To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GSVA")
In most cases, you don't need to download the package archive at all.
GSVA
Gene Set Variation Analysis for microarray and RNA-seq data
Bioconductor version: 2.14
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:
source("http://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")
| R Script | Gene Set Variation Analysis | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GeneSetEnrichment, Microarray, Pathways, Software |
| Version | 1.12.0 |
| In Bioconductor since | BioC 2.8 (R-2.13) |
| License | GPL (>= 2) |
| Depends | R (>= 2.13.0), methods, GSEABase(>= 1.17.4) |
| Imports | methods, BiocGenerics, Biobase, GSEABase |
| Suggests | limma, RColorBrewer, genefilter, mclust, edgeR, GSVAdata |
| System Requirements | |
| URL | http://www.sagebase.org |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | GSVA_1.12.0.tar.gz |
| Windows Binary | GSVA_1.12.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | GSVA_1.12.0.tgz |
| Mac OS X 10.9 (Mavericks) | GSVA_1.12.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
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