Flexible empirical Bayes models for differential gene expression

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Flexible empirical Bayes models for differential gene expression

MOTIVATION Inference about differential expression is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular for this type of problem. The two most common hierarchical models are the hierarchical Gamma-Gamma (GG) and Lognormal-Normal (LNN) models. However, to facilitate inference, some unrealistic assumptions have been ma...

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The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practic...

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In recent years the new technology of microarrays has made it feasible to measure expression of thousands of genes to identify changes between different biological states. In such biological experiments we are confronted with the problem of high-dimensionality because of thousands of genes involved and at the same time with small sample sizes (due to limited availability of cases). The set of d...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2006

ISSN: 1367-4803,1460-2059

DOI: 10.1093/bioinformatics/btl612