An immune cell's phenotype expresses through its high-dimensional marker signature. Cluster analyses of data from high-throughput mass and flow cytometry marker panels permit discovery of previously undescribed immune cell phenotypes. Impactful reporting of new phenotypes demands low-dimensional visualization tools that preserve with integrity phenotypes' original high-dimensional structure. For this purpose, we introduce penalized supervised star plots. As designed and as we demonstrate, penalized supervised star plots are two-dimensional projections that tend to preserve separation of clusters as well as information on the relative contributions of various markers in differentiating phenotypes. The new method is robust to markers that do not differentiate phenotypes at all, as shown in a challenge data set. Results include comparison with other popular procedures. Penalized supervised star plots incorporate cross-validation to permit portability of estimated optimal projections to new samples. Supervised star plots are further illustrated with a featured influenza-specific T cell data set as well as a peripheral blood mononuclear cell phenotyping data set.
Keywords: penalty; flow cytometry; immune cell phenotypes; influenza; mass cytometry; regularization.