Classifying flow cytometry data using Bayesian analysis helps to distinguish ALS patients from healthy controls

Frontiers in Immunology, vol. 14, pp. 1198860

Abstract

Introduction: Given its wide availability and cost-effectiveness, multidimensional flow cytometry (mFC) became a core method in the field of immunology allowing for the analysis of a broad range of individual cells providing insights into cell subset composition, cellular behavior, and cell-to-cell interactions. Formerly, the analysis of mFC data solely relied on manual gating strategies. With the advent of novel computational approaches, (semi-)automated gating strategies and analysis tools complemented manual approaches. Methods: Using Bayesian network analysis, we developed a mathematical model for the dependencies of different obtained mFC markers. The algorithm creates a Bayesian network that is a HC tree when including raw, ungated mFC data of a randomly selected healthy control cohort (HC). The HC tree is used to classify whether the observed marker distribution (either patients with amyotrophic lateral sclerosis (ALS) or HC) is predicted. The relative number of cells where the probability q is equal to zero is calculated reflecting the similarity in the marker distribution between a randomly chosen mFC file (ALS or HC) and the HC tree. Results: Including peripheral blood mFC data from 68 ALS and 35 HC, the algorithm could correctly identify 64/68 ALS cases. Tuning of parameters revealed that the combination of 7 markers, 200 bins, and 20 patients achieved the highest AUC on a significance level of p < 0.0001. The markers CD4 and CD38 showed the highest zero probability. We successfully validated our approach by including a second, independent ALS and HC cohort (55 ALS and 30 HC). In this case, all ALS were correctly identified and side scatter and CD20 yielded the highest zero probability. Finally, both datasets were analyzed by the commercially available algorithm 'Citrus', which indicated superior ability of Bayesian network analysis when including raw, ungated mFC data. Discussion: Bayesian network analysis might present a novel approach for classifying mFC data, which does not rely on reduction techniques, thus, allowing to retain information on the entire dataset. Future studies will have to assess the performance when discriminating clinically relevant differential diagnoses to evaluate the complementary diagnostic benefit of Bayesian network analysis to the clinical routine workup.

Authors 18

1. Saskia Jania Räuber corresponding

   Heinrich Heine University Düsseldorf

   Affiliation as printed

   Department of Neurology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany

2. Christopher Nelke

   Heinrich Heine University Düsseldorf

   Affiliation as printed

   Department of Neurology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany

3. Christina B. Schroeter

   Heinrich Heine University Düsseldorf

   Affiliation as printed

   Department of Neurology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany

4. Sumanta Barman

   Heinrich Heine University Düsseldorf

   Affiliation as printed

   Department of Neurology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany

5. Marc Pawlitzki

   Heinrich Heine University Düsseldorf

   Affiliation as printed

   Department of Neurology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany

6. Jens Ingwersen

   Heinrich Heine University Düsseldorf

   Affiliation as printed

   Department of Neurology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany

7. Katja Akgün

   Technische Universität Dresden · University Hospital Carl Gustav Carus

   Affiliation as printed

   Department of Neurology, Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany

8. René Günther

   Technische Universität Dresden · University Hospital Carl Gustav Carus

   Affiliation as printed

   Department of Neurology, Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany

9. Alejandra P. Garza

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Institute of Inflammation and Neurodegeneration, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

10. Michaela Marggraf

    Technische Universität Dresden · University Hospital Carl Gustav Carus

    Affiliation as printed

    Department of Neurology, Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany

11. Ildikò Rita Dunay

    Otto-von-Guericke-Universität Magdeburg

    Affiliation as printed

    Institute of Inflammation and Neurodegeneration, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

12. Stefanie Schreiber

    Otto-von-Guericke-Universität Magdeburg

    Affiliation as printed

    Department of Neurology, Otto von Guericke University, Magdeburg, Germany

13. Stefan Vielhaber

    Otto-von-Guericke-Universität Magdeburg

    Affiliation as printed

    Department of Neurology, Otto von Guericke University, Magdeburg, Germany

14. Tjalf Ziemssen

    Technische Universität Dresden · University Hospital Carl Gustav Carus

    Affiliation as printed

    Department of Neurology, Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden University of Technology, Dresden, Germany

15. Nico Melzer

    Heinrich Heine University Düsseldorf

    Affiliation as printed

    Department of Neurology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany

16. Tobias Ruck

    Heinrich Heine University Düsseldorf

    Affiliation as printed

    Department of Neurology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany

17. Sven Guenther Meuth

    Heinrich Heine University Düsseldorf

    Affiliation as printed

    Department of Neurology, Medical Faculty, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany

18. Michaël Herty Aachen Department of Mathematics Institute of Geometry and Applied Mathematics

    RWTH Aachen University

    Affiliation as printed

    Department of Mathematics, Institute of Geometry and Applied Mathematics, RWTH Aachen University, Aachen, Germany