Name Mode Size
..
atomic 040000
cpp_tests 040000
data_structures 040000
file_parser 040000
gibbs_sampler 040000
include 040000
math 040000
utils 040000
Cogaps.cpp 100755 7 kb
GapsParameters.cpp 100755 4 kb
GapsParameters.h 100755 3 kb
GapsResult.cpp 100755 2 kb
GapsResult.h 100755 1 kb
GapsRunner.cpp 100755 14 kb
GapsRunner.h 100755 1 kb
GapsStatistics.cpp 100755 4 kb
GapsStatistics.h 100755 5 kb
Makevars.in 100755 0 kb
Makevars.win 100755 1 kb
RcppExports.cpp 100755 3 kb
test-runner.cpp 100755 0 kb
README.md
# CoGAPS Version: 3.2.40 [![Bioc](https://bioconductor.org/images/logo_bioconductor.gif)](https://bioconductor.org/packages/CoGAPS) [![downloads](https://bioconductor.org/shields/downloads/release/CoGAPS.svg)](http://bioconductor.org/packages/stats/bioc/CoGAPS/) [![Build Status](https://travis-ci.org/FertigLab/CoGAPS.svg?branch=master)](https://travis-ci.org/FertigLab/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. # Installing CoGAPS *CoGAPS* is a bioconductor R package and so the release version can be installed as follows: ``` install.packages("BiocManager") BiocManager::install("CoGAPS") ``` The most up-to-date version of *CoGAPS* can be installed directly from the *FertigLab* Github Repository: ``` ## Method 1 using BiocManager BiocManager::install("FertigLab/CoGAPS") ## Method 2 using devtools package devtools::install_github("FertigLab/CoGAPS") ``` # Using CoGAPS Follow the vignette here: http://htmlpreview.github.io/?https://github.com/FertigLab/CoGAPS/blob/develop/vignettes/CoGAPS.html