Package: GSVA
Version: 2.4.4
Title: Gene Set Variation Analysis for Microarray and RNA-Seq Data
Authors@R: c(person("Robert", "Castelo", role=c("aut", "cre"),
                    comment=c(ORCID="0000-0003-2229-4508"),
                    email="robert.castelo@upf.edu"),
             person("Justin", "Guinney", role="aut", email="jguinney@gmail.com"),
             person("Alexey", "Sergushichev", role="ctb", email="alsergbox@gmail.com"),
             person("Pablo Sebastian", "Rodriguez", role="ctb", email="pablo.rodriguez.bio2@gmail.com"),
             person("Axel", "Klenk", role="ctb", email="axel.klenk@gmail.com"),
             person("Chan Zuckerberg Initiative (CZI)", role="fnd"),
             person("Spanish Ministry of Science, Innovation and Universities (MCIU)", role="fnd"))
Depends: R (>= 4.0.0)
Imports: methods, stats, utils, graphics, parallel, BiocGenerics,
        MatrixGenerics, S4Vectors, S4Arrays, HDF5Array, SparseArray,
        DelayedArray, IRanges, Biobase, SummarizedExperiment, GSEABase,
        Matrix (>= 1.5-0), BiocParallel, SingleCellExperiment,
        BiocSingular, SpatialExperiment, sparseMatrixStats, cli, memuse
Suggests: RUnit, BiocStyle, knitr, rmarkdown, limma, RColorBrewer,
        org.Hs.eg.db, genefilter, edgeR, GSVAdata, sva, TENxPBMCData,
        TENxVisiumData, scuttle, scran, igraph, shiny, shinydashboard,
        ggplot2, data.table, plotly, future, promises, shinybusy,
        shinyjs
LinkingTo: cli
Description: 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.
License: Artistic-2.0
VignetteBuilder: knitr
URL: https://github.com/rcastelo/GSVA
BugReports: https://github.com/rcastelo/GSVA/issues
Encoding: UTF-8
biocViews: FunctionalGenomics, Microarray, RNASeq, Pathways,
        GeneSetEnrichment
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
git_url: https://git.bioconductor.org/packages/GSVA
git_branch: RELEASE_3_22
git_last_commit: e48e822
git_last_commit_date: 2025-12-14
Repository: Bioconductor 3.22
Date/Publication: 2025-12-15
NeedsCompilation: yes
Packaged: 2025-12-15 23:12:49 UTC; biocbuild
Author: Robert Castelo [aut, cre] (ORCID:
    <https://orcid.org/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@upf.edu>
