A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies

BMC Bioinformatics

Methodology Article

Andrew E. Teschendorff¹  Shijie C. Zheng²  Stephan Beck³  Charles E. Breeze³ 

[1] 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;Department of Women’s Cancer, University College London, 74 Huntley Street, WC1E 6 AU, London, United Kingdom;Statistical Cancer Genomics, Paul O’Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, WC1E 6BT, London, United Kingdom;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;Medical Genomics, Paul O’Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, WC1E 6BT, London, United Kingdom;

关键词: Cellular heterogeneity;    DNA methylation;    EWAS;   

DOI  :  10.1186/s12859-017-1511-5

 received in 2016-05-27, accepted in 2017-01-31,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundIntra-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.ResultsHere 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.ConclusionsEstimating cell-type fractions and subsequent inference in EWAS may benefit from the use of non-constrained reference-based cell-type deconvolution methods.

【 授权许可】

CC BY   
© The Author(s). 2017