CoGAPS

DOI: 10.18129/B9.bioc.CoGAPS    

This package is for version 3.9 of Bioconductor; for the stable, up-to-date release version, see CoGAPS.

Coordinated Gene Activity in Pattern Sets

Bioconductor version: 3.9

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: 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

Maintainer: Elana J. Fertig <ejfertig at jhmi.edu>, Thomas D. Sherman <tomsherman159 at gmail.com>

Installation

To install this package, start R (version "3.6") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("CoGAPS")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("CoGAPS")

 

HTML	R Script	CoGAPS
PDF		Reference Manual
Text		NEWS

Details

biocViews	Bayesian, Clustering, DifferentialExpression, DimensionReduction, GeneExpression, GeneSetEnrichment, ImmunoOncology, Microarray, MultipleComparison, RNASeq, Software, TimeCourse, Transcription
Version	3.4.1
In Bioconductor since	BioC 2.7 (R-2.12) (9 years)
License	GPL (==2)
Depends	R (>= 3.5.0)
Imports	BiocParallel, cluster, data.table, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5
LinkingTo	Rcpp
Suggests	testthat, knitr, rmarkdown, BiocStyle
SystemRequirements
Enhances
URL
Depends On Me
Imports Me	projectR
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	CoGAPS_3.4.1.tar.gz
Windows Binary	CoGAPS_3.4.1.zip
Mac OS X 10.11 (El Capitan)	CoGAPS_3.4.1.tgz
Source Repository	git clone https://git.bioconductor.org/packages/CoGAPS
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/CoGAPS
Package Short Url	https://bioconductor.org/packages/CoGAPS/
Package Downloads Report	Download Stats