recipe bioconductor-cogaps

Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.

Homepage

https://bioconductor.org/packages/3.9/bioc/html/CoGAPS.html

License

GPL (==2)

Recipe

/bioconductor-cogaps/meta.yaml

package bioconductor-cogaps

(downloads) docker_bioconductor-cogaps

Versions

3.2.1-0

Depends bioconductor-biocparallel

>=1.16.0,<1.17.0

Depends bioconductor-s4vectors

>=0.20.0,<0.21.0

Depends bioconductor-singlecellexperiment

>=1.4.0,<1.5.0

Depends bioconductor-summarizedexperiment

>=1.12.0,<1.13.0

Depends libgcc-ng

>=7.3.0

Depends libstdcxx-ng

>=7.3.0

Depends r-base

>=3.5.1,<3.5.2.0a0

Depends r-bh

Depends r-cluster

Depends r-data.table

Depends r-gplots

Depends r-rcolorbrewer

Depends r-rcpp

Requirements

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-cogaps

and update with:

conda update bioconductor-cogaps

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-cogaps:<tag>

(see bioconductor-cogaps/tags for valid values for <tag>)