Adaptive Bayesian Variable Clustering via Structural Learning of Breast Cancer Data

TL;DR

This paper introduces a hierarchical Bayesian model for protein clustering based on correlation structures, utilizing RJMCMC for inference, and demonstrates its effectiveness on simulated and breast cancer data.

Contribution

It presents a novel Bayesian clustering approach that simultaneously estimates protein clusters and their number using correlation-based priors and RJMCMC inference.

Findings

Effective clustering of proteins in simulated data

Successful application to breast cancer protein expression data

Flexible estimation of the number of protein clusters

Abstract

Clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize the large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlo (RJMCMC) based technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as the estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.