Recursively partitioned mixture model clustering of DNA methylation data using biologically informed correlation structures

Koestler Devin C. (), Christensen Brock C., Marsit Carmen J., Kelsey Karl T. and Houseman E. Andres
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Koestler Devin C.: Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
Christensen Brock C.: Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA Department of Pharmacology and Toxicology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA
Marsit Carmen J.: Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA Department of Pharmacology and Toxicology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA
Kelsey Karl T.: Department of Pathology and Laboratory Medicine, Brown University, Providence, RI, USA Department of Epidemiology, Brown University, Providence, RI, USA
Houseman E. Andres: Department of Public Health, Oregon State University, Corvallis, OR, USA

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 2, 225-240

Abstract: DNA methylation is a well-recognized epigenetic mechanism that has been the subject of a growing body of literature typically focused on the identification and study of profiles of DNA methylation and their association with human diseases and exposures. In recent years, a number of unsupervised clustering algorithms, both parametric and non-parametric, have been proposed for clustering large-scale DNA methylation data. However, most of these approaches do not incorporate known biological relationships of measured features, and in some cases, rely on unrealistic assumptions regarding the nature of DNA methylation. Here, we propose a modified version of a recursively partitioned mixture model (RPMM) that integrates information related to the proximity of CpG loci within the genome to inform correlation structures from which subsequent clustering analysis is based. Using simulations and four methylation data sets, we demonstrate that integrating biologically informative correlation structures within RPMM resulted in improved goodness-of-fit, clustering consistency, and the ability to detect biologically meaningful clusters compared to methods which ignore such correlation. Integrating biologically-informed correlation structures to enhance modeling techniques is motivated by the rapid increase in resolution of DNA methylation microarrays and the increasing understanding of the biology of this epigenetic mechanism.

Keywords: finite mixture models epigenetics; genomic data; model-based clustering (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/sagmb-2012-0068

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