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Cell type prioritization in single-cell data
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Abstract
We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensional space. We validate our method on single-cell RNA sequencing, chromatin accessibility and imaging transcriptomics datasets, and show that Augur outperforms existing methods based on differential gene expression. Augur identified the neural circuits restoring locomotion in mice following spinal cord neurostimulation.
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In the citing paper
“This unbiased analysis indicated that endothelial cells underwent the most profound transcriptional changes in the spinal cord below the injury (Fig. 5e ).”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [27]
Experimental — accuracy not yet measuredIn the citing paper
“The method is versatile, accommodating various molecular feature modalities such as scRNA-seq, proteomics, epigenomics, and imaging transcriptomics datasets.”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [31]
In the citing paper
“Augur employs a Random Forest classifier, known for its non-parametric nature, making it robust to different molecular measurements and pre-processing steps, surpassing the Wilcoxon test usually used for differential expression analysis in these scenarios.”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [31]
In the citing paper
“Augur[31] prioritises cell types based on their molecular response to perturbations in highly multidimensional single-cell data.”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [31]
In the citing paper
“Our taxonomy recapitulated the known hierarchical organization of neuronal subpopulations in the spinal cord9,12,31–33 (Extended Data Fig. 4a–g).”
From The neurons that restore walking after paralysis (2022) · cites this paper as [12]
In the citing paper
“Five of these six comparisons involved acute perturbations on the timescale of transcription (minutes to hours), raising a risk that the total transcriptional output of each cell may not yet fully reflect the impact of the perturbation at the time of snRNA-seq12. Consequently, for these five comparisons, we applied Augur to a matrix of RNA velocity75, as previously described12,13,75.”
From The neurons that restore walking after paralysis (2022) · cites this paper as [12]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785
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
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969
Research works printed but not linked: 2
- Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017). · DOI · Paper page
- Zappia, L., Phipson, B. & Oshlack, A. Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 18, 174 (2017). · DOI · Paper page
Cited by
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Self-citationLin, P., Yan, P., Zhu, J., Huang, S., Wang, Z., Hu, O., Jin, H., Li, Y., Zhang, L., Zhao, J., et al. (2023) · Cell Proliferation · doi:10.1111/cpr.13464 · Cited by 9
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Hu, X., Li, G., Toledo, M. P., Sanchez, P. S., Xie, G., Liu, C., Naji, A., Irianto, J., Roper, M. G., & Wang, Y. J. (2025) · Diabetes · doi:10.2337/db24-1038 · Cited by 10
Deng, Y., Chen, Q., Guo, C., Chen, J., Li, X., Li, Z., Zhang, Y., Zhao, J., Zhou, J., Cai, J., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02231-y · Cited by 14
Jiang, L., Jiang, Y., Zhou, X., Wang, L., Zhang, S., Jiang, C., Meng, H., Gu, Y., Fu, Y., Xu, K., et al. (2025) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.70685 · Cited by 10
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Lei, I., Sicim, H., Gao, W., Huang, W., Noly, P., Pergande, M. R., Wilson, M. C., Lee, A., Liu, L., Ela, A. S. A. A. A. E., et al. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00653-x · Cited by 10
Chen, Y., Fan, B., Zeng, J., Zou, Y., Tao, C., Chen, C., Zhang, P., Liang, J., Qi, F., Tang, H., et al. (2025) · Research · doi:10.34133/research.0741 · Cited by 9
Asenjo-Martínez, A., Dragičević, K., Hou, W., Ozsvár, A., Hansen, N. W., Pfisterer, U., Rydbirk, R., Demharter, S., Møller, B. E., Gasthaus, J., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.028 · Cited by 9
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Self-citationSimon, R. C., Fleming, W., Senthilkumar, P., Briones, B. A., Ishii, K. K., Hjort, M. M., Martin, M. M., Hashikawa, K., Sanders, A. D., Golden, S. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.15.575766 · Cited by 9
Larionova, I., Iamshchikov, P. S., Казакова, А., Rakina, M. A., Menyalo, M., Enikeeva, K., Альбертовна, Р. Г., Вакилевна, Ш. Ю., Павлов, В. Н., Виллерт, А. Б., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1414716 · Cited by 8
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Müller‐Miny, L., Heming, M., Lautwein, T., Ruck, T., Lu, I., Wiendl, H., & zu Hörste, G. M. (2023) · Journal of Neuroimmunology · doi:10.1016/j.jneuroim.2023.578088 · Cited by 8
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Zhao, J., Jaffe, A., Li, H., Lindenbaum, O., Sefik, E., