Lecture on "A Marginalized Two-Part Beta Regression Model for Microbiome Compositional Data"
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Lecture on "A Marginalized Two-Part Beta Regression Model for Microbiome Compositional Data"
DateandTime: 2017-06-19 21:36:38

Speaker: Liu Lei, Associate Professor, Feinberg School of Medicine, Northwestern University, US

Date: June 19, 2017

Time: 3:00 pm-4:00 pm

Location: Room 1238, Block B, Zhixin Building, Central Campus

Sponsor: ZhongTai Securities Institute for Financial Studies 


Abstract:

In microbiome studies, one important goal is to detect differential abundance of microbes across clinical conditions and treatment options. However, the microbiome compositional data (denoted by relative abundance) are highly skewed, bounded in [0, 1), and often with many zeros. A two-part model is commonly used to separate zeros and positive values explicitly by two submodels: a logistic model for the probability of a specie being present in Part I, and a Beta regression model for the relative abundance conditional on the presence of the specie in Part II. However, the regression coefficients in Part II cannot provide a marginal (unconditional) interpretation of covariate effects on the microbial abundance, which is of great interest in many applications. In this paper, we propose a marginalized two-part Beta regression model which captures the zero-inflation and skewness of microbiome data and also allows investigators to examine covariate effects on the marginal (unconditional) mean. We demonstrate its practical performance using simulation studies and apply the model to a real metagenomic dataset on mouse skin microbiota. We find that under the proposed marginalized model, without loss in power, the likelihood ratio test performs better in controlling the type I error than those under conventional methods.


Bio:   

Dr. Liu completed his Ph.D. from University of Michigan in 2004. He is currently associate professor of Feinberg School of Medicine at Northwestern University. His main research interests include survival analysis, longitudinal data analysis, spline regression methods, with applications to clinical and health services studies. He is particularly interested in the analysis of recurrent event data, medical cost data, and joint models of multi-process data. He has published in top journals such as Environmental Research, Journal of NeuroVirology, and so on.


For more information, please visit:

http://mathfinance.sdu.edu.cn/teacher/report_doReportContentDeatil.action?reportDto.reportId=de53e5b7-1ceb-478a-9ea9-5ad008a59b49 


Edited by: Shi Yajie




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