BAREB: A Bayesian repulsive biclustering model for periodontal data

TL;DR

BAREB is a Bayesian biclustering method that identifies diverse periodontal disease patterns by considering spatial dependence, covariates, and non-ignorable missing data, providing interpretable results for clinical data.

Contribution

This paper introduces BAREB, a novel Bayesian biclustering model using DPP priors, spatial dependence, and non-ignorable missing data handling for periodontal disease analysis.

Findings

BAREB accurately estimates biclusters in simulations.

It outperforms alternative methods in clustering accuracy.

Applied to clinical data, BAREB yields interpretable disease patterns.

Abstract

Preventing periodontal diseases (PD) and maintaining the structure and function of teeth are important goals for personal oral care. To understand the heterogeneity in patients with diverse PD patterns, we develop BAREB, a Bayesian repulsive biclustering method that can simultaneously cluster the PD patients and their tooth sites after taking the patient- and site- level covariates into consideration. BAREB uses the determinantal point process (DPP) prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Since PD progression is hypothesized to be spatially-referenced, BAREB factors in the spatial dependence among tooth sites. In addition, since PD is the leading cause for tooth loss, the missing data mechanism is non-ignorable. Such nonrandom missingness is incorporated into BAREB. For the posterior inference, we design an efficient reversible jump Markov chain Monte Carlo sampler. Simulation studies show that BAREB is able to accurately estimate the biclusters, and compares favorably to alternatives. For real world application, we apply BAREB to a dataset from a clinical PD study, and obtain desirable and interpretable results. A major contribution of this paper is the Rcpp implementation of BAREB, available at https://github.com/YanxunXu/ BAREB.