A generalized Bayesian stochastic block model for microbiome community detection

TL;DR

This paper introduces a Bayesian stochastic block model tailored for microbiome data, effectively uncovering community structures in high-dimensional, compositional metagenomic datasets, and incorporates taxonomic tree information for improved analysis.

Contribution

The paper presents a novel Bayesian stochastic block model that accounts for microbiome data characteristics and integrates taxonomic tree information using a Markov random field prior.

Findings

Model outperforms existing methods in simulations.

Successfully applied to urinary microbiome data.

Provides new insights into microbiome community structures.

Abstract

Advances in next-generation sequencing technology have enabled the high-throughput profiling of metagenomes and accelerated the microbiome study. Recently, there has been a rise in quantitative studies that aim to decipher the microbiome co-occurrence network and its underlying community structure based on metagenomic sequence data. Uncovering the complex microbiome community structure is essential to understanding the role of the microbiome in disease progression and susceptibility. Taxonomic abundance data generated from metagenomic sequencing technologies are high-dimensional and compositional, suffering from uneven sampling depth, over-dispersion, and zero-inflation. These characteristics often challenge the reliability of the current methods for microbiome community detection. To this end, we propose a Bayesian stochastic block model to study the microbiome co-occurrence network based on the recently developed modified centered-log ratio transformation tailored for microbiome data analysis. Our model allows us to incorporate taxonomic tree information using a Markov random field prior. The model parameters are jointly inferred by using Markov chain Monte Carlo sampling techniques. Our simulation study showed that the proposed approach performs better than competing methods even when taxonomic tree information is non-informative. We applied our approach to a real urinary microbiome dataset from postmenopausal women, the first time the urinary microbiome co-occurrence network structure has been studied. In summary, this statistical methodology provides a new tool for facilitating advanced microbiome studies.