Source: r-bioc-gsva
Standards-Version: 4.7.4
Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Uploaders:
 Steffen Moeller <moeller@debian.org>,
Section: gnu-r
Testsuite: autopkgtest-pkg-r
Build-Depends:
 debhelper-compat (= 14),
 dh-r,
 r-base-dev,
 r-bioc-biocgenerics,
 r-bioc-matrixgenerics,
 r-bioc-s4vectors,
 r-bioc-s4arrays,
 r-bioc-hdf5array,
 r-bioc-sparsearray,
 r-bioc-delayedarray,
 r-bioc-iranges,
 r-bioc-biobase,
 r-bioc-summarizedexperiment,
 r-bioc-gseabase,
 r-cran-matrix,
 r-cran-memuse,
 r-bioc-delayedmatrixstats,
 r-bioc-biocparallel,
 r-bioc-singlecellexperiment,
 r-bioc-biocsingular,
 r-bioc-spatialexperiment,
 r-bioc-sparsematrixstats,
 r-cran-cli,
 r-pkg-team-core-architecture,
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-bioc-gsva
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-bioc-gsva.git
Homepage: https://bioconductor.org/packages/GSVA/

Package: r-bioc-gsva
Architecture: any
Depends:
 ${R:Depends},
 ${shlibs:Depends},
 ${misc:Depends},
 r-pkg-team-core-architecture,
Recommends:
 ${R:Recommends},
Suggests:
 ${R:Suggests},
Description: Gene Set Variation Analysis for Microarray and RNA-Seq Data
 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.
