HTS experiments have the ability to screen tens of thousands (or even millions) of compounds rapidly. Hence, it is a challenge to glean chemical/biochemical significance from mounds of data in the process of hit selection. To address this challenge, appropriate analytic methods have been adopted for hit selection. There are two main strategies of selecting hits with large effects. One is to use certain metric(s) to rank and/or classify the compounds by their effects and then to select the largest number of potent compounds that is practical for validation assays. The other strategy is to test whether a compound has effects strong enough to reach a pre-set level. In this strategy, false-negative rates (FNRs) and/or false-positive rates (FPRs) must be controlled.
There are two major types of HTS experiments, one without replicates (usually in primary screens) and one with replicates (usually in confirmatory screens). The analytic methods for hit selection differ in those two types of HTS experiments. For example, the z-score method is suitable for screens without replicates whereas the t-statistic is suitable for screens with replicate. The calculation of SSMD for screens without replicates also differs from that for screens with replicates.