In a screen with replicates, we can directly estimate data variability for each compound, and thus we can use more powerful methods, such as SSMD for cases with replicates and t-statistic that does not rely on the strong assumption that the z-score and z*-score rely on. One issue with the use of t-statistic and associated p-values is that they are affected by both sample size and effect size. They come from testing for no mean difference, thus are not designed to measure the size of small molecule or siRNA effects. For hit selection, the major interest is the size of effect in a tested small molecule or siRNA. SSMD directly assesses the size of effects. SSMD has also been shown to be better than other commonly used effect sizes. The population value of SSMD is comparable across experiments and thus we can use the same cutoff for the population value of SSMD to measure the size of siRNA effects.
SSMD can overcome the drawback of average fold change not being able to capture data variability. On the other hand, because SSMD is the ratio of mean to standard deviation, we may get a large SSMD value when the standard deviation is very small, even if the mean is small. In some cases, a too small mean value may not have a biological impact. As such, the compounds with large SSMD values (or differentiations) but too small mean values may not be of interest. The concept of dual-flashlight plot has been proposed to address this issue. In a dual-flashlight plot, we plot the SSMD versus average log fold-change (or average percent inhibition/activation) on the y- and x-axes, respectively, for all compounds investigated in an experiment. With the dual-flashlight plot, we can see how the genes or compounds are distributed into each category in effect sizes, as shown in the figure. Meanwhile, we can also see the average fold-change for each compound.