Calling differential DNA methylation at cell-type resolution: avoiding misconceptions and promoting best practices
Calling differential DNA methylation at cell-type resolution: avoiding misconceptions and promoting best practices
Abstract The accurate detection of cell-type specific DNA methylation alterations in the context of general epigenome studies is an important task to improve our understanding of epigenomics in disease development. Although a number of statistical algorithms designed to address this problem have emerged, the task remains challenging. Here we show that a recent commentary by Rahmani et al, that aims to address misconceptions and best practices in the field, continues to suffer from critical misconceptions in how statistical algorithms should be compared and evaluated. In addition, we report contradictory results on real EWAS datasets.
Jing Han、Beck Stephan、Teschendorff Andrew E.、Zheng Shijie C.、Breeze Charles E.
CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational BiologyUCL Cancer Institute, University College LondonCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology||UCL Cancer Institute, University College LondonCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology||John Hopkins School of MedicineAltius Institute for Biomedical Sciences
基础医学生物科学研究方法、生物科学研究技术分子生物学
EWASDNA methylationcell-type heterogeneitycell-type deconvolution
Jing Han,Beck Stephan,Teschendorff Andrew E.,Zheng Shijie C.,Breeze Charles E..Calling differential DNA methylation at cell-type resolution: avoiding misconceptions and promoting best practices[EB/OL].(2025-03-28)[2025-05-09].https://www.biorxiv.org/content/10.1101/2021.02.28.433245.点此复制
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