To compare the sensitivity and precision results further, we calculated the precision of algorithms along a grid of nominal adjusted P values (Additional file 1: Figure S19). We then found the nominal adjusted P value for each algorithm, which resulted in a median actual precision of 0.9 (FDR = 0.1). Having thus calibrated each algorithm to a target FDR, we evaluated the sensitivity of calling, as shown in Additional file 1: Figure S20. As expected, here the algorithms performed more similarly to each other. This analysis revealed that, for a given target precision, DESeq2 often was among the top algorithms by median sensitivity, though the variability across random replicates was larger than the differences between algorithms.
