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Functional Hierarchical Models for Identifying Genes with Different TimeâCourse Expression Profiles
- Hong, F., Li, H.
- Biometrics 2006 v.62 no.2 pp. 534-544
- Caenorhabditis elegans, algorithms, analysis of variance, biological properties and phenomena, biomedical research, biometry, data collection, gene expression, gene expression regulation, genes, microarray technology, models, probability
- Timeâcourse studies of gene expression are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion. We introduce a functional hierarchical model for detecting temporally differentially expressed (TDE) genes between two experimental conditions for crossâsectional designs, where the gene expression profiles are treated as functional data and modeled by basis function expansions. A Monte Carlo EM algorithm was developed for estimating both the geneâspecific parameters and the hyperparameters in the second level of modeling. We use a direct posterior probability approach to bound the rate of false discovery at a preâspecified level and evaluate the methods by simulations and application to microarray timeâcourse gene expression data on Caenorhabditis elegans developmental processes. Simulation results suggested that the procedure performs better than the twoâway ANOVA in identifying TDE genes, resulting in both higher sensitivity and specificity. Genes identified from the C. elegans developmental data set show clear patterns of changes between the two experimental conditions.