Clustering gene expression time series data using an infinite Gaussian process mixture model.

Ian C Mcdowell,Dinesh Manandhar,Christopher M Vockley,Timothy E Reddy,Amy K Schmid,Barbara E Engelhardt,Qing Nie

doi:10.1371/journal.pcbi.1005896

Abstract

Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

Highlights

The analysis of time series gene expression has enabled insights into development [1,2,3], response to environmental stress [4], cell cycle progression [5, 6], pathogenic infection [7], cancer [8], circadian rhythm [9, 10], and other biomedically important processes
Transcriptome-wide measurement of gene expression dynamics can reveal regulatory mechanisms that control how cells respond to changes in the environment
Two challenges in clustering time series gene expression data are selecting the number of clusters and modeling dependencies in gene expression levels between time points

Summary

Introduction

The analysis of time series gene expression has enabled insights into development [1,2,3], response to environmental stress [4], cell cycle progression [5, 6], pathogenic infection [7], cancer [8], circadian rhythm [9, 10], and other biomedically important processes. Gene expression is a tightly regulated spatiotemporal process. Genes with similar expression dynamics have been shown to share biological functions [11]. Clustering reduces the complexity of a transcriptional response by grouping genes into a small number of response types. Regulatory mechanisms characterizing shared response types can be explored using these clusters by, for example, comparing sequence motifs or other features within and across clusters

Methods

Results

Discussion

Conclusion