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.

翻译：人体由与多种疾病发展和预防相关的微生物组构成。这些微生物形成多种复杂的相互作用，对科学界解释疾病的进展与预防具有重要意义。与传统将微生物组视为单一、同配性网络的观点不同，我们引入一种新颖的统计方法——加权随机无限块模型——来分析微生物共现互作网络中复杂的群落结构。我们的模型采用一种新型半参数秩基相关方法，在全连接网络框架下基于微生物分类群转化后的相对丰度定义它们之间的连接。通过运用贝叶斯非参数方法，所提出的模型能够有效地将分类群聚类为不同群落，同时估算群落的数量。基于后验概率矩阵获得分类群群落成员的后验摘要，该方法可自然解决标签交换问题。通过模拟研究以及对复发性尿路感染绝经后患者微生物组数据的实际应用，我们证明该方法在聚类准确性上优于其他方法。这一进展为理解微生物组织提供了更细致的视角，对疾病研究具有重要意义。