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Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometrydoi:10.3389/fimmu.2021.787574 pmid:35046945 pmcid:PMC8761933 fatcat:e5mjjybfprfkbgrcincmbbimp4