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Microbiome `omics approaches can reveal intriguing relationships between thehuman microbiome and certain disease states. Along with the identification ofspecific bacteria taxa associated with diseases, recent scientific advancementsprovide mounting evidence that metabolism, genetics and environmental factorscan all modulate these microbial effects. However, the current methods forintegrating microbiome data and other covariates are severely lacking. Hence,we present an integrative Bayesian zero-inflated negative binomial regressionmodel that can both distinguish differentially abundant taxa with distinctphenotypes and quantify covariate-taxa effects. Our model demonstrates goodperformance using simulated data. Furthermore, we successfully integratedmicrobiome taxonomies and metabolomics in two real microbiome datasets toprovide biologically interpretable findings. In all, we proposed a novelintegrative Bayesian regression model that features bacterial differentialabundance analysis and microbiome-covariate effects quantifications, whichmakes it suitable for general microbiome studies.

A Bayesian Zero-Inflated Negative Binomial Regression Model for the Integrative Analysis of Microbiome Data