A Dirichlet Process Mixture Model for Clustering Longitudinal Gene Expression Data

TL;DR

This paper introduces BClustLonG, a Bayesian clustering method utilizing longitudinal gene expression data to improve subgroup identification in biomedical research, accounting for intra-individual variability and gene correlations.

Contribution

It develops a novel Bayesian model combining linear mixed-effects and factor analysis with Dirichlet process priors for effective clustering of longitudinal gene expression data.

Findings

BClustLonG outperforms existing clustering methods in simulations.

Successfully distinguishes burn from trauma patients.

Identifies meaningful subgroups within trauma patients.

Abstract

Subgroup identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to define subgroups. Longitudinal gene expression profiles might provide additional information on disease progression than what is captured by baseline profiles alone. Moreover, the longitudinal gene expression data allows for intra-individual variability to be accounted for when grouping patients. Therefore, subgroup identification could be more accurate and effective with the aid of longitudinal gene expression data. However, existing statistical methods are unable to fully utilize these data for patient clustering. In this article, we introduce a novel subgroup identification method in the Bayesian setting based on longitudinal gene expression profiles. This method, called BClustLonG, adopts a linear mixed-effects framework to model the trajectory of genes over time while clustering is jointly conducted based on the regression coefficients obtained from all genes. In order to account for the correlations among genes and alleviate the high dimensionality challenges, we adopt a factor analysis model for the regression coefficients. The Dirichlet process prior distribution is utilized for the means of the regression coefficients to induce clustering. Through extensive simulation studies, we show that BClustLonG has improved performance over other clustering methods. When applied to a dataset of severely injured (burn or trauma) patients, our model is able to distinguish burn patients from trauma patients and identify interesting subgroups in trauma patients.