Integrating AUROC and SSMD for quality control in high-throughput screening assays

Area of Science:

• Biomedical research
• Drug discovery
• Genomics

Background:

• High-throughput screening (HTS) assays are crucial for drug discovery and functional genomics.
• Ensuring data quality and reliability in HTS is vital, particularly with small sample sizes.
• Current quality control (QC) methods may need enhancement for small sample scenarios.

Purpose of the Study:

• To explore the integration of Strictly Standardized Mean Difference (SSMD) and Area Under the Receiver Operating Characteristic Curve (AUROC) for HTS QC.
• To establish the theoretical and empirical relationships between SSMD and AUROC.
• To demonstrate the utility of the combined metrics for improving HTS data reliability.

Main Methods:

• Investigated the statistical properties and relationships between SSMD and AUROC.
• Developed parametric, semi-parametric, and non-parametric estimation methods for these metrics.
• Applied the integrated framework to real-world high-throughput screening datasets.

Main Results:

• Demonstrated that SSMD and AUROC provide complementary insights into HTS data quality.
• Established a robust framework for joint application of SSMD and AUROC.
• Validated the effectiveness of the integrated QC approach using empirical HTS data.

Conclusions:

• The joint application of SSMD and AUROC offers a powerful and interpretable QC strategy for HTS.
• This integrated approach enhances data reliability, especially when dealing with limited control sample sizes.
• Recommends the adoption of SSMD and AUROC for improved QC in HTS assays.