题目：Cell-type-specific single cell gene regulatory network inference based on mixture Poisson log-normal graphical model

主讲人：北京大学 席瑞斌副教授

主持人：统计学院 常晋源教授

时间：2021年11月23日（周二）上午9:30-10:30

地点：腾讯会议，164 354 660

报告摘要：

Gene regulatory network (GRN), representing the regulatory relationships between genes, is important for understanding the complex biological system. Single-cell RNA sequencing (scRNA-seq) technologies can now allow unveiling GRNs at the single cell level. However, most scRNA-seq data contain cells of more than one cell type and each cell’s cell type is unknown. The GRNs of different cell types are different. A common practice to infer GRNs in different cell types is to first cluster the cells and infer GRNs for every cluster separately. However, the clustering can be inaccurate especially for clusters that are close to each other, thus leading to inferior performance of GRN inference. Here, we propose to model scRNA-seq by the mixture multivariate Poisson Log-Normal (MPLN) distribution. The precision matrices of the MPLN are the cell-type-specific GRNs and maximizing its lasso-penalized likelihood can provide the estimates of the cell-type-specific GRNs without first clustering the cells. To avoid the intractable evaluation and optimization of the MPLN’s log-likelihood, we develop an uncentered variational inferential approach (called VMPLN) to estimate the precision matrices. Under an irrepresentability condition and a few other mild conditions, we establish the convergence rates and sign consistency of the estimator of the precision matrices. Comprehensive simulation analyses and scRNA-seq data analyses further reveal that VMPLN gives better performance for estimating the cell-type-specific GRNs than other methods.

主讲人简介：

席瑞斌，北京大学数学科学学院、统计科学中心长聘副教授、研究员。2014年入选国家级重大人才青年计划。 2009年毕业于美国圣路易斯华盛顿大学，同年以助理研究员身份加入哈佛大学医学院从事生物医学信息学方面的研究。2012年9月加入北京大学。席瑞斌的主要研究方向是癌基因组及高通量测序数据计算分析、生物医学大数据的统计分析、大数据、贝叶斯统计、高维统计。席瑞斌近年来有40多篇文章发表于Nature, Nature Genetics, PNAS, Science Translational Medicine, Nature Communications, Nucleic Acids Research, Journal of Hepatology, Bioinformatics, Biometrika, IEEE Transactions on Knowledge and Data Engineering等顶级或权威学术期刊。席瑞斌先后主持或参与过科技部973项目、国家重点研发项目、基金委重点项目及基金委面上项目等多个科研基金项目。