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Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model
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Data visualizations to detect systematic errors in laboratory assay results.

Jörn Lötsch1,2

  • 1Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany.

Pharmacology Research & Perspectives
|December 12, 2017
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Standard data quality checks may miss systematic lab errors. Novel data visualizations, like dotplots, can reveal hidden issues in drug and substance concentration measurements, improving research accuracy.

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R programming languagedata quality checkdata science

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Area of Science:

  • Pharmacology and Toxicology
  • Data Science and Visualization
  • Laboratory Medicine

Background:

  • Accurate measurement of drug and endogenous substance concentrations is crucial for pharmacology research and clinical services.
  • Traditional statistical and visual methods for inspecting laboratory assay results may fail to detect systematic errors.
  • Certain data anomalies, like constant values across all probes in an assay run, can evade standard quality control checks.

Purpose of the Study:

  • To demonstrate the limitations of standard data inspection methods in identifying systematic laboratory errors.
  • To introduce and advocate for the use of advanced data visualizations for enhanced error detection.
  • To propose a specific visualization technique for improving the quality assessment of laboratory assay data.

Main Methods:

  • Application of data science-derived visualizations to real-life laboratory assay data.
  • Comparison of standard data inspection techniques with alternative visualization methods.
  • Development and evaluation of a dotplot visualization for assay data analysis.

Main Results:

  • Standard methods may not detect systematic laboratory errors, such as consistent erroneous values across all probes.
  • Alternative data visualizations offer improved sensitivity in detecting various types of systematic errors.
  • A proposed dotplot visualization effectively highlights data range, outliers, and systematic measurement issues.

Conclusions:

  • Enhanced data visualization strategies are essential for robust quality control in laboratory measurements.
  • The proposed dotplot offers a valuable tool for identifying systematic errors in drug and substance concentration assays.
  • Improved data inspection methods contribute to the reliability of basic and clinical pharmacology research.