Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer

TL;DR

This paper introduces a Bayesian clustering method with feature selection and tree structure integration to identify microbiome-based subgroups in pancreatic cancer patients, improving over existing models.

Contribution

The novel unsupervised clustering approach incorporates feature selection, learns the number of clusters, and uses tree structure information, advancing microbiome data analysis.

Findings

Effective in simulated microbiome data

Identifies meaningful patient subgroups

Outperforms existing clustering methods

Abstract

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.