2009/03/31 by Shane T. Jensen, Ibrahim Erkan, Erna S. Arnardottir +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayes' theorem #Bayesian Modeling and Causal Inference #Bayesian inference #Bayesian probability #Bayesian statistics #Expression (computer science) #Gene expression and cancer classification #Genetic Associations and Epidemiology #Measure (data warehouse) #Multiple comparisons problem #Sleep apnea #Statistical hypothesis testing #stat.AP #stat.ME
paper · pdf · doi:10.1214/09-aoas241
published as Annals of Applied Statistics 2009, Vol. 3, No. 3, 1080-1101 · Published in at http://dx.doi.org/10.1214/09-AOAS241 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2009/09/01 · arxiv created 2009/10/08 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Substantial statistical research has recently been devoted to the analysis of large-scale microarray experiments which provide a measure of the simultaneous expression of thousands of genes in a particular condition. A typical goal is the comparison of gene expression between two conditions (e.g., diseased vs. nondiseased) to detect genes which show differential expression. Classical hypothesis testing procedures have been applied to this problem and more recent work has employed sophisticated models that allow for the sharing of information across genes. However, many recent gene expression studies have an experimental design with several conditions that requires an even more involved hypothesis testing approach. In this paper, we use a hierarchical Bayesian model to address the situation where there are many hypotheses that must be simultaneously tested for each gene. In addition to having many hypotheses within each gene, our analysis also addresses the more typical multiple comparison issue of testing many genes simultaneously. We illustrate our approach with an application to a study of genes involved in obstructive sleep apnea in humans.