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

Bioinformatics. 2017 Jun 15;33(12):1892-1894. doi: 10.1093/bioinformatics/btx058.


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 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.

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Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

  • Algorithms*
  • Bayes Theorem
  • Biomarkers
  • Gene Expression Profiling / methods*
  • Humans
  • Sequence Analysis, RNA / methods
  • Software*


  • Biomarkers