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Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments |
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| Authors: | Maureen A Sartor Craig R Tomlinson Scott C Wesselkamper Siva Sivaganesan George D Leikauf Mario Medvedovic |
| Institution: | (1) Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA;(2) Center of Environmental Genetics, University of Cincinnati, Cincinnati, OH, USA;(3) Dartmouth College, Departments of Medicine and Pharmacology & Toxicology, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA;(4) Mathematical Sciences Department, University of Cincinnati, Cincinnati, OH, USA;(5) Biomedical Informatics Division, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA |
| Abstract: | BackgroundThe 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. |
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