To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("GSVA")

In most cases, you don't need to download the package archive at all.

GSVA

   

This package is for version 3.1 of Bioconductor; for the stable, up-to-date release version, see GSVA.

Gene Set Variation Analysis for microarray and RNA-seq data

Bioconductor version: 3.1

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 with contributions from Robert Castelo

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

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

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("GSVA")

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 1.16.0
In Bioconductor since BioC 2.8 (R-2.13) (5 years)
License GPL (>= 2)
Depends R (>= 2.13.0)
Imports methods, BiocGenerics, Biobase, GSEABase(>= 1.17.4)
LinkingTo
Suggests limma, RColorBrewer, genefilter, mclust, edgeR, snow, parallel, GSVAdata
SystemRequirements
Enhances
URL http://www.sagebase.org
Depends On Me
Imports Me
Suggests Me
Build Report  

Package Archives

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

Package Source GSVA_1.16.0.tar.gz
Windows Binary GSVA_1.16.0.zip (32- & 64-bit)
Mac OS X 10.6 (Snow Leopard) GSVA_1.16.0.tgz
Mac OS X 10.9 (Mavericks) GSVA_1.16.0.tgz
Subversion source (username/password: readonly)
Git source https://github.com/Bioconductor-mirror/GSVA/tree/release-3.1
Package Short Url http://bioconductor.org/packages/GSVA/
Package Downloads Report Download Stats

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