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DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors
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Abstract
Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as "doublets," which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present "best practices" for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with "hybrid" expression features.
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“Doublets were identified using the DoubletFinder package [24] and removed assuming a doublet rate formation of 3%.”
From Distinct tau neuropathology and cellular profiles of an APOE3 Christchurch homozygote protected against autosomal dominant Alzheimer’s dementia (2022) · cites this paper as [24]
The cited source says what the claim attributes to it.
In the citing paper
“Doublets were identified using a modified version of the DoubletFinder algorithm132 (available in scrattch.hicat, https://github.com/AllenInstitute/scrattch.hicat, v1.0.9) and removed when doublet score >0.3.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [132]
The cited source says what the claim attributes to it.
In the citing paper
“A dual-strategy approach was employed to remove doublets. Algorithmic detection was performed per sample using DoubletFinder [20], which was re-implemented by the authors from R to Python as pydoubletfinder [21] (10 principal components, pN = 0.25, pK = 0.09, dynamic doublet rate base of 3%). To address high-ambient RNA levels in heart tissue, this was supplemented with biological signature-based”
From Cardiac Macrophages Exhibit Dynamic Heterogeneity and Functional Specialization During Experimental Autoimmune Myocarditis. (2026) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“To identify and remove doublets, we employed DoubletFinder v2.0 (McGinnis et al., 2019) using an estimated multiplet rate derived from the total number of recovered nuclei.”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as McGinnis et al., 2019
In the citing paper
“To remove suspected doublets, DoubletFinder (v2.0.3)63 was applied and detected doublets were excluded from the analysis.”
From Cellular senescence in white matter microglia is induced during ageing in mice and exacerbates the neuroinflammatory phenotype (2023) · cites this paper as [63]
In the citing paper
“To detect and discard doublets, we used Doublet Finder62, which removes nuclei with expression profiles that resemble synthetically mixed nuclei from the dataset.”
From Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy (2021) · cites this paper as [62]
In the citing paper
“For both datasets, doublet cell detection was performed per sample using DoubletFinder (100) and Scrublet (101).”
From Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia (2020) · cites this paper as [100]
In the citing paper
“Three recent doublet detection tools offer more elegant and potentially better solutions (DoubletDecon: preprint: DePasquale et al, 2018; Scrublet: Wolock et al, 2019; Doublet Finder: McGinnis et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as McGinnis et al, 2018
In the citing paper
“Nucleus doublets were identified using the R package DoubletFinder (version DoubletFinder_2.0.2, (McGinnis et al., 2019).”
From A cellular and spatial map of the choroid plexus across brain ventricles and ages (2021) · cites this paper as McGinnis et al., 2019
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Citations
Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Building Predictive Models in R Using the caret Package
Kuhn, M. (2008) · Journal of Statistical Software · doi:10.18637/jss.v028.i05 · Cited by 8,863
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
ROCR: visualizing classifier performance in R
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti623 · Cited by 3,172
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494
GARCÍA-ALONSO, C. R., Pérez, L. M., & Fernández, J. C. F. (2011) · Annals of Operations Research · doi:10.1007/s10479-011-0841-3 · Cited by 1,435
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402
Quantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
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
Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease
Park, J., Shrestha, R., Qiu, C., Kondo, A., Huang, S., Werth, M., Li, M., Barasch, J., & Suszták, K. (2018) · Science · doi:10.1126/science.aar2131 · Cited by 1,065
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046
Splatter: simulation of single-cell RNA sequencing data
Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905
Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput
Gierahn, T. M., Wadsworth, M. H., Hughes, T. K., Bryson, B. D., Butler, A., Satija, R., Fortune, S. M., Love, J. C., & Shalek, A. K. (2017) · Nature Methods · doi:10.1038/nmeth.4179 · Cited by 882
SAVER: gene expression recovery for single-cell RNA sequencing
Huang, M. C., Wang, J., Torre, E. A., Dueck, H., Shaffer, S. M., Bonasio, R., Murray, J. I., Raj, A., Li, M., & Zhang, N. R. (2018) · Nature Methods · doi:10.1038/s41592-018-0033-z · Cited by 739
Classification of low quality cells from single-cell RNA-seq data
Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A. (2016) · Genome Biology · doi:10.1186/s13059-016-0888-1 · Cited by 693
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643
Self-citationData exploration, quality control and testing in single-cell qPCR-based gene expression experiments
McDavid, A., Finak, G., Chattopadyay, P. K., Dominguez, M., Lamoreaux, L., Ma, S. S., Roederer, M., & Gottardo, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts714 · Cited by 395
