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Open Access Highly Accessed Methodology article

Systematic error detection in experimental high-throughput screening

Plamen Dragiev1, Robert Nadon23 and Vladimir Makarenkov1*

Author Affiliations

1 Département d'informatique, Université du Québec à Montréal, C.P. 8888 succ. Centre-Ville, Montreal (QC) H3C 3P8, Canada

2 Department of Human Genetics, McGill University, 1205 Dr. Penfield Ave. Montreal, QC, H3A 1B1

3 McGill University and Genome Quebec Innovation Centre, 740 Dr. Penfield Ave., Montreal, QC, H3A 1A4, Canada

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BMC Bioinformatics 2011, 12:25  doi:10.1186/1471-2105-12-25

Published: 19 January 2011

Abstract

Background

High-throughput screening (HTS) is a key part of the drug discovery process during which thousands of chemical compounds are screened and their activity levels measured in order to identify potential drug candidates (i.e., hits). Many technical, procedural or environmental factors can cause systematic measurement error or inequalities in the conditions in which the measurements are taken. Such systematic error has the potential to critically affect the hit selection process. Several error correction methods and software have been developed to address this issue in the context of experimental HTS [1-7]. Despite their power to reduce the impact of systematic error when applied to error perturbed datasets, those methods also have one disadvantage - they introduce a bias when applied to data not containing any systematic error [6]. Hence, we need first to assess the presence of systematic error in a given HTS assay and then carry out systematic error correction method if and only if the presence of systematic error has been confirmed by statistical tests.

Results

We tested three statistical procedures to assess the presence of systematic error in experimental HTS data, including the χ2 goodness-of-fit test, Student's t-test and Kolmogorov-Smirnov test [8] preceded by the Discrete Fourier Transform (DFT) method [9]. We applied these procedures to raw HTS measurements, first, and to estimated hit distribution surfaces, second. The three competing tests were applied to analyse simulated datasets containing different types of systematic error, and to a real HTS dataset. Their accuracy was compared under various error conditions.

Conclusions

A successful assessment of the presence of systematic error in experimental HTS assays is possible when the appropriate statistical methodology is used. Namely, the t-test should be carried out by researchers to determine whether systematic error is present in their HTS data prior to applying any error correction method. This important step can significantly improve the quality of selected hits.