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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]
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“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]
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“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]
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“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
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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Items 701–800 of 1,021
Single-cell RNA and protein profiling of immune cells from the mouse brain and its border tissues
Scheyltjens, I., Hove, H. V., Vlaminck, K. D., Kancheva, D., Bastos, J., Vara‐Pérez, M., Antunes, A. R. P., Martens, L., Scott, C. L., Ginderachter, J. A. V., et al. (2022) · Nature Protocols · doi:10.1038/s41596-022-00716-4 · Cited by 44
Ichino, L., Picard, C. L., Yun, J., Chotai, M., Wang, S., Lin, E. K., Papareddy, R. K., Xue, Y., & Jacobsen, S. E. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111699 · Cited by 38
Conte, T. C., Duran-Bishop, G., Orfi, Z., Mokhtari, I., Deprez, A., Côté, I., Molina, T., Taeyeon, K., Tellier, L., Roussel, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39663-3 · Cited by 39
Cai, Z., Chen, L., Chen, S., Fang, R., Chen, X., & Lei, W. (2023) · Cell Communication and Signaling · doi:10.1186/s12964-023-01312-z · Cited by 41
Live birth of chimeric monkey with high contribution from embryonic stem cells
Cao, J., Li, W., Li, J., Mazid, M. A., Li, C., Jiang, Y., Jia, W., Wu, L. L., Liao, Z., Sun, S., et al. (2023) · Cell · doi:10.1016/j.cell.2023.10.005 · Cited by 38
Protracted neuronal recruitment in the temporal lobes of young children
Nascimento, M. A., Biagiotti, S. W., Herranz‐Pérez, V., Santiago, S., Bueno, R., Ye, C., Abel, T. J., Zhang, Z., Rubio-Moll, J. S., Kriegstein, A. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06981-x · Cited by 38
Hu, C., Zhang, N., Hong, Y. M., Tie, R., Fan, D., Lin, A., Chen, Y., Xiang, L., & Shao, J.-Z. (2024) · eLife · doi:10.7554/elife.92424 · Cited by 39
Mosaic loss of Y chromosome is associated with aging and epithelial injury in chronic kidney disease
Wilson, P. C., Verma, A. K., Yoshimura, Y., Muto, Y., Li, H., Malvin, N. P., Dixon, E. E., & Humphreys, B. D. (2024) · Genome biology · doi:10.1186/s13059-024-03173-2 · Cited by 38
Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model
Sun, T., Chen, J., Yang, F., Zhang, G., Chen, J., Wang, X., & Zhang, J. (2024) · EMBO Molecular Medicine · doi:10.1038/s44321-024-00052-y · Cited by 39
Batorsky, R., Ceasrine, A. M., Shook, L. L., Kışlal, S., Bordt, E. A., Devlin, B. A., Perlis, R. H., Slonim, D. K., Bilbo, S. D., & Edlow, A. G. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114326 · Cited by 38
Grossmann, L. D., Chen, C.-H., Uzun, Y., Thadi, A., Wolpaw, A. J., Louault, K., Goldstein, Y., Surrey, L. F., Martínez, D., Calafatti, M., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-0046 · Cited by 38
Microglial heterogeneity in the ischemic stroke mouse brain of both sexes
del Águila, Á., Zhang, R., Yu, X., Dang, L., Xu, F., Zhang, J., Jain, V., Tian, J., Zhong, X., Sheng, H., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01368-7 · Cited by 42
Dehestani, M., Kozareva, V., Blauwendraat, C., Fraenkel, E., Gasser, T., & Bansal, V. (2024) · Molecular Brain · doi:10.1186/s13041-024-01128-z · Cited by 38
Maternal circadian rhythms during pregnancy dictate metabolic plasticity in offspring
