EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data
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EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data Andrew E. Teschendorff1,2*† , Tianyu Zhu1†, Charles E. Breeze3 and Stephan Beck2 * Correspondence: [email protected]. cn † Andrew E. Teschendorff and Tianyu Zhu contributed equally to this work. 1 CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China 2 UCL Cancer Institute, Paul O’Gorman Building, University College London, 72 Huntley Street, London WC1E 6BT, UK Full list of author information is available at the end of the article
Abstract Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types. Keywords: DNA methylation, EWAS, Single-cell RNA-Seq
Background DNA methylation is a key cell type-specific epigenetic mark associated with gene expression that plays a key role in development and differentiation [1]. Epigenome-wide association studies (EWAS) have demonstrated altered DNA methylation (DNAm) patterns in a wide range of diseases [2, 3], but interpretation is severely hampered by cell type heterogeneity [4–7]. Indeed, due to cost and logistical reasons, almost all of the genome-wide DNA profiles generated to date have been performed in complex tissues that are composed of many different cell types, which can confound analysis and prevent the identification of cell type-specific changes underlying disease. In principle, the challenge posed by cell type heterogeneity is best addressed with single-cell technologies [8, 9], which consortia such as the Human and Mouse Cell Atlas (HCA/MCA) projects [10–13] are using to generate tissue-specific single-cell RNA-Seq (scRNA-Seq) atlases at high cellular resolution. Such tissue-specific scRNA-Seq atlases provide a nearly unbiased catalog of all major cell types present in a tissue and constitute a resource that is already being exploited to enable cell type deconvolution of bulk mRNA expression profiles [14]. However, the generation of single-cell atlases at the DNAm © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution a
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