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Generalized and scalable trajectory inference in single-cell omics data with VIA

Author

Listed:
  • Shobana V. Stassen

    (The University of Hong Kong)

  • Gwinky G. K. Yip

    (The University of Hong Kong)

  • Kenneth K. Y. Wong

    (The University of Hong Kong
    Hong Kong Science Park)

  • Joshua W. K. Ho

    (The University of Hong Kong
    Hong Kong Science Park)

  • Kevin K. Tsia

    (The University of Hong Kong
    Hong Kong Science Park)

Abstract

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

Suggested Citation

  • Shobana V. Stassen & Gwinky G. K. Yip & Kenneth K. Y. Wong & Joshua W. K. Ho & Kevin K. Tsia, 2021. "Generalized and scalable trajectory inference in single-cell omics data with VIA," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25773-3
    DOI: 10.1038/s41467-021-25773-3
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    Cited by:

    1. Zehua Zeng & Yuqing Ma & Lei Hu & Bowen Tan & Peng Liu & Yixuan Wang & Cencan Xing & Yuanyan Xiong & Hongwu Du, 2024. "OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Jolene S. Ranek & Wayne Stallaert & J. Justin Milner & Margaret Redick & Samuel C. Wolff & Adriana S. Beltran & Natalie Stanley & Jeremy E. Purvis, 2024. "DELVE: feature selection for preserving biological trajectories in single-cell data," Nature Communications, Nature, vol. 15(1), pages 1-26, December.
    3. Simone Puccio & Giorgio Grillo & Giorgia Alvisi & Caterina Scirgolea & Giovanni Galletti & Emilia Maria Cristina Mazza & Arianna Consiglio & Gabriele De Simone & Flavio Licciulli & Enrico Lugli, 2023. "CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    4. Vishnu Muraleedharan Saraswathy & Lili Zhou & Mayssa H. Mokalled, 2024. "Single-cell analysis of innate spinal cord regeneration identifies intersecting modes of neuronal repair," Nature Communications, Nature, vol. 15(1), pages 1-21, December.

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