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>

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