Improved Statistical Methods for Hit Selection in High-Throughput Screening

TLDR

High‑throughput screening rapidly tests large compound libraries using advanced automation and detection, yet hit selection remains largely based on simple statistical methods. The study aims to critique conventional hit‑selection techniques and propose modern statistical alternatives. They introduce the Stat Server HTS application, a web‑based tool that applies advanced statistical analyses to HTS data and presents results as intuitive graphs and tables.

Abstract

High-throughput screening (HTS) plays a central role in modern drug discovery, allowing the rapid screening of large compound collections against a variety of putative drug targets. HTS is an industrial-scale process, relying on sophisticated automation, control, and state-of-the art detection technologies to organize, test, and measure hundreds of thousands to millions of compounds in nano- to microliter volumes. Despite this high technology, hit selection for HTS is still typically done using simple data analysis and basic statistical methods. The authors discuss in this article some shortcomings of these methods and present alternatives based on modern methods of statistical data analysis. Most important, they describe and show numerous real examples from the biologist-friendly Stat Server HTS application (SHS), a custom-developed software tool built on the commercially available S-PLUS and StatServer statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables.