Skip to content
Bayesian MCMC matrix factorization algorithm
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
data compress R data Jun 24, 2019
inst passing debug tests locally Jun 24, 2019
man fixed documentation and vignette errors Jun 24, 2019
src correct typo in windows Makevars Jul 2, 2019
tests fix typo that caused sample names to be unread Jun 24, 2019
vignettes version bump Jun 26, 2019
.Rbuildignore ignore new object files in R builds Jun 24, 2019
.gitignore fixed windows build issues Feb 8, 2019
.travis.yml test on osx as well as linux Jul 2, 2019
COPYING updated git config so that permissions get committed Oct 29, 2018
ChangeLog version bumP Jul 2, 2019
DESCRIPTION version bumP Jul 2, 2019
NAMESPACE updated docs Jun 26, 2019
NEWS updated git config so that permissions get committed Oct 29, 2018 version bumP Jul 2, 2019
cleanup fixed cleanup script and removed incorrect assert statements Jun 19, 2019
configure version bumP Jul 2, 2019 temporarily disable checkpoints to get clean builds Jul 2, 2019 make sure windows tests are passing Oct 30, 2018

CoGAPS Version: 3.5.6

Bioc downloads Build Status

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.

Installing CoGAPS

CoGAPS is a bioconductor R package (link) and so the release version can be installed as follows:


The most up-to-date version of CoGAPS can be installed directly from the FertigLab Github Repository:


Using CoGAPS

Follow the vignette here

You can’t perform that action at this time.