报告人: 王涛教授（上海交通大学）

报告时间：2023年11月18日，13:30-14:20

报告地点：金融工程研究中心105报告厅

报告题目：Analysis of sparse compositions of microbiomes

报告摘要：A central objective in microbiome research is to identify microbes that play crucial roles in both health and disease, with the potential of these microbes to serve as biomarkers for preventing, diagnosing, and treating diseases. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed, and contain a substantial proportion of zeros. The presence of compositionality, sparsity, and over-dispersion presents formidable challenges for absolute abundance analysis, leading to potentially misleading results when classical data analysis methods are applied. To address these challenges, we introduce a model-based approach called mbDecoda, for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Simulated examples and real-world data applications are used to comprehensively demonstrate the robustness and effectiveness of mbDecoda in the context of absolute abundance analysis.

个人简介：王涛博士，上海交通大学教授，博士生导师；交大-耶鲁生物统计与数据科学联合中心研究员。主要研究领域是生物医学大数据的统计共性算法和理论，尤其是微生物组数据的统计学基础和分析方法。相关成果发表在统计学期刊JASA、JRSSB、Biometrika，以及生物信息学期刊Genome Biology、Briefings in Bioinformatics、Bioinformatics。获国家自然科学基金优秀青年科学基金项目，是国际统计学会Elected Member，担任中国现场统计研究会统计交叉科学研究分会副理事长。

邀请人：徐礼柏