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Systematic error detection in experimental high-throughput screening

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 ca...

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Autores principales: Dragiev, Plamen, Nadon, Robert, Makarenkov, Vladimir
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3034671/
https://www.ncbi.nlm.nih.gov/pubmed/21247425
http://dx.doi.org/10.1186/1471-2105-12-25
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author Dragiev, Plamen
Nadon, Robert
Makarenkov, Vladimir
author_facet Dragiev, Plamen
Nadon, Robert
Makarenkov, Vladimir
author_sort Dragiev, Plamen
collection PubMed
description 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 χ(2 )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.
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spelling pubmed-30346712011-02-17 Systematic error detection in experimental high-throughput screening Dragiev, Plamen Nadon, Robert Makarenkov, Vladimir BMC Bioinformatics Methodology Article 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 χ(2 )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. BioMed Central 2011-01-19 /pmc/articles/PMC3034671/ /pubmed/21247425 http://dx.doi.org/10.1186/1471-2105-12-25 Text en Copyright ©2011 Dragiev et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Dragiev, Plamen
Nadon, Robert
Makarenkov, Vladimir
Systematic error detection in experimental high-throughput screening
title Systematic error detection in experimental high-throughput screening
title_full Systematic error detection in experimental high-throughput screening
title_fullStr Systematic error detection in experimental high-throughput screening
title_full_unstemmed Systematic error detection in experimental high-throughput screening
title_short Systematic error detection in experimental high-throughput screening
title_sort systematic error detection in experimental high-throughput screening
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3034671/
https://www.ncbi.nlm.nih.gov/pubmed/21247425
http://dx.doi.org/10.1186/1471-2105-12-25
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