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

ABSTRACT:

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. 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. Additionally, a manual version of the GWCoGAPS algorithm contains analytic and visualization tools including patternMatcher, a Sh

SUBMITTER: Stein-O'Brien GL 

PROVIDER: S-EPMC5860188 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature