报告题目：A MarginalizedTwo-Part Beta Regression Model for Microbiome Compositional Data
报 告 人：刘磊教授(美国西北大学)
报告摘要：Inmicrobiome studies, one important goal is to detect differential abundance ofmicrobes across clinical conditions and treatment options. However, themicrobiome compositional data (denoted by relative abundance) are highlyskewed, bounded in [0, 1), and often with many zeros. A two-part model iscommonly used to separate zeros and positive values explicitly by twosubmodels: a logistic model for the probability of a specie being present inPart I, and a Beta regression model for the relative abundance conditional onthe presence of the specie in Part II. However, the regression coefficients inPart II cannot provide a marginal (unconditional) interpretation of covariate effectson the microbial abundance, which is of great interest in many applications. Inthis paper, we propose a marginalized two-part Beta regression model whichcaptures the zero-inflation and skewness of microbiome data and also allowsinvestigators to examine covariate effects on the marginal (unconditional)mean. We demonstrate its practical performance using simulation studies andapply the model to a real metagenomic dataset on mouse skin microbiota. We findthat under the proposed marginalized model, without loss in power, thelikelihood ratio test performs better in controlling the type I error thanthose under conventional methods.