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.
UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT, UK.
Genome Biol. 2020 Sep 4;21(1):221. doi: 10.1186/s13059-020-02126-9.
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.
细胞类型异质性为表观基因组数据的解读带来了挑战,而大量细胞和样本可靠的单细胞 DNA 甲基化组的生成更加剧了这一挑战。我们提出了 EPISCORE,这是一种计算算法,可针对任何实体组织以单细胞类型分辨率对批量组织 DNA 甲基化数据执行虚拟显微切割。EPISCORE 将基因调控的概率表观遗传模型应用于单细胞 RNA-seq 组织图谱,以生成组织特异性 DNA 甲基化参考矩阵,从而能够对批量组织数据中的细胞类型比例和细胞类型特异性差异甲基化信号进行定量。我们在多个表观基因组研究和组织类型中验证了 EPISCORE。