The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Decontamination of ambient RNA in single-cell RNA-seq with DecontX
Open accessPublished version of a preprint
Abstract
Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq). However, ambient RNA present in the cell suspension can be aberrantly counted along with a cell’s native mRNA and result in cross-contamination of transcripts between different cell populations. DecontX is a novel Bayesian method to estimate and remove contamination in individual cells. DecontX accurately predicts contamination levels in a mouse-human mixture dataset and removes aberrant expression of marker genes in PBMC datasets. We also compare the contamination levels between four different scRNA-seq protocols. Overall, DecontX can be incorporated into scRNA-seq workflows to improve downstream analyses.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DecontX can be applied when empty droplet counts are not available but its results are potentially heavily dependent on the accuracy of the clustering provided.
“This allows DecontX to be applied when empty droplet counts are not available but also means that the results are potentially heavily dependent on the accuracy of the clustering provided.”
From SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2020) · cites this paper as [23]
DecontX relies on accurate clustering of the data to estimate and remove the background without the need for gene counts from empty droplets.
“Finally, DecontX [23] relies on accurate clustering of the data to estimate and remove the background without the need for gene counts from empty droplets.”
From SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2020) · cites this paper as [23]
Removing ambient RNA as an initial quality-control step improves downstream analyses for many tissues.
“one should consider removing ambient RNA as an initial analysis step in quality control to improve downstream analyses for many tissues21–23.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [23]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826
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
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1285773 · Cited by 4,498
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
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 · cited 8×
“Interestingly, cells from cluster 17 were predicted to be doublets by a doublet prediction method Scrublet (Wolock et al,2019) (Figure 6A).”
MethodInfluentialScrublet: 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
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. (2023) · Figshare · doi:10.6084/m9.figshare.5203696.v2 · Cited by 2,088
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. (2017) · Figshare · doi:10.6084/m9.figshare.5203696 · Cited by 2,048
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
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 · cited 1×
“Other approaches such as demuxlet [12] and scds [13] model gene expression from the original data, then assign doublet to barcodes that have observed expression from genes that are likely to not occur simultaneously.”
MethodDefining cell types and states with single-cell genomics
Trapnell, C. (2015) · Genome Research · doi:10.1101/gr.190595.115 · Cited by 813
Estimating a Dirichlet Distribution
Minka, T. P. (2000) · Cited by 758
The unconventional lifestyle of NKT cells
Kronenberg, M., & Gapin, L. (2002) · Nature reviews. Immunology · doi:10.1038/nri854 · Cited by 750
Single-cell barcoding and sequencing using droplet microfluidics
Žilionis, R., Nainys, J., Veres, A., Savova, V., Zemmour, D., Klein, A. M., & Mažutis, L. (2016) · Nature Protocols · doi:10.1038/nprot.2016.154 · Cited by 735
Advances and Applications of Single-Cell Sequencing Technologies
Wang, Y., & Navin, N. E. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.05.005 · Cited by 624
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T., & Saul, L. K. (1998) · Springer eBooks · doi:10.1007/978-94-011-5014-9_5 · Cited by 604
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
Mereu, E., Lafzi, A., Moutinho, C., Ziegenhain, C., McCarthy, D. J., Álvarez-Varela, A., Batlle, E., Sagar, Grün, D., Lau, J. K., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0469-4 · Cited by 499
Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments
Tian, L., Dong, X., Freytag, S., Cao, K. L., Su, S., JalalAbadi, A., Amann‐Zalcenstein, D., Weber, T. S., Seidi, A., Jabbari, J. S., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0425-8 · Cited by 355
Detection and removal of barcode swapping in single-cell RNA-seq data
Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L., & Marioni, J. C. (2018) · Nature Communications · doi:10.1038/s41467-018-05083-x · Cited by 324
scds: 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
Costello, M., Fleharty, M., Abreu, J., Farjoun, Y., Ferriera, S., Holmes, L., Granger, B., Green, L., Howd, T., Mason, T., et al. (2018) · BMC Genomics · doi:10.1186/s12864-018-4703-0 · Cited by 260
An Enhanced Density Based Spatial Clustering of Applications with Noise
Ram, A., Sharma, A., Jalal, A. S., Agrawal, A. K., & Singh, R. (2009) · IEEE International Advance Computing Conference · doi:10.1109/iadcc.2009.4809235 · Cited by 144
scds: Computational Annotation of Doublets in Single Cell RNA Sequencing DataPreprint
Bais, A., & Kostka, D. (2019) · bioRxiv · doi:10.1101/564021 · Cited by 122
Decontamination of ambient RNA in single-cell RNA-seq with DecontXPreprint
Yang, S., Corbett, S. E., Koga, Y., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/704015 · Cited by 56
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
Young, M. D., & Behjati, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa151 · Cited by 1,619
“Finally, De-contX [23] relies on accurate clustering of the data to estimate and remove the background without the need for gene counts from empty droplets.”
