Literature DB >> 20810601

CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data.

Elana J Fertig1, Jie Ding, Alexander V Favorov, Giovanni Parmigiani, Michael F Ochs.   

Abstract

SUMMARY: Coordinated Gene Activity in Pattern Sets (CoGAPS) provides an integrated package for isolating gene expression driven by a biological process, enhancing inference of biological processes from transcriptomic data. CoGAPS 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. The software is provided as open source C++ code built on top of JAGS software with an R interface. AVAILABILITY: The R package CoGAPS and the C++ package GAPS-JAGS are provided open source under the GNU Lesser Public License (GLPL) with a users manual containing installation and operating instructions. CoGAPS is available through Bioconductor and depends on the rjags package available through CRAN to interface CoGAPS with GAPS-JAGS. URL: http://www.cancerbiostats.onc.jhmi.edu/cogaps.cfm .

Mesh:

Year:  2010        PMID: 20810601      PMCID: PMC3025742          DOI: 10.1093/bioinformatics/btq503

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

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Authors:  Jelle J Goeman; Peter Bühlmann
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7.  High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics.

Authors:  Carlos M Carvalho; Jeffrey Chang; Joseph E Lucas; Joseph R Nevins; Quanli Wang; Mike West
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8.  Detection of treatment-induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data.

Authors:  Michael F Ochs; Lori Rink; Chi Tarn; Sarah Mburu; Takahiro Taguchi; Burton Eisenberg; Andrew K Godwin
Journal:  Cancer Res       Date:  2009-11-10       Impact factor: 12.701

9.  Matrix factorization for recovery of biological processes from microarray data.

Authors:  Andrew V Kossenkov; Michael F Ochs
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Authors:  Ghislain Bidaut; Karsten Suhre; Jean-Michel Claverie; Michael F Ochs
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  10 in total
  30 in total

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3.  Updating annotations with the distributed annotation system and the automated sequence annotation pipeline.

Authors:  William Speier; Michael F Ochs
Journal:  Bioinformatics       Date:  2012-09-03       Impact factor: 6.937

4.  Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.

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Journal:  Cell Syst       Date:  2019-05-22       Impact factor: 10.304

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7.  Hypoxia tolerance in the Norrin-deficient retina and the chronically hypoxic brain studied at single-cell resolution.

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8.  Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets.

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9.  Identifying context-specific transcription factor targets from prior knowledge and gene expression data.

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Journal:  Cancer Cell       Date:  2019-02-11       Impact factor: 31.743

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