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CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose–response curves

Author

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  • Florian P. Bayer

    (Technical University of Munich)

  • Manuel Gander

    (Technical University of Munich)

  • Bernhard Kuster

    (Technical University of Munich
    Partner Site Munich)

  • Matthew The

    (Technical University of Munich)

Abstract

Dose-response curves are key metrics in pharmacology and biology to assess phenotypic or molecular actions of bioactive compounds in a quantitative fashion. Yet, it is often unclear whether or not a measured response significantly differs from a curve without regulation, particularly in high-throughput applications or unstable assays. Treating potency and effect size estimates from random and true curves with the same level of confidence can lead to incorrect hypotheses and issues in training machine learning models. Here, we present CurveCurator, an open-source software that provides reliable dose-response characteristics by computing p-values and false discovery rates based on a recalibrated F-statistic and a target-decoy procedure that considers dataset-specific effect size distributions. The application of CurveCurator to three large-scale datasets enables a systematic drug mode of action analysis and demonstrates its scalable utility across several application areas, facilitated by a performant, interactive dashboard for fast data exploration.

Suggested Citation

  • Florian P. Bayer & Manuel Gander & Bernhard Kuster & Matthew The, 2023. "CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose–response curves," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43696-z
    DOI: 10.1038/s41467-023-43696-z
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