  Microbiome `omics approaches can reveal intriguing relationships between the
human microbiome and certain disease states. Along with the 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 differentially abundant taxa with distinct
phenotypes and quantify covariate-taxa effects. Our model demonstrates 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
makes it suitable for general microbiome studies.
