Single-cell Differential Expression

Single-Cell Differential Expression is a computational method in transcriptomics that identifies statistically significant differences in gene expression levels at the resolution of individual cells, distinguished from bulk-level approaches by its ability to detect heterogeneous expression patterns across cell populations.

202

Publications

22.8K

Citations

1.3K

Authors

475

Institutions

Publications per year

2017–2026

162

Authors

1.3K

  1. MD
    Mark D. Robinson

    University of Zurich, Switzerland

    8 publications · 1.7K citations

    8

    h-index

  2. CS
    Charlotte Soneson

    SIB Swiss Institute of Bioinformatics, Switzerland

    6 publications · 1.7K citations

    6

    h-index

  3. JC
    John C. Marioni

    European Bioinformatics Institute, United Kingdom

    5 publications · 1.3K citations

    5

    h-index

  4. SD
    Sandrine Dudoit

    University of California, Berkeley, United States

    4 publications · 1.2K citations

    4

    h-index

  5. BV
    Beate Vieth

    Ludwig-Maximilians-Universität München, Germany

    4 publications · 502 citations

    4

    h-index

1–5 of 1.3K

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 28 publications · 857 citations

    17

    h-index

  2. Bioinformatics

    14 publications · 1.8K citations

    13

    h-index

  3. Nature Communications

    12 publications · 3.1K citations

    12

    h-index

  4. Genome biology

    10 publications · 1.2K citations

    10

    h-index

  5. Nature Methods

    7 publications · 5.2K citations

    7

    h-index

1–5