The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Doublet identification in single-cell sequencing data using scDblFinder
Open accessAbstract
Doublets are prevalent in single-cell sequencing data and can lead to artifactual findings. A number of strategies have therefore been proposed to detect them. Building on the strengths of existing approaches, we developed scDblFinder , a fast, flexible and accurate Bioconductor-based doublet detection method. Here we present the method, justify its design choices, demonstrate its performance on both single-cell RNA and accessibility (ATAC) sequencing data, and provide some observations on doublet formation, detection, and enrichment analysis. Even in complex datasets, scDblFinder can accurately identify most heterotypic doublets, and was already found by an independent benchmark to outcompete alternatives.
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“Quality control was performed to remove low-quality cells based on the number of detected genes and unique molecular identifiers (UMIs). Cells with an unusually high number of UMIs, suggesting potential doublet events, were also excluded using scDblFinder [18].”
From Apolipoprotein E abundance is elevated in the brains of individuals with Down syndrome–Alzheimer’s disease (2025) · cites this paper as [18]
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“Doublets were identified via scDblFinder v1.12.068 with default parameters.”
From CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis (2024) · cites this paper as [68]
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McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758
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Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,101
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945
Self-citationscds: computational annotation of doublets in single-cell RNA sequencing data
Bais, A. S., & Kostka, D. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz698 · Cited by 308
Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments
Bloom, J. D. (2018) · PeerJ · doi:10.7717/peerj.5578 · Cited by 46
Flagged first, then by citations
Research works printed but not linked: 16
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Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051
Spatial multi-omic map of human myocardial infarction
Kuppe, C., Ramirez Flores, R. O., Li, Z., Hayat, S., Levinson, R. T., Liao, X., Hannani, M. T., Tanevski, J., Wünnemann, F., Nagai, J. S., et al. (2022) · Nature · doi:10.1038/s41586-022-05060-x · Cited by 772
Mosquera, J. V., Auguste, G., Wong, D., Turner, A. W., Hodonsky, C. J., Alvarez-Yela, A. C., Song, Y., Cheng, Q., Lino Cardenas, C. L., Theofilatos, K., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113380 · Cited by 123
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Guo, J. L., Braun, D., Fitzgerald, G. A., Hsieh, Y.-T., Rougé, L., Litvinchuk, A., Steffek, M., Propson, N. E., Heffner, C. M., Discenza, C., et al. (2025) · Cell · doi:10.1016/j.cell.2024.10.027 · Cited by 91
Metadata
- Authors
- Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W., & Robinson, M. D.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Genentech (US)
- F. Hoffmann-La Roche AG (CH)
- Corresponding author
- Germain, Pierre-Luc
- Robinson, Mark D.
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2022-05-16
- Volume
- 10
- Pages
- 979
- ISSN
- 2046-1402 (ISSN-L)
- PMC
- PMC9204188
- OpenAlex
- W4280619656
- Cited by
- 684
- References
- 6
- Citations by year
- 2026: 270 · 2025: 240 · 2024: 104 · 2023: 59 · 2022: 11
- FWCI
- 33.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- F1000Research (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (310030_175841)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- doublet detection, single-cell sequencing, single-cell RNA sequencing, single-cell ATAC-seq, Bioconductor, enrichment analysis, benchmarking
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