PatternMarkers and Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS) for data-driven detection of novel biomarkers via whole transcriptome Non-negative matrix factorization (NMF)

Stein-O'Brien, G.; Carey, J.; Lee, W.-s.; Considine, M.; Favorov, A.; Flam, E.; Guo, T.; Li, L.; Marchionni, L.; Sherman, T.; Gaykalova, D.; McKay, R.; Ochs, M.; Colantuoni, C.; Fertig, E.

doi:10.1101/083717

bioRxiv · 10.1101/083717

PatternMarkers and Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS) for data-driven detection of novel biomarkers via whole transcriptome Non-negative matrix factorization (NMF)

Stein-O'Brien, G.·Carey, J.·Lee, W.-s.·Considine, M.·Favorov, A.·Flam, E.·Guo, T.·Li, L.·Marchionni, L.·Sherman, T.·Gaykalova, D.·McKay, R.·Ochs, M.·Colantuoni, C.·Fertig, E.

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Abstract

SummaryNon-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.\n\nAvailabilityPatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license.\n\nContactgsteinobrien@jhmi.edu; ccolantu@jhmi.edu; ejfertig@jhmi.edu

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Stein-O'Brien, G., Carey, J., Lee, W.-s., Considine, M., Favorov, A., Flam, E., Guo, T., Li, L., Marchionni, L., Sherman, T., Gaykalova, D., McKay, R., Ochs, M., Colantuoni, C., Fertig, E.. 2016-10-26. PatternMarkers and Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS) for data-driven detection of novel biomarkers via whole transcriptome Non-negative matrix factorization (NMF). https://doi.org/10.1101/083717

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