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Open Access Publications from the University of California

Functional Empirical Bayes Methods for Identifying Genes with Different Time-course Expression Profiles


Time course studies of gene expression are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion. Microarray technology makes it possible to study genome-wide temporal differences in gene expression profiles between different experimental conditions/groups. In this paper, we introduce a functional hierarchical model and empirical Bayes approach to model gene expression trajectories over time and to detect temporally differentially expressed (TDE) genes. Monte Carlo EM algorithm is developed for estimating both the gene-specific parameters and the hyperparameters. We use the posterior probability based false discovery rate (FDR) criterion to identify the TDE genes in order to control for the over FDR. We illustrate the methods by using both simulated data sets and a data set from a microarray based gene expression time course study of C. elegans developmental processes. Simulation results suggested that the procedure have low false discovery rate but could potentially have high false negative rate when the noise variance is relatively large. Results from both simulations and analysis of C. elegans data indicated that the procedure performed better than the two-way ANOVA in identifying TDE genes between the dauer exit process and starved L1 worms response to feeding process.

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