主 题:Factor augmented inverse regression and its application to microbiome data analysis(因子增广逆回归及其在微生物组数据分析中的应用)
主讲人:上海交通大学王涛教授
主持人:统计学院林华珍教授
时间:2023年6月6日(周五)下午16:00-17:00
举办地点:柳林校区弘远楼408会议室
主办单位:统计研究中心和统计学院 科研处
主讲人简介:
王涛博士,上海交通大学长聘副教授,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;国际统计学会Elected Member。研究方向为生物统计和高维数据统计推断,在JASA,JRSSB,Biometrika,Genome Biology,Briefings in Bioinformatics,Bioinformatics等期刊发表论文五十余篇;主持国家自然科学基金面上项目和优秀青年科学基金项目,参与中央高校优秀青年团队项目。
内容简介:
We investigate the relationship between count data that inform the relative abundance of features of a composition, and factors that influence the composition. We introduce multinomial Factor Augmented Inverse Regression (FAIR) of the count vector onto response factors as a general framework for obtaining low-dimensional summaries of the count vector that preserve information relevant to the response. By augmenting known response factors with random latent factors, FAIR extends multinomial logistic regression to account for overdispersion and general correlations among counts. The method of maximum variational likelihood and a fast variational expectation-maximization algorithm are proposed for approximate inference based on variational approximation, and the asymptotic properties of the resulting estimator are derived. The effectiveness of FAIR is illustrated through application to a microbiome data set.
主讲人研究了代表成分特征相对丰度的计数数据与影响成分的因子之间的关系。主讲人将计数向量的多项因子增广逆回归(FAIR)引入到响应因子上,作为获得计数向量的低维总结特征的一般框架,该框架保留了与响应相关的信息。通过用随机潜在因子增加已知的响应因子,FAIR扩展了多项逻辑回归来说明计数之间的过离散和广义相关性。基于变分逼近提出了最大变分似然和快速变分期望最大化算法来进行近似推断,并给出了估计量的渐近性质。通过将其应用于微生物组数据集,说明了FAIR的有效性。