Guidelines on optimizing DNA methylation reference panels for cell-type deconvolution

doi:10.1038/s42003-026-09745-1

2026-02-19

Xiaolong Guo *

Andrew E. Teschendorff *

DOI：10.1038/s42003-026-09745-1

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摘要

En 中文

准确进行细胞类型解卷积对于正确解释表观基因组-wide关联研究至关重要。对于所有细胞类型解卷积任务，需要估计样本中的基础细胞类型比例，这通常通过使用由分选或单细胞DNA甲基化（DNAm）数据构建的DNA甲基化参考面板来实现。两种竞争方法已出现以构建此类参考面板：一种使用机器学习，另一种基于优化效应量和细胞类型特异性。本文证明后一种方法更优，因为构建面板时使用的分选样本数量相对较少，标准机器学习无法优化效应量和细胞类型特异性，导致模型过拟合，并在独立数据测试中表现不佳。此外，与基于高甲基化标志物构建的参考面板相比，基于细胞类型特异性低甲基化标志物构建的成人血液面板能更准确地估计细胞类型比例。这些见解为优化未来DNAm参考面板的构建提供了重要指导原则。为辅助此任务，我们已在EpiDISH R软件包中添加了构建优化DNAm参考面板的功能。对用于细胞类型解卷积的DNA甲基化参考面板构建方法的比较分析强调了优化其构建的重要指导原则。

Keyword:

Epigenomics

Microarrays

Life Sciences

general

期刊

Communications Biology

IF:

5.1

论文数:

1.1W

被引数:

3.2W

机构

Yueyang Road

学者数:

论文数: 1

被引数: 0

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