GSVA
This is thereleased version of GSVA; for the devel version, seeGSVA.
Gene Set Variation Analysis for Microarray and RNA-Seq Data
Bioconductor version: Release (3.22)
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("GSVA")):Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")BiocManager::install("GSVA")For older versions of R, please refer to the appropriateBioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GSVA")| Gene set variation analysis | HTML | R Script |
| GSVA on single-cell RNA-seq data | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | FunctionalGenomics,GeneSetEnrichment,Microarray,Pathways,RNASeq,Software |
| Version | 2.4.3 |
| In Bioconductor since | BioC 2.8 (R-2.13) (14.5 years) |
| License | Artistic-2.0 |
| Depends | R (>= 3.5.0) |
| Imports | methods, stats, utils, graphics,BiocGenerics,MatrixGenerics,S4Vectors,S4Arrays,HDF5Array,SparseArray,DelayedArray,IRanges,Biobase,SummarizedExperiment,GSEABase,Matrix (>= 1.5-0), parallel,BiocParallel,SingleCellExperiment,SpatialExperiment,sparseMatrixStats,DelayedMatrixStats,BiocSingular,cli,memuse |
| System Requirements | |
| URL | https://github.com/rcastelo/GSVA |
| Bug Reports | https://github.com/rcastelo/GSVA/issues |
See More
| Suggests | RUnit,BiocStyle,knitr,rmarkdown,limma,RColorBrewer,org.Hs.eg.db,genefilter,edgeR,GSVAdata,sva,ggplot2,TENxPBMCData,scuttle,scran,igraph,shiny,shinydashboard,ggplot2,data.table,plotly,future,promises,shinybusy,shinyjs |
| Linking To | cli |
| Enhances | |
| Depends On Me | SMDIC |
| Imports Me | consensusOV,EGSEA,octad,oppar,pathMED,signifinder,singleCellTK,TBSignatureProfiler,autoGO,clustermole,DRviaSPCN,GSEMA,psSubpathway,scMappR,SIGN,sigQC,spatialGE,ssdGSA |
| Suggests Me | decoupleR,escape,MCbiclust,mitology,sparrow,SPONGE,ReporterScore |
| Links To Me | |
| Build Report | Build Report |
Package Archives
FollowInstallation instructions to use this package in your R session.
| Source Package | GSVA_2.4.3.tar.gz |
| Windows Binary (x86_64) | GSVA_2.4.1.zip (64-bit only) |
| macOS Binary (x86_64) | GSVA_2.4.3.tgz |
| macOS Binary (arm64) | GSVA_2.4.3.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/ |
| Package Downloads Report | Download Stats |
| Old Source Packages for BioC 3.22 | Source Archive |