Shen Ning, Korthauer Keegan
Department of Statistics, University of British Columbia, Vancouver, Canada.
Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, Vancouver, Canada.
Genome Biol. 2024 Dec 30;25(1):321. doi: 10.1186/s13059-024-03457-7.
Single-cell DNA methylation measurements reveal genome-scale inter-cellular epigenetic heterogeneity, but extreme sparsity and noise challenges rigorous analysis. Previous methods to detect variably methylated regions (VMRs) have relied on predefined regions or sliding windows and report regions insensitive to heterogeneity level present in input. We present vmrseq, a statistical method that overcomes these challenges to detect VMRs with increased accuracy in synthetic benchmarks and improved feature selection in case studies. vmrseq also highlights context-dependent correlations between methylation and gene expression, supporting previous findings and facilitating novel hypotheses on epigenetic regulation. vmrseq is available at https://github.com/nshen7/vmrseq .
单细胞DNA甲基化测量揭示了基因组规模的细胞间表观遗传异质性,但极端的稀疏性和噪声给严格分析带来了挑战。以前检测可变甲基化区域(VMR)的方法依赖于预定义区域或滑动窗口,并报告对输入中存在的异质性水平不敏感的区域。我们提出了vmrseq,这是一种统计方法,它克服了这些挑战,在合成基准测试中以更高的准确性检测VMR,并在案例研究中改进了特征选择。vmrseq还突出了甲基化与基因表达之间的上下文依赖性相关性,支持了以前的发现,并促进了关于表观遗传调控的新假设。可在https://github.com/nshen7/vmrseq获得vmrseq。