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. © 2007 Blangiardo and Richardson; licensee BioMed Central Ltd.
CITATION STYLE
Blangiardo, M., & Richardson, S. (2007). Statistical tools for synthesizing lists of differentially expressed features in related experiments. Genome Biology, 8(4). https://doi.org/10.1186/gb-2007-8-4-r54
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