A clustering hybrid method to identify cellular populations and their phenotypic signatures

Flow cytometers have enabled researchers to measure 8 to 16 different cellular markers at the single-cell level. Due to the encoded complexity in flow cytometry dataset across diverse cellular subtypes, new computational methods are required to extract biological insights and potentially rare subpopulations. In this paper, we present a hybrid clustering algorithm that generates a 2-dimensional distillation of flow cy-tometry data and then automatically extracts the subtypes and their phenotypic signatures based on the markers' distribution.