CoGAPS/CoGAPS: Coordinated Gene Activity in Pattern Sets
Version 2.5.2

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.

Getting started

Package details

AuthorWai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig
Bioconductor views Bayesian Clustering DifferentialExpression DimensionReduction GeneExpression GeneSetEnrichment Microarray MultipleComparison RNASeq TimeCourse Transcription
MaintainerElana J. Fertig <[email protected]>
LicenseGPL (==2)
Version2.5.2
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("CoGAPS/CoGAPS")
CoGAPS/CoGAPS documentation built on Sept. 23, 2017, 6:45 a.m.