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

ABSTRACT: 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.

SUBMITTER: Mazoure B

PROVIDER: S-EPMC5607347 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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

Mazoure Bogdan B Nadon Robert R Makarenkov Vladimir V

Scientific reports 20170920 1

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 u ...[more]

PMID: 28931934

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