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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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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2011
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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. |
format | Text |
id | pubmed-3034671 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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