To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("CoGAPS")
In most cases, you don't need to download the package archive at all.
CoGAPS
Coordinated Gene Activity in Pattern Sets
Bioconductor version: 3.0
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.
Author: Elana J. Fertig, Michael F. Ochs
Maintainer: Elana J. Fertig <ejfertig at jhmi.edu>, Michael F. Ochs <ochsm at tcnj.edu>
Citation (from within R,
enter citation("CoGAPS")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("CoGAPS")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("CoGAPS")
| R Script | GAPS/CoGAPS Users Manual | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GeneExpression, Microarray, Software |
| Version | 2.0.0 |
| In Bioconductor since | BioC 2.7 (R-2.12) |
| License | GPL (==2) |
| Depends | R (>= 3.0.1), Rcpp (>= 0.11.2), RColorBrewer (>= 1.0.5), gplots (>= 2.8.0) |
| Imports | graphics, grDevices, methods, stats, utils |
| LinkingTo | Rcpp, BH |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | CoGAPS_2.0.0.tar.gz |
| Windows Binary | CoGAPS_2.0.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | CoGAPS_2.0.0.tgz |
| Mac OS X 10.9 (Mavericks) | CoGAPS_2.0.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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