ArticleJournal of biomolecular screening2014
Rank ordering plate data facilitates data visualization and normalization in high-throughput screening.
Article in Journal of biomolecular screening, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
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- Screening under infection-relevant conditions reveals chemical sensitivity in multidrug resistant invasive non-typhoidalRSC chemical biology · 2023Article
- Nutrient Limitation SensitizesACS infectious diseases · 2023Article
- Inhibiting fatty acid synthesis overcomes colistin resistance.Nature microbiology · 2023Article
- Protective Effect of Neutral Electrolyzed Saline on Gentamicin-Induced Nephrotoxicity: Evaluation of Histopathologic Parameters in a Murine Model.Medicina (Kaunas, Lithuania) · 2023Article
- Targeting non-canonical pathways as a strategy to modulate the sodium iodide symporter.Cell chemical biology · 2022Article
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- Large-scale data analysis for robotic yeast one-hybrid platforms and multi-disciplinary studies using GateMultiplex.BMC biology · 2021Article
- Identification of Bioactive SNM1A Inhibitors.ACS omega · 2021Article
- Physicochemical and Structural Parameters Contributing to the Antibacterial Activity and Efflux Susceptibility of Small-Molecule Inhibitors of Escherichia coli.Antimicrobial agents and chemotherapy · 2021Article
- AFrontiers in molecular biosciences · 2021Article
- Development and validation of a high-throughput whole cell assay to investigateThe Journal of biological chemistry · 2020Article
- Article
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4 authors.
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Abstract
High-throughput screening (HTS) of chemical and microbial strain collections is an indispensable tool for modern chemical and systems biology; however, HTS data sets have inherent systematic and random error, which may lead to false-positive or false-negative results. Several methods of normalization of data exist; nevertheless, due to the limitations of each, no single method has been universally adopted. Here, we present a method of data visualization and normalization that is effective, intuitive, and easy to implement in a spreadsheet program. For each plate, the data are ordered by ascending values and a plot thereof yields a curve that is a signature of the plate data. Curve shape characteristics provide intuitive visualization of the frequency and strength of inhibitors, activators, and noise on the plate, allowing potentially problematic plates to be flagged. To reduce plate-to-plate variation, the data can be normalized by the mean of the middle 50% of ordered values, also called the interquartile mean (IQM) or the 50% trimmed mean of the plate. Positional effects due to bias in columns, rows, or wells can be corrected using the interquartile mean of each well position across all plates (IQMW) as a second level of normalization. We illustrate the utility of this method using data sets from biochemical and phenotypic screens.
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