GSVA

DOI: 10.18129/B9.bioc.GSVA    

This package is for version 3.9 of Bioconductor; for the stable, up-to-date release version, see GSVA.

Gene Set Variation Analysis for microarray and RNA-seq data

Bioconductor version: 3.9

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.

Author: Justin Guinney [aut, cre], Robert Castelo [aut], Joan Fernandez [ctb]

Maintainer: Justin Guinney <justin.guinney at sagebase.org>

Citation (from within R, enter citation("GSVA")):

Installation

To install this package, start R (version "3.6") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GSVA")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("GSVA")

 

PDF R Script Gene Set Variation Analysis
PDF   Reference Manual
Text   NEWS

Details

biocViews GeneSetEnrichment, Microarray, Pathways, Software
Version 1.32.0
In Bioconductor since BioC 2.8 (R-2.13) (8.5 years)
License GPL (>= 2)
Depends R (>= 3.0.0)
Imports methods, BiocGenerics, Biobase, GSEABase(>= 1.17.4), geneplotter, shiny, shinythemes
LinkingTo
Suggests limma, RColorBrewer, genefilter, mclust, edgeR, snow, parallel, GSVAdata
SystemRequirements
Enhances
URL https://github.com/rcastelo/GSVA
BugReports https://github.com/rcastelo/GSVA/issues
Depends On Me SISPA
Imports Me consensusOV, EGSEA, oppar, singleCellTK, TNBC.CMS
Suggests Me MCbiclust
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package GSVA_1.32.0.tar.gz
Windows Binary GSVA_1.32.0.zip
Mac OS X 10.11 (El Capitan) GSVA_1.32.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/GSVA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/GSVA
Package Short Url https://bioconductor.org/packages/GSVA/
Package Downloads Report Download Stats

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