A0552
Title: Clustering microbiome data using finite mixture of Dirichlet-multinomial regression models
Authors: Zeny Feng - University of Guelph (Canada) [presenting]
Abstract: The human microbiome is a fundamental component of our physiology, and exploring the relationship between biological/environmental covariates and the resulting taxonomic composition of a given microbial community is an active area of research. The advancement of biology techniques, allow us to sequence the high throughput microbial metagenomic with an affordable cost, such that microbiome data is available and accessible for the study. Previously, a Dirichlet-multinomial regression framework has been suggested to model this relationship, but it did not account for any latent group structure which has been observed across microbiome samples which share similar biota compositions (such as enterotypes). A finite mixture of Dirichlet-multinomial regression models is proposed and illustrated in order to account for this group structure and to allow for a probabilistic investigation of the relationship between bacterial abundance and biological/environmental covariates within each inferred group. Furthermore, finite mixtures of regression models which incorporate the concomitant effect of the covariates on the resulting mixing proportions are also proposed and examined within the Dirichlet-multinomial framework.
