Multi-parametric flow cytometry is a key technology for characterization of immune cell
phenotypes. However, robust high-dimensional post-analytic strategies for automated data
analysis in large numbers of donors are still lacking. Here, we report a computational
pipeline, called FlowGM, which minimizes operator input, is insensitive to compensation
settings, and can be adapted to different analytic panels. A Gaussian Mixture Model (GMM)-
based approach was utilized for initial clustering, with the number of clusters determined …