# HG changeset patch # User modencode-dcc # Date 1358455473 18000 # Node ID 369b8aa2f7bdbc223baff1baa71a2831a06436fc Uploaded diff -r 000000000000 -r 369b8aa2f7bd idrToolDef.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/idrToolDef.xml Thu Jan 17 15:44:33 2013 -0500 @@ -0,0 +1,61 @@ + + + + Consistency Analysis on a pair of narrowPeak files + batch-consistency-analysis.r $input1 $input2 $halfwidth $overlap $option $sigvalue $gtable $rout $aboveIDR $ratio $emSav $uriSav + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +Reproducibility is essential to reliable scientific discovery in high-throughput experiments. The IDR (Irreproducible Discovery Rate) framework is a unified approach to measure the reproducibility of findings identified from replicate experiments and provide highly stable thresholds based on reproducibility. Unlike the usual scalar measures of reproducibility, the IDR approach creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates. In layman's terms, the IDR method compares a pair of ranked lists of identifications (such as ChIP-seq peaks). These ranked lists should not be pre-thresholded i.e. they should provide identifications across the entire spectrum of high confidence/enrichment (signal) and low confidence/enrichment (noise). The IDR method then fits the bivariate rank distributions over the replicates in order to separate signal from noise based on a defined confidence of rank consistency and reproducibility of identifications i.e the IDR threshold. For more information on IDR, see https://sites.google.com/site/anshulkundaje/projects/idr + + +