recipe bioconductor-cogaps

Coordinated Gene Activity in Pattern Sets

Homepage

https://bioconductor.org/packages/3.11/bioc/html/CoGAPS.html

License

GPL (==2)

Recipe

/bioconductor-cogaps/meta.yaml

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.

package bioconductor-cogaps

Versions

3.8.0-0,  3.6.0-0,  3.4.1-0,  3.2.1-0

Depends

• bioconductor-biocparallel >=1.22.0,<1.23.0

• bioconductor-rhdf5 >=2.32.0,<2.33.0

• bioconductor-s4vectors >=0.26.0,<0.27.0

• bioconductor-singlecellexperiment >=1.10.0,<1.11.0

• bioconductor-summarizedexperiment >=1.18.0,<1.19.0

• libblas >=3.8.0,<4.0a0

• libgcc-ng >=7.3.0

• liblapack >=3.8.0,<3.9.0a0

• libstdcxx-ng >=7.3.0

• r-base >=4.0,<4.1.0a0

• r-cluster

• r-gplots

• r-rcolorbrewer

• r-rcpp

Required By

• bioconductor-projectr

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>)

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-cogaps/README.html)