VariantExperiment_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VariantExperiment_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VariantExperiment_1.8.0.tgz 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: 80 Package: VariantFiltering Version: 1.30.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.25.1), VariantAnnotation (>= 1.13.29) Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>= 1.19.13), SummarizedExperiment, GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, GenomicScores (>= 1.0.0), Gviz, shiny, shinythemes, shinyjs, DT, shinyTree LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit, BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.1000genomes.hs37d5, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: i386, x64 MD5sum: d83409a07079fafbe076cc5f5ab8eb1e 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: RELEASE_3_14 git_last_commit: a95fdc0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VariantFiltering_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VariantFiltering_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VariantFiltering_1.30.0.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: 170 Package: VariantTools Version: 1.36.0 Depends: 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) License: Artistic-2.0 MD5sum: 48c85fbfebb35a94117f1dcd3e1d969f 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: RELEASE_3_14 git_last_commit: 2756ffb git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VariantTools_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VariantTools_1.35.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VariantTools_1.36.0.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, MMAPPR2 suggestsMe: VariantToolsData dependencyCount: 98 Package: VaSP Version: 1.6.0 Depends: R (>= 4.0), ballgown Imports: IRanges, GenomicRanges, S4Vectors, Sushi, parallel, matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats, graphics, methods Suggests: knitr, rmarkdown License: GPL (>= 2.0) MD5sum: 32360b7af859bcaf96dc90b96b3b76bd 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. 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: RELEASE_3_14 git_last_commit: c68dc71 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VaSP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VaSP_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VaSP_1.6.0.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: 111 Package: vbmp Version: 1.62.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) Archs: i386, x64 MD5sum: 5e1c38504956a8d824eb45ca5029ffdf 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: RELEASE_3_14 git_last_commit: 175aa5e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/vbmp_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vbmp_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vbmp_1.62.0.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: 1.10.0 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: b3c9cdde27f9480b981d856c54859e36 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: RELEASE_3_14 git_last_commit: 4d7caea git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VCFArray_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VCFArray_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VCFArray_1.10.0.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: 99 Package: VegaMC Version: 3.32.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods License: GPL-2 Archs: i386, x64 MD5sum: d7c44adbfaee606e2f94b1f88597a00f 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: RELEASE_3_14 git_last_commit: 843928d git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VegaMC_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VegaMC_3.32.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VegaMC_3.32.0.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: 71 Package: velociraptor Version: 1.4.0 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: 04414fdd42363c724129a63b212b0fd5 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: RELEASE_3_14 git_last_commit: ca6d3dc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/velociraptor_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/velociraptor_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/velociraptor_1.4.0.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 dependsOnMe: OSCA.advanced dependencyCount: 58 Package: veloviz Version: 1.0.0 Depends: R (>= 4.1) Imports: Rcpp, Matrix, igraph, mgcv, RSpectra, grDevices, graphics, stats LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 7ddc94961ab377cf7b360d8973a74731 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: RELEASE_3_14 git_last_commit: 8c25afc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/veloviz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/veloviz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/veloviz_1.0.0.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: 17 Package: VennDetail Version: 1.10.0 Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble, magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger Suggests: knitr, rmarkdown, testthat, markdown License: GPL-2 MD5sum: 70d25b00db989cb6b6fb39b52a28d3ac 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: RELEASE_3_14 git_last_commit: 4701342 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VennDetail_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VennDetail_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VennDetail_1.10.0.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: 51 Package: VERSO Version: 1.4.0 Depends: R (>= 4.1.0) Imports: ape, parallel, Rfast, stats Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: cab61bcb67cd6a697dca8b75369e819c 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: RELEASE_3_14 git_last_commit: f50b524 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VERSO_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VERSO_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VERSO_1.4.0.tgz vignettes: vignettes/VERSO/inst/doc/vignette.pdf vignetteTitles: VERSO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VERSO/inst/doc/vignette.R dependencyCount: 16 Package: vidger Version: 1.14.0 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 Archs: i386, x64 MD5sum: e528e47fc132a016ff9573ed0007251e 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: RELEASE_3_14 git_last_commit: be96db4 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/vidger_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vidger_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vidger_1.14.0.