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
The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans
The Tabula Sapiens Consortium*, Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., et al. (2022) · Science · doi:10.1126/science.abl4896 · Cited by 1,056
Self-citationFleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2023) · Nature Methods · doi:10.1038/s41592-023-01943-7 · Cited by 867
Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly
Li, H., Janssens, J., Waegeneer, M. D., Kolluru, S. S., Davie, K., Gardeux, V., Saelens, W., David, F. P. A., Brbić, M., Spanier, K. I., et al. (2022) · Science · doi:10.1126/science.abk2432 · Cited by 826
Denisenko, E., Guo, B. B., Jones, M., Hou, R., de Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02048-6 · Cited by 686
Joint probabilistic modeling of single-cell multi-omic data with totalVI
Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592
Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients
Bost, P., Giladi, A., Liu, Y., Bendjelal, Y., Xu, G., David, E., Blecher-Gonen, R., Cohen, M., Medaglia, C., Li, H., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.006 · Cited by 471
Mathys, H., Peng, Z., Boix, C. A., Victor, M. B., Leary, N., Babu, S., Abdelhady, G., Jiang, X., Ng, A. P., Ghafari, K., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.039 · Cited by 495
Applications of single-cell RNA sequencing in drug discovery and development
de Sande, B. V., Lee, J. S., Mutasa-Gottgens, E., Naughton, B. J., Bacon, W., Manning, J. R., Wang, Y., Pollard, J. R., Mendez, M., Hill, J., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00688-4 · Cited by 462
The enteric nervous system relays psychological stress to intestinal inflammation
Schneider, K. M., Blank, N., Alvarez, Y., Thum, K., Lundgren, P., Litichevskiy, L., Sleeman, M., Bahnsen, K., Kim, J., Kardo, S., et al. (2023) · Cell · doi:10.1016/j.cell.2023.05.001 · Cited by 344
Organization of the human intestine at single-cell resolution
Hickey, J. W., Becker, W. R., Nevins, S. A., Horning, A. M., Espín-Pérez, A., Zhu, C., Zhu, B., Wei, B., Chiu, R., Chen, D. C., et al. (2023) · Nature · doi:10.1038/s41586-023-05915-x · Cited by 330
Uzquiano, A., Kedaigle, A. J., Pigoni, M., Paulsen, B. D. S., Adiconis, X., Kim, K., Faits, T., Nagaraja, S., Antón-Bolaños, N., Gerhardinger, C., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.010 · Cited by 257
Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data
Xi, N. M., & Li, J. J. (2021) · Cell Systems · doi:10.1016/j.cels.2020.11.008 · Cited by 208
Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing
Blattman, S. B., Jiang, W., Oikonomou, P., & Tavazoie, S. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0729-6 · Cited by 217
Single-cell multi-omics in the medicinal plant Catharanthus roseus
Li, C., Wood, J. C., Vu, A. H., Hamilton, J. P., López, C. E. R., Payne, R. M. E., Guerrero, D. A. S., Gase, K., Yamamoto, K. T., Vaillancourt, B., et al. (2023) · Nature Chemical Biology · doi:10.1038/s41589-023-01327-0 · Cited by 210
Reprogramming roadmap reveals route to human induced trophoblast stem cells
Liu, X., Ouyang, J. F., Rossello, F. J., Tan, J. P., Davidson, K. C., Valdes, D. S., Schröder, J., Sun, Y., Chen, J., Knaupp, A. S., et al. (2020) · Nature · doi:10.1038/s41586-020-2734-6 · Cited by 202
Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M. (2019) · doi:10.1101/791699 · Cited by 164
Single-cell RNA sequencing of human liver reveals hepatic stellate cell heterogeneity
Payen, V. L., Lavergne, A., Alevra Sarika, N., Colonval, M., Karim, L., Deckers, M., Najimi, M., Coppieters, W., Charloteaux, B., Sokal, E. M., et al. (2021) · JHEP Reports · doi:10.1016/j.jhepr.2021.100278 · Cited by 160
