A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data

• Activity Number: 611 - Nonparametric Priors for Exchangeable Data and Beyond
• Type: Topic Contributed
• Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Juhee Lee* and Marilou Sison-Mangus
• Companies: UC Santa Cruz and Unversity of California, Santa Cruz
• Keywords: Count data; Laplace Prior; Metagenomic; Process convolution; Negative binomial model; 16S ribosomal DNA sequencing

Abstract

The successional dynamics of microbial communities are influenced by the synergistic interactions of physical and biological factors. We develop a Bayesian semiparametric regression model to investigate how microbial abundance and succession change with covarying physical and biological factors using 16S rDNA sequencing data. A generalized linear regression model is built using the Laplace prior to improve estimation of covariate effects on mean abundances of microbial species represented by operational taxonomic units (OTUs). A nonparametric prior model is used to facilitate borrowing strength across OTUs, across samples and across time points. It flexibly estimates baseline mean abundances of OTUs and provides the basis for improved quantification of covariate effects. The proposed method does not require prior normalization of OTU counts to adjust differences in sample total counts. Instead, the normalization and estimation of covariate effects on OTU abundance levels are simultaneously carried out for joint analysis of all OTUs. Using simulation studies and a real data analysis, we demonstrate improved inference compared to an existing method.