Statistical Methods for Microbiome Association Analysis
报告人：Xiang Zhan (Peking University)
时间：2021-11-11 14:00-16:00
地点：Room 1418, Sciences Building No. 1
Abstract: Advancement in next generation high-throughput sequencing technologies—such as genomics, transcriptomics, proteomics, metabolomics and metagenomics—allows characterization of the human omics profile at an extraordinarily detailed molecular level. Among the fields of omics studies, a very popular mode of analysis is the association analysis, which tries to establish associative relationships between omics features and disease outcomes as the first step to study the underlying biological omics mechanism of the disease. Despite its popularity, the field of omics association studies, however, has not yet reached enough maturity for making the leap from omics survey to rational omics-based personalized therapeutics. One primary limitation to leverage this large body of omics sequencing data is computational and statistical challenges, including high-dimensionality, sparse data structure, relatively small effect size or sample size and complex dependence/correlation structure among omics features. Taking microbiome and metagenomics data as examples, in this talk, we discuss some recent statistical methods to combat these challenges in microbiome association analysis. Our proposed methods are both powerful and robust, while maintaining both statistical rigor and biological relevance. Using comprehensive numerical simulation studies, we will show that the proposed methods are superior than existing counterparts in literature. We will also demonstrate the potential usefulness of our methods by applications to several real data sets.
Biography: 占翔，tyc234cc 太阳成集团公共卫生学院生物统计系及北京国际数学研究中心副教授。近年来一直从事生物统计和生物信息学，统计遗传，高维分子生物组学数据分析与统计推断研究。先后主持2项美国国家科学基金委及美国国家卫生研究院的科研基金项目，在生物统计和生物信息学等相关领域的国际SCI杂志上发表科研论文近40篇，其中约一半为第一作者或通讯作者。
