Doublet Classification

Doublet Classification is a methodological framework in computational biology that distinguishes genuine single-cell profiles from artificial doublet artifacts arising from the co-encapsulation of multiple cells in high-throughput single-cell sequencing experiments.

57

Publications

12.1K

Citations

162

Authors

109

Institutions

Publications per year

2017–2026

22

Authors

162

  1. AT
    Aaron T. L. Lun

    University of Cambridge, United Kingdom

    2 publications · 1.5K citations

    2

    h-index

  2. AB
    Abha Bais

    University of Pittsburgh, United States

    2 publications · 345 citations

    2

    h-index

  3. MH
    Martin Hemberg

    Wellcome Sanger Institute, United Kingdom

    2 publications · 86 citations

    2

    h-index

  4. PG
    Pierre‐Luc Germain

    University of Zurich, Switzerland

    2 publications · 1.5K citations

    2

    h-index

  5. JT
    Jimmy Tsz Hang Lee

    Wellcome Sanger Institute, United Kingdom

    2 publications · 86 citations

    2

    h-index

1–5 of 162

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 5 publications · 301 citations

    5

    h-index

  2. The Astrophysical Journal

    ApJ · 5 publications · 101 citations

    5

    h-index

  3. Cell Systems

    4 publications · 8K citations

    4

    h-index

  4. Physical Review

    2 publications · 74 citations

    2

    h-index

  5. 2

    h-index

1–5