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VoPo leverages cellular heterogeneity for predictive modeling of single-cell data

Author

Listed:
  • Natalie Stanley

    (Stanford University
    Stanford University
    Stanford University)

  • Ina A. Stelzer

    (Stanford University
    Stanford University)

  • Amy S. Tsai

    (Stanford University)

  • Ramin Fallahzadeh

    (Stanford University
    Stanford University
    Stanford University)

  • Edward Ganio

    (Stanford University
    Stanford University)

  • Martin Becker

    (Stanford University
    Stanford University
    Stanford University)

  • Thanaphong Phongpreecha

    (Stanford University
    Stanford University
    Stanford University)

  • Huda Nassar

    (Stanford University
    Stanford University
    Stanford University)

  • Sajjad Ghaemi

    (Stanford University
    Stanford University
    Stanford University
    National Research Council Canada)

  • Ivana Maric

    (Stanford University)

  • Anthony Culos

    (Stanford University
    Stanford University
    Stanford University)

  • Alan L. Chang

    (Stanford University
    Stanford University
    Stanford University)

  • Maria Xenochristou

    (Stanford University
    Stanford University
    Stanford University)

  • Xiaoyuan Han

    (Stanford University
    Stanford University)

  • Camilo Espinosa

    (Stanford University
    Stanford University
    Stanford University)

  • Kristen Rumer

    (Stanford University
    Stanford University)

  • Laura Peterson

    (Stanford University
    Stanford University)

  • Franck Verdonk

    (Stanford University
    Stanford University)

  • Dyani Gaudilliere

    (Stanford University
    Stanford University
    Stanford University)

  • Eileen Tsai

    (Stanford University
    Stanford University)

  • Dorien Feyaerts

    (Stanford University
    Stanford University)

  • Jakob Einhaus

    (Stanford University
    Stanford University)

  • Kazuo Ando

    (Stanford University
    Stanford University)

  • Ronald J. Wong

    (Stanford University)

  • Gerlinde Obermoser

    (Stanford University)

  • Gary M. Shaw

    (Stanford University)

  • David K. Stevenson

    (Stanford University)

  • Martin S. Angst

    (Stanford University)

  • Brice Gaudilliere

    (Stanford University
    Stanford University)

  • Nima Aghaeepour

    (Stanford University
    Stanford University
    Stanford University)

Abstract

High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo ( https://github.com/stanleyn/VoPo ), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

Suggested Citation

  • Natalie Stanley & Ina A. Stelzer & Amy S. Tsai & Ramin Fallahzadeh & Edward Ganio & Martin Becker & Thanaphong Phongpreecha & Huda Nassar & Sajjad Ghaemi & Ivana Maric & Anthony Culos & Alan L. Chang , 2020. "VoPo leverages cellular heterogeneity for predictive modeling of single-cell data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17569-8
    DOI: 10.1038/s41467-020-17569-8
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