Ancuța, P., Liu, K., Misra, V., Wacleche, V. S., Gosselin, A., Zhou, X., & Gabuzda, D. H. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-403 · Cited by 284
Good things peak in pairs: a note on the bimodality coefficient
Pfister, R., Schwarz, K. A., Janczyk, M., Dale, R. A. C., & Freeman, J. B. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00700 · Cited by 258
Nuclei multiplexing with barcoded antibodies for single-nucleus genomics
Gaublomme, J. T., Li, B., Aubin, C., Knecht, A., Yang, Y., Drokhlyansky, E., Wittenberghe, N. V., Waldman, J., Dionne, D. A., Nguyễn, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10756-2 · Cited by 186
Lineage dynamics of murine pancreatic development at single-cell resolution
Byrnes, L. E., Wong, D. M., Subramaniam, M., Meyer, N. P., Gilchrist, C. L., Knox, S. M., Tward, A. D., Ye, C., & Sneddon, J. B. (2018) · Nature Communications · doi:10.1038/s41467-018-06176-3 · Cited by 185
Shin, D., Lee, W., Lee, J. H., & Bang, D. (2019) · Science Advances · doi:10.1126/sciadv.aav2249 · Cited by 101
Jeevan-Raj, B. P., Gehrig, J., Charmoy, M., Chennupati, V., Grandclément, C., Angelino, P., Delorenzi, M., & Held, W. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.06.071 · Cited by 93
CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics
Guo, C., Kong, W., Kamimoto, K., Rivera-Gonzalez, G. C., Yang, X., Kirita, Y., & Morris, S. A. (2019) · Genome biology · doi:10.1186/s13059-019-1699-y · Cited by 93
The Human Cell Atlas White PaperPreprint
Regev, A., Teichmann, S. A., Rozenblatt–Rosen, O., Stubbington, M. J. T., Ardlie, K., Amit, I., Arlotta, P., Bader, G. D., Benoist, C. O., Biton, M., et al. (2018) · arXiv (Cornell University) · doi:10.17863/cam.40032 · Cited by 88
Stoeckle, C., Gouttefangeas, C., Hammer, M., Weber, E., Melms, A., & Tolosa, E. (2008) · Experimental Hematology · doi:10.1016/j.exphem.2008.10.011 · Cited by 81
MULTI-seq: Scalable sample multiplexing for single-cell RNA sequencing using lipid-tagged indicesPreprint
McGinnis, C. S., Patterson, D. M., Winkler, J., et al. (2018) · bioRxiv · doi:10.1101/387241 · Cited by 66
Self-citationZhao, C., Tan, Y.-C., Wong, W.-C., Sem, X., Zhang, H., Han, H., Ong, S.-M., Wong, K.-L., Yeap, W.-H., Sze, S. K., et al. (2010) · Cell Death and Disease · doi:10.1038/cddis.2010.69 · Cited by 57
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
Highly Multiplexed Single-Cell RNA-seq for Defining Cell Population and Transcriptional SpacesPreprint
Gehring, J., Park, J. H., Chen, S., Thomson, M., & Pachter, L. S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/315333 · Cited by 25
Nuclei multiplexing with barcoded antibodies for single-nucleus genomicsPreprint
Gaublomme, J. T., Li, B., Aubin, C., Knecht, A., Drokhlyansky, E., Wittenberghe, N. V., Waldman, J., Dionne, D. A., Nguyễn, L., Jager, P. L. D., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/476036 · Cited by 25
Characterization of MHC-II antigen presentation by B cells and monocytes from older individuals
Clark, H. L., Banks, R. E., Jones, L., Hornick, T. R., Higgins, P. A., Burant, C., & Canaday, D. H. (2012) · Clinical Immunology · doi:10.1016/j.clim.2012.06.005 · Cited by 18
CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomicsPreprint
Guo, C., Kong, W., Kamimoto, K., Rivera-Gonzalez, G. C., Yang, X., Kirita, Y., & Morris, S. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/335547 · Cited by 6
Multiplexed single-cell RNA-seq via transient barcoding for drug screeningPreprint
Shin, D., Lee, W., Lee, J. H., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/359851 · Cited by 5
Flagged first, then by citations
Research works printed but not linked: 1
- Cao, 2017, Comprehensive single-cell transcriptional profiling of a multicellular organism, Science, 357, 661, 10.1126/science.aam8940 · DOI · Paper page
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Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
Doublet identification in single-cell sequencing data using scDblFinder
Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W., & Robinson, M. D. (2022) · F1000Research · doi:10.12688/f1000research.73600.2 · Cited by 684
ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392
Spatiotemporal Immune Landscape of Colorectal Cancer Liver Metastasis at Single-Cell Level
Wu, Y., Yang, S., Ma, J., Chen, Z., Song, G., Rao, D., Cheng, Y., Huang, S., Liu, Y., Jiang, S., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0316 · Cited by 1,238
Metadata
- Authors
- McGinnis, C. S., Murrow, L. M., & Gartner, Z. J.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Gartner, Zev J.
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 8
- Issue
- 4
- Pages
- 329-337.e4
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC6853612
- OpenAlex
- W2953151710
- Cited by
- 4,521
- References
- 40
- Citations by year
- 2026: 1009 · 2025: 1128 · 2024: 982 · 2023: 635 · 2022: 397 · 2021: 224 · 2020: 107 · 2019: 31
- FWCI
- 64.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Department of Defense (W81XWH-10-1-1023, W81XWH-13-1-0221)
- NIH (DP2 HD080351-01)
- NSF (MCB-1330864)
- UCSF (DBI-1548297)
- Damon Runyon Cancer Research Foundation (DRG-2239-15)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- doublet detection, single-cell RNA sequencing, nearest neighbors, technical artifacts, differentially expressed genes, parameter estimation, kidney cells
- MeSH terms
- RNA-Seq, Humans, Software, Single-Cell Analysis
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