DOI: 10.18129/B9.bioc.CoGAPS    

Coordinated Gene Activity in Pattern Sets

Bioconductor version: Release (3.6)

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.

Author: Wai-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

Maintainer: Elana J. Fertig <ejfertig at>

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PDF R Script GAPS/CoGAPS Users Manual
PDF   Reference Manual
Text   NEWS


biocViews Bayesian, Clustering, DifferentialExpression, DimensionReduction, GeneExpression, GeneSetEnrichment, Microarray, MultipleComparison, RNASeq, Software, TimeCourse, Transcription
Version 2.12.0
In Bioconductor since BioC 2.7 (R-2.12) (7.5 years)
License GPL (==2)
Depends R (>= 3.0.1), Rcpp (>= 0.11.2)
Imports RColorBrewer (>= 1.0.5), gplots (>= 2.8.0), graphics, grDevices, methods, cluster, shiny, stats, utils, doParallel, foreach, ggplot2, reshape2
LinkingTo Rcpp, BH, RcppArmadillo
Suggests testthat, iterators, parallel, lintr
Depends On Me
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Suggests Me
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