Yao, N., Kinouchi, K., Katoh, M., Ashtiani, K. C., Abdelkarim, S., Morimoto, H., Torimitsu, T., Kozuma, T., Iwahara, A., Kosugi, S., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2024.12.002 · Cited by 39
Yu, W., Gui, S., Peng, L., Luo, H., Xie, J., Xiao, J., Yilamu, Y., Sun, Y. E., Cai, S., Cheng, Z., et al. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.01.014 · Cited by 38
Hobson, R., Levy, S. H. S., Singal, C. M. S., Flaherty, D., Xiao, H., Ciener, B., Reddy, H., Zabinyakov, N., Kim, C. Y., Teich, A. F., et al. (2026) · Nature Immunology · doi:10.1038/s41590-025-02401-6 · Cited by 42
Single-cell RNA sequencing of SARS–CoV-2 cell entry factors in the preconceptional human endometrium
Vilella, F., Wang, W., Moreno, I., Rosón, B., Quake, S. R., & Simón, C. (2021) · Human Reproduction · doi:10.1093/humrep/deab183 · Cited by 38
Davies, M., Garcia, S. M., Liu, M., Chi, H. M., Kim, H. T., Raffaı̈, R. L., Liu, X., & Feeley, B. T. (2022) · The American Journal of Sports Medicine · doi:10.1177/03635465221095568 · Cited by 38
Wang, W., Ložar, T., Golfinos, A. E., Lee, D., Gronski, E., Ward-Shaw, E. T., Hayes, M., Bruce, J. Y., Kimple, R. J., Hu, R., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-3039 · Cited by 38
Fu, W., Chen, S., Yang, R., Li, C., Gao, H., Li, J. H., & Zhang, X. (2022) · eLife · doi:10.7554/elife.79585 · Cited by 39
Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogrammingPreprint
Nair, S., Ameen, M., Sundaram, L., Pampari, A., Schreiber, J. M., Balsubramani, A., Wang, Y. X., Burns, D., Blau, H. M., Karakikes, I., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.04.560808 · Cited by 38
Kleeman, S. O., Thakir, T. M., Demestichas, B., Mourikis, N., Loiero, D., Ferrer, M., Bankier, S., Riazat-Kesh, Y. J. R. A., Lee, H., Chantzichristos, D., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100347 · Cited by 37
Mitroi, D. N., Tian, M., Kawaguchi, R., Lowry, W. E., & Carmichael, S. T. (2022) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.17315 · Cited by 38
Tokura, M., Nakayama, J., Prieto‐Vila, M., Shiino, S., Yoshida, M., Yamamoto, T., Watanabe, N., Takayama, S., Suzuki, Y., Okamoto, K., et al. (2022) · Cancer Research · doi:10.1158/0008-5472.can-22-0090 · Cited by 38
Single-cell transcriptome profiles of Drosophila fruitless-expressing neurons from both sexes
Palmateer, C. M., Artikis, C., Brovero, S. G., Friedman, B., Gresham, A., & Arbeitman, M. N. (2023) · eLife · doi:10.7554/elife.78511 · Cited by 41
Pajanoja, C., Hsin, J., Olinger, B., Schiffmacher, A. T., Yazejian, R. M., Abrams, S. R., Dapkunas, A., Zainul, Z., Doyle, A. D., Martı́n, D., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41384-6 · Cited by 37
Hall, M. S., Teer, J. K., Yu, X., Branthoover, H., Snedal, S. A., Rodríguez-Valentín, M., Nagle, L., Scott, E. N., Schachner, B. I., Innamarato, P., et al. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-007288 · Cited by 39
Immunological link between periodontitis and type 2 diabetes deciphered by single‐cell RNA analysis
Lee, H., Joo, J., Song, J., Kim, H., Kim, Y. H., & Park, H. R. (2023) · Clinical and Translational Medicine · doi:10.1002/ctm2.1503 · Cited by 37
Yuan, H., Mao, X., Yan, Y., Huang, R., Zhang, Q., Zeng, Y., Bao, M., Dai, Y., Fang, B., Mi, J., et al. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04860-1 · Cited by 37
Liang, J., Bi, G., Sui, Q., Zhao, G., Zhang, H., Bian, Y., Chen, Z., Huang, Y., Xi, J., Shi, Y., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113771 · Cited by 38
