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Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM

Author

Listed:
  • Huidong Chen

    (Massachusetts General Hospital Research Institute and Harvard Medical School
    Dana-Farber Cancer Institute
    Harvard T.H. Chan School of Public Health
    Tongji University)

  • Luca Albergante

    (PSL Research University
    INSERM, U900
    PSL Research University, CBIO-Centre for Computational Biology)

  • Jonathan Y. Hsu

    (Massachusetts General Hospital Research Institute and Harvard Medical School
    Massachusetts Institute of Technology)

  • Caleb A. Lareau

    (Massachusetts General Hospital Research Institute and Harvard Medical School
    Broad Institute of MIT and Harvard)

  • Giosuè Lo Bosco

    (University of Palermo
    Euro-Mediterranean Institute of Science and Technology)

  • Jihong Guan

    (Tongji University)

  • Shuigeng Zhou

    (Fudan University)

  • Alexander N. Gorban

    (University of Leicester
    Lobachevsky University)

  • Daniel E. Bauer

    (Broad Institute of MIT and Harvard
    Harvard Medical School)

  • Martin J. Aryee

    (Massachusetts General Hospital Research Institute and Harvard Medical School
    Harvard T.H. Chan School of Public Health
    Broad Institute of MIT and Harvard)

  • David M. Langenau

    (Massachusetts General Hospital Research Institute and Harvard Medical School
    Harvard Stem Cell Institute)

  • Andrei Zinovyev

    (PSL Research University
    INSERM, U900
    PSL Research University, CBIO-Centre for Computational Biology
    Lobachevsky University)

  • Jason D. Buenrostro

    (Broad Institute of MIT and Harvard
    Harvard University)

  • Guo-Cheng Yuan

    (Dana-Farber Cancer Institute
    Harvard T.H. Chan School of Public Health
    Harvard Stem Cell Institute)

  • Luca Pinello

    (Massachusetts General Hospital Research Institute and Harvard Medical School
    Broad Institute of MIT and Harvard)

Abstract

Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. We have tested STREAM on several synthetic and real datasets generated with different single-cell technologies. We further demonstrate its utility for understanding myoblast differentiation and disentangling known heterogeneity in hematopoiesis for different organisms. STREAM is an open-source software package.

Suggested Citation

  • Huidong Chen & Luca Albergante & Jonathan Y. Hsu & Caleb A. Lareau & Giosuè Lo Bosco & Jihong Guan & Shuigeng Zhou & Alexander N. Gorban & Daniel E. Bauer & Martin J. Aryee & David M. Langenau & Andre, 2019. "Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09670-4
    DOI: 10.1038/s41467-019-09670-4
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    Cited by:

    1. Zhenchao Tang & Guanxing Chen & Shouzhi Chen & Jianhua Yao & Linlin You & Calvin Yu-Chian Chen, 2024. "Modal-nexus auto-encoder for multi-modality cellular data integration and imputation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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