A Bayesian Semiparametric Mixture Model for Clustering Zero-Inflated Microbiome Data

TL;DR

This paper introduces a Bayesian semiparametric mixture model tailored for zero-inflated microbiome data, enabling automatic determination of the number of clusters and improving clustering accuracy over existing methods.

Contribution

The novel model accommodates zero-inflation and learns the number of clusters simultaneously, addressing limitations of prior clustering approaches in microbiome research.

Findings

Outperforms existing clustering methods in simulations

Effectively identifies meaningful microbiome-based clusters

Highlights importance of modeling zero-inflation

Abstract

Microbiome research has immense potential for unlocking insights into human health and disease. A common goal in human microbiome research is identifying subgroups of individuals with similar microbial composition that may be linked to specific health states or environmental exposures. However, existing clustering methods are often not equipped to accommodate the complex structure of microbiome data and typically make limiting assumptions regarding the number of clusters in the data which can bias inference. Designed for zero-inflated multivariate compositional count data collected in microbiome research, we propose a novel Bayesian semiparametric mixture modeling framework that simultaneously learns the number of clusters in the data while performing cluster allocation. In simulation, we demonstrate the clustering performance of our method compared to distance- and model-based alternatives and the importance of accommodating zero-inflation when present in the data. We then apply the model to identify clusters in microbiome data collected in a study designed to investigate the relation between gut microbial composition and enteric diarrheal disease.