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

Marta Blangiardo*, Alberto Cassese, Sylvia Richardson

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


BACKGROUND: 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. RESULTS: 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 (three experiments). For the first example, we found between 68 and 104 genes commonly perturbed between the two tissues, using the two methods described above, and enrichment of the inflammation pathways, which are related to obesity and diabetes. For the second example, looking at three lists of features, we found 110 genes commonly perturbed between the three tissues, using the same two methods, and enrichment on genes involved in cell development. CONCLUSIONS: sdef is an R package that provides researchers with an easy and powerful methodology to find lists of features commonly perturbed in two or more experiments to be further investigated. The package is provided with plots and tables to help the user visualize and interpret the results. The Windows, Linux and MacOS versions of the package, together with the documentation are available on the website
Original languageEnglish
Article number270
JournalBMC Bioinformatics
Publication statusPublished - Jun 2010
Externally publishedYes


  • Databases
  • Gene Expression Profiling
  • Gene Expression Profiling: methods
  • Genetic
  • Oligonucleotide Array Sequence Analysis
  • Oligonucleotide Array Sequence Analysis: methods
  • Software

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