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Studying the human microbiome has gained substantial interest in recentyears, and a common task in the analysis of these data is to cluster microbiomecompositions into subtypes. This subdivision of samples into subgroups servesas an intermediary step in achieving personalized diagnosis and treatment. Inapplying existing clustering methods to modern microbiome studies including theAmerican Gut Project (AGP) data, we found that this seemingly standard task,however, is very challenging in the microbiome composition context due toseveral key features of such data. Standard distance-based clusteringalgorithms generally do not produce reliable results as they do not take intoaccount the heterogeneity of the cross-sample variability among the bacterialtaxa, while existing model-based approaches do not allow sufficient flexibilityfor the identification of complex within-cluster variation from cross-clustervariation. Direct applications of such methods generally lead to overlydispersed clusters in the AGP data and such phenomenon is common for othermicrobiome data. To overcome these challenges, we introduce Dirichlet-treemultinomial mixtures (DTMM) as a Bayesian generative model for clusteringamplicon sequencing data in microbiome studies. DTMM models the microbiomepopulation with a mixture of Dirichlet-tree kernels that utilizes thephylogenetic tree to offer a more flexible covariance structure incharacterizing within-cluster variation, and it provides a means foridentifying a subset of signature taxa that distinguish the clusters. Weperform extensive simulation studies to evaluate the performance of DTMM andcompare it to state-of-the-art model-based and distance-based clusteringmethods in the microbiome context. Finally, we report a case study on the fecaldata from the AGP to identify compositional clusters among individuals withinflammatory bowel disease and diabetes.

Dirichlet-tree multinomial mixtures for clustering microbiome compositions