Statistical and Graphical Methods for Quality Control Determination of High-Throughput Screening Data

Abstract

High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good quality data and minimize assay variability while maintaining assay sensitivity. The authors describe new QC methods and show numerous real examples from their biologist-friendly StatServer ® HTS application, a custom-developed software tool built from 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. It allows users to visualize HTS data and examine assay performance during the HTS campaign to quickly react to or avoid quality problems. (Journal of Biomolecular Screening 2003:624-633)

Chelation of intracellular iron with the antifungal agent ciclopirox olamine induces cell death in leukemia and myeloma cells

“…Results were normalized and corrected for systematic errors using the B score. 18 Compounds with a B score value lower than 3 times the SD were empirically considered hits in the assay.…”

Integrating Experimental and Analytic Approaches to Improve Data Quality in Genome-wide RNAi Screens

“…Studies have shown that Z factor is the most favorable among these classical metrics 16 B4: Adjusted data of a plate in 2nd screen…”

“…Position effects, especially edge effects, commonly exist in HTS assays. [15][16][17][18]22 Smoothing methods or robust regressions are usually adopted to adjust for systematic errors due to position effects. In this study, we used local polynomial regression fitting 36 of measured intensities on row and column numbers to adjust for the systematic errors related to positions in the ApoA1 experiments.…”

“…The existence of systematic errors of measurement is not uncommon in HTS experiments. [14][15][16][17][18][19][20][21] A good plate design helps to identify systematic errors (especially those linked with well position) and to determine what normalization should be used to remove/reduce the impact of systematic errors on both quality control (QC) and hit selection. Thus, the second process is to construct and adopt effective plate designs.…”

Novel Analytic Criteria and Effective Plate Designs for Quality Control in Genome-Scale RNAi Screens

“…The existence of systematic errors of measurement is not uncommon in HTS experiments. 13,[26][27][28] A good plate design helps to identify systematic errors (especially those linked with well position) and to determine what normalization we should use to adjust the data so that we can remove/reduce the impact of systematic errors on both QC and hit selection. Currently, the commonly used plate designs cannot effectively identify and adjust for systematic errors.…”