How to cluster gene expression dynamics in response to environmental signals

Y Wang,L Wang,M Xu,R Li,Z Wang,J Zhu,S A Berceli,R Wu,M Tao

doi:10.1093/bib/bbr032

Abstract

Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments.

Highlights

The development of high-throughput technologies, such as DNA microarrays and proteomics platforms, has made it possible to ask and address many fundamental but difficult questions in developmental biology and biomedicine
There is a pressing need for computational tools that can unravel the developmental machinery of time-dependent gene expression profiles, despite a vast body of literature presenting these tools [4,5,6, 8, 10,11,12]
One significant lack is the unavailability of models for analyzing gene–environment interactions for gene expression dynamics

Summary

INTRODUCTION

These genes each have an expression trajectory over four time points as the sum of the mean trajectory of the underlying cluster and residual errors whose covariance structure follows the first-order AR model, but assuming the value of variance that triples the estimated variance. Expression trajectories of each gene cluster can be reasonably well estimated (Figure 4), despite a tripled variance used This shows that the results from the real data set analyzed by the model are convincing from a statistical point of view. This suggests that the FPR of our tool is acceptably low

DISCUSSION

Findings

Key Points