PT  - JOURNAL ARTICLE
AU  - Genevieve L Stein-O’Brien
AU  - Jacob L Carey
AU  - Wai-shing Lee
AU  - Michael Considine
AU  - Alexander V Favorov
AU  - Emily Flam
AU  - Theresa Guo
AU  - Sijia Li
AU  - Luigi Marchionni
AU  - Thomas Sherman
AU  - Shawn Sivy
AU  - Daria A Gaykalova
AU  - Ronald D McKay
AU  - Michael F Ochs
AU  - Carlo Colantuoni
AU  - Elana J Fertig
TI  - PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF
AID  - 10.1101/083717
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 083717
4099  - http://biorxiv.org/content/early/2016/10/28/083717.short
4100  - http://biorxiv.org/content/early/2016/10/28/083717.full
AB  - 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