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Gene Set Variation Analysis for Microarray and RNA-Seq Data

Bioconductor version: Release (3.18)

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")):


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Gene set variation analysis HTML R Script
Reference Manual PDF


biocViews FunctionalGenomics, GeneSetEnrichment, Microarray, Pathways, RNASeq, Software
Version 1.50.0
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, sparseMatrixStats, DelayedArray, DelayedMatrixStats, HDF5Array, BiocSingular
System Requirements
URL https://github.com/rcastelo/GSVA
Bug Reports https://github.com/rcastelo/GSVA/issues
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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
Depends On Me SISPA
Imports Me consensusOV, EGSEA, escape, octad, oppar, scFeatures, signifinder, singleCellTK, TBSignatureProfiler, TNBC.CMS
Suggests Me decoupleR, MCbiclust, sparrow, SPONGE
Links To Me
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Package Archives

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

Source Package GSVA_1.50.0.tar.gz
Windows Binary GSVA_1.50.0.zip
macOS Binary (x86_64) GSVA_1.50.0.tgz
macOS Binary (arm64) GSVA_1.50.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/GSVA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/GSVA
Bioc Package Browser https://code.bioconductor.org/browse/GSVA/
Package Short Url https://bioconductor.org/packages/GSVA/
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