DIRICHLET-TREE MULTINOMIAL MIXTURES FOR CLUSTERING MICROBIOME COMPOSITIONS

• 成果类型: Article
• 署名作者: Mao, Jialiang; Ma, Li
• 署名单位: Duke University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1552
• 发表日期: 2022
• 页码: 1476-1499
• 关键词: gut microbiota; sampling methods; metagenome

摘要

Studying the human microbiome has gained substantial interest in recent years, and a common task in the analysis of these data is to cluster microbiome compositions into subtypes. This subdivision of samples into subgroups serves as an intermediary step in achieving personalized diagnosis and treatment. In applying existing clustering methods to modern microbiome studies, including the American Gut Project (AGP) data, we found that this seemingly standard task, however, is very challenging in the microbiome composition context, due to several key features of such data. Standard distance-based clustering algorithms generally do not produce reliable results, as they do not take into account the heterogeneity of the crosssample variability among the bacterial taxa, while existing model-based approaches do not allow sufficient flexibility for the identification of complex within-cluster variation from cross-cluster variation. Direct applications of such methods generally lead to overly dispersed clusters in the AGP data, and such a phenomenon is common for other microbiome data. To overcome these challenges, we introduce Dirichlet-tree multinomial mixtures (DTMM) as a Bayesian generative model for clustering amplicon sequencing data in microbiome studies. DTMM models the microbiome population with a mixture of Dirichlet-tree kernels that utilizes the phylogenetic tree to offer a more flexible covariance structure in characterizing within-cluster variation, and it provides a means for identifying a subset of signature taxa that distinguish the clusters. We perform extensive simulation studies to evaluate the performance of DTMM, compare it to state-of-the-art model-based and distancebased clustering methods in themicrobiome context and carry out a validation study on a publicly available longitudinal data set to confirm the biological relevance of the clusters. Finally, we report a case study on the fecal data from the AGP to identify compositional clusters among individuals with inflammatory bowel disease and diabetes. Among our most interesting findings is that enterotypes (i.e., gut microbiome clusters) are not always defined by the most dominant taxa, as previous analyses had assumed, but can involve a number of less abundant taxa which cannot be identified with existing distance-based and method-based approaches.