Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments

Background: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework. Results: We present a novel Bayesian moderated-T, which we show to perform favorably in simulations, with two real, dual-channel microarray experiments and in two controlled single-channel experiments. In simulations, the new metho...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Maureen A. Sartor, Craig R. Tomlinson, Scott C. Wesselkamper, Siva Sivaganesan, George D. Leikauf, Mario Medvedovic
• Keywords: BMCBI 2006, Empirical Bayes, Gene Expression, Gene Expression Measurements