GSVA

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

All versions

3.24 (devel), 3.23 (release), 3.22, 3.21, 3.20, 3.19, 3.18, 3.17, 3.16, 3.15, 3.14, 3.13, 3.12, 3.11, 3.10, 3.9, 3.8, 3.7, 3.6, 3.5, 3.4, 3.3, 3.2, 3.1, 3.0, 2.14, 2.13, 2.12, 2.11, 2.10, 2.9, 2.8

Gene Set Variation Analysis for Microarray and RNA-Seq Data

Bioconductor version: Development (3.24)

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] ORCID: 0000-0003-2229-4508 , Justin Guinney [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb], Axel Klenk [ctb], Chan Zuckerberg Initiative (CZI) [fnd], Spanish Ministry of Science, Innovation and Universities (MCIU) [fnd]

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

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

Robert Castelo, Justin Guinney. GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data. doi:10.18129/B9.bioc.GSVA, R package version 2.7.22, https://bioconductor.org/packages/GSVA.

Generated from the package metadata; it may differ from the package's own citation.

Installation

To install this package, start R (version "4.6") and enter:

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

## The following initializes the development version of Bioconductor
BiocManager::install(version = "devel")

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")

Running GSVA in HPC and cloud environments	HTML	R Script
GSVA on proteomics data	HTML	R Script
GSVA on single-cell RNA-seq data	HTML	R Script
GSVA on spatial omics data	HTML	R Script
Gene set variation analysis	HTML	R Script
Reference Manual	PDF
NEWS	Text

Need some help? Ask on the Bioconductor Support site!

Details

biocViews	FunctionalGenomics, GeneSetEnrichment, Microarray, Pathways, RNASeq, Software
Version	2.7.22
In Bioconductor since	BioC 2.8 (R-2.13) (15.5 years)
License	Artistic-2.0
Depends	R (>= 4.0.0)
Imports	methods, stats, utils, graphics, tools, BiocGenerics, MatrixGenerics, S4Vectors, S4Arrays, HDF5Array, SparseArray, DelayedArray, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix (>= 1.5-0), DelayedMatrixStats, BiocParallel, SingleCellExperiment, SpatialExperiment, sparseMatrixStats, cli, memuse
System Requirements
URL	https://github.com/rcastelo/GSVA
Bug Reports	https://github.com/rcastelo/GSVA/issues

Suggests	RUnit, BiocStyle, knitr, rmarkdown, limma, RColorBrewer, arrow, duckdb, DBI, org.Hs.eg.db, genefilter, edgeR, GSVAdata, sva, ExperimentHub, TENxPBMCData, TENxVisiumData, spatialLIBD, scrapper, bluster, igraph, ggspavis, patchwork, ggplot2, shiny, shinydashboard, data.table, plotly, future, promises, shinybusy, shinyjs, batchtools
Linking To	cli
Enhances
Depends On Me	SMDIC
Imports Me	autoGO, consensusOV, ctdR, DRviaSPCN, EGSEA, EMTscore, GSABenchmark, GSEMA, IOBR, octad, oppar, pathMED, plaid, psSubpathway, scFeatures, scMappR, SIGN, signifinder, singleCellTK, spatialGE, SubtypeDrug, TBSignatureProfiler
Suggests Me	clustermole, decoupleR, escape, futurize, MCbiclust, mitology, ReporterScore, sparrow, SPONGE
Links To Me
Build Report	Build Report, r-universe

Package Archives

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

Source Package	GSVA_2.7.22.tar.gz
Windows Binary (x86_64)	GSVA_2.7.17.zip
macOS Binary (big-sur-x86_64)	GSVA_2.7.22.tgz
macOS Binary (sonoma-arm64)	GSVA_2.7.22.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