  There is a keen interest in characterizing variation in the microbiome across
cancer patients, given increasing evidence of its important role in determining
treatment outcomes. Here our goal is to discover subgroups of patients with
similar microbiome profiles. We propose a novel unsupervised clustering
approach in the Bayesian framework that innovates over existing model-based
clustering approaches, such as the Dirichlet multinomial mixture model, in
three key respects: we incorporate feature selection, learn the appropriate
number of clusters from the data, and integrate information on the tree
structure relating the observed features. We compare the performance of our
proposed method to existing methods on simulated data designed to mimic real
microbiome data. We then illustrate results obtained for our motivating data
set, a clinical study aimed at characterizing the tumor microbiome of
pancreatic cancer patients.
