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Microfluidics in Assessing Platelet Function
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Normalization methods in time series of platelet function assays: A SQUIRE compliant study.

Sven Van Poucke1, Zhongheng Zhang, Mark Roest

  • 1Department of Anesthesiology, Intensive Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium Department of Critical Care Medicine, Jinhua Hospital of Zhejiang University, Zhejiang, P.R. China Synapse Research Institute, Maastricht, The Netherlands Department of Organizational Sciences, University of Belgrade, Belgrade, Serbia Department of Infection and Liver Diseases, Liver Research Center, Wenzhou Medical University, Wenzhou, China Central Diagnostic Laboratory, Maastricht University Medical Centre (MUMC+) Department of Anaesthesiology & Pain Treatment, Maastricht University Medical Centre, Maastricht, The Netherlands Department of Anesthesiology, ICU and Perioperative Medicine, HMC, Doha, Qatar.

Medicine
|July 19, 2016
PubMed
Summary

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Data normalization is key for analyzing complex platelet function test results. Normalizing data per assay over all time points, using methods like IQR, range, or z-transformation, best reveals correlations in these multivariate time series.

Area of Science:

  • Biomedical Engineering
  • Clinical Laboratory Science
  • Data Science

Background:

  • Platelet function assays (e.g., aggregometry, thromboelastometry) generate complex, high-dimensional multivariate time series data.
  • Extracting meaningful clinical and statistical information from such data is challenging.
  • Effective data visualization relies on appropriate normalization techniques.

Purpose of the Study:

  • To present and visualize various data normalization methods for platelet function data.
  • To identify the most suitable normalization approach for multivariate time series analysis of platelet function tests.
  • To evaluate the impact of different normalization strategies on correlation analysis.

Main Methods:

  • Exploration of data normalization techniques: z-transformation, range transformation, proportion transformation, and interquartile range (IQR).

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  • Application of normalization strategies: per assay across all time points, and per time point across all assays.
  • Statistical analysis using Spearman correlation test to assess data relationships post-normalization.

    • Main Results:

    • Normalization per assay across all time points using IQR, range transformation, and z-transformation revealed significant correlations.
    • Normalization per time point across all tests did not yield discernible correlations.
    • Treating all data as a single dataset for normalization also failed to produce interpretable correlations.

    Conclusions:

    • The choice of normalization strategy significantly impacts the ability to detect correlations in platelet function data.
    • Normalizing data per assay over all time points is a more effective approach for analyzing multivariate time series from platelet function tests.
    • Specific methods like IQR, range, and z-transformation are suitable for preserving correlational information when applied appropriately.