recipe bioconductor-gsva

Gene Set Variation Analysis for Microarray and RNA-Seq Data

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/GSVA.html

License:

GPL (>= 2)

Recipe:

/bioconductor-gsva/meta.yaml

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

(downloads) docker_bioconductor-gsva

versions:
1.50.0-01.48.2-01.46.0-11.46.0-01.42.0-21.42.0-11.42.0-01.40.0-01.38.2-0

1.50.0-01.48.2-01.46.0-11.46.0-01.42.0-21.42.0-11.42.0-01.40.0-01.38.2-01.38.0-01.36.0-01.34.0-01.32.0-11.30.0-11.30.0-01.28.0-01.26.0-01.24.2-01.24.1-0

depends bioconductor-biobase:

>=2.62.0,<2.63.0

depends bioconductor-biobase:

>=2.62.0,<2.63.0a0

depends bioconductor-biocparallel:

>=1.36.0,<1.37.0

depends bioconductor-biocparallel:

>=1.36.0,<1.37.0a0

depends bioconductor-biocsingular:

>=1.18.0,<1.19.0

depends bioconductor-biocsingular:

>=1.18.0,<1.19.0a0

depends bioconductor-delayedarray:

>=0.28.0,<0.29.0

depends bioconductor-delayedarray:

>=0.28.0,<0.29.0a0

depends bioconductor-delayedmatrixstats:

>=1.24.0,<1.25.0

depends bioconductor-delayedmatrixstats:

>=1.24.0,<1.25.0a0

depends bioconductor-gseabase:

>=1.64.0,<1.65.0

depends bioconductor-gseabase:

>=1.64.0,<1.65.0a0

depends bioconductor-hdf5array:

>=1.30.0,<1.31.0

depends bioconductor-hdf5array:

>=1.30.0,<1.31.0a0

depends bioconductor-iranges:

>=2.36.0,<2.37.0

depends bioconductor-iranges:

>=2.36.0,<2.37.0a0

depends bioconductor-s4vectors:

>=0.40.0,<0.41.0

depends bioconductor-s4vectors:

>=0.40.2,<0.41.0a0

depends bioconductor-singlecellexperiment:

>=1.24.0,<1.25.0

depends bioconductor-singlecellexperiment:

>=1.24.0,<1.25.0a0

depends bioconductor-sparsematrixstats:

>=1.14.0,<1.15.0

depends bioconductor-sparsematrixstats:

>=1.14.0,<1.15.0a0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0a0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc-ng:

>=12

depends liblapack:

>=3.9.0,<4.0a0

depends r-base:

>=4.3,<4.4.0a0

depends r-matrix:

>=1.5-0

requirements:

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 myenvname being 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>``)

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