Name Mode Size
..
AtomicSupport.cc 100644 20 kb
AtomicSupport.h 100644 4 kb
GAPSNorm.cpp 100644 29 kb
GAPSNorm.h 100644 3 kb
GibbsSampler-atomic.cpp 100644 5 kb
GibbsSampler-init.cpp 100644 5 kb
GibbsSampler-update.cpp 100644 43 kb
GibbsSampler-util.cpp 100644 12 kb
GibbsSampler.h 100644 11 kb
GibbsSamplerMap.cpp 100755 20 kb
GibbsSamplerMap.h 100755 3 kb
Matrix.cpp 100644 8 kb
Matrix.h 100644 2 kb
RcppExports.cpp 100644 5 kb
cogapsR.cpp 100644 21 kb
cogapsmapR.cpp 100644 21 kb
cogapsmaptestR.cpp 100755 26 kb
cogapstestR.cpp 100755 26 kb
randgen.cpp 100644 1 kb
randgen.h 100644 0 kb
sub_func.cpp 100644 2 kb
sub_func.h 100644 1 kb
README.md
# 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.