Fig. 4

Comparing DGCA to alternatives in the differential correlation simulation study. a-d: Representative receiver operating characteristic (ROC) curves show the ability of DGCA, EBcoexpress, and Discordant with and without the use of the Fisher z-transformation (FT) to accurately detect truly differential correlated gene pairs at different simulated sample sizes in each condition, including n = 10 a, n = 30 b, n = 50 c, and n = 100 d. Black lines show the ROC curves of DGCA, while blue lines show the ROC curves of EBcoexpress, red lines show Discordant without the Fisher transformation, and orange lines show Discordant with the Fisher transformation. e: Comparison of area under curve (AUC) statistics for 5 runs of the simulation study using each of the methods at different numbers of samples, where errors bars represent the standard error of the mean. Asterisks indicate a Bonferroni-adjusted significant difference in the AUCs (p < 0.0083) between DGCA and each of the other methods, tested using a two-sided t-test