IBMT is a Bayesian hierarchical normal model to define a novel Intensity-Based Moderated T-statistic.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.
Laboratory for Statistical Genomics, Univ. Cincinnati
- R Package
:: MORE INFORMATION
BMC Bioinformatics. 2006 Dec 19;7:538.
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments.
Sartor MA, Tomlinson CR, Wesselkamper SC, Sivaganesan S, Leikauf GD, Medvedovic M.