使用 EpiSCORE 对批量 DNA 甲基化数据进行细胞类型去卷积。
Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE.
机构信息
CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
UCL Cancer Institute, Paul O'Gorman Building, University College London, London, UK.
出版信息
Methods Mol Biol. 2023;2629:23-42. doi: 10.1007/978-1-0716-2986-4_3.
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.
从大量组织中生成的 DNA 甲基化数据代表了许多不同细胞类型的混合物。因此，当推断差异 DNA 甲基化时，组织中细胞类型组成的变化是一个主要的混杂因素。由于单细胞甲基组测序的成本很高，因此能够剖析大量 DNA 甲基组异质性的计算方法提供了一种高效且具有成本效益的解决方案，特别是在大规模 EWAS 的背景下。在本章中，我们将介绍一种使用单细胞 Omic 参考物（EpiSCORE）进行表观遗传细胞类型去卷积的分步教程，这是一种基于参考的方法，利用单细胞 RNA-Seq 数据集的高分辨率性质来促进批量组织 DNA 甲基组的微切割。
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