New criteria for selecting differentially expressed genes

Two new criteria for identifying differentially expressed genes, the average difference score (ADS) and the mean difference score (MDS), are formulated. The performance of ADS and MDS were compared to that of several commonly used criteria, including Welch t-statistic (WTS), Fisher correlation score (FCS), Wilcoxon rank sum (WRS) and independently consistent expression (ICE) on simulated and real biological datasets. We find that ADS and MDS outperform these existing criteria. When high-sensitivity screening is required, ADS appears to be preferable to WTS. When a false positive rate (FPR) similar to WTS is desired, MDS should be used. The popular Wilcoxon rank sum is a more conservative approach that should be employed when the lowest FPR is desired, even at the expense of lower true positive rate (TPR). ICE is a less desirable criterion because it does not perform well for data generated by the normal model. FCS gave results similar to those of WTS. Evaluation of these algorithms using real biological datasets showed that ADS and MDS flagged several biologically significant genes that were missed by WTS, besides selecting most of the genes that are also selected by WTS