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.
This package is for version 3.3 of Bioconductor; for the stable, up-to-date release version, see GSVA.
Bioconductor version: 3.3
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")
):
To install this package, start R and enter:
## try http:// if https:// URLs are not supported source("https://bioconductor.org/biocLite.R") biocLite("GSVA")
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GSVA")
R Script | Gene Set Variation Analysis | |
Reference Manual | ||
Text | NEWS |
biocViews | GeneSetEnrichment, Microarray, Pathways, Software |
Version | 1.20.0 |
In Bioconductor since | BioC 2.8 (R-2.13) (5.5 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 | SISPA |
Imports Me | EGSEA, oppar |
Suggests Me | |
Build Report |
Follow Installation instructions to use this package in your R session.
Package Source | GSVA_1.20.0.tar.gz |
Windows Binary | GSVA_1.20.0.zip (32- & 64-bit) |
Mac OS X 10.9 (Mavericks) | GSVA_1.20.0.tgz |
Subversion source | (username/password: readonly) |
Git source | https://github.com/Bioconductor-mirror/GSVA/tree/release-3.3 |
Package Short Url | http://bioconductor.org/packages/GSVA/ |
Package Downloads Report | Download Stats |
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