An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data.
The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development.



In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available.



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.
Department of Statistics, Rice University, Houston, TX, USA.
W Duncan
WD
Wadsworth
ESOMAS Department, University of Torino and Collegio Carlo Alberto, Torino, Italy.
Raffaele
R
Argiento
Department of Statistics, University of California, Irvine, CA, USA.
Michele
M
Guindani
Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA.
Jessica
J
Galloway-Pena
Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA.
Samuel A
SA
Shelburne
Department of Statistics, Rice University, Houston, TX, USA. marina@rice.edu.
Marina
0000-0002-7360-5321
M
Vannucci
D000465:Algorithms; D001419:Bacteria / Q000145:classification*; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008390:Markov Chains; D064307:Microbiota; D009010:Monte Carlo Method
Bayesian hierarchical model; Data integration; Dirichlet-multinomial; Microbiome data; Variable selection