An efficient method for the detection and elimination of systematic error in high-throughput screening
Open accessAbstract
Motivation: High-throughput screening (HTS) is an early-stage process in drug discovery which allows thousands of chemical compounds to be tested in a single study. We report a method for correcting HTS data prior to the hit selection process (i.e. selection of active compounds). The proposed correction minimizes the impact of systematic errors which may affect the hit selection in HTS. The introduced method, called a well correction, proceeds by correcting the distribution of measurements within wells of a given HTS assay. We use simulated and experimental data to illustrate the advantages of the new method compared to other widely-used methods of data correction and hit selection in HTS. Contact: makarenkov.vladimir@uqam.ca Supplementary information: Supplementary data are available at Bioinformatics online.
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Positional effects due to technical variability manifest as distinct spatial patterns across rows, columns and edges in different plates.
“In contrast, positional effects due to technical variability manifest as distinct spatial patterns across the rows, columns and edges in different plates, a common challenge in multi-well-based assays30–33.”
From A statistical framework for high-content phenotypic profiling using cellular feature distributions (2022) · cites this paper as [32]
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A statistical framework for high-content phenotypic profiling using cellular feature distributions
Pearson, Y. E., Kremb, S., Butterfoss, G. L., Xie, X., Fahs, H., & Gunsalus, K. C. (2022) · Communications Biology · doi:10.1038/s42003-022-04343-3 · Cited by 19 · see citing works
- Authors
- Makarenkov, V., Zentilli, P., Kevorkov, D., Gagarin, A., Malo, N., & Nadon, R.
- Affiliations
- Université du Québec à Montréal (CA)
- ORCID
- Makarenkov, Vladimir
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-04-26
- Volume
- 23
- Issue
- 13
- Pages
- 1648-1657
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2166393666
- Cited by
- 97
- References
- 18
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 1 · 2022: 3 · 2021: 2 · 2020: 2 · 2018: 3 · 2017: 6 · 2016: 6 · 2015: 8 · 2014: 7 · 2013: 12 · 2012: 7
- FWCI
- 7.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- high-throughput screening, bias correction, hit selection, drug discovery
- MeSH terms
- Biological Assay, Data Interpretation, Statistical, Drug Evaluation, Preclinical, Sensitivity and Specificity, Technology, Pharmaceutical, Drug Design, Artifacts
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