Issues Of Z-factor and an approach to avoid them for quality control in high-throughput screening studies.

MOTIVATION High throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that z-factor has major issues. RESULTS In this article, the following four major issues are explored and demonstrated so that researchers may use the z-factor appropriately. First, the z-factor violates the Pythagorean Theorem of Statistics. Second, there is no adjustment of sampling error in the application of the z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based z-factor does not exist. Fourth, the thresholds in the z-factor based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. AVAILABILITY The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.