Related Experiment Video
Updated: Mar 17, 2026
Microfluidics in Assessing Platelet Function
Published on: November 8, 2024
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.
Platelet function can be quantitatively assessed by specific assays such as light-transmission aggregometry, multiple-electrode aggregometry measuring the response to adenosine diphosphate (ADP), arachidonic acid, collagen, and thrombin-receptor activating peptide and viscoelastic tests such as rotational thromboelastometry (ROTEM).The task of extracting meaningful statistical and clinical information from high-dimensional data spaces in temporal multivariate clinical data represented in multivariate time series is complex. Building insightful visualizations for multivariate time series demands adequate usage of normalization techniques.In this article, various methods for data normalization (z-transformation, range transformation, proportion transformation, and interquartile range) are presented and visualized discussing the most suited approach for platelet function data series.Normalization was calculated per assay (test) for all time points and per time point for all tests.Interquartile range, range transformation, and z-transformation demonstrated the correlation as calculated by the Spearman correlation test, when normalized per assay (test) for all time points. When normalizing per time point for all tests, no correlation could be abstracted from the charts as was the case when using all data as 1 dataset for normalization.
Platelet function can be quantitatively assessed by specific assays such as light-transmission aggregometry, multiple-electrode aggregometry measuring the response to adenosine diphosphate (ADP), arachidonic acid, collagen, and thrombin-receptor activating peptide and viscoelastic tests such as rotational thromboelastometry (ROTEM).The task of extracting meaningful statistical and clinical information from high-dimensional data spaces in temporal multivariate clinical data represented in multivariate time series is complex. Building insightful visualizations for multivariate time series demands adequate usage of normalization techniques.In this article, various methods for data normalization (z-transformation, range transformation, proportion transformation, and interquartile range) are presented and visualized discussing the most suited approach for platelet function data series.Normalization was calculated per assay (test) for all time points and per time point for all tests.Interquartile range, range transformation, and z-transformation demonstrated the correlation as calculated by the Spearman correlation test, when normalized per assay (test) for all time points. When normalizing per time point for all tests, no correlation could be abstracted from the charts as was the case when using all data as 1 dataset for normalization.
