Statistical tools for synthesizing lists of differentially expressed features in related experiments.
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.
Blangiardo
Marta
M
Centre for Biostatistics, Imperial College, St Mary's Campus, Norfolk Place, London, UK. m.blangiardo@imperial.ac.uk
Richardson
Sylvia
S
D000821:Animal Feed; D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004041:Dietary Fats / Q000378:metabolism; D020869:Gene Expression Profiling; D008168:Lung / Q000378:metabolism; D055370:Lung Injury; D051379:Mice; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D012123:Pulmonary Ventilation; D051381:Rats; D012122:Ventilators, Mechanical / Q000009:adverse effects
D004041:Dietary Fats