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Statistical tools for synthesizing lists of differentially expressed features in related experiments

Marta Blangiardo* and Sylvia Richardson

Author Affiliations

Centre for Biostatistics, Imperial College, St Mary's Campus, Norfolk Place, London W2 1PG, UK

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Genome Biology 2007, 8:R54  doi:10.1186/gb-2007-8-4-r54

Published: 11 April 2007

Abstract

We propose a novel approach for finding a list of features that are commonly perturbed in two or more experiments, quantifying the evidence of dependence between the experiments by a ratio. We present a Bayesian analysis of this ratio, which leads us to suggest two rules for choosing a cut-off on the ranked list of p values. We evaluate and compare the performance of these statistical tools in a simulation study, and show their usefulness on two real datasets.