Hit selection

In high-throughput screening (HTS), one of the major goals is to select compounds (including small molecules, siRNAs, shRNA, genes, et al.) with a desired size of inhibition or activation effects. A compound with a desired size of effects in an HTS screen is called a hit. The process of selecting hits is called hit selection. In high-throughput screening (HTS), one of the major goals is to select compounds (including small molecules, siRNAs, shRNA, genes, et al.) with a desired size of inhibition or activation effects. A compound with a desired size of effects in an HTS screen is called a hit. The process of selecting hits is called hit selection. 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. 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.