Spatial artifact detection improves the reproducibility of drug screening experiments.

Reliable and reproducible drug screening experiments are essential for drug discovery and personalized medicine. We demonstrate how systematic experimental errors in drug plates negatively impact data reproducibility, and that conventional quality control (QC) methods based on plate controls fail to detect these spatial errors. To address this limitation, we developed a control-independent QC approach that uses normalized residual fit error (NRFE) to identify systematic artifacts in drug screening experiments. Analysis of >100,000 duplicate measurements from the PRISM pharmacogenomic study revealed that NRFE-flagged experiments show 3-fold lower reproducibility among technical replicates. By integrating NRFE with QC methods to analyze 41,762 matched drug-cell line pairs between two datasets from the Genomics of Drug Sensitivity in Cancer project, we improved the cross-dataset correlation from 0.66 to 0.76. Available as an R package at https://github.com/IanevskiAleksandr/plateQC, plateQC provides a robust toolset for enhancing drug screening data reliability and consistency for basic research and translational applications.