Does intensity-based hierarchical Bayes method improve testing for differentially expressed genes in microarray experiments?
We use a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic (IBMT). The method is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. IBMT has the strength of balancing two important factors in the analysis of microarray data: the degree of independence of variances relative to the degree of identity (i.e. t-tests vs. equal variance assumption), and the relationship between variance and signal intensity. When this variance-intensity relationship is weak or does not exist, IBMT reduces to a previously described moderated t-statistic. Furthermore, our method may be directly applied to any array platform and experimental design. Together, these properties show IBMT to be a valuable option in the analysis of virtually any microarray experiment.
Our results of the reporter assays using 2R, 3Rc, and 3Rg repeat sequences prompted us to classify 3Rg as a high TS expression allele, and 2R and 3Rc as low TS expression alleles. The cells with low TS expression alleles were shown to exhibit significantly higher FUdR sensitivity than the cells with high TS expression alleles for the first time. These results were consistent with numerous previous in vitro and in vivo findings that tumors showing high TS expression were less sensitive to fluoropyrimidines. These results support the idea that genotyping the tandem repeat sequences of TYMS in the 5'-untranslated region is useful for individualized therapy involving fluoropyrimidine antitumor drugs.