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TCM visualizes trajectories and cell populations from single cell data

Author

Listed:
  • Wuming Gong

    (University of Minnesota)

  • Il-Youp Kwak

    (University of Minnesota)

  • Naoko Koyano-Nakagawa

    (University of Minnesota)

  • Wei Pan

    (University of Minnesota)

  • Daniel J. Garry

    (University of Minnesota)

Abstract

Profiling single cell gene expression data over specified time periods are increasingly applied to the study of complex developmental processes. Here, we describe a novel prototype-based dimension reduction method to visualize high throughput temporal expression data for single cell analyses. Our software preserves the global developmental trajectories over a specified time course, and it also identifies subpopulations of cells within each time point demonstrating superior visualization performance over six commonly used methods.

Suggested Citation

  • Wuming Gong & Il-Youp Kwak & Naoko Koyano-Nakagawa & Wei Pan & Daniel J. Garry, 2018. "TCM visualizes trajectories and cell populations from single cell data," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05112-9
    DOI: 10.1038/s41467-018-05112-9
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