Abstract
Mass cytometry enables high-throughput characterization of heterogeneous cell populations at single-cell resolution, using metal isotopes to capture cellular signals and avoiding the spectral overlap common in flow cytometry. Despite advancements, conventional data analysis often focuses on manual gating or clustering within specific samples, overlooking disparities across subjects or biological samples. To address this gap, we propose a novel framework that treats the cell-by-protein matrix as a high-dimensional distribution, using Quantized Optimal Transport (QOT) to quantify distances between samples based on their cellular protein expression profiles. This approach allows for a direct comparison of distributions without relying on predefined gating strategies, capturing subtle variations in the data. We validated our method through two experiments using real-world time-series Coronavirus Disease 2019 (COVID-19) cytometry data. First, we conducted a leave-one-out analysis to identify immunologically unstable proteins over time, revealing CD3 and CD45 as the proteins changing the most during the vaccine response. Second, we aimed to capture individual immune fingerprints over time by calculating pairwise Wasserstein distances between samples and applying hierarchical clustering. Using silhouette scores to evaluate clustering effectiveness, we identified optimal combinations of immunological markers that effectively grouped samples from the same participant across different time points. Our findings demonstrate that the QOT framework provides a robust and flexible tool for cohort-level analysis of mass cytometry data, enabling the identification of unstable immunological markers and capturing immune response heterogeneity among vaccinated cohorts.