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 and removed assuming a doublet rate formation of 3%.
“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]
Doublets were identified using a modified version of DoubletFinder algorithm and removed when doublet score >0.3.
“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]
Doublet detection used a dual-strategy approach: algorithmic detection with DoubletFinder and biological signature-based filtering.
“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]
DoubletFinder v2.0 was employed to identify and remove doublets, using an estimated multiplet rate derived from the total number of recovered nuclei.
“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
Suspected doublets were detected with DoubletFinder and excluded from the scRNA-seq analysis.
“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]
Doublets were detected and discarded with Doublet Finder, which removes nuclei whose expression profiles resemble synthetically mixed nuclei.
“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]
Doublet cell detection was performed per sample using DoubletFinder and Scrublet for both datasets.
“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]
Three recent doublet detection tools offer more elegant and potentially better solutions than high-count depth thresholds.
“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
Nucleus doublets were identified using DoubletFinder and removed from downstream analysis.
“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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- 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 · 2018: 4 · 2014: 1
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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