Z-factor

Abstract: In this article, the authors compare the assay performance measures, signal window, Z' factor, and assay variability ratio. They examine their mathematical formulae for similarities and differences, describe their statistical sampling properties using the results of a computer simulation, and illustrate their use with example data. Based on these results, the authors recommend the Z' factor as a preferred measure of assay performance for screening assays and point out that none of these measures are adequate for characterizing concentration-response assays.

Abstract: It is generally accepted that the conversion of substrate should be kept at less than 10% of the total substrate used when studying enzyme kinetics. However, 10% or less substrate conversion often will not produce sufficient signal changes required for robust high-throughput screening (HTS). To increase the signal-to-background ratio, HTS is often performed at higher than 10% substrate conversion. Because the consequences of high substrate conversion are poorly understood, the screening results are sometimes questioned by enzymologists. The quality of an assay is judged by the ability to detect an inhibitor under HTS conditions, which depends on the robustness of the primary detection signal (Z factor) and the sensitivity to an inhibitor. The assay sensitivity to an inhibitor is reflected in the observed IC(50) value or percent inhibition at a fixed compound concentration when single-point data are collected. The major concern for an enzymatic assay under high substrate conversion is that the sensitivity of the screen may be compromised. Here we derive the relationship between the IC(50) value for a given inhibitor and the percentage of substrate conversion using a first-order kinetic model under conditions that obey Henri-Michaelis-Menten kinetics. The derived theory was further verified experimentally with a cAMP-dependent protein kinase. This model provides guidance for assay developers to choose an appropriate substrate conversion in designing an enzymatic assay, balancing the needs for robust signal and sensitivity to inhibitors.

Abstract: One of the most fundamental challenges in genome-wide RNA interference (RNAi) screens is to glean biological significance from mounds of data, which relies on the development and adoption of appropriate analytic methods and designs for quality control (QC) and hit selection. Currently, a Z-factor-based QC criterion is widely used to evaluate data quality. However, this criterion cannot take into account the fact that different positive controls may have different effect sizes and leads to inconsistent QC results in experiments with 2 or more positive controls with different effect sizes. In this study, based on a recently proposed parameter, strictly standardized mean difference (SSMD), novel QC criteria are constructed for evaluating data quality in genome-wide RNAi screens. Two good features of these novel criteria are: (1) SSMD has both clear original and probability meanings for evaluating the differentiation between positive and negative controls and hence the SSMD-based QC criteria have a solid prob...