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
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Doublet identification in single-cell sequencing data using scDblFinder
Pierre‐Luc Germain, Aaron T. L. Lun, Will Macnair et al. · F1000Research · 2021 · 919 citations · Full text
Doublet identification in single-cell sequencing data using scDblFinder
Pierre‐Luc Germain, Aaron T. L. Lun, Carlos Garcia Meixide et al. · F1000Research · 2022 · 618 citations · Full text
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Chan Zuckerberg Initiative (United States)
Redwood City, United States
4 h-index
4 publications
4.9K citations
Harvard University
Cambridge, United States
4 h-index
8 publications
9K citations
University of California, San Francisco
San Francisco, United States
4 h-index
8 publications
14.4K citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
4 publications
92 citations
University of Zurich
Zurich, Switzerland
2 h-index
4 publications
3.1K citations
Garvan Institute of Medical Research
Darlinghurst, Australia
2 h-index
7 publications
325 citations
University Medical Center Groningen
Groningen, The Netherlands
2 h-index
2 publications
86 citations

Brigham and Women's Hospital
Boston, United States
2 h-index
2 publications
86 citations
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Repository · 5 publications · 301 citations
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ApJ · 5 publications · 101 citations
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4 publications · 8K citations
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2 publications · 74 citations
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2 publications · 21 citations
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