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

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

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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 Shiny web application. The decomposition in the manual pipeline can be replaced with any NMF algorithm, for further generalization of the software. 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 and Implementation: PatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license.

Original languageEnglish (US)
Pages (from-to)1892-1894
Number of pages3
Issue number12
StatePublished - Jun 15 2017

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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