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CoGAPS | An R/C++ package to identify patterns and biological process activity in transcriptomic data

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An R/C++ package to identify patterns and biological process activity in transcriptomic data. CoGAPS provides an integrated package for isolating gene expression driven by a biological process, enhancing inference of biological processes from transcriptomic data. It improves on other enrichment measurement methods by combining a Markov chain Monte Carlo (MCMC) matrix factorization algorithm (GAPS) with a threshold-independent statistic inferring activity on gene sets. coGAPS infers biological activity by identifying overlapping, coregulated sets of genes and applying Z-score based statistics. It can be used to isolate transcription factor (TF) or BP activity in datasets of thousands of genes and tens to thousands of samples. The software is provided as open source C++ code built on top of JAGS software with an R interface.

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CoGAPS classification

CoGAPS specifications

Unique identifier:
OMICS_01973
Software type:
Package/Module
Restrictions to use:
None
Output data:
p-values for upregulation and for downregulation of each gene set in each pattern, conversion of p-values to activity estimates of each gene set in each pattern
Programming languages:
C++, R
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes
Name:
Coordinated Gene Activity in Pattern Sets
Interface:
Command line interface
Input data:
List or data frame containing the genes in each gene set, number of permutations used for the N/A distribution in the gene set statistic
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU General Public License version 2.0
Version:
2.8.0
Requirements:
Rcpp

CoGAPS distribution

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CoGAPS support

Documentation

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Publications

Institution(s)

McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD; Lieber Institute for Brain Development, Baltimore, MD, USA; Department of Oncology and Division of Biostatistics and Bioinformatics, Johns Hopkins School of Medicine, Baltimore, MD, USA; Vavilov Institute of General Genetics, Moscow, Russia; Research Institute of Genetics and Selection of Industrial Microorganisms, Moscow, Russia; Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Mathematics and Statistics, The College of New Jersey, Ewing Township, NJ, USA; Department of Neurology and Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA

Funding source(s)

This work was supported by the National Institutes of Health [NCI R01CA177669 and K25CA141053, NLM R01LM001100 and NCI P30 CA006973] and the Cleveland Foundation and Johns Hopkins University Discovery Awards.

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