An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data.

Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.