Figure 1

Area under the ROC curve (AUC). Area under the ROC curve (AUC) for the eleven evaluated methods, in simulation studies $B_0^{1250}$ (panel A), $B_{625}^{625}$ (panel B), $B_0^{4000}$ (panel C), $B_{2000}^{2000}$ (panel D), $S_{625}^{625}$ (panel E) and $R_{625}^{625}$ (panel F). The boxplots summarize the AUCs obtained across 10 independently simulated instances of each simulation study. Each panel shows the AUCs across three sample sizes (|S₁| = |S₂| = 2, 5 and 10, respectively, signified by the last number in the tick labels). The methods are ordered according to their median AUC for the largest sample size. When all DE genes were regulated in the same direction, increasing the number of DE genes from 1,250 (panel A) to 4,000 (panel C) impaired the performance of all methods. In contrast, when the DE genes were regulated in different directions (panels B and D), the number of DE genes had much less impact. The variability of the performance of baySeq was much higher when all genes were regulated in the same direction (panels A and C) compared to when the DE genes were regulated in different directions (panels B and D). Including outliers (panels E and F) decreased the AUC for most methods (compare to panel B), but less so for the transformation-based methods (voom+limma and vst+limma) and SAMseq.