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

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. 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. 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. 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.

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Copyright © 2023 Räuber, Nelke, Schroeter, Barman, Pawlitzki, Ingwersen, Akgün, Günther, Garza, Marggraf, Dunay, Schreiber, Vielhaber, Ziemssen, Melzer, Ruck, Meuth and Herty.

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SR received travel reimbursements from Merck Healthcare Germany GmbH and Alexion Pharmaceuticals. She served on a scientific advisory board from Merck Healthcare Germany GmbH and her research was supported by Novartis and the Stiftung zur Förderung junger Neurowissenschaftler. CS received travel reimbursements from Merck Healthcare Germany GmbH and Alexion Pharmaceuticals. She served on a scientific advisory board from Merck Healthcare Germany GmbH. MP received honoraria for lecturing from Argenx, Biogen, Bayer, Novartis, Hexal, Sanofi and Merck. He received research funding from Biogen. KA received personal compensation for consulting services and speaker honoraria from Roche, Novartis, Merck, Sanofi, Teva, BMS, Biogen, and Celgene. SM received honoraria for lecturing and travel expenses for attending meetings from Almirall, Amicus Therapeutics Germany, Bayer HealthCare, Biogen, Celgene, Diamed, Genzyme, MedDay Pharmaceuticals, Merck Serono, Novartis, Novo Nordisk, ONO Pharma, Roche, Sanofi-Aventis, Chugai Pharma, QuintilesIMS, and Teva. His research is funded by the German Ministry for Education and Research BMBF, Bundesinstitut für Risikobewertung BfR, Deutsche Forschungsgemeinschaft DFG, Else Kröner Fresenius Foundation, Gemeinsamer Bundesausschuss G-BA, German Academic Exchange Service, Hertie Foundation, Interdisciplinary Center for Clinical Studies IZKF Muenster, German Foundation Neurology and by Alexion, Almirall, Amicus Therapeutics Germany, Biogen, Diamed, Fresenius Medical Care, Genzyme, HERZ Burgdorf, Merck Serono, Novartis, ONO Pharma, Roche, and Teva. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.