ArticleScientific reports2017
Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Spatial artifact detection improves the reproducibility of drug screening experiments.iScience · 2025Article
- Advancing Organic Chemistry Using High-Throughput Experimentation.Angewandte Chemie (International ed. in English) · 2025Review
- Large-scale human myeloma cell line small molecule compound screen dataset.Scientific data · 2025Article
- Development of an automated 3D high content cell screening platform for organoid phenotyping.SLAS discovery : advancing life sciences R & D · 2024Article
- A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments.PloS one · 2024Article
- A 384-Well Microtiter Plate Model for Candida Biofilm Formation and Its Application to High-Throughput Screening.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach.Biostatistics (Oxford, England) · 2022Article
- DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks.Scientific reports · 2022Article
- Organoids in image-based phenotypic chemical screens.Experimental & molecular medicine · 2021Review
- Double Empirical Bayes Testing.International statistical review = Revue internationale de statistique · 2020Article
- Statistical models for identifying frequent hitters in high throughput screening.Scientific reports · 2020Article
- Probe-target hybridization depends on spatial uniformity of initial concentration condition across large-format chips.Scientific reports · 2020Article
- A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines.Cell systems · 2019Article
- The Distribution of Standard Deviations Applied to High Throughput Screening.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
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Registered trials
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