Miller, T. E., Lareau, C. A., Verga, J. A., DePasquale, E. A. K., Liu, V., Ssozi, D., Sándor, K., Yin, Y., Ludwig, L. S., Farran, C. A. E., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01210-8 · Cited by 150
Landscape of mast cell populations across organs in mice and humans
Tauber, M., Basso, L., Martin, J.-C., Bostan, L. E., Pinto, M. M., Thierry, G. R., Houmadi, R., Serhan, N., Loste, A., Blériot, C., et al. (2023) · The Journal of Experimental Medicine · doi:10.1084/jem.20230570 · Cited by 151
Thumkeo, D., Punyawatthananukool, S., Prasongtanakij, S., Matsuura, R., Arima, K., Nie, H., Yamamoto, R., Aoyama, N., Hamaguchi, H., Sugahara, S., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110914 · Cited by 146
The developmental basis of fingerprint pattern formation and variation
Glover, J. D., Sudderick, Z. R., Shih, B. B.-J., Batho-Samblas, C., Charlton, L., Krause, A. L., Anderson, C., Riddell, J., Balic, A., Li, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.01.015 · Cited by 136
Caglayan, E., Liu, Y., & Konopka, G. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.010 · Cited by 123
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
Single-cell transcriptomics for the assessment of cardiac disease
Miranda, A. M. A., Janbandhu, V., Maatz, H., Kanemaru, K., Cranley, J., Teichmann, S. A., Hübner, N., Schneider, M. D., Harvey, R. P., & Noseda, M. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00805-7 · Cited by 117
Wienke, J., Visser, L. L., Kholosy, W. M., Keller, K. M., Barisa, M., Poon, E., Munnings-Tomes, S., Himsworth, C., Calton, E. A., Rodríguez, A. I. E., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2023.12.008 · Cited by 129
Lee, Y., Bogdanoff, D., Wang, Y., Hartoularos, G. C., Woo, J. M., Mowery, C. T., Nisonoff, H. M., Lee, D. S., Sun, Y., Lee, J., et al. (2021) · Science Advances · doi:10.1126/sciadv.abg4755 · Cited by 110
Jevitt, A. M., Chatterjee, D., Xie, G., Wang, X., Otwell, T., Huang, Y., & Deng, W. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000538 · Cited by 101
Pei, S., Shelton, I. T., Gillen, A. E., Stevens, B. M., Gasparetto, M., Wang, Y., Liu, L., Liu, J., Brunetti, T. M., Engel, K. L., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-1297 · Cited by 118
An Integrated Map of Cell Type–Specific Gene Expression in Pancreatic Islets
Elgamal, R. M., Kudtarkar, P., Melton, R. L., Mummey, H. M., Benaglio, P., Okino, M.-L., & Gaulton, K. J. (2023) · Diabetes · doi:10.2337/db23-0130 · Cited by 105
Tissue morphology influences the temporal program of human brain organoid development
Chiaradia, I., Imaz-Rosshandler, I., Nilges, B. S., Boulanger, J., Pellegrini, L., Das, R., Kashikar, N. D., & Lancaster, M. A. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.09.003 · Cited by 106
Netskar, H., Pfefferle, A., Goodridge, J. P., Sohlberg, E., Dufva, O., Teichmann, S. A., Brownlie, D., Michaëlsson, J., Marquardt, N., Clancy, T., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01884-z · Cited by 126
Korrapati, S., Taukulis, I., Olszewski, R. T., Pyle, M. P., Gu, S., Singh, R., Griffiths, C., Martı́n, D., Boger, E. T., Morell, R. J., et al. (2019) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2019.00316 · Cited by 98
Xiong, H., Luo, Y., Wang, Q., Yu, X., & He, A. (2021) · Nature Methods · doi:10.1038/s41592-021-01129-z · Cited by 100
Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 104
Abe, Y., Sakata‐Yanagimoto, M., Fujisawa, M., Miyoshi, H., Suehara, Y., Hattori, K., Kusakabe, M., Sakamoto, T., Nishikii, H., Nguyen, T. B., et al. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00866-3 · Cited by 103
Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
Su, M., Pan, T., Chen, Q.-Z., Zhou, W., Gong, Y., Xu, G., Yan, H.-Y., Li, S., Shi, Q.-Z., Zhang, Y., et al. (2022) · Military Medical Research · doi:10.1186/s40779-022-00434-8 · Cited by 114
Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling
Yamawaki, T., Lu, D., Ellwanger, D. C., Bhatt, D. M., Manzanillo, P. S., Arias, V., Zhou, H., Yoon, O. K., Homann, O. R., Wang, S., et al. (2021) · BMC Genomics · doi:10.1186/s12864-020-07358-4 · Cited by 94
Whole-body gene expression atlas of an adult metazoan
Ghaddar, A., Armingol, E., Huynh, C., Gevirtzman, L., Lewis, N. E., Waterston, R., & O’Rourke, E. J. (2023) · Science Advances · doi:10.1126/sciadv.adg0506 · Cited by 107
Genetic risk converges on regulatory networks mediating early type 2 diabetes
Walker, J. T., Saunders, D. C., Rai, V., Chen, H., Orchard, P., Dai, C., Pettway, Y. D., Hopkirk, A. L., Reihsmann, C., Tao, Y., et al. (2023) · Nature · doi:10.1038/s41586-023-06693-2 · Cited by 92
Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas
Hrovatin, K., Bastidas-Ponce, A., Bakhti, M., Zappia, L., Büttner, M., Salinno, C., Sterr, M., Böttcher, A., Migliorini, A., Lickert, H., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00876-x · Cited by 89
SpotClean adjusts for spot swapping in spatial transcriptomics data
Ni, Z., Prasad, A., Chen, S., Halberg, R. B., Arkin, L. M., Drolet, B. A., Newton, M. A., & Kendziorski, C. M. (2022) · Nature Communications · doi:10.1038/s41467-022-30587-y · Cited by 83
Lim, R. G., Al-Dalahmah, O., Wu, J., Gold, M. P., Reidling, J. C., Tang, G., Adam, M., Dansu, D. K., Park, H.-J., Casaccia, P., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35388-x · Cited by 84
Ober-Reynolds, B., Wang, C., Ko, J. M., Rios, E. J., Aasi, S. Z., Davis, M. M., Oro, A. E., & Greenleaf, W. J. (2023) · Nature Genetics · doi:10.1038/s41588-023-01445-4 · Cited by 83
Feng, D., Zhu, W., Wang, J., Li, D., Shi, X., Xiong, Q., You, J., Han, P., Qiu, S., Wei, Q., et al. (2024) · Military Medical Research · doi:10.1186/s40779-024-00526-7 · Cited by 94
DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data
Muskovic, W., & Powell, J. E. (2021) · Genome biology · doi:10.1186/s13059-021-02547-0 · Cited by 82
Hong, R., Koga, Y., Bandyadka, S., Leshchyk, A., Wang, Y., Akavoor, V., Cao, X., Sarfraz, I., Wang, Z., Alabdullatif, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29212-9 · Cited by 82
Self-citationGuimarães, G. R., Maklouf, G. R., Teixeira, C. E., Santos, L. D. O., Tessarollo, N. G., de Toledo, N. E., Serain, A. F., de Lanna, C. A., Pretti, M. A. M., da Cruz, J. G. V., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49916-4 · Cited by 93
Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics
Subramanian, A., Alperovich, M., Yang, Y., & Li, B. (2022) · Genome biology · doi:10.1186/s13059-022-02820-w · Cited by 93
Nault, R., Fader, K. A., Bhattacharya, S., & Zacharewski, T. R. (2020) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2020.07.012 · Cited by 71
Durán, R. C., González-Orozco, J. C., Velasco, I., & Wu, J. Q. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.884748 · Cited by 65
Integrated single-cell atlas of human atherosclerotic plaques
Traeuble, K., Munz, M., Pauli, J., Sachs, N., Vafadarnejad, E., Carrillo‐Roa, T., Mäegdefessel, L., Kastner, P., & Heinig, M. (2025) · Nature Communications · doi:10.1038/s41467-025-63202-x · Cited by 72
The effect of background noise and its removal on the analysis of single-cell expression data
Janssen, P., Kliesmete, Z., Vieth, B., Adiconis, X., Simmons, S., Marshall, J. L., Aubin, C., Heyn, H., Levin, J. Z., Enard, W., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02978-x · Cited by 64
