Clustering of time-course gene expression profiles using normal mixture models with AR(1) random effects

TL;DR

This paper introduces a new mixture model with AR(1) random effects for clustering time-course gene expression data, addressing limitations of Fourier series approaches by modeling temporal dependence and correlation.

Contribution

The paper proposes a novel mixture model with AR(1) random effects specifically designed for clustering complex time-course gene expression profiles.

Findings

The model effectively captures temporal dependence in gene expression data.

Simulations and real data demonstrate improved clustering performance.

The approach addresses limitations of Fourier series methods.

Abstract

Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with AR(1) random effects for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models.