  The human body consists of microbiomes associated with the development and
prevention of several diseases. These microbial organisms form several complex
interactions that are informative to the scientific community for explaining
disease progression and prevention. Contrary to the traditional view of the
microbiome as a singular, assortative network, we introduce a novel statistical
approach using a weighted stochastic infinite block model to analyze the
complex community structures within microbial co-occurrence microbial
interaction networks. Our model defines connections between microbial taxa
using a novel semi-parametric rank-based correlation method on their
transformed relative abundances within a fully connected network framework.
Employing a Bayesian nonparametric approach, the proposed model effectively
clusters taxa into distinct communities while estimating the number of
communities. The posterior summary of the taxa community membership is obtained
based on the posterior probability matrix, which could naturally solve the
label switching problem. Through simulation studies and real-world application
to microbiome data from postmenopausal patients with recurrent urinary tract
infections, we demonstrate that our method has superior clustering accuracy
over alternative approaches. This advancement provides a more nuanced
understanding of microbiome organization, with significant implications for
disease research.
