Multimodal Single-cell Visualization

Multimodal Single-Cell Visualization is a computational framework that integrates and visually represents multiple types of single-cell data modalities—such as gene expression, chromatin accessibility, and protein abundance—within a unified spatial or low-dimensional representation to enable comparative and integrative analysis at the single-cell level.

10

Publications

421

Citations

165

Authors

67

Institutions

Publications per year

2017–2026

9

Authors

165

  1. IG
    Ilan Gold

    Broad Institute, United States

    2 publications · 101 citations

    2

    h-index

  2. MS
    Mark S. Keller

    Harvard University, United States

    2 publications · 101 citations

    2

    h-index

  3. PV
    Peter V. Kharchenko

    Harvard University, United States

    2 publications · 101 citations

    2

    h-index

  4. TM
    Trevor Manz

    Harvard University, United States

    2 publications · 101 citations

    2

    h-index

  5. NG
    Nils Gehlenborg

    Harvard University, United States

    2 publications · 101 citations

    2

    h-index

1–5 of 165

Venues

  1. NAR Genomics and Bioinformatics

    1 publications · 20 citations

    1

    h-index

  2. Cell Reports Methods

    1 publications · 13 citations

    1

    h-index

  3. Nucleic Acids Research

    1 publications · 45 citations

    1

    h-index

  4. The Analyst

    1 publications · 20 citations

    1

    h-index

  5. Cytometry Part B Clinical Cytometry

    1 publications · 15 citations

    1

    h-index

1–5