RT Journal Article
SR Electronic
T1 PatternMarkers and Genome-Wide CoGAPS in Analysis in Parallel Sets (GWCoGAPS) for data-driven detection of novel biomarkers via whole transcriptome Non-negative matrix factorization (NMF)
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 083717
DO 10.1101/083717
A1 Genevieve Stein-O’Brien
A1 Jacob Carey
A1 Wai-shing Lee
A1 Michael Considine
A1 Alexander Favorov
A1 Emily Flam
A1 Theresa Guo
A1 Lucy Li
A1 Luigi Marchionni
A1 Thomas Sherman
A1 Shawn Sivy
A1 Daria Gaykalova
A1 Ronald McKay
A1 Michael Ochs
A1 Carlo Colantuoni
A1 Elana Fertig
YR 2016
UL http://biorxiv.org/content/early/2016/10/26/083717.abstract
AB Summary NMF algorithms associate gene expression changes with biological processes (e.g., time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers identification. Therefore, we developed a novel PatternMarkers statistic to extract unique 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 GWCoGAPS, the first robust Bayesian NMF technique for whole genome transcriptomics using the sparse, MCMC algorithm, CoGAPS. This software contains additional 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 unique ability to detect data-driven biomarkers from whole genome data.Availability PatternMarkers and GWCoGAPS are in the CoGAPS Bioconductor package as of version 3.5 under the GPL license.Contact CColantu{at}jhmi.edu; ejfertig{at}jhmi.eduSupplementary information Supplementary data is available at Bioinformatics online.