Effect-Size Measures as Descriptors of Assay Quality in High-Content Screening: A Brief Review of Some Available...

The field of high-content screening (HCS) typically uses measures of screen quality conceived for fairly straightforward high-throughput screening (HTS) scenarios. However, in contrast to HTS, image-based HCS systems rely on multidimensional readouts reporting biological responses associated with complex cellular phenotypes. Not only is the dimensionality in which the screens operate higher, but also the scale of the individual features describing the quantified phenotypic changes is often smaller than what is seen in one-dimensional HTS platforms. Therefore, the use of HTS-type quality measures to characterize HCS screens may severely underestimate the detection power of the assays used, and it may mislead the screening scientists regarding the necessary screen optimization. Also, traditional HTS-based measures are typically reported without any estimation of precision, which makes them unsuitable for computation of confidence intervals or for meta-analysis. This review summarizes the well-established statistical techniques for reporting effect sizes and argues that this broadly accepted methodology could be seamlessly integrated into HCS quality reporting, supplanting or even replacing measures such as Z' or S_w (assay window).