There is a need to integrate statistical significance with biological relevance when considering dynamic changes in transcriptional response in biomedical research. Towards this end, methodologies based on analysis of variance (ANOVA) will be developed to detect differentially expressed genes in time course microarray data through focused contrasts and trend tests within and between treatment conditions. The efficiency of these ANOVA-based analyses will be compared with empirical Bayes methods and B-spline fitting analyses to synthesize new methodologies that incorporate the best features of the various approaches. Next, statistically relevant associations between gene lists derived from the ANOVA method and known biological processes will be used to uncover relationships important in studies of sleep deprivation in brain tissue sampled for flies and mice and factors influencing osteocyte and adipocyte differentiation in UAMS-33 mouse cell cultures. Finally, confirmatory real-time quantitative PCR assays will be designed and applied to the UAMS-33 mouse cell line experiments with higher frequency time point sampling in order to build graphical models that finely characterize the time dynamics of this system. ? ?
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