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Modeling error in experimental assays using the bootstrap principle: understanding discrepancies between assays using
Sonya M Hanson1, Sean Ekins2, John D Chodera3
1Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. sonya.hanson@choderalab.org.
Journal of Computer-Aided Molecular Design
|December 19, 2015
Summary
This study introduces bootstrap-based assay modeling to simulate and track experimental error and bias. These methods help identify how simple procedures can amplify imprecision and bias, improving assay accuracy.
Area of Science:
- Biotechnology
- Laboratory Science
- Data Analysis
Background:
- Experimental assay data inherently contains errors, the sources of which are often unclear.
- Understanding and quantifying assay error is crucial for reliable scientific results.
Purpose of the Study:
- To present simple assay modeling techniques using the bootstrap principle.
- To simulate and propagate sources of error and bias in experimental assay data.
- To aid in understanding and correcting discrepancies between assays.
Main Methods:
- Utilized bootstrap-based modeling to simulate error and bias.
- Analyzed the impact of robotic liquid handling in dilution series on assay precision.
- Examined the influence of dispensing technology on assay measurements.
Main Results:
- Demonstrated that simple operations like robotic dilutions can significantly amplify imprecision and bias.
- Showed how dispensing technology choices impact assay measurements.
- Quantified contributions to discrepancies between different assays.
Conclusions:
- The described modeling techniques allow for the understanding of expected error and bias in experimental datasets.
- These methods can assist experimentalists in designing assays to meet accuracy and imprecision goals.
- Provides a framework for identifying and potentially correcting assay discrepancies.
Keywords:
Acoustic droplet ejectionAssay modelingBootstrap principleDirect dispensingDispensing technologiesError modelingLiquid handlingMore Related Videos
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