May 22, 2010

sdef: an R package to synthesize lists of significant features in related experiments

BMC Bioinformatics
Marta BlangiardoSylvia Richardson


In microarray studies researchers are often interested in the comparison of relevant quantities between two or more similar experiments, involving different treatments, tissues, or species. Typically each experiment reports measures of significance (e.g. p-values) or other measures that rank its features (e.g genes). Our objective is to find a list of features that are significant in all experiments, to be further investigated. In this paper we present an R package called sdef, that allows the user to quantify the evidence of communality between the experiments using previously proposed statistical methods based on the ranked lists of p-values. sdef implements two approaches that address this objective: the first is a permutation test of the maximal ratio of observed to expected common features under the hypothesis of independence between the experiments. The second approach, set in a Bayesian framework, is more flexible as it takes into account the uncertainty on the number of genes differentially expressed in each experiment. We used sdef to re-analyze publicly available data i) on Type 2 diabetes susceptibility in mice on liver and skeletal muscle (two experiments); ii) on molecular similarities between mammalian sexes (thre...Continue Reading

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Mentioned in this Paper

BTG1 gene
Diabetes Mellitus, Non-Insulin-Dependent
ATF3 gene
Biochemical Pathway
SMC1A gene
Ncbi Taxonomy
Computer Programs and Programming
STAG2 gene
Cell Development
GPC4 gene

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