Name Mode Size
..
cpp_tests 040000
data_structures 040000
file_parser 040000
math 040000
Archive.h 100644 4 kb
AtomicDomain.cpp 100644 5 kb
AtomicDomain.h 100644 3 kb
Cogaps.cpp 100644 2 kb
GapsAssert.h 100644 2 kb
GapsRunner.cpp 100644 11 kb
GapsRunner.h 100644 3 kb
GapsStatistics.cpp 100644 6 kb
GapsStatistics.h 100644 1 kb
GibbsSampler.cpp 100644 6 kb
GibbsSampler.h 100644 17 kb
Makevars 100644 1 kb
Makevars.in 100644 1 kb
ProposalQueue.cpp 100644 6 kb
ProposalQueue.h 100644 2 kb
RcppExports.cpp 100644 6 kb
test-runner.cpp 100644 0 kb
README.md
# CoGAPS [![Bioc](https://bioconductor.org/images/logo_bioconductor.gif)](https://bioconductor.org/packages/CoGAPS) [![downloads](https://bioconductor.org/shields/downloads/CancerInSilico.svg)](https://bioconductor.org/packages/CoGAPS) [![Travis-CI Build Status](https://travis-ci.org/CoGAPS/CoGAPS.svg?branch=master)](https://travis-ci.org/CoGAPS/CoGAPS) [![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/CoGAPS/CoGAPS?branch=master&svg=true)](https://ci.appveyor.com/project/CoGAPS/CoGAPS) [![Coverage Status](https://img.shields.io/codecov/c/github/CoGAPS/CoGAPS/master.svg)](https://codecov.io/github/CoGAPS/CoGAPS?branch=master) 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.