EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data

Genome Biol. 2020 Sep 4;21(1):221. doi: 10.1186/s13059-020-02126-9.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • DNA Methylation*
  • Endothelial Cells
  • Epigenesis, Genetic
  • Epigenome*
  • Epigenomics*
  • Humans
  • Lung
  • Lung Neoplasms / genetics
  • RNA, Messenger
  • RNA-Seq / methods*
  • Single-Cell Analysis / methods*

Substances

  • RNA, Messenger