Melchinger, I., Guo, K., Li, X., Guo, J.-K., Cantley, L. G., & Xu, L. (2024) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00076.2024 · Cited by 37
Integrative mapping of human CD8+ T cells in inflammation and cancer
Xue, Z., Wu, L., Tian, R., Gao, B. X., Zhao, Y. S., He, B., Sun, D., Zhao, B., Li, Y., Zhu, K., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02530-0 · Cited by 38
Zhang, G., Zhang, X., Pan, W., Chen, X., Wan, L., Liu, C., Yong, Y., Zhao, Y., Sang, S., Zhang, L., et al. (2025) · Advanced Science · doi:10.1002/advs.202413019 · Cited by 37
Cheng, X., Lai, H., Luo, W., Zhang, M., Miao, J., Song, W., Xing, S., Wang, J., & Gao, W. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-03740-6 · Cited by 37
Guan, Y., Du, Y., Wang, G., Gou, H., Xue, Y., Xu, J., Li, E., Chan, D. W., Wu, D., Xu, P., et al. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.673295 · Cited by 38
Azzoni, E., Frontera, V., Anselmi, G., Rode, C., James, C. T., Deltcheva, E. M., Demian, A. S., Brown, J., Barone, C., Patelli, A., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110103 · Cited by 38
Magaletta, M. E., Lobo, M., Kernfeld, E. M., Aliee, H., Huey, J. D., Parsons, T. J., Theis, F. J., & Maehr, R. (2022) · Nature Communications · doi:10.1038/s41467-022-28067-4 · Cited by 38
Zhang, M., Wu, L., Deng, Y., Peng, F., Wang, T., Zhao, Y., Chen, P., Liu, J., Cai, G., Wang, L., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.857025 · Cited by 37
Xu, X., Hua, X., Han, M., Hu, S., & Song, J. (2023) · Basic Research in Cardiology · doi:10.1007/s00395-022-00972-1 · Cited by 38
Weber, L. M., Hippen, A. A., Hickey, P. F., Berrett, K. C., Gertz, J., Doherty, J. A., Greene, C. S., & Hicks, S. C. (2021) · GigaScience · doi:10.1093/gigascience/giab062 · Cited by 37
SFRP4+ stromal cell subpopulation with IGF1 signaling in human endometrial regeneration
Wu, B., Yu, L. J., Nie, N., Shen, X., Jiang, W. M., Liu, Y., Gong, L., An, C., Zhao, K., Yao, X., et al. (2022) · Cell Discovery · doi:10.1038/s41421-022-00438-7 · Cited by 36
Remarkable sex-specific differences at single-cell resolution in neonatal hyperoxic lung injury
Cantu, A., Gutierrez, M. E. C., Dong, X., Leek, C. C., Sajti, E., & Lingappan, K. (2022) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00269.2022 · Cited by 38
Sweat, M., Cao, Y.-P., Zhang, X.-R., Burnicka-Turek, O., Perez-Cervantes, C., Arulsamy, K., Lu, F.-J., Keating, E. M., Akerberg, B. N., Ma, Q., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00334-7 · Cited by 31
Wang, Y., Ma, X., Xu, E., Huang, Z. Y., Chen, Y., Zhu, K., Dong, Y., & Zhang, C. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.1586 · Cited by 36
Liver X receptor unlinks intestinal regeneration and tumorigenesis
Das, S., Parigi, S. M., Luo, X., Fransson, J., Kern, B. C., Okhovat, M. A., Diaz, O. E., Sorini, C., Czarnewski, P., Webb, A., et al. (2024) · Nature · doi:10.1038/s41586-024-08247-6 · Cited by 39
Spatiotemporal transcriptome and metabolome landscapes of cotton somatic embryos
Ge, X., Yu, X., Liu, Z., Yuan, J., Qin, A., Wang, Y., Chen, Y., Qin, W., Liu, Y., Liu, X., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-55870-6 · Cited by 36
Yang, X., Ding, W., Lou, F., Xu, H., Sheng, A., Sun, Y., Cai, X., Zhou, M., Lin, F., Jin, R., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.05.018 · Cited by 36
Melton, R. L., Jiménez, S., Elison, W. S., Tucciarone, L., Howell, A., Wang, G., Berti, D. A., Beebe, E., Miller, M., Zeng, C., et al. (2025) · Science Advances · doi:10.1126/sciadv.ady0080 · Cited by 36
