Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data

Li-Ping Tian,Li-Zhi Liu,Fang-Xiang Wu

doi:10.1155/2014/313747

Li-Ping Tian, Li-Zhi Liu + Show 1 more

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https://doi.org/10.1155/2014/313747

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Abstract

Microarray technology has produced a huge body of time-course gene expression data and will continue to produce more. Such gene expression data has been proved useful in genomic disease diagnosis and drug design. The challenge is how to uncover useful information from such data by proper analysis methods such as significance analysis and clustering analysis. Many statistic-based significance analysis methods and distance/correlation-based clustering analysis methods have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterizes such data and that should be considered in analysis of such data. In this paper, we employ a nonlinear model to analyse time-course gene expression data. We firstly develop an efficient method for estimating the parameters in the nonlinear model. Then we utilize this model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles. The verification with two synthetic datasets shows that our developed significance analysis method and cluster analysis method outperform some existing methods. The application to one real-life biological dataset illustrates that the analysis results of our developed methods are in agreement with the existing results.

Highlights

To understand the mechanisms of dynamic biological processes, DNA microarray experiments have been employed to produce gene expression profiles at a series of time points, for example, the cell division cycle processes of yeast Saccharomyces cerevisiae [1, 2], bacterium Caulobacter crescentus [3], and human being [4]
To evaluate the significance analysis method, we generate one synthetic dataset that consists of 2000 noisy gene expression profiles based on model (13) and 2000 time-course gene expression profiles based on model (1)
We have presented a significance analysis method and a cluster analysis method for time-course gene expression profiles

Summary

Introduction

To understand the mechanisms of dynamic biological processes, DNA microarray experiments have been employed to produce gene expression profiles at a series of time points, for example, the cell division cycle processes of yeast Saccharomyces cerevisiae [1, 2], bacterium Caulobacter crescentus [3], and human being [4] Such time-course gene expression data provides a dynamic snapshot of most (if not all) of the genes related to the biological development process and can be useful in genomic disease diagnosis and genomic drug design. The “Rfold change” method determines a gene to be significantly expressed if the ratio of expression values under two different conditions is greater than R or less than 1/R, where R is a userpreset positive number This approach has been improved by a resampling (bootstrapping) method called SAM [8, 9].

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