Multi-modal Single-cell Clustering

Multi-Modal Single-Cell Clustering is a computational approach that integrates multiple distinct molecular measurement types (such as transcriptome, epigenome, and proteome) from the same single cells to jointly identify cellular subpopulations, thereby enabling more comprehensive and biologically resolved cell type or state classification than unimodal clustering methods.

4

Publications

579

Citations

58

Authors

38

Institutions

Publications per year

2017–2026

4

Variants

Multimodal Single-cell Clustering

Authors

58

  1. KD
    Kevin D. Deane

    University of Colorado Denver, United States

    1 publications · 393 citations

    1

    h-index

  2. FZ
    Fan Zhang

    Harvard University, United States

    1 publications · 393 citations

    1

    h-index

  3. ZD
    Zhibin Dong

    National University of Defense Technology, China

    1 publications · 61 citations

    1

    h-index

  4. LG
    Laura Geraldino‐Pardilla

    Columbia University, United States

    1 publications · 393 citations

    1

    h-index

  5. JB
    Joyce B. Kang

    Harvard University, United States

    1 publications · 393 citations

    1

    h-index

1–5 of 58

Venues

  1. Nucleic Acids Research

    2 publications · 125 citations

    2

    h-index

  2. Briefings in Bioinformatics

    1 publications · 61 citations

    1

    h-index

  3. Nature

    1 publications · 393 citations

    1

    h-index

  4. Nature Communications

    1 publications · 11 citations

1–4