Hayford, C. E., Tyson, D. R., Robbins, C. T., Frick, P. L., Quaranta, V., & Harris, L. A. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3000797 · Cited by 38
Zarkoob, H., Allué‐Guardia, A., Chen, Y., Garcia‐Vilanova, A., Jung, O., Coon, S. L., Song, M. J., Park, J., Oladunni, F. S., Miller, J., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03753-7 · Cited by 37
From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis
Carangelo, G., Magi, A., & Semeraro, R. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.994069 · Cited by 39
The molecular consequences of androgen activity in the human breast
Raths, F., Karimzadeh, M., Ing, N., Martinez, A., Yang, Y., Qu, Y., Lee, T.-Y., Mulligan, B., Devkota, S., Tilley, W. T., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100272 · Cited by 39
Lin, J.-P., Brake, A., Donadieu, M., Lee, A., Smith, G., Hu, K., Nair, G., Kawaguchi, R., Sati, P., Geschwind, D. H., et al. (2025) · Science · doi:10.1126/science.adp6325 · Cited by 35
Ramirez-Valdez, R. A., Baharom, F., Khalilnezhad, A., Fussell, S. C., Hermans, D., Schrager, A. M., Tobin, K. K. S., Lynn, G. M., Khalilnezhad, S., Ginhoux, F., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112599 · Cited by 37
Kunkel, T. J., Townsend, A., Sullivan, K. A., Merlet, J., Schuchman, E. H., Jacobson, D., & Lieberman, A. P. (2023) · Nature Communications · doi:10.1038/s41467-023-39733-6 · Cited by 37
Wang, W., Zhang, M., Ren, X., Song, Y., Xu, Y., Zhuang, K., Xiao, T., Guo, X., Wang, S., Hong, Q., et al. (2023) · Molecular Therapy · doi:10.1016/j.ymthe.2023.07.024 · Cited by 37
Zhou, J., Abedini, A., Balzer, M. S., Shrestha, R., Dhillon, P., Liu, H., Hu, H., & Suszták, K. (2023) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000217 · Cited by 37
Li, Y., Luo, C., Jiang, J., He, S., Liu, Y., Yan, W., Xia, Y., Cui, Q., Huang, Y., Lim, J. Q., et al. (2023) · Advanced Science · doi:10.1002/advs.202303913 · Cited by 38
Degagné, É., Donohoue, P. D., Roy, S., Scherer, J., Fowler, T. W., Davis, R. T., Reyes, G. A., Kwong, G., Stanaway, M., Vicena, V. L., et al. (2024) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-23-0679 · Cited by 35
Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing
Xie, Y., Chen, H., Chellamuthu, V. R., Lajam, A. B. M., Albani, S., Low, A. H. L., Petretto, E. G., & Behmoaras, J. V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25073828 · Cited by 39
Mandula, J. K., Sierra-Mondragon, R. A., Jimenez, R. V., Chang, D., Mohamed, E. T., Chang, S., Vázquez-Martínez, J. A., Cao, Y. W., Anadon, C. M., Lee, S. B., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.03.020 · Cited by 35
Cui, Y., Su, Y., Bian, J., Han, X., Guo, H., Yang, Z., Chen, Y., Li, L., Li, T., Deng, X. W., et al. (2024) · Plant Communications · doi:10.1016/j.xplc.2024.100979 · Cited by 35
Montserrat-Ayuso, T., & Esteve‐Codina, A. (2024) · BMC Genomics · doi:10.1186/s12864-024-11015-5 · Cited by 37
CRISPRi-based screens in iAssembloids to elucidate neuron-glia interactions
Li, E., Benitez, C., Boggess, S. C., Koontz, M., Rose, I. V. L., Martinez, D., Dräger, N. M., Teter, O. M., Samelson, A. J., Pierce, N., et al. (2025) · Neuron · doi:10.1016/j.neuron.2024.12.016 · Cited by 35
Gálvez‐Cancino, F., Navarrete, M., Beattie, G., Puccio, S., Conde, E., Foster, K. A., Morris, Y., Sahwangarrom, T., Karagianni, D., Liu, J., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.03.021 · Cited by 37
