Characterizing cell subsets using marker enrichment modeling.

ABSTRACT: Learning cell identity from high-content single-cell data presently relies on human experts. 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 outperforms 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.

SUBMITTER: Diggins KE 

PROVIDER: S-EPMC5330853 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature