Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE

Tianyu Zhu; Andrew E Teschendorff

doi:10.1007/978-1-0716-2986-4_3

Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE

Methods Mol Biol. 2023:2629:23-42. doi: 10.1007/978-1-0716-2986-4_3.

Authors

Tianyu Zhu¹, Andrew E Teschendorff^{2

3}

Affiliations

¹ CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
² CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. andrew@picb.ac.cn.
³ UCL Cancer Institute, Paul O'Gorman Building, University College London, London, UK. andrew@picb.ac.cn.

PMID: 36929072
DOI: 10.1007/978-1-0716-2986-4_3

Abstract

DNA methylation data generated from bulk tissue represents a mixture of many different cell types. Variation in the cell-type composition of tissues is thus a major confounder when inferring differential DNA methylation. Due to the high cost of single-cell methylome sequencing, computational methods that can dissect the cell-type heterogeneity of bulk DNA methylomes offer an efficient and cost-effective solution, especially in the context of large-scale EWAS. In this chapter, we present a step-by-step tutorial of Epigenetic cell-type deconvolution using Single-Cell Omic References (EpiSCORE), a reference-based method that leverages the high-resolution nature of single-cell RNA-Seq datasets to facilitate microdissection of bulk-tissue DNA methylomes.

Keywords: Cell-type deconvolution; Cell-type heterogeneity; DNA methylation; EWAS; Single-cell RNA-Seq.

MeSH terms

Algorithms*
DNA Methylation*
Epigenome
Epigenomics