There are many metrics used for hit selection in primary screens without replicates.
The easily interpretable ones are fold change, mean difference, percent inhibition, and percent activity. However, the drawback common to all of these metrics is that they do not capture data variability effectively. To address this issue, researchers then turned to the z-score method or SSMD, which can capture data variability in negative references.
The z-score method is based on the assumption that the measured values (usually fluorescent intensity in log scale) of all investigated compounds in a plate have a normal distribution. SSMD also works the best under the normality assumption. However, true hits with large effects should behave very different from the majority of the compounds and thus are outliers. Strong assay artifacts may also behave as outliers. Thus, outliers are not uncommon in HTS experiments. The regular versions of z-score and SSMD are sensitive to outliers and can be problematic. Consequently, robust methods such as the z*-score method, SSMD*, B-score method, and quantile-based method have been proposed and adopted for hit selection in primary screens without replicates.
In a primary screen without replicates, every compound is measured only once. Consequently, we cannot directly estimate the data variability for each compound. Instead, we indirectly estimate data variability by making a strong assumption that every compound has the same variability as a negative reference in a plate in the screen. The z-score, z*-score and B-score relies on this strong assumption; so are the SSMD and SSMD* for cases without replicates.