Considerations for building and using integrated single-cell atlases
Hrovatin, K., Sikkema, L., Shitov, V. A., Heimberg, G., Shulman, M., Oliver, A. J., Mueller, M. F., Ibarra, I. L., Wang, H., Ramírez-Suástegui, C., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02532-y · Cited by 68
Brown, D. A., Altermatt, M., Dobreva, T., Chen, S., Wang, A. Z., Thomson, M. W., & Gradinaru, V. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.730825 · Cited by 61
Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age
Wells, S. B., Rainbow, D. B., Mark, M., Szabo, P. A., Ergen, C., Caron, D. P., Maceiras, A. R., Rahmani, E., Benuck, E., Amiri, V. V. P., et al. (2025) · Nature Immunology · doi:10.1038/s41590-025-02241-4 · Cited by 67
Song, H., Bucher, S., Rosenberg, K., Tsui, M., Burhan, D., Hoffman, D. B., Cho, S., Rangaswami, A. A., Breese, M. R., Leung, S. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32473-z · Cited by 60
Orchard, P., Manickam, N., Varshney, A., Vadlamudi, S., Burant, C., Parker, S. C. J., Kaplan, J., Lalancette, C., Mohlke, K. L., Gallagher, K., et al. (2021) · Genome Research · doi:10.1101/gr.268482.120 · Cited by 58
Single-cell joint profiling of multiple epigenetic proteins and gene transcription
Xiong, H., Wang, Q., Li, C. C., & He, A. (2024) · Science Advances · doi:10.1126/sciadv.adi3664 · Cited by 58
CD163 + Macrophages Induce Endothelial-to-Mesenchymal Transition in Atheroma
Mori, M., Sakamoto, A., Kawakami, R., Guo, L., Slenders, L., Mosquera, J. V., Ghosh, S., Wesseling, M., Shiraki, T., Bellissard, A., et al. (2024) · Circulation Research · doi:10.1161/circresaha.123.324082 · Cited by 57
Maura, F., Boyle, E. M., Coffey, D. G., Maclachlan, K. H., Gagler, D. C., Diamond, B. T., Ghamlouch, H., Blaney, P., Ziccheddu, B., Cirrincione, A. M., et al. (2023) · Nature Cancer · doi:10.1038/s43018-023-00657-1 · Cited by 59
Yu, W., Biyik‐Sit, R., Uzun, Y., Chen, C.-H., Thadi, A., Sussman, J., Pang, M., Wu, C.-Y., Grossmann, L. D., Gao, P., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02158-6 · Cited by 62
Consequences and opportunities arising due to sparser single-cell RNA-seq datasets
Bouland, G. A., Mahfouz, A. E., & Reinders, M. J. T. (2023) · Genome biology · doi:10.1186/s13059-023-02933-w · Cited by 52
Single-cell and spatially resolved transcriptomics for liver biology
Lin, P., Yan, X., Jing, S., Wu, Y., Shan, Y., Guo, W., Gu, J., Li, Y., Zhang, H., & Li, H. (2023) · Hepatology · doi:10.1097/hep.0000000000000387 · Cited by 56
Dissecting the polygenic basis of atherosclerosis via disease-associated cell state signatures
Örd, T., Lönnberg, T., Nurminen, V., Ravindran, A., Niskanen, H., Kiema, M., Õunap, K., Maria, M., Moreau, P. R., Mishra, P. P., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.03.013 · Cited by 49
scRNA-seq of colorectal cancer shows regional immune atlas with the function of CD20+ B cells
Ji, L., Fu, G., Huang, M., Kao, X., Zhu, J., Dai, Z., Chen, Y., Li, H., Zhou, J., Chu, X., et al. (2024) · Cancer Letters · doi:10.1016/j.canlet.2024.216664 · Cited by 50
The spatial landscape of glial pathology and T cell response in Parkinson’s disease substantia nigra
Ma, M., Paryani, F., Jakubiak, K. A., Xia, S., Antoku, S., Kannan, A., Lee, J., Madden, N., Kumar, S. S., Li, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62478-3 · Cited by 55
Wang, Z., Yang, S., Koga, Y., Corbett, S. E., Shea, C. V., Johnson, W. E., Yajima, M., & Campbell, J. D. (2022) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqac066 · Cited by 47
Self-citationSaraswathy, V. M., Zhou, L., & Mokalled, M. H. (2024) · Nature Communications · doi:10.1038/s41467-024-50628-y · Cited by 50
