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PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF

By Genevieve L Stein-O’Brien, Jacob L Carey, Wai-shing Lee, Michael Considine, Alexander V Favorov, Emily Flam, Theresa Guo, Sijia Li, Luigi Marchionni, Thomas Sherman, Shawn Sivy, Daria A Gaykalova, Ronald D McKay, Michael F. Ochs, Carlo Colantuoni, Elana J. Fertig

Posted 26 Oct 2016
bioRxiv DOI: 10.1101/083717 (published DOI: 10.1093/bioinformatics/btx058)

Non-negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g., time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel PatternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with PatternMarkers requires whole-genome data. However, NMF algorithms typically do not converge for the tens of thousands of genes in genome-wide profiling. Therefore, we also developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. This software contains analytic and visualization tools including a Shiny web application, patternMatcher, which are generalized for any NMF. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTex data, illustrating GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data. Availability: PatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license.

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