AUTHOR=Räuber Saskia , Nelke Christopher , Schroeter Christina B. , Barman Sumanta , Pawlitzki Marc , Ingwersen Jens , Akgün Katja , Günther Rene , Garza Alejandra P. , Marggraf Michaela , Dunay Ildiko Rita , Schreiber Stefanie , Vielhaber Stefan , Ziemssen Tjalf , Melzer Nico , Ruck Tobias , Meuth Sven G. , Herty Michael 

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

JOURNAL=Frontiers in Immunology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1198860

DOI=10.3389/fimmu.2023.1198860

ISSN=1664-3224

ABSTRACT=This is a provisional file, not the final typeset article 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.