Vanuytsel, K., Villacorta-Martín, C., Lindstrom-Vautrin, J., Wang, Z., García-Beltrán, W. F., Vrbanac, V. D., Parsons, D., Lam, E. C., Matte, T. M., Dowrey, T. W., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28616-x · Cited by 54
Self-citationMulti-omic analysis of Huntington’s disease reveals a compensatory astrocyte state
Paryani, F., Kwon, J., Ng, C., Jakubiak, K. A., Madden, N., Ofori, K., Tang, A., Lü, H., Xia, S., Li, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50626-0 · Cited by 56
Haglund, A., Zuber, V., Abouzeid, M., Yang, Y., Ko, J. H., Wiemann, L., Otero‐Jimenez, M., Muhammed, L., Feleke, R., Nott, A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-024-02050-9 · Cited by 46
Lost in space: what single-cell RNA sequencing cannot tell you
Adema, K., Schon, M. A., Nodine, M. D., & Kohlen, W. (2024) · Trends in Plant Science · doi:10.1016/j.tplants.2024.03.010 · Cited by 44
Chen, D., Jin, Z., Han, C., Wu, Y., Bian, Y., Yuan, T., Lv, H., Xia, Q., Wang, L., Chu, Q., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.07.014 · Cited by 47
Bosi, E., Marchetti, P., Rutter, G. A., & Eizirik, D. L. (2022) · iScience · doi:10.1016/j.isci.2022.105056 · Cited by 45
Automated quality control and cell identification of droplet-based single-cell data using dropkickPreprint
Heiser, C. N., Wang, V. M., Chen, B., Hughey, J., & Lau, K. S. (2021) · Genome Research · doi:10.1101/gr.271908.120 · Cited by 43
Single‐cell transcriptome analysis reveals thyrocyte diversity in the zebrafish thyroid gland
Gillotay, P., Shankar, M., Haerlingen, B., Eski, S. E., Pozo‐Morales, M., Suñer, I. G., Reinhardt, S., Kränkel, A., Bläsche, J., Petzold, A., et al. (2020) · EMBO Reports · doi:10.15252/embr.202050612 · Cited by 42
HIV integration in the human brain is linked to microglial activation and 3D genome remodeling
Plaza-Jennings, A. L., Valada, A., O’Shea, C. P., Iskhakova, M., Hu, B., Javidfar, B., Hutta, G. B., Lambert, T. Y., Murray, J., Kassim, B. S., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.11.016 · Cited by 42
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
APOE genotype determines cell-type-specific pathological landscape of Alzheimer’s disease
Li, Z., Martens, Y. A., Ren, Y., Jin, Y., Sekiya, H., Doss, S. V., Kouri, N., Castanedes-Casey, M., Christensen, T. A., Miller Nevalainen, L. B., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.02.017 · Cited by 39
Single-cell mapping of combinatorial target antigens for CAR switches using logic gates
Kwon, J., Kang, J., Jo, A., Seo, K., An, D., Baykan, M. Y., Lee, J. H., Kim, N., Eum, H. H., Hwang, S., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01686-y · Cited by 38
Single cell genomics as a transformative approach for aquaculture research and innovation
Daniels, R. R., Taylor, R. S., Robledo, D., & Macqueen, D. J. (2023) · Reviews in Aquaculture · doi:10.1111/raq.12806 · Cited by 37
Spatial enhancer activation influences inhibitory neuron identity during mouse embryonic development
Dvoretskova, E., Ho, M. C., Kittke, V., Neuhaus, F., Vitali, I., Lam, D. D., Delgado, I., Feng, C., Torres, M., Winkelmann, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01611-9 · Cited by 36
Durable lymph-node expansion is associated with the efficacy of therapeutic vaccination
Najibi, A. J., Lane, R. S., Sobral, M. C., Bovone, G., Kang, S. J., Freedman, B. R., Estupinan, J. G., Elósegui-Artola, A., Tringides, C. M., Dellacherie, M. O., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01209-3 · 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
Marteau, V., Nemati, N., Handler, K., Raju, D., Kirchmair, A., Rieder, D., Soto, E. K., Fotakis, G., de Lange, G., Carollo, S., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2025.12.003 · Cited by 40
