报告题目：Microbiome data analysis using zero-inflated probabilistic PCA models

报告人：王涛 研究员 （上海交通大学）

报告时间：2022年7月15日 （星期五）上午10：00-11：00

腾讯会议 ID：804-278-399

邀请人：王晓光 副教授 联系电话：84708351-8213

报告摘要：The analysis of microbiome data is complicated by several statistical challenges. In particular, microbiome data produced by high-throughput sequencing are count-valued, correlated, high-dimensional, over-dispersed with excess zeros, and compositional. To describe and simulate microbial community data, we introduce a general framework called Zero-Inflated Probabilistic PCA (ZIPPCA) by extending probabilistic PCA from the Gaussian setting to multivariate and sparse count data. We propose a negative binomial ZIPPCA model for dimension reduction and data visualization, and a logistic normal multinomial ZIPPCA model for inferring microbial compositions. We develop efficient variational approximation algorithms for maximum likelihood estimation and inference. We demonstrate the performance of the proposed methods on real microbiome data sets.

报告人简介：王涛博士，上海交通大学统计系、生物信息和生物统计系长聘副教授/博导，交大-耶鲁生物统计与数据科学联合中心研究员。东南大学学士、华东师范大学硕士、香港浸会大学博士、美国耶鲁大学博士后；国际统计学会Elected Member；获2021年度上海市生物信息学会青年新星奖。研究方向为生物统计和高维数据统计推断，主要成果发表在JASA，JRSSB，Biometrika，AOAS，Biometrics，Bioinformatics，Genome Biology，Briefings in Bioinformatics等。