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QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold
Jean F Fontaine1, Bernhard Suter, Miguel A Andrade-Navarro
1Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany. jean-fred.fontaine@mdc-berlin.de.
Background:
High-throughput biological experiments can produce a large amount of data showing little overlap with current knowledge. This may be a problem when evaluating alternative scoring mechanisms for such data according to a gold standard dataset because standard statistical tests may not be appropriate.
Findings:
To address this problem we have implemented the QiSampler tool that uses a repetitive sampling strategy to evaluate several scoring schemes or experimental parameters for any type of high-throughput data given a gold standard. We provide two example applications of the tool: selection of the best scoring scheme for a high-throughput protein-protein interaction dataset by comparison to a dataset derived from the literature, and evaluation of functional enrichment in a set of tumour-related differentially expressed genes from a thyroid microarray dataset.
Conclusions:
QiSampler is implemented as an open source R script and a web server, which can be accessed at http://cbdm.mdc-berlin.de/tools/sampler/.
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