Murrow, L. M., Weber, R. J., Caruso, J. A., McGinnis, C. S., Phong, K. T., Gascard, P. D., Rabadam, G., Borowsky, A. D., Desai, T. A., Thomson, M., et al. (2022) · Cell Systems · doi:10.1016/j.cels.2022.06.005 · Cited by 36
Self-citationLiu, X., Xu, X., Wu, Z., Shan, Q., Wang, Z., Wu, Z., Ding, X., Huang, W., & Wang, Z. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.961350 · Cited by 35
Huo, Y., Wu, L., Pang, A., Li, Q., Hong, F., Zhu, C., Yang, Z., Dai, W., Zheng, Y., Meng, Q., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.abn6429 · Cited by 37
He, S., Li, X., Mittra, N., Bhattacharjee, A., Wang, H., Song, S., Zhao, S., Liu, F., & Han, X. (2025) · Advanced Science · doi:10.1002/advs.202410910 · Cited by 34
Single-cell chromatin accessibility and transcriptome atlas of mouse embryos
Jiang, S., Huang, Z., Li, Y., Yu, C., Yu, H., Ke, Y., Jiang, L., & Liu, J. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112210 · Cited by 34
Rudman-Melnick, V., Adam, M., Stowers, K., Potter, A., Ma, Q., Chokshi, S. M., Vanhoutte, D., Valiente-Alandí, Í., Lindquist, D. M., Nieman, M. L., et al. (2024) · Scientific Reports · doi:10.1038/s41598-023-50195-0 · Cited by 34
Su, Q., Huang, W., Huang, Y., Dai, R., Chen, C., Li, Q., Liu, H., Li, Z., Zhao, Y., Wu, Q., et al. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02233-y · Cited by 37
Liu, Y., Zhang, M., Wang, C., Chen, H., Su, D., Yang, C. H., Tao, Y., Lv, X., Zhou, Z., Li, J., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c01401 · Cited by 34
Molecular pathways and diagnosis in spatially resolved Alzheimer’s hippocampal atlas
Wang, P., Han, L., Wang, L., Tao, Q., Guo, Z., Luo, T., He, Y., Xu, Z. P., Yu, J., Liu, Y., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.03.002 · Cited by 36
Sun, S., Lai, C., Huang, C., Ren, X., Zhang, T., Zou, J., Tong, Y., Zhou, Q., Lu, J., Shen, Z., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67443-8 · Cited by 36
Xi, N. M., & Li, J. J. (2021) · STAR Protocols · doi:10.1016/j.xpro.2021.100699 · Cited by 37
Tian, S., Zheng, N., Zu, X., Wu, G., Zhong, J., Zhang, J., Sheng, L., Liu, W., Wang, C., Ge, G., et al. (2022) · Phytomedicine · doi:10.1016/j.phymed.2021.153922 · Cited by 34
Nederlof, R., Reidel, S., Spychala, A., Gödecke, S., Heinen, A., Lautwein, T., Petzsch, P., Köhrer, K., & Gödecke, A. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.908023 · Cited by 34
Shim, J., Park, J., Abudureyimu, G., Kim, M.-H., Shim, J. S., Jang, K., Kwon, E. J., Jang, H., Yeo, E., Lee, J. H., et al. (2022) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2022.06.022 · Cited by 35
Wu, H., Xie, C., Wang, R., Cheng, J., Xu, Q., & Zhao, H. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1095757 · Cited by 34
Siddique, K., Ager-Wick, E., Fontaine, R., Weltzien, F., & Henkel, C. V. (2021) · Scientific Data · doi:10.1038/s41597-021-01058-8 · Cited by 34
Sfaira accelerates data and model reuse in single cell genomics
Fischer, D. S., Dony, L., König, M., Moeed, A., Zappia, L., Heumos, L., Tritschler, S., Holmberg, O., Aliee, H., & Theis, F. J. (2021) · Genome Biology · doi:10.1186/s13059-021-02452-6 · Cited by 33
Fink, M., Njah, K., Patel, S. J., Cook, D. P., Man, V., Ruso, F., Rajan, A., Narimatsu, M., Obersterescu, A., Pye, M. J., et al. (2024) · Nature Cell Biology · doi:10.1038/s41556-024-01550-4 · Cited by 33
Balzer, M. S., Pavkovic, M., Frederick, J., Abedini, A., Freyberger, A., Vienenkoetter, J., Mathar, I., Siudak, K., Eitner, F., Sandner, P., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.100992 · Cited by 34