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: 125 Package: viper Version: 1.28.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: b41027a67e0097b1e80b0e0ee38e5660 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: RELEASE_3_14 git_last_commit: a8250ff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/viper_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/viper_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/viper_1.28.0.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, dorothea suggestsMe: decoupleR, MethReg, MOMA dependencyCount: 21 Package: ViSEAGO Version: 1.8.0 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 Archs: i386, x64 MD5sum: a429b0e4a8ead0caf774fa3335ed8e78 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: RELEASE_3_14 git_last_commit: c5fd4b9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/ViSEAGO_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/ViSEAGO_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/ViSEAGO_1.8.0.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: 155 Package: vissE Version: 1.2.2 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: 9700ae26b8e0563d806e1ea3badbbfef 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] () 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: RELEASE_3_14 git_last_commit: 690dee2 git_last_commit_date: 2021-10-27 Date/Publication: 2021-10-28 source.ver: src/contrib/vissE_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/vissE_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/vissE_1.2.2.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: 159 Package: VplotR Version: 1.4.0 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: e116b1e5930bd102e296cf35a45bb190 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: RELEASE_3_14 git_last_commit: 2aee3df git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/VplotR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/VplotR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/VplotR_1.4.0.tgz vignettes: vignettes/VplotR/inst/doc/VplotR.html vignetteTitles: VplotR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VplotR/inst/doc/VplotR.R dependencyCount: 74 Package: vsn Version: 3.62.0 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: 1b9030239dd822c114a31456a441fa2c 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: RELEASE_3_14 git_last_commit: 6ae7f4e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/vsn_3.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vsn_3.62.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vsn_3.62.0.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, bnem, DAPAR, DEP, Doscheda, imageHTS, MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, pvca, Ringo, tilingArray, ExpressionNormalizationWorkflow, RNAseqQC suggestsMe: adSplit, beadarray, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, scp, twilight, estrogen, wrMisc dependencyCount: 46 Package: vtpnet Version: 0.34.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: e31d045178198f95c04329b6322e2aee 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: RELEASE_3_14 git_last_commit: 5fba459 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/vtpnet_0.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vtpnet_0.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vtpnet_0.34.0.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: 134 Package: vulcan Version: 1.16.0 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: 5fe26aca22b3f4d63cbbc39817b23df1 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: RELEASE_3_14 git_last_commit: b25ca79 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/vulcan_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/vulcan_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/vulcan_1.16.0.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: 177 Package: waddR Version: 1.8.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), eva, BiocFileCache, BiocParallel, SingleCellExperiment, parallel, methods, stats LinkingTo: Rcpp, RcppArmadillo, Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown, scater License: MIT + file LICENSE MD5sum: af9b21f538b15e500b80049c298c5b91 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: RELEASE_3_14 git_last_commit: 540ab09 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/waddR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/waddR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.1/waddR_1.8.0.tgz 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: 116 Package: wateRmelon Version: 2.0.0 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: c9d4485c55aa79bd2c8c880f815bfd95 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] Maintainer: Leo C Schalkwyk VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wateRmelon git_branch: RELEASE_3_14 git_last_commit: f6a331b git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/wateRmelon_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/wateRmelon_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.1/wateRmelon_2.0.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.html vignetteTitles: wateRmelon User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: bigmelon, skewr importsMe: ChAMP, MEAT suggestsMe: RnBeads dependencyCount: 169 Package: wavClusteR Version: 2.28.0 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 Archs: i386, x64 MD5sum: 4046f9106efb35f5155b9687aaad24d1 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: RELEASE_3_14 git_last_commit: 93c12e2 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/wavClusteR_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/wavClusteR_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.1/wavClusteR_2.28.0.