QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Findings</p> <p>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.</p> <p>Conclusions</p> <p>QiSampler is implemented as an open source R script and a web server, which can be accessed at <url>http://cbdm.mdc-berlin.de/tools/sampler/</url>.</p