Data Quality Assurance and Statistical Analysis of High Throughput Screenings for Drug Discovery

High throughput screening (HTS) is an important tool in modern drug discovery processes. Many recent, successful drugs can be traced back to HTS [1]. This platform has proliferated from pharmaceutical industry to national labs (e.g. NIH Molecular Libraries Screening Centers Network), and to academic institutions. Besides throughput improvements from thousand molecules in early times to multimillion molecules now, it has been adapted to increasingly sophisticated biological assays such as high content imaging. The vast amount of biological data from these screens presents a significant challenge for identifying interesting molecules in various biological processes. Due to the intrinsic noise of HTS and complex biological processes in most assays, HTS results need careful analysis to identify reliable hit molecules. Various data normalization and analysis algorithms have been developed by different groups over the years. In this chapter, we briefly describe some common issues encountered in HTS and related analysis.

Keywords: Bayesian model, dose-response analysis, High-throughput Screening, hit analysis, hit identification, normalization, ontology-based pattern identification (OPI) method, quality control, strictly standardized mean difference (SSMD) metric, t-test.