A comprehensive re-analysis of the Golden Spike data: Towards a benchmark for differential expression methods-7

Robesets are used as true negatives, genes with FC of 1.2 are used as true positives, and no post-summarization normalization is used. We only show results for the FC DE detection method. The different charts show a.) probesets selected using a 2-sided test of DE, b.) probesets selected using a 1-sided test of up-regulation and c.) probesets selected using a 1-sided test of down-regulation. The diagonal line shows the "line of no-discrimination". This shows how well we would expect random guessing of class labels to perform.<p><b>Copyright information:</b></p><p>Taken from "A comprehensive re-analysis of the Golden Spike data: Towards a benchmark for differential expression methods"</p><p>http://www.biomedcentral.com/1471-2105/9/164</p><p>BMC Bioinformatics 2008;9():164-164.</p><p>Published online 26 Mar 2008</p><p>PMCID:PMC2324099.</p><p>