Ali Rahnavard1, Suvo Chatterjee2, Bahar Sayoldin3, Keith A Crandall1, Fasil Tekola-Ayele2, Himel Mallick4. 1. Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA. 2. Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. 3. School of Systems Biology, George Mason University, Fairfax, VA 22030, USA. 4. Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA.
Abstract
MOTIVATION: The discovery of biologically interpretable and clinically actionable communities in heterogeneous omics data is a necessary first step towards deriving mechanistic insights into complex biological phenomena. Here we present a novel clustering approach, omeClust, for community detection in omics profiles by simultaneously incorporating similarities among measurements and the overall complex structure of the data. RESULTS: We show that omeClust outperforms published methods in inferring the true community structure as measured by both sensitivity and misclassification rate on simulated datasets. We further validated omeClust in diverse, multiple omics datasets, revealing new communities and functionally related groups in microbial strains, cell line gene expression patterns, and fetal genomic variation. We also derived enrichment scores attributable to putatively meaningful biological factors in these datasets that can serve as hypothesis generators facilitating new sets of testable hypotheses. AVAILABILITY: omeClust is open-source software, and the implementation is available online at http://github.com/omicsEye/omeClust. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The discovery of biologically interpretable and clinically actionable communities in heterogeneous omics data is a necessary first step towards deriving mechanistic insights into complex biological phenomena. Here we present a novel clustering approach, omeClust, for community detection in omics profiles by simultaneously incorporating similarities among measurements and the overall complex structure of the data. RESULTS: We show that omeClust outperforms published methods in inferring the true community structure as measured by both sensitivity and misclassification rate on simulated datasets. We further validated omeClust in diverse, multiple omics datasets, revealing new communities and functionally related groups in microbial strains, cell line gene expression patterns, and fetal genomic variation. We also derived enrichment scores attributable to putatively meaningful biological factors in these datasets that can serve as hypothesis generators facilitating new sets of testable hypotheses. AVAILABILITY: omeClust is open-source software, and the implementation is available online at http://github.com/omicsEye/omeClust. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.