Trajectory-aware Clustering

Trajectory-Aware Clustering is a single-cell clustering method that incorporates developmental or dynamic trajectory information to guide the identification of cell populations, thereby distinguishing itself from conventional clustering approaches by explicitly modeling the temporal or pseudotemporal progression of cells.

16

Publications

1K

Citations

59

Authors

33

Institutions

Publications per year

2017–2026

11

Authors

59

  1. MG
    Maxime Gariel

    Georgia Institute of Technology, United States

    2 publications · 286 citations

    2

    h-index

  2. AN
    Ashok N. Srivastava

    Ames Research Center, United States

    2 publications · 286 citations

    2

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  3. ÉF
    Éric Féron

    Massachusetts Institute of Technology, United States

    2 publications · 286 citations

    2

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  4. HC
    Hui Ci

    China University of Mining and Technology, China

    1 publications · 24 citations

    1

    h-index

  5. XD
    Xiaoxiao Dong

    Southeast University, China

    1 publications · 38 citations

    1

    h-index

1–5 of 59

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 33 citations

    2

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  2. Journal of Marine Science and Engineering

    2 publications · 62 citations

    2

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  3. 2

    h-index

  4. 1

    h-index

  5. Aerospace Science and Technology

    AST · 1 publications · 83 citations

    1

    h-index

1–5