Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies

Spatial bias continues to be a major challenge in high-throughput screening technologies. Its successful detection and elimination are critical for identifying the most promising drug candidates. Here, we examine experimental small molecule assays from the popular ChemBank database and show that scr...

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Bibliographic Details
Published inScientific reports Vol. 7; no. 1; p. 11921
Main Authors Mazoure, Bogdan, Nadon, Robert, Makarenkov, Vladimir
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 20.09.2017
Nature Publishing Group
Subjects
631/114/1314
631/61/32
Bias
Drug development
High-throughput screening
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
Statistical methods
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Summary:Spatial bias continues to be a major challenge in high-throughput screening technologies. Its successful detection and elimination are critical for identifying the most promising drug candidates. Here, we examine experimental small molecule assays from the popular ChemBank database and show that screening data are widely affected by both assay-specific and plate-specific spatial biases. Importantly, the bias affecting screening data can fit an additive or multiplicative model. We show that the use of appropriate statistical methods is essential for improving the quality of experimental screening data. The presented methodology can be recommended for the analysis of current and next-generation screening data.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-11940-4