GSVA

This is the development version of GSVA; for the stable release version, see GSVA.

Gene Set Variation Analysis for Microarray and RNA-Seq Data

Bioconductor version: Development (3.19)

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: Robert Castelo [aut, cre], Justin Guinney [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb], Axel Klenk [ctb]

Maintainer: Robert Castelo <robert.castelo at upf.edu>

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

Installation

To install this package, start R (version "4.4") and enter:


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

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("GSVA")

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

Documentation


Reference Manual	PDF

Need some help? Ask on the Bioconductor Support site!

Details

biocViews	FunctionalGenomics, GeneSetEnrichment, Microarray, Pathways, RNASeq, Software
Version	1.51.14
In Bioconductor since	BioC 2.8 (R-2.13) (13 years)
License	GPL (>= 2)
Depends	R (>= 3.5.0)
Imports	methods, stats, utils, graphics, S4Vectors, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix (>= 1.5-0), parallel, BiocParallel, SingleCellExperiment, SpatialExperiment, sparseMatrixStats, DelayedArray, DelayedMatrixStats, HDF5Array, BiocSingular
System Requirements
URL	https://github.com/rcastelo/GSVA
Bug Reports	https://github.com/rcastelo/GSVA/issues

Suggests	BiocGenerics, RUnit, BiocStyle, knitr, rmarkdown, limma, RColorBrewer, org.Hs.eg.db, genefilter, edgeR, GSVAdata, shiny, shinydashboard, ggplot2, data.table, plotly, future, promises, shinybusy, shinyjs
Linking To
Enhances
Depends On Me
Imports Me	EGSEA, escape, octad, oppar, scFeatures, signifinder, singleCellTK, TBSignatureProfiler, TNBC.CMS
Suggests Me	decoupleR, MCbiclust, sparrow, SPONGE
Links To Me
Build Report	Build Report

Package Archives

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

Source Package
Windows Binary
macOS Binary (x86_64)
macOS Binary (arm64)
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