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: 142 Package: weaver Version: 1.60.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 232531f1f3567303a0e44177241dec75 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: RELEASE_3_14 git_last_commit: 387ca0c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/weaver_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/weaver_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.1/weaver_1.60.0.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: 1.66.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) MD5sum: 7bc92ae3495ec30fa86d5b13cf228fc6 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: RELEASE_3_14 git_last_commit: bbe6a8c git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/webbioc_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/webbioc_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.1/webbioc_1.66.0.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: 87 Package: weitrix Version: 1.6.0 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: i386, x64 MD5sum: 980ae21056a49246d2c1d8e96be376f3 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: RELEASE_3_14 git_last_commit: 51e45d9 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/weitrix_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/weitrix_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.1/weitrix_1.6.0.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: 82 Package: widgetTools Version: 1.72.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: b5923ca9255d410e3ddd88bb5a212e06 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: RELEASE_3_14 git_last_commit: b898577 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/widgetTools_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/widgetTools_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.1/widgetTools_1.72.0.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: 1.18.0 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: f6c2256bf0b294f9da1b148f6b4e168b 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: RELEASE_3_14 git_last_commit: abca417 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/wiggleplotr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/wiggleplotr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/wiggleplotr_1.18.0.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 dependencyCount: 79 Package: wpm Version: 1.4.0 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: 141be6037e13eed570ee89dcec7c28dd 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: RELEASE_3_14 git_last_commit: 154d31e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/wpm_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/wpm_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/wpm_1.4.0.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: 121 Package: wppi Version: 1.2.0 Depends: R(>= 4.1) Imports: dplyr, igraph, logger, methods, magrittr, Matrix, OmnipathR(>= 2.99.8), progress, purrr, rlang, RCurl, stats, tibble, tidyr Suggests: knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: fa7e8f002f59219506c0d94e9ec6fdfe 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 git_url: https://git.bioconductor.org/packages/wppi git_branch: RELEASE_3_14 git_last_commit: 18c6e65 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/wppi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/wppi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.1/wppi_1.2.0.tgz vignettes: vignettes/wppi/inst/doc/wppi_workflow.html vignetteTitles: WPPI workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/wppi/inst/doc/wppi_workflow.R dependencyCount: 64 Package: Wrench Version: 1.12.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: 51467b040e3a72f2203785b577c1c878 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: RELEASE_3_14 git_last_commit: e69c2cc git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Wrench_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Wrench_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Wrench_1.12.0.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 suggestsMe: PLNmodels dependencyCount: 10 Package: xcms Version: 3.16.1 Depends: R (>= 4.0.0), BiocParallel (>= 1.8.0), MSnbase (>= 2.19.1) Imports: mzR (>= 2.25.3), methods, Biobase, BiocGenerics, ProtGenerics (>= 1.25.1), lattice, RColorBrewer, plyr, RANN, MassSpecWavelet (>= 1.5.2), S4Vectors, robustbase, IRanges, SummarizedExperiment, MsCoreUtils, MsFeatures Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>= 0.25.1), ncdf4, testthat, pander, magrittr, rmarkdown, multtest, MALDIquant, pheatmap, Spectra (>= 1.1.17), MsBackendMgf, progress Enhances: Rgraphviz, rgl, XML License: GPL (>= 2) + file LICENSE MD5sum: c9e87528867ee0acf4ca84c3086001a0 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 [ctb], Ralf Tautenhahn [ctb], Steffen Neumann [aut, cre] (), Paul Benton [ctb], Christopher Conley [ctb], Johannes Rainer [ctb] (), Michael Witting [ctb], William Kumler [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: RELEASE_3_14 git_last_commit: ed02588 git_last_commit_date: 2021-11-19 Date/Publication: 2021-11-21 source.ver: src/contrib/xcms_3.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/xcms_3.16.1.zip mac.binary.ver: bin/macosx/contrib/4.1/xcms_3.16.1.tgz vignettes: vignettes/xcms/inst/doc/LC-MS-feature-grouping.html, vignettes/xcms/inst/doc/xcms-direct-injection.html, vignettes/xcms/inst/doc/xcms-lcms-ms.html, vignettes/xcms/inst/doc/xcms.html vignetteTitles: LC-MS feature grouping, Grouping FTICR-MS data with xcms, LC-MS/MS data analysis with xcms, LCMS 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, Metab, metaMS, ncGTW, proFIA, faahKO, PtH2O2lipids importsMe: CAMERA, cliqueMS, cosmiq, Risa suggestsMe: CluMSID, MassSpecWavelet, msPurity, RMassBank, msdata, mtbls2, RforProteomics, CorrectOverloadedPeaks, enviGCMS, isatabr, RAMClustR dependencyCount: 94 Package: XDE Version: 2.40.0 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: b1db04370776951d52178e467017df73 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: RELEASE_3_14 git_last_commit: bfc3c54 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/XDE_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/XDE_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/XDE_2.40.0.