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Characterizing cell subsets using marker enrichment modeling

Nature Methods volume 14, pages 275–278 (2017)Cite this article

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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.

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Figure 1: Marker enrichment modeling (MEM) automatically labels human blood cell populations in data set A.
Figure 2: Hierarchical clustering based solely on MEM label groups of T cells and B cells measured in diverse studies using different cytometry platforms.
Figure 3: MEM correctly grouped immune and cancer cell populations from glioma tumors using nine proteins expressed on cancer cells in data set D.

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Change history

  • 10 February 2017

    In the version of this article initially published online, Jonathan M Irish was misspelled as Jonathon M Irish. The error has been corrected in the print, PDF and HTML versions of this article as of 10 Feburary 2017.

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Acknowledgements

This study was supported by R25 CA136440-04 (K.E.D.), F31 CA199993 (A.R.G.), R00 CA143231-03 (J.M.I.), the Vanderbilt-Ingram Cancer Center (VICC, P30 CA68485), VICC Ambassadors, a VICC Hematology Helping Hands award (J.M.I. and K.E.D.), and the Vanderbilt International Scholars Program (N.L.). Thanks to M. Roussel for helpful discussions of myeloid cell identity markers, to D. Doxie for helpful discussions of MEM analysis of tumor and immune cell subsets, and to L. Chambless and R. Ihrie for use of glioma tumor data generated by N.L.

Author information

Authors and Affiliations

  1. Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA

    Kirsten E Diggins, Nalin Leelatian & Jonathan M Irish

  2. Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA

    Kirsten E Diggins, Allison R Greenplate, Nalin Leelatian, Cara E Wogsland & Jonathan M Irish

  3. Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA

    Allison R Greenplate, Cara E Wogsland & Jonathan M Irish

Authors
  1. Kirsten E Diggins
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  2. Allison R Greenplate
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  3. Nalin Leelatian
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  4. Cara E Wogsland
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  5. Jonathan M Irish
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Contributions

All authors designed experiments, discussed data visualization, contributed intellectually to the manuscript, and approved the final manuscript. J.M.I. and K.E.D. performed computational analyses, developed analytical tools and protocols, conceived and designed the study, and wrote the manuscript. A.R.G. contributed to Figures 2 and 3 and assisted with manuscript revisions. N.L. contributed to Figure 3 and manuscript revisions. C.E.W. contributed to R code implementation and manuscript revisions.

Corresponding author

Correspondence to Jonathan M Irish.

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Competing interests

J.M.I. is cofounder and board member and Cytobank Inc. and received research support from Incyte Corp.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Tables 1–5 and Supplementary Notes 1–5 (PDF 3915 kb)

Source data to Supplementary Figure 2

Source data to Supplementary Figure 4

Source data to Supplementary Figure 5

Supplementary Data 1

Supplementary Note 2 Underlying Data (XLSX 28 kb)

Supplementary Data 2

Supplementary Note 5-Figure 1 Underlying Data (XLSX 18 kb)

Supplementary Data 3

Supplementary Note 5-Figure 2 Underlying Data (XLSX 13 kb)

Supplementary Software

R package for MEM implementation (ZIP 4346 kb)

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Diggins, K., Greenplate, A., Leelatian, N. et al. Characterizing cell subsets using marker enrichment modeling. Nat Methods 14, 275–278 (2017). https://doi.org/10.1038/nmeth.4149

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