%0 Journal Article %A Genevieve L Stein-O’Brien %A Jacob L Carey %A Wai-shing Lee %A Michael Considine %A Alexander V Favorov %A Emily Flam %A Theresa Guo %A Sijia Li %A Luigi Marchionni %A Thomas Sherman %A Shawn Sivy %A Daria A Gaykalova %A Ronald D McKay %A Michael F Ochs %A Carlo Colantuoni %A Elana J Fertig %T PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF %D 2016 %R 10.1101/083717 %J bioRxiv %P 083717 %X Summary 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.Contact gsteinobrien{at}jhmi.edu; ccolantu{at}jhmi.edu; ejfertig{at}jhmi.edu %U https://www.biorxiv.org/content/biorxiv/early/2016/10/28/083717.full.pdf