- recipe bioconductor-gsva
Gene Set Variation Analysis for Microarray and RNA-Seq Data
- Homepage:
- License:
GPL (>= 2)
- Recipe:
- Links:
biotools: gsva
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.
- package bioconductor-gsva¶
- versions:
2.0.0-1,2.0.0-0,1.50.0-1,1.50.0-0,1.48.2-0,1.46.0-1,1.46.0-0,1.42.0-2,1.42.0-1,2.0.0-1,2.0.0-0,1.50.0-1,1.50.0-0,1.48.2-0,1.46.0-1,1.46.0-0,1.42.0-2,1.42.0-1,1.42.0-0,1.40.0-0,1.38.2-0,1.38.0-0,1.36.0-0,1.34.0-0,1.32.0-1,1.30.0-1,1.30.0-0,1.28.0-0,1.26.0-0,1.24.2-0,1.24.1-0- depends bioconductor-biobase:
>=2.66.0,<2.67.0- depends bioconductor-biobase:
>=2.66.0,<2.67.0a0- depends bioconductor-biocparallel:
>=1.40.0,<1.41.0- depends bioconductor-biocparallel:
>=1.40.0,<1.41.0a0- depends bioconductor-biocsingular:
>=1.22.0,<1.23.0- depends bioconductor-biocsingular:
>=1.22.0,<1.23.0a0- depends bioconductor-delayedarray:
>=0.32.0,<0.33.0- depends bioconductor-delayedarray:
>=0.32.0,<0.33.0a0- depends bioconductor-delayedmatrixstats:
>=1.28.0,<1.29.0- depends bioconductor-delayedmatrixstats:
>=1.28.0,<1.29.0a0- depends bioconductor-gseabase:
>=1.68.0,<1.69.0- depends bioconductor-gseabase:
>=1.68.0,<1.69.0a0- depends bioconductor-hdf5array:
>=1.34.0,<1.35.0- depends bioconductor-hdf5array:
>=1.34.0,<1.35.0a0- depends bioconductor-iranges:
>=2.40.0,<2.41.0- depends bioconductor-iranges:
>=2.40.0,<2.41.0a0- depends bioconductor-s4vectors:
>=0.44.0,<0.45.0- depends bioconductor-s4vectors:
>=0.44.0,<0.45.0a0- depends bioconductor-singlecellexperiment:
>=1.28.0,<1.29.0- depends bioconductor-singlecellexperiment:
>=1.28.0,<1.29.0a0- depends bioconductor-sparsematrixstats:
>=1.18.0,<1.19.0- depends bioconductor-sparsematrixstats:
>=1.18.0,<1.19.0a0- depends bioconductor-spatialexperiment:
>=1.16.0,<1.17.0- depends bioconductor-spatialexperiment:
>=1.16.0,<1.17.0a0- depends bioconductor-summarizedexperiment:
>=1.36.0,<1.37.0- depends bioconductor-summarizedexperiment:
>=1.36.0,<1.37.0a0- depends libblas:
>=3.9.0,<4.0a0- depends libgcc:
>=13- depends liblapack:
>=3.9.0,<4.0a0- depends r-base:
>=4.4,<4.5.0a0- depends r-cli:
- depends r-matrix:
>=1.5-0- requirements:
- additional platforms:
linux-aarch64
Installation
You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).
While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.
Given that you already have a conda environment in which you want to have this package, install with:
mamba install bioconductor-gsva and update with:: mamba update bioconductor-gsva
To create a new environment, run:
mamba create --name myenvname bioconductor-gsva
with
myenvnamebeing a reasonable name for the environment (see e.g. the mamba docs for details and further options).Alternatively, use the docker container:
docker pull quay.io/biocontainers/bioconductor-gsva:<tag> (see `bioconductor-gsva/tags`_ for valid values for ``<tag>``)
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/bioconductor-gsva/README.html)