Wang, L., Si, W., Yu, X., Piffkó, A., Dou, X., Ding, X., Bugno, J., Yang, K., Wen, C., Zhang, L., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci172919 · Cited by 34
MBD2 couples DNA methylation to transposable element silencing during male gametogenesis
Wang, S., Wang, M., Ichino, L., Boone, B. A., Zhong, Z., Papareddy, R. K., Lin, E. K., Yun, J., Feng, S., & Jacobsen, S. E. (2024) · Nature Plants · doi:10.1038/s41477-023-01599-3 · Cited by 34
Chen, C., Chung, W., Chang, J. W., Yang, C., Hui‐Kang, D., Teng, Y.-C., Lu, C., Chen, W., Yang, H.-Y., Tsai, C.-C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54180-7 · Cited by 33
Multi-omics reveals immune response and metabolic profiles during high-altitude mountaineering
Yin, J., Lv, J., Yang, S., Wang, Y., Huang, Z., Wang, X., Hou, G., Zhou, W., Liu, Y., Wang, W., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.115134 · Cited by 36
Single-cell atlas of human pancreatic islet and acinar endothelial cells in health and diabetes
Craig‐Schapiro, R., Li, G., Chen, K., Gómez-Salinero, J. M., Nachman, R., Kopacz, A., Schreiner, R. P., Chen, X., Zhou, Q., Rafii, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55415-3 · Cited by 34
Huang, R. J., Wichmann, I. A., Su, A., Sathe, A., Shum, M. V., Grimes, S. M., Meka, R. S., Almeda, A. F., Bai, X., Shen, J., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00816-w · Cited by 33
Chai, H., Huang, X., Xiong, G., Huang, J., Pels, K. K., Meng, L., Han, J., Tang, D., Pan, G., Deng, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02658-7 · Cited by 35
Immunosuppressive Tumor Microenvironment of Osteosarcoma
Taylor, A. M., Sheng, J., Ng, P. K. S., Harder, J. M., Kumar, P., Ahn, J. Y., Cao, Y., Dzis, A. M., Jillette, N., Goodspeed, A. E., et al. (2025) · Cancers · doi:10.3390/cancers17132117 · Cited by 38
Single-cell atlas of domestic pig cerebral cortex and hypothalamus
Zhu, J., Chen, F., Luo, L., Wu, W., Dai, J., Zhong, J., Lin, X., Chai, C., Ding, P., Liang, L., et al. (2021) · 中国科学通报：英文版 · doi:10.1016/j.scib.2021.04.002 · Cited by 35
de Jesus, D. S., Mak, T. C. S., Wang, Y.-F., von Ohlen, Y., Bai, Y., Kane, E., CHABOSSEAU, P. L., Chahrour, C., Distaso, W., Salem, V., et al. (2021) · Molecular Metabolism · doi:10.1016/j.molmet.2021.101248 · Cited by 36
Basile, G., Kahraman, S., Dirice, E., Pan, H., Dreyfuss, J. M., & Kulkarni, R. N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00941-8 · Cited by 33
Prospects and challenges of cancer systems medicine: from genes to disease networks
Karimi, M. R., Karimi, A. H., Abolmaali, S., Sadeghi, M., & Schmitz, U. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab343 · Cited by 33
Wang, R., Wang, R., Li, L., Li, L., Yong, J., Yong, J., Fan, X., Hu, Y., Zhai, F., Tang, F., et al. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.04.002 · Cited by 34
Yin, W., Ping, Y., Li, F., Lv, S., Zhang, X., Li, X., Guo, Y. J., Liu, Q., Li, T., Yang, L., et al. (2022) · The Journal of Pathology · doi:10.1002/path.5984 · Cited by 33
deMULTIplex2: robust sample demultiplexing for scRNA-seq
Zhu, Q., Conrad, D. N., & Gartner, Z. J. (2024) · Genome biology · doi:10.1186/s13059-024-03177-y · Cited by 32
Self-citationReinisch, I., Michenthaler, H., Sulaj, A., Moyschewitz, E., Krstić, J., Galhuber, M., Xu, R., Riahi, Z., Wang, T., Vujić, N., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45724-y · Cited by 33
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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