Characterizing cell subsets in heterogeneous tissues using marker enrichment modeling

Learning cell identity from single-cell data presently relies on human experts. Here, we present Marker Enrichment Modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human and machine-readable text label. MEM outperformed traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.