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: 62 Package: Xeva Version: 1.10.0 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: f2db8b9d37d1e192423d61be18e077a3 NeedsCompilation: no Title: Analysis of patient-derived xenograft (PDX) data Description: Contains set of functions to perform analysis of patient-derived xenograft (PDX) data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Arvind Mer, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/Xeva/issues git_url: https://git.bioconductor.org/packages/Xeva git_branch: RELEASE_3_14 git_last_commit: 6cfb49e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/Xeva_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/Xeva_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Xeva_1.10.0.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: 149 Package: XINA Version: 1.12.0 Depends: R (>= 3.5) Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 MD5sum: d6c7f08a8606cee588508a0d03da8834 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: RELEASE_3_14 git_last_commit: 232d72e git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/XINA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/XINA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.1/XINA_1.12.0.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: 68 Package: xmapbridge Version: 1.52.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: 6575158536ffcd8dc42cb5481a397789 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: RELEASE_3_14 git_last_commit: fe32fcd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/xmapbridge_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/xmapbridge_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.1/xmapbridge_1.52.0.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: 1.2.2 Depends: R (>= 4.1) Imports: utils, Biostrings, 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 Archs: i386, x64 MD5sum: b479157d68c0ad67dc6d08a60f0d330a 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 (distributed under its own \licence{https://github.com/ViennaRNA/ViennaRNA/blob/master/license.txt}; \citation{https://github.com/plucinskam/XNAString/blob/master/README.md#dependencies}). 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 package. 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: RELEASE_3_14 git_last_commit: 4685f4c git_last_commit_date: 2021-11-29 Date/Publication: 2021-11-30 source.ver: src/contrib/XNAString_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.1/XNAString_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.1/XNAString_1.2.2.tgz vignettes: vignettes/XNAString/inst/doc/XNAString_vignette.html vignetteTitles: XNAString_vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/XNAString/inst/doc/XNAString_vignette.R dependencyCount: 77 Package: XVector Version: 0.34.0 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: i386, x64 MD5sum: a3fa3501c2ceb5ad140c628cad2344d3 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: RELEASE_3_14 git_last_commit: 06adb25 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/XVector_0.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/XVector_0.34.0.zip mac.binary.ver: bin/macosx/contrib/4.1/XVector_0.34.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, triplex importsMe: BSgenome, ChIPsim, CNEr, dada2, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, HiLDA, IONiseR, IsoformSwitchAnalyzeR, kebabs, MatrixRider, Modstrings, monaLisa, ProteoDisco, R453Plus1Toolbox, ribosomeProfilingQC, Rsamtools, rtracklayer, Structstrings, TFBSTools, tracktables, tRNA, tRNAscanImport, VariantAnnotation, simMP suggestsMe: IRanges, musicatk linksToMe: Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 10 Package: yamss Version: 1.20.0 Depends: R (>= 3.3.0), 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: b9dbcda5a94c2a256ab190521823fe3f 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. biocViews: MassSpectrometry, Metabolomics, ImmunoOncology, 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: RELEASE_3_14 git_last_commit: 42e2ecd git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/yamss_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/yamss_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/yamss_1.20.0.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: 47 Package: YAPSA Version: 1.20.1 Depends: R (>= 3.6.0), GenomicRanges, ggplot2, grid Imports: limSolve, SomaticSignatures, VariantAnnotation, GenomeInfoDb, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, doParallel, PMCMRplus, ggbeeswarm, ComplexHeatmap, KEGGREST, grDevices, Biostrings, BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: f6b15750450f910df2c3d3efd659e69f 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, cre], Lea Jopp-Saile [aut], Carolin Andresen [aut], Zuguang Gu [aut], Matthias Schlesner [aut] Maintainer: Daniel Huebschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/YAPSA git_branch: RELEASE_3_14 git_last_commit: 6c3f437 git_last_commit_date: 2021-11-29 Date/Publication: 2021-11-30 source.ver: src/contrib/YAPSA_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.1/YAPSA_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.1/YAPSA_1.20.1.tgz vignettes: vignettes/YAPSA/inst/doc/index.html, 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: index.html, 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: 192 Package: yarn Version: 1.20.0 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: ee144ef85f49d08f2c26a4e611466b25 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: RELEASE_3_14 git_last_commit: b41e4ef git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-27 