  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.
