Single-cell Time-series Visualization

Single-Cell Time-Series Visualization is a computational framework that enables the longitudinal graphical representation of gene expression dynamics at the individual cell level over sequential time points, distinguishing it from static single-cell visualization methods by explicitly capturing temporal evolution in cellular states.

7

Publications

1.1K

Citations

54

Authors

21

Institutions

Publications per year

2017–2026

4

Authors

54

  1. SW
    Shao’ang Wen

    Chinese Academy of Sciences, China

    1 publications · 346 citations

    1

    h-index

  2. PF
    Paul F. Worley

    Johns Hopkins University, United States

    1 publications · 61 citations

    1

    h-index

  3. JY
    Jun Yan

    Chinese Academy of Sciences, China

    1 publications · 346 citations

    1

    h-index

  4. RL
    Ren Liu

    University of California San Diego, United States

    1 publications · 43 citations

    1

    h-index

  5. AM
    A.R. Mitz

    Washington University in St. Louis, United States

    1 publications · 12 citations

    1

    h-index

1–5 of 54

Venues

  1. Cell

    1 publications · 43 citations

    1

    h-index

  2. Nature Neuroscience

    1 publications · 346 citations

    1

    h-index

  3. Nature Communications

    1 publications · 14 citations

    1

    h-index

  4. Methods in molecular biology

    Book series · 1 publications · 16 citations

    1

    h-index

  5. Journal of Neuroscience Methods

    1 publications · 61 citations

    1

    h-index

1–5