Testing for differential abundance in mass cytometry data

When comparing biological conditions using mass cytometry data, one key challenge is to identify cellular populations that change in abundance. Here, we present a novel computational strategy for detecting these “differentially abundant” populations, by assigning cells to hyperspheres, testing for significant differences between conditions and controlling the spatial false discovery rate. The method’s performance is established using simulations and real data where it finds novel patterns of differential abundance.