title: Reference-free deconvolution of DNA methylation data and mediation by cell composition effects.
abstract: BACKGROUND
  Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying cell types.
  
  
  RESULTS
  We propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying methylomes, the number of these constituent cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of cells. We demonstrate these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent cell types.
  
  
  CONCLUSIONS
  Our methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by cell composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.
authors.affiliation: School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA. andres.houseman@oregonstate.edu.
authors.forename: E Andres
authors.identifier: http://orcid.org/0000-0003-0703-1830
authors.initials: EA
authors.lastname: Houseman
authors.affiliation: School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA.
authors.forename: Molly L
authors.initials: ML
authors.lastname: Kile
authors.affiliation: Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
authors.forename: David C
authors.initials: DC
authors.lastname: Christiani
authors.affiliation: Department of Pathology, University of Miami, Miller School of Medicine, Miami, FL, USA.
authors.forename: Tan A
authors.initials: TA
authors.lastname: Ince
authors.affiliation: Department of Epidemiology, Department of Pathology and Laboratory Medicine, Brown University, Providence, USA.
authors.forename: Karl T
authors.initials: KT
authors.lastname: Kelsey
authors.affiliation: Department of Community and Family Medicine, Dartmouth Medical School, Hanover, NH, USA.
authors.forename: Carmen J
authors.initials: CJ
authors.lastname: Marsit
mesh_terms: D000465:Algorithms; D019175:DNA Methylation; D057890:Epigenomics; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology
keywords: DNA methylation; Deconvolution; Epigenetics; Non-negative matrix factorization