CoGAPS
Coordinated Gene Activity in Pattern Sets
Bioconductor version: 3.24 · Package version: 3.33.0
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CoGAPS")Details
| Maintainer | Elana J. Fertig <ejfertig@jhmi.edu>, Thomas D. Sherman <tomsherman159@gmail.com>, Jeanette Johnson <jjohn450@jhmi.edu>, Dmitrijs Lvovs <dlvovs1@jh.edu> |
| 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 |
| License | BSD_3_clause + file LICENSE |
| Downloads rank | 659 |
| Source branch | devel |
| biocViews | Bayesian, Clustering, DifferentialExpression, DimensionReduction, GeneExpression, GeneSetEnrichment, ImmunoOncology, Microarray, MultipleComparison, RNASeq, Software, TimeCourse, Transcription |
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | CoGAPS_3.33.0.tar.gz |
| Windows binary (x86_64) | CoGAPS_3.33.0.zip |
| macOS binary (arm64) | CoGAPS_3.33.0.tgz |
| macOS binary (x86_64) | CoGAPS_3.33.0.tgz |
Dependencies
Depends: R (>= 3.5.0)
Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5, dplyr, fgsea, forcats, ggplot2
Suggests: testthat, knitr, rmarkdown, BiocStyle, SeuratObject, BiocFileCache, xml2
Reverse dependencies
Suggests Me (2): projectR, SpaceMarkers