CoGAPS/CoGAPS: Coordinated Gene Activity in Pattern Sets
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
Browse package contents
Package details |
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| Author | Jeanette Johnson, Ashley Tsang, Jacob Mitchell, Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig |
| Bioconductor views | Bayesian Clustering DifferentialExpression DimensionReduction GeneExpression GeneSetEnrichment ImmunoOncology Microarray MultipleComparison RNASeq TimeCourse Transcription |
| Maintainer | Elana J. Fertig <ejfertig@jhmi.edu>, Thomas D. Sherman <tomsherman159@gmail.com>, Jeanette Johnson <jjohn450@jhmi.edu>, Dmitrijs Lvovs <dlvovs1@jh.edu> |
| License | BSD_3_clause + file LICENSE |
| Version | 3.24.0 |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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