title: A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies.
abstract: BACKGROUND
  Intra-sample cellular heterogeneity presents numerous challenges to the identification of biomarkers in large Epigenome-Wide Association Studies (EWAS). While a number of reference-based deconvolution algorithms have emerged, their potential remains underexplored and a comparative evaluation of these algorithms beyond tissues such as blood is still lacking.
  
  
  RESULTS
  Here we present a novel framework for reference-based inference, which leverages cell-type specific DNAse Hypersensitive Site (DHS) information from the NIH Epigenomics Roadmap to construct an improved reference DNA methylation database. We show that this leads to a marginal but statistically significant improvement of cell-count estimates in whole blood as well as in mixtures involving epithelial cell-types. Using this framework we compare a widely used state-of-the-art reference-based algorithm (called constrained projection) to two non-constrained approaches including CIBERSORT and a method based on robust partial correlations. We conclude that the widely-used constrained projection technique may not always be optimal. Instead, we find that the method based on robust partial correlations is generally more robust across a range of different tissue types and for realistic noise levels. We call the combined algorithm which uses DHS data and robust partial correlations for inference, EpiDISH (Epigenetic Dissection of Intra-Sample Heterogeneity). Finally, we demonstrate the added value of EpiDISH in an EWAS of smoking.
  
  
  CONCLUSIONS
  Estimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constrained reference-based cell-type deconvolution methods.
authors.affiliation: CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 200031, Shanghai, China. a.teschendorff@ucl.ac.uk.|Department of Women's Cancer, University College London, 74 Huntley Street, London, WC1E 6 AU, United Kingdom. a.teschendorff@ucl.ac.uk.|Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, WC1E 6BT, London, United Kingdom. a.teschendorff@ucl.ac.uk.
authors.forename: Andrew E
authors.initials: AE
authors.lastname: Teschendorff
authors.affiliation: Medical Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, WC1E 6BT, London, United Kingdom.
authors.forename: Charles E
authors.initials: CE
authors.lastname: Breeze
authors.affiliation: CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 200031, Shanghai, China.|University of Chinese Academy of Sciences, 19A Yuquan Road, 100049, Beijing, China.
authors.forename: Shijie C
authors.initials: SC
authors.lastname: Zheng
authors.affiliation: Medical Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, WC1E 6BT, London, United Kingdom.
authors.forename: Stephan
authors.initials: S
authors.lastname: Beck
mesh_terms: D000465:Algorithms; D057890:Epigenomics / Q000379:methods*; D005819:Genetic Markers; D055106:Genome-Wide Association Study; D006801:Humans
chemical_list: D005819:Genetic Markers
keywords: Cellular heterogeneity; DNA methylation; EWAS