Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM)

Curr Protoc Cytom. 2018 Jan 18;83:10.21.1-10.21.28. doi: 10.1002/cpcy.34.


Multiplexed single-cell experimental techniques like mass cytometry measure 40 or more features and enable deep characterization of well-known and novel cell populations. However, traditional data analysis techniques rely extensively on human experts or prior knowledge, and novel machine learning algorithms may generate unexpected population groupings. Marker enrichment modeling (MEM) creates quantitative identity labels based on features enriched in a population relative to a reference. While developed for cell type analysis, MEM labels can be generated for a wide range of multidimensional data types, and MEM works effectively with output from expert analysis and diverse machine learning algorithms. MEM is implemented as an R package and includes three steps: (1) calculation of MEM values that quantify each feature's relative enrichment in the population, (2) reporting of MEM labels as a heatmap or as a text label, and (3) quantification of MEM label similarity between populations. The protocols here show MEM analysis using datasets from immunology and oncology. These MEM implementations provide a way to characterize population identity and novelty in the context of computational and expert analyses. © 2018 by John Wiley & Sons, Inc.

Keywords: MEM; bioinformatics; cell identity; computational biology; cytotype; flow cytometry; machine learning; marker enrichment modeling; mass cytometry; single cell.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Electronic Data Processing / methods*
  • Flow Cytometry / methods*
  • Humans
  • Machine Learning*
  • Models, Theoretical*