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Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types

Author

Listed:
  • Vincent Unen

    (Leiden University Medical Center)

  • Thomas Höllt

    (Delft University of Technology
    Leiden University Medical Center)

  • Nicola Pezzotti

    (Delft University of Technology)

  • Na Li

    (Leiden University Medical Center)

  • Marcel J. T. Reinders

    (Delft University of Technology)

  • Elmar Eisemann

    (Delft University of Technology)

  • Frits Koning

    (Leiden University Medical Center)

  • Anna Vilanova

    (Delft University of Technology)

  • Boudewijn P. F. Lelieveldt

    (Delft University of Technology
    Leiden University Medical Center)

Abstract

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets.

Suggested Citation

  • Vincent Unen & Thomas Höllt & Nicola Pezzotti & Na Li & Marcel J. T. Reinders & Elmar Eisemann & Frits Koning & Anna Vilanova & Boudewijn P. F. Lelieveldt, 2017. "Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01689-9
    DOI: 10.1038/s41467-017-01689-9
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    Cited by:

    1. Honglei Ren & Benjamin L. Walker & Zixuan Cang & Qing Nie, 2022. "Identifying multicellular spatiotemporal organization of cells with SpaceFlow," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Juan Du & Junlei Zhang & Lin Wang & Xun Wang & Yaxing Zhao & Jiaoying Lu & Tingmin Fan & Meng Niu & Jie Zhang & Fei Cheng & Jun Li & Qi Zhu & Daoqiang Zhang & Hao Pei & Guang Li & Xingguang Liang & He, 2023. "Selective oxidative protection leads to tissue topological changes orchestrated by macrophage during ulcerative colitis," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. Nannan Guo & Na Li & Li Jia & Qinyue Jiang & Mette Schreurs & Vincent Unen & Susana M. Chuva Sousa Lopes & Alexandra A. Vloemans & Jeroen Eggermont & Boudewijn Lelieveldt & Frank J. T. Staal & Noel F., 2023. "Immune subset-committed proliferating cells populate the human foetal intestine throughout the second trimester of gestation," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Lijun Cheng & Pratik Karkhanis & Birkan Gokbag & Yueze Liu & Lang Li, 2022. "DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data," PLOS Computational Biology, Public Library of Science, vol. 18(4), pages 1-22, April.

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