A Bayesian Semiparametric Factor Analysis Model for Subtype Identification

TL;DR

This paper introduces BCSub, a Bayesian semiparametric factor analysis model for disease subtype identification from gene expression data, improving clustering accuracy and clinical relevance.

Contribution

The paper presents a novel Bayesian method that reduces high-dimensional gene data to factors for better disease subtype clustering, outperforming existing methods.

Findings

BCSub outperforms traditional clustering methods in simulations.

Identifies more clinically relevant subtypes in real datasets.

Effective in high-dimensional, correlated gene expression data.

Abstract

Disease subtype identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to infer disease subtypes, which often lead to biologically meaningful insights into disease. Despite many successes, existing clustering methods may not perform well when genes are highly correlated and many uninformative genes are included for clustering due to the high dimensionality. In this article, we introduce a novel subtype identification method in the Bayesian setting based on gene expression profiles. This method, called BCSub, adopts an innovative semiparametric Bayesian factor analysis model to reduce the dimension of the data to a few factor scores for clustering. Specifically, the factor scores are assumed to follow the Dirichlet process mixture model in order to induce clustering. Through extensive simulation studies, we show that BCSub has improved performance over commonly used clustering methods. When applied to two gene expression datasets, our model is able to identify subtypes that are clinically more relevant than those identified from the existing methods.