Classifying flow cytometry data using Bayesian analysis helps to distinguish ALS patients from healthy controls - 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; et al. (Frontiers in Immunology, 2023)
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.