Introduction

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.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.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.

Publications

  1. Reference-free deconvolution of DNA methylation data and mediation by cell composition effects.
    Cite this
    Houseman EA, Kile ML, Christiani DC, Ince TA, Kelsey KT, Marsit CJ, 2016-06-01 - BMC bioinformatics
  2. Reference-free cell mixture adjustments in analysis of DNA methylation data.
    Cite this
    Houseman EA, Molitor J, Marsit CJ, 2014-05-01 - Bioinformatics (Oxford, England)

Credits

  1. E Andrés Houseman
    Developer

    School of Biological and Population Health Sciences, College of Public Health and Human Sciences, United States of America

  2. Molly L Kile
    Developer

    School of Biological and Population Health Sciences, College of Public Health and Human Sciences, United States of America

  3. David C Christiani
    Developer

    Department of Environmental Health, Harvard T. H. Chan School of Public Health, United States of America

  4. Tan A Ince
    Developer

    Department of Pathology, University of Miami, United States of America

  5. Karl T Kelsey
    Developer

    Department of Epidemiology, Department of Pathology and Laboratory Medicine, United States of America

  6. Carmen J Marsit
    Investigator

    Department of Community and Family Medicine, Dartmouth Medical School, United States of America

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Summary
AccessionBT006446
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByCarmen J Marsit