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

doi:10.1093/bioinformatics/btx058

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

School of Medicine

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	1892-1894
Number of pages	3
Journal	Bioinformatics
Volume	33
Issue number	12
DOIs	https://doi.org/10.1093/bioinformatics/btx058
State	Published - 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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10.1093/bioinformatics/btx058

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Stein-O'Brien, G. L., Carey, J. L., Lee, W. S., Considine, M., Favorov, A. V., Flam, E., Guo, T., Li, S., Marchionni, L., Sherman, T., Sivy, S., Gaykalova, D. A., McKay, R. D., Ochs, M. F., Colantuoni, C., & Fertig, E. J. (2017). PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF. Bioinformatics, 33(12), 1892-1894. https://doi.org/10.1093/bioinformatics/btx058

Stein-O'Brien, GL, Carey, JL, Lee, WS, Considine, M, Favorov, AV, Flam, E, Guo, T, Li, S, Marchionni, L, Sherman, T, Sivy, S, Gaykalova, DA, McKay, RD, Ochs, MF, Colantuoni, C & Fertig, EJ 2017, 'PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF', Bioinformatics, vol. 33, no. 12, pp. 1892-1894. https://doi.org/10.1093/bioinformatics/btx058

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title = "PatternMarkers & GWCoGAPS for novel data-driven biomarkers via whole transcriptome NMF",

abstract = "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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AU - Favorov, Alexander V.

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AU - Guo, Theresa

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AU - Sherman, Thomas

AU - Sivy, Shawn

AU - Gaykalova, Daria A.

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AU - Colantuoni, Carlo

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

Abstract

ASJC Scopus subject areas

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