source.ver: src/contrib/yarn_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/yarn_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.1/yarn_1.20.0.tgz vignettes: vignettes/yarn/inst/doc/yarn.pdf vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yarn/inst/doc/yarn.R dependencyCount: 160 Package: zellkonverter Version: 1.4.0 Imports: Matrix, basilisk, reticulate, SingleCellExperiment (>= 1.11.6), SummarizedExperiment, DelayedArray, methods, S4Vectors, utils, cli Suggests: covr, spelling, testthat, knitr, rmarkdown, BiocStyle, scRNAseq, HDF5Array, rhdf5, BiocFileCache License: MIT + file LICENSE Archs: i386, x64 MD5sum: fdf72d7f1606811939ba211058579537 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] () 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: RELEASE_3_14 git_last_commit: bcef367 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/zellkonverter_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/zellkonverter_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.1/zellkonverter_1.4.0.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: velociraptor suggestsMe: HDF5Array dependencyCount: 42 Package: zFPKM Version: 1.16.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr, rmarkdown License: GPL-3 | file LICENSE Archs: i386, x64 MD5sum: eb6d1b394afb4c5afbb4a991b3a88aac 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: RELEASE_3_14 git_last_commit: 0da4cc5 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/zFPKM_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/zFPKM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/zFPKM_1.16.0.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: 63 Package: zinbwave Version: 1.16.0 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 License: Artistic-2.0 MD5sum: 169db34940de5634addf1be86efb2d94 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: RELEASE_3_14 git_last_commit: 17a2d03 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/zinbwave_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/zinbwave_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.1/zinbwave_1.16.0.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 suggestsMe: MAST, splatter dependencyCount: 72 Package: zlibbioc Version: 1.40.0 License: Artistic-2.0 + file LICENSE MD5sum: 33b033a0149ad5f89340b20cc000847a 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 BugReports: https://github.com/Bioconductor/zlibbioc/issues git_url: https://git.bioconductor.org/packages/zlibbioc git_branch: RELEASE_3_14 git_last_commit: 3f116b3 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 source.ver: src/contrib/zlibbioc_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.1/zlibbioc_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.1/zlibbioc_1.40.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: SimRAD importsMe: affy, affyio, affyPLM, bamsignals, ChemmineOB, FLAMES, MADSEQ, makecdfenv, NanoMethViz, oligo, polyester, qckitfastq, Rhtslib, Rsamtools, rtracklayer, ShortRead, snpStats, TransView, VariantAnnotation, XVector, jackalope suggestsMe: metacoder linksToMe: bamsignals, csaw, diffHic, epialleleR, FLAMES, maftools, methylKit, mzR, NxtIRFcore, Rfastp, Rhtslib, scPipe, seqTools, ShortRead, jackalope dependencyCount: 0 Package: ENVISIONQuery Version: 1.42.0 Depends: rJava, XML, utils License: GPL-2 NeedsCompilation: no Title: Retrieval from the ENVISION bioinformatics data portal into R Description: Tools to retrieve data from ENVISION, the Database for Annotation, Visualization and Integrated Discovery portal biocViews: Annotation Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/ENVISIONQuery git_branch: RELEASE_3_14 git_last_commit: 5d48286 git_last_commit_date: 2021-10-26 Date/Publication: 2021-11-16 win.binary.ver: bin/windows/contrib/4.1/ENVISIONQuery_1.42.0.zip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Herper Version: 1.4.0 Depends: R (>= 4.0), reticulate Imports: utils, rjson, withr, stats Suggests: BiocStyle, testthat, knitr, rmarkdown, seqCNA License: GPL-3 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: RELEASE_3_14 git_last_commit: f9c3ca8 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 win.binary.ver: bin/windows/contrib/4.1/Herper_1.4.0.zip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MethCP Version: 1.8.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, S4Vectors, bsseq, DSS, methylKit, DNAcopy, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel Suggests: testthat, knitr, rmarkdown License: Artistic-2.0 NeedsCompilation: no Title: Differential methylation anlsysis for bisulfite sequencing data Description: MethCP is a differentially methylated region (DMR) detecting method for whole-genome bisulfite sequencing (WGBS) data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. biocViews: DifferentialMethylation, Sequencing, WholeGenome, TimeCourse Author: Boying Gong [aut, cre] Maintainer: Boying Gong VignetteBuilder: knitr BugReports: https://github.com/boyinggong/methcp/issues git_url: https://git.bioconductor.org/packages/MethCP git_branch: RELEASE_3_14 git_last_commit: b9b32e0 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 win.binary.ver: bin/windows/contrib/4.1/MethCP_1.8.0.zip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Onassis Version: 1.15.0 Depends: R (>= 4.0), rJava, OnassisJavaLibs Imports: GEOmetadb, RSQLite, data.table, methods, tools, utils, AnnotationDbi, RCurl, stats, DT, data.table, knitr, Rtsne, dendextend, clusteval, ggplot2, ggfortify Suggests: BiocStyle, rmarkdown, htmltools, org.Hs.eg.db, gplots, GenomicRanges, kableExtra License: GPL-2 NeedsCompilation: no Title: OnASSIs Ontology Annotation and Semantic SImilarity software Description: A package that allows the annotation of text with ontology terms (mainly from OBO ontologies) and the computation of semantic similarity measures based on the structure of the ontology between different annotated samples. biocViews: Annotation, DataImport, Clustering, Network, Software, GeneTarget Author: Eugenia Galeota Maintainer: Eugenia Galeota SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Onassis git_branch: master git_last_commit: 1d388ce git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-27 win.binary.ver: bin/windows/contrib/4.1/Onassis_1.15.0.zip mac.binary.ver: bin/macosx/contrib/4.1/Onassis_1.15.