报告题目：scDMV: A zero-one inflated beta mixture modeling strategy for differential methylation analysis with single-cell bisulfite sequencing data

报告时间：2023年6月27日（周二）上午9:00-10:00

报告链接：https://meeting.tencent.com/dm/X0BfLGDqx6AO

腾讯会议：915 598 707

报告摘要：The whole genome bisulfite sequencing has been the gold standard of DNA methylation detection at single-nucleotide resolution on a genome-wide scale. Traditionally, sequencing methods can only get the average expression level of many cells and therefore ignore heterogeneity among individual cells. To observe the multilayered status of single cells, single-cell bisulfite sequencing (scBS-seq) technologies have been rapidly developed and proven to be an effective and powerful tool in identification of differentially methylated region (DMR). However, DMR recognition with scBS-seq has low precision accuracy since data are often sparse and have excess zeros and ones, due to the relatively low sequencing depth and low coverage. A new differential methylation analysis approach that can well accommodate the special features of such data and enhance recognition accuracy is most desirable. A new beta mixture approach (scDMV) that incorporates excess zeros and ones and allows low-input sequencing is proposed for single-cell bisulfite sequencing data to analyze methylation differences between samples from different groups for a site or region. Compared with several alternative methods, the scDMV approach performs favorably in terms of both sensitivity and precision and also has a good control of the false positive rate as shown in our extensive simulation studies. In real data applications, we also find that scDMV method exhibits higher precision and sensitivity in identifying differentially methylation regions, even for low-input samples. Furthermore, scDMV can delineate important information that is missed by other methods for GO enrichment analysis with single cell whole genome sequencing data. scDMV is available as an R package along with the tutorial at https://github.com/PLX-m/scDMV.

报告人简介：周彦，深圳大学数学与统计学院特聘研究员，博士生导师。2015年美国伊利诺伊大学香槟分校从事博士后研究，后入职深圳大学。先后多次访问香港大学、香港浸会大学和香港城市大学等。主要从事生物统计和机器学习等方面的研究。获得深圳市孔雀计划奖励C类和“南山区领航人才”。主持国家面上项目、国家青年项目等数项。以第一作者身份在Genome Research（影响因子：14.38），Bioinformatics（影响因子：7.38），Statistics in Medicine等国际顶级期刊上发表高水平SCI论文近40篇。单篇最高引用次数150多次。目前任广东省高等学校教学指导委员会委员、中国现场统计协会理事、广东省现场统计协会副理事长和常务理事和中国环境资源统计协会和教育统计协会常务理事。