报告时间：2021年10月14日 19：00-20：00

腾讯会议：912 710 219

报告人：罗翔宇（中国人民大学）

报告摘要：Inferring gene regulatory networks can elucidate how genes work cooperatively. The gene-gene collaboration information is often learned by Gaussian graphical models (GGM) that aim to identify whether the expression levels of any pair of genes are dependent given other genes' expression values. One basic assumption that guarantees the validity of GGM is data normality, and this often holds for bulk-level expression data which aggregate biological signals from a collection of cells. However, fine-grained cell-level expression profiles collected in single-cell RNA-sequencing (scRNA-seq) reveal non-normality features---cellular heterogeneity and zero-inflation. We propose a Bayesian latent mixture GGM to jointly estimate multiple gene regulatory networks accounting for the zero-inflation and unknown heterogeneity of single-cell expression data. The proposed approach outperforms competing methods on synthetic data in terms of network structure and precision matrix estimation accuracy and provides biological insights when applied to two real-world scRNA-seq datasets.

报告人介绍：罗翔宇，中国人民大学统计与大数据研究院助理教授、博士生导师。2018年博士毕业于香港中文大学统计系，2014年本科毕业于中国科学技术大学统计与金融系。研究兴趣为贝叶斯统计、生物信息学、统计计算等。已有研究成果发表在Journal of the American Statistical Association, Annals of Applied Statistics, Nature Communications等统计或生物信息国际期刊上。

邀请人：马学俊