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: synapter Version: 2.18.0 Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2) Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools, Biobase, knitr, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2), rmarkdown (>= 1.0) Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN, BiocStyle License: GPL-2 Archs: i386, x64 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: RELEASE_3_14 git_last_commit: 1ee8f37 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 win.binary.ver: bin/windows/contrib/4.1/synapter_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.1/synapter_2.18.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ScISI Version: 1.66.0 Depends: R (>= 2.10), GO.db, RpsiXML, annotate, apComplex Imports: AnnotationDbi, GO.db, RpsiXML, annotate, methods, org.Sc.sgd.db, utils Suggests: ppiData, xtable License: LGPL Title: In Silico Interactome Description: Package to create In Silico Interactomes biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree Author: Tony Chiang Maintainer: Tony Chiang Package: affyPara Version: 1.54.0 Depends: R (>= 2.5.0), methods, affy (>= 1.20.0), snow (>= 0.2-3), vsn (>= 3.6.0), aplpack (>= 1.1.1), affyio Suggests: affydata Enhances: affy License: GPL-3 Title: Parallelized preprocessing methods for Affymetrix Oligonucleotide Arrays Description: The package contains parallelized functions for exploratory oligonucleotide array analysis. The package is designed for large numbers of microarray data. biocViews: Microarray, Preprocessing Author: Markus Schmidberger , Esmeralda Vicedo , Ulrich Mansmann Maintainer: Markus Schmidberger URL: http://www.ibe.med.uni-muenchen.de PackageStatus: Deprecated Package: RmiR Version: 1.50.0 Depends: R (>= 2.7.0), RmiR.Hs.miRNA, RSVGTipsDevice Imports: DBI, methods, stats Suggests: hgug4112a.db,org.Hs.eg.db License: Artistic-2.0 Title: Package to work with miRNAs and miRNA targets with R Description: Useful functions to merge microRNA and respective targets using differents databases biocViews: Software,GeneExpression,Microarray,TimeCourse,Visualization Author: Francesco Favero Maintainer: Francesco Favero Package: BrainStars Version: 1.38.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 Title: query gene expression data and plots from BrainStars (B*) Description: This package can search and get gene expression data and plots from BrainStars (B*). BrainStars is a quantitative expression database of the adult mouse brain. The database has genome-wide expression profile at 51 adult mouse CNS regions. biocViews: Microarray, OneChannel, DataImport Author: Itoshi NIKAIDO Maintainer: Itoshi NIKAIDO PackageStatus: Deprecated Package: KEGGprofile Version: 1.36.0 Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt,RCurl,ggplot2,reshape2 License: GPL (>= 2) Title: An annotation and visualization package for multi-types and multi-groups expression data in KEGG pathway Description: KEGGprofile is an annotation and visualization tool which integrated the expression profiles and the function annotation in KEGG pathway maps. The multi-types and multi-groups expression data can be visualized in one pathway map. KEGGprofile facilitated more detailed analysis about the specific function changes inner pathway or temporal correlations in different genes and samples. biocViews: Pathways, KEGG Author: Shilin Zhao, Yan Guo, Yu Shyr Maintainer: Shilin Zhao PackageStatus: Deprecated Package: SwimR Version: 1.32.0 Depends: R (>= 3.0.0), methods, gplots (>= 2.10.1), heatmap.plus (>= 1.3), signal (>= 0.7), R2HTML (>= 2.2.1) Imports: methods License: LGPL-2 Title: SwimR: A Suite of Analytical Tools for Quantification of C. elegans Swimming Behavior Description: SwimR is an R-based suite that calculates, analyses, and plots the frequency of C. elegans swimming behavior over time. It places a particular emphasis on identifying paralysis and quantifying the kinetic elements of paralysis during swimming. Data is input to SwipR from a custom built program that fits a 5 point morphometric spine to videos of single worms swimming in a buffer called Worm Tracker. biocViews: Visualization Author: Jing Wang , Andrew Hardaway and Bing Zhang Maintainer: Randy Blakely PackageStatus: Deprecated Package: dualKS Version: 1.54.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) Title: Dual KS Discriminant Analysis and Classification Description: This package implements a Kolmogorov Smirnov rank-sum based algorithm for training (i.e. discriminant analysis--identification of genes that discriminate between classes) and classification of gene expression data sets. One of the chief strengths of this approach is that it is amenable to the "multiclass" problem. That is, it can discriminate between more than 2 classes. biocViews: Microarray, Classification Author: Eric J. Kort, Yarong Yang Maintainer: Eric J. Kort , Yarong Yang PackageStatus: Deprecated Package: compEpiTools Version: 1.28.0 Depends: R (>= 3.1.1), 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 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, cre] Maintainer: Kamal Kishore VignetteBuilder: knitr Package: methylPipe Version: 1.28.0 Depends: R (>= 3.2.0), 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) 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: Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr Package: ENCODExplorer Version: 2.20.0 Depends: R (>= 3.6) Imports: methods, tools, jsonlite, RCurl, tidyr, data.table, dplyr, stringr, stringi, utils, AnnotationHub, GenomicRanges, rtracklayer, S4Vectors, GenomeInfoDb, ENCODExplorerData Suggests: RUnit,BiocGenerics,knitr, curl, httr, shiny, shinythemes, DT License: Artistic-2.0 Title: A compilation of ENCODE metadata Description: This