Isolation and RNA sequencing of single nuclei from Drosophila tissues
McLaughlin, C. N., Qi, Y., Quake, S. R., Luo, L., & Li, H. (2022) · STAR Protocols · doi:10.1016/j.xpro.2022.101417 · 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
Wang, X., Wang, Z., Liao, Q., Yuan, P., Mei, J., Zhang, Y., Wu, C., Kang, X., Zheng, S., Yang, C., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58511-0 · Cited by 40
Self-citationAging human abdominal subcutaneous white adipose tissue at single cell resolution
Whytock, K. L., Divoux, A., Sun, Y., Pino, M. F., Yu, G., Jin, C. A., Robino, J. J., Plekhanov, A., Varlamov, O., Smith, S. R., et al. (2024) · Aging Cell · doi:10.1111/acel.14287 · Cited by 39
Repeated head trauma causes neuron loss and inflammation in young athletes
Butler, M. L. M. D., Pervaiz, N., Breen, K., Calderazzo, S. M., Ypsilantis, P., Wang, Y., Breda, J. C., Mazzilli, S. A., Nicks, R., Spurlock, E., et al. (2025) · Nature · doi:10.1038/s41586-025-09534-6 · Cited by 57
Self-citationProbabilistic machine learning ensures accurate ambient denoising in droplet-based single-cell omicsPreprint
Sheng, C., Lopes, R., Li, G., Schuierer, S., Waldt, A., Cuttat, R., Dimitrieva, S., Kauffmann, A., Durand, E., Galli, G. G., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.14.476312 · Cited by 37
Montserrat-Ayuso, T., & Esteve‐Codina, A. (2024) · BMC Genomics · doi:10.1186/s12864-024-11015-5 · Cited by 37
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
Private information leakage from single-cell count matrices
Walker, C. R., Li, X., Chakravarthy, M., Lounsbery-Scaife, W., Choi, Y. A., Singh, R., & Gürsoy, G. (2024) · Cell · doi:10.1016/j.cell.2024.09.012 · Cited by 33
Kenney, D., O’Connell, A. K., Turcinovic, J., Montanaro, P., Hekman, R. M., Tamura, T., Berneshawi, A. R., Cafiero, T. R., Abdullatif, S. A., Blum, B. C., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110714 · Cited by 35
Self-citationYan, Y., Chen, L., Zheng, L., Wang, G., Zhang, H., Feng, X., Liu, Y., Li, L., Zhang, Y. S., Ji, P., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202315590 · Cited by 31
Divergent WNT signaling and drug sensitivity profiles within hepatoblastoma tumors and organoids
Kluiver, T. A., Lu, Y., Schubert, S. A., Kraaier, L. J., Ringnalda, F. C. A. S., Lijnzaad, P., DeMartino, J., Megchelenbrink, W., Amo-Addae, V., Eising, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52757-w · Cited by 35
Metadata
- Authors
- Yang, S., Corbett, S. E., Koga, Y., Wang, Z., Johnson, W. E., Yajima, M., & Campbell, J. D.
- Affiliations
- Boston University (US)
- Corresponding author
- Campbell, Joshua D.
- Venue
- Genome Biology
- Published
- 2020-03-05
- Volume
- 21
- Issue
- 1
- Pages
- 57-57
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC7059395
- OpenAlex
- W3010766518
- Cited by
- 635
- References
- 27
- Citations by year
- 2026: 177 · 2025: 158 · 2024: 131 · 2023: 77 · 2022: 52 · 2021: 23 · 2020: 15
- FWCI
- 14.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OpenBU (Boston University) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Library of Medicine (R01LM013154-01)
- Informatics Technology for Cancer Research (1U01 CA220413- 01)
- 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
- single-cell RNA sequencing, pollutant removal, Bayesian estimation, droplet microfluidics, cross-contamination, peripheral blood mononuclear cells
- MeSH terms
- RNA-Seq, Animals, Bayes Theorem, Cell Line, Humans, RNA, Mice, Lab-On-A-Chip Devices, 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 ›
Are you an author, or do you see a problem with this page? Request a review
