A Parsimonious Method to Identify Differentially Expressed Genes in Time Course Microarray Studies Author: Peigang Li, Mingyu Liang, Daniel A. Beard, Andrew S. Greene, Biotechnology and Bioengineering Center, Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226 Abstract Temporal patterns of gene expression can be obtained by repeated measurements over distinct time points with several replicates. One goal of such experiments is to identify genes that are differentially expressed between the samples obtained from different physiological protocols. In this work, a dataset from salt-induced hypertension in Dahl rats (Liang M. et al., Physiol Genomics. 12:229-37, 2003) is studied using population statistical analysis in terms of a nonlinear model. The model is constructed to fit gene expression data, which tend to show large changes immediately following a stimulus, followed by alteration of expression differences later in the time course. Modeling this expression pattern with a generic 3-parameter gamma function, nonlinear mixed-effects (nlme) statistical analysis reveals that two parameters account for the individual-gene mixed-effects while one parameter accounts for the population fixed-effects, thus reducing the number of parameters required to describe a single gene expression profile in the observed data. A synthetic dataset was constructed to mimic the statistical properties of the real data and to determine optimal criteria for selecting differentially expressed genes. It is shown that genes that are significantly differentially expressed are effectively segregated from other genes in the two dimensional space of the mixed-effects parameters. To account for the effects of replicates and improve the prediction accuracy, we fit the same generic nonlinear model for each gene separately against the replicated data. The distribution patterns of the difference in each parameter from each data set serve as features in identifying differentially expressed genes. Send email to Peigang Li for detailed information |
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