package allows user to quickly access ENCODE project files metadata and give access to helper functions to query the ENCODE rest api, download ENCODE datasets and save the database in SQLite format. biocViews: Infrastructure, DataImport Author: Charles Joly Beauparlant [aut, cre], Audrey Lemacon [aut], Eric Fournier [aut], Louis Gendron [ctb], Astrid-Louise Deschenes [ctb], Arnaud Droit [aut] Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/ENCODExplorer/issues PackageStatus: Deprecated Package: FindMyFriends Version: 1.24.0 Imports: methods, BiocGenerics, Biobase, tools, dplyr, IRanges, Biostrings, S4Vectors, kebabs, igraph, Matrix, digest, filehash, Rcpp, ggplot2, gtable, grid, reshape2, ggdendro, BiocParallel, utils, stats LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown, reutils License: GPL (>=2) NeedsCompilation: yes Title: Microbial Comparative Genomics in R Description: A framework for doing microbial comparative genomics in R. The main purpose of the package is assisting in the creation of pangenome matrices where genes from related organisms are grouped by similarity, as well as the analysis of these data. FindMyFriends provides many novel approaches to doing pangenome analysis and supports a gene grouping algorithm that scales linearly, thus making the creation of huge pangenomes feasible. biocViews: ComparativeGenomics, Clustering, DataRepresentation, GenomicVariation, SequenceMatching, GraphAndNetwork Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/FindMyFriends VignetteBuilder: knitr BugReports: https://github.com/thomasp85/FindMyFriends/issues PackageStatus: Deprecated Package: CountClust Version: 1.22.0 Depends: R (>= 3.4), ggplot2 (>= 2.1.0) Imports: SQUAREM, slam, maptpx, plyr(>= 1.7.1), cowplot, gtools, flexmix, picante, limma, parallel, reshape2, stats, utils, graphics, grDevices Suggests: knitr, kableExtra, BiocStyle, Biobase, roxygen2, RColorBrewer, devtools, xtable License: GPL (>= 2) Title: Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models Description: Fits grade of membership models (GoM, also known as admixture models) to cluster RNA-seq gene expression count data, identifies characteristic genes driving cluster memberships, and provides a visual summary of the cluster memberships. biocViews: ImmunoOncology, RNASeq, GeneExpression, Clustering, Sequencing, StatisticalMethod, Software, Visualization Author: Kushal Dey [aut, cre], Joyce Hsiao [aut], Matthew Stephens [aut] Maintainer: Kushal Dey URL: https://github.com/kkdey/CountClust VignetteBuilder: knitr Package: GenoGAM Version: 2.12.0 Depends: R (>= 3.5), SummarizedExperiment (>= 1.1.19), HDF5Array (>= 1.8.0), rhdf5 (>= 2.21.6), S4Vectors (>= 0.23.18), Matrix (>= 1.2-8), data.table (>= 1.9.4) Imports: Rcpp (>= 0.12.14), sparseinv (>= 0.1.1), Rsamtools (>= 1.18.2), GenomicRanges (>= 1.23.16), BiocParallel (>= 1.5.17), DESeq2 (>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb (>= 1.7.6), GenomicAlignments (>= 1.7.17), IRanges (>= 2.5.30), Biostrings (>= 2.39.14), DelayedArray (>= 0.3.19), methods, stats LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, chipseq (>= 1.21.2), LSD (>= 3.0.0), genefilter (>= 1.54.2), ggplot2 (>= 2.1.0), testthat, knitr, rmarkdown License: GPL-2 NeedsCompilation: no Title: A GAM based framework for analysis of ChIP-Seq data Description: This package allows statistical analysis of genome-wide data with smooth functions using generalized additive models based on the implementation from the R-package 'mgcv'. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Estimation of dispersion and smoothing parameters is performed by cross-validation. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals. biocViews: Regression, DifferentialPeakCalling, ChIPSeq, DifferentialExpression, Genetics, Epigenetics, WholeGenome, ChipOnChip, ImmunoOncology Author: Georg Stricker [aut, cre], Alexander Engelhardt [aut], Julien Gagneur [aut] Maintainer: Georg Stricker URL: https://github.com/gstricker/GenoGAM VignetteBuilder: knitr BugReports: https://github.com/gstricker/GenoGAM/issues Package: SRGnet Version: 1.20.0 Depends: R (>= 3.3.1), EBcoexpress, MASS, igraph, pvclust (>= 2.0-0), gbm (>= 2.1.1), limma, DMwR (>= 0.4.1), matrixStats, Hmisc Suggests: knitr, rmarkdown License: GPL-2 NeedsCompilation: no Title: SRGnet: An R package for studying synergistic response to gene mutations from transcriptomics data Description: We developed SRGnet to analyze synergistic regulatory mechanisms in transcriptome profiles that act to enhance the overall cell response to combination of mutations, drugs or environmental exposure. This package can be used to identify regulatory modules downstream of synergistic response genes, prioritize synergistic regulatory genes that may be potential intervention targets, and contextualize gene perturbation experiments. biocViews: Software, StatisticalMethod, Regression Author: Isar Nassiri [aut, cre], Matthew McCall [aut, cre] Maintainer: Isar Nassiri VignetteBuilder: knitr PackageStatus: Deprecated Package: coexnet Version: 1.16.0 Depends: R (>= 3.6) Imports: affy, siggenes, GEOquery, vsn, igraph, acde, Biobase, limma, graphics, stats, utils, STRINGdb, SummarizedExperiment, minet, rmarkdown Suggests: RUnit, BiocGenerics, knitr License: LGPL Title: coexnet: An R package to build CO-EXpression NETworks from Microarray Data Description: Extracts the gene expression matrix from GEO DataSets (.CEL files) as a AffyBatch object. Additionally, can make the normalization process using two different methods (vsn and rma). The summarization (pass from multi-probe to one gene) uses two different criteria (Maximum value and Median of the samples expression data) and the process of gene differentially expressed analisys using two methods (sam and acde). The construction of the co-expression network can be conduced using two different methods, Pearson Correlation Coefficient (PCC) or Mutual Information (MI) and choosing a threshold value using a graph theory approach. biocViews: GeneExpression, Microarray, DifferentialExpression, GraphAndNetwork, NetworkInference, SystemsBiology, Normalization, Network Author: Juan David Henao [aut,cre], Liliana Lopez-Kleine [aut], Andres Pinzon-Velasco [aut] Maintainer: Juan David Henao VignetteBuilder: knitr Package: perturbatr Version: 1.14.0 Depends: R (>= 3.5), methods, stats Imports: dplyr, ggplot2, tidyr, assertthat, lme4, splines, igraph, foreach, parallel, doParallel, diffusr, lazyeval, tibble, grid, utils, graphics, scales, magrittr, formula.tools, rlang Suggests: testthat, lintr, knitr, rmarkdown, BiocStyle License: GPL-3 Title: Statistical Analysis of High-Throughput Genetic Perturbation Screens Description: perturbatr does stage-wise analysis of large-scale genetic perturbation screens for integrated data sets consisting of multiple screens. For multiple integrated perturbation screens a hierarchical model that considers the variance between different biological conditions is fitted. The resulting list of gene effects is then further extended using a network propagation algorithm to correct for false negatives. biocViews: ImmunoOncology, Regression, CellBasedAssays, Network Author: Simon Dirmeier [aut, cre] Maintainer: Simon Dirmeier URL: https://github.com/cbg-ethz/perturbatr VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/perturbatr/issues Package: HiCBricks Version: 1.12.0 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 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 Package: CHETAH Version: 1.10.0 Depends: R (>= 3.6), ggplot2, SingleCellExperiment Imports: gplots, shiny, plotly, pheatmap, bioDist, dendextend, cowplot, corrplot, grDevices, stats, graphics, reshape2, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr License: file LICENSE 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 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 Package: XCIR Version: 1.8.0 Depends: methods Imports: stats, utils, tools, data.table, Biostrings, IRanges, VariantAnnotation, seqminer, ggplot2, biomaRt, readxl, S4Vectors Suggests: knitr, rmarkdown License: GPL-2 Title: XCI-inference Description: Models and tools for subject level analysis of X chromosome inactivation (XCI) and XCI-escape inference. biocViews: StatisticalMethod, RNASeq, Sequencing, Coverage Author: Renan Sauteraud, Dajiang Liu Maintainer: Renan Sauteraud URL: https://github.com/SRenan/XCIR VignetteBuilder: knitr BugReports: https://github.com/SRenan/XCIR/issues Package: Autotuner Version: 1.8.0 Depends: R (>= 4.0.0), methods, Biobase, MSnbase (>= 2.14.2) Imports: RColorBrewer, mzR, assertthat, scales, entropy, cluster, grDevices, graphics, stats, utils Suggests: testthat (>= 2.1.0), covr, devtools, knitr, rmarkdown, mtbls2 License: MIT + file LICENSE Title: Automated parameter selection for untargeted metabolomics data processing Description: This package is designed to help faciliate data processing in untargeted metabolomics. To do this, the algorithm contained within the package performs statistical inference on raw data to come up with the best set of parameters to process the raw data. biocViews: MassSpectrometry, Metabolomics Author: Craig McLean Maintainer: Craig McLean URL: https://github.com/crmclean/Autotuner/ VignetteBuilder: knitr BugReports: https://github.com/crmclean/Autotuner/issues Package: gramm4R Version: 1.8.0 Depends: R (>= 3.6.0) Imports: basicTrendline,investr,minerva,psych,grDevices, graphics, stats,DelayedArray,SummarizedExperiment,DMwR,phyloseq Suggests: knitr, rmarkdown License: GPL-2 Title: Generalized correlation analysis and model construction strategy for metabolome and microbiome Description: Generalized Correlation Analysis for Metabolome and Microbiome (GRaMM), for inter-correlation pairs discovery among metabolome and microbiome. biocViews: GraphAndNetwork,Microbiome Author: Mengci Li, Dandan Liang, Tianlu Chen and Wei Jia Maintainer: Tianlu Chen VignetteBuilder: knitr PackageStatus: Deprecated Package: ALPS Version: 1.8.0 Depends: R (>= 3.6) Imports: assertthat, BiocParallel, ChIPseeker, corrplot, data.table, dplyr, GenomicRanges, GGally, genefilter, gghalves, ggplot2, ggseqlogo, Gviz, magrittr, org.Hs.eg.db, plyr, reshape2, rtracklayer, stats, stringr, tibble, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, utils Suggests: knitr, rmarkdown, ComplexHeatmap, circlize, testthat License: MIT + file LICENSE Title: AnaLysis routines for ePigenomicS data Description: The package provides analysis and publication quality visualization routines for genome-wide epigenomics data such as histone modification or transcription factor ChIP-seq, ATAC-seq, DNase-seq etc. The functions in the package can be used with any type of data that can be represented with bigwig files at any resolution. The goal of the ALPS is to provide analysis tools for most downstream analysis without leaving the R environment and most tools in the package require a minimal input that can be prepared with basic R, unix or excel skills. biocViews: Epigenetics, Sequencing, ChIPSeq, ATACSeq, Visualization, Transcription, HistoneModification Author: Venu Thatikonda, Natalie Jäger Maintainer: Venu Thatikonda URL: https://github.com/itsvenu/ALPS VignetteBuilder: knitr BugReports: https://github.com/itsvenu/ALPS/issues PackageStatus: Deprecated Package: MSstatsTMTPTM Version: 1.4.0 Depends: R (>= 4.0) Imports: dplyr, gridExtra, stringr, reshape2, stats, utils, ggplot2, grDevices, graphics, MSstatsTMT, Rcpp LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat, MSstats, covr License: Artistic-2.0 Title: Post Translational Modification (PTM) Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: Tools for Post Translational Modification (PTM) and protein significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. The functions in this package should be used after PTM/protein summarization. They can be used to both plot the summarized results and model the summarized datasets. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Tsung-Heng Tsai [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsTMTPTM/issues PackageStatus: Deprecated