# Spatial artifact detection improves the reproducibility of drug screening experiments

## TL;DR

A new quality control method called NRFE improves drug screening reproducibility by detecting spatial errors missed by traditional methods.

## Contribution

A control-independent QC approach using NRFE to detect spatial artifacts in drug screening experiments.

## Key findings

- NRFE-flagged experiments show 3-fold lower reproducibility among technical replicates.
- Integrating NRFE with QC methods improves cross-dataset correlation from 0.66 to 0.76.
- PlateQC R-package provides a toolset for enhancing drug screening data reliability.

## Abstract

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.

•NRFE metric detects systematic spatial artifacts missed by existing quality control methods•Integrating NRFE with existing methods improves cross-study correlation and data reliability•NRFE-flagged low-quality plates exhibit 3-fold higher variability among technical replicates•PlateQC R-package provides QC implementation and guidelines for drug screening workflows

NRFE metric detects systematic spatial artifacts missed by existing quality control methods

Integrating NRFE with existing methods improves cross-study correlation and data reliability

NRFE-flagged low-quality plates exhibit 3-fold higher variability among technical replicates

PlateQC R-package provides QC implementation and guidelines for drug screening workflows

Natural sciences; Biological sciences; Bioinformatics; Pharmacoinformatics