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Activity Number: 310 - SPEED:Statistical Methods for GWAs, Genetics, Genomics, and Other Omics Studies, Part 2
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 9:25 AM to 10:10 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #307685
Title: A Bayesian Zero-Inflated Negative Binomial Regression Model for the Integrative Analysis of Microbiome Data
Author(s): Shuang Jiang*
Companies: Southern Methodist University
Keywords: Bayesian regression; Count data; Feature selection; Integrative analysis; Microbiome; Mixture models

Microbiome ‘omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with identification of specific bacteria taxa associated with diseases, recent scientific advancements provide mounting evidence that metabolism, genetics and environmental factors can all modulate these microbial effects. However, the current methods for integrating microbiome data and other covariates are severely lacking. Hence, we present an integrative Bayesian zero-inflated negative binomial regression model that can both distinguish differential abundant taxa with distinct phenotypes and quantify covariate-taxa effects. Our model has good performance using simulated data. Furthermore, we successfully integrated microbiome taxonomies and metabolomics in two real microbiome datasets to provide biologically interpretable findings. In all, we proposed a novel integrative Bayesian regression model that features bacterial differential abundance analysis and microbiome-covariate effects quantifications which is suitable for general microbiome studies.

Authors who are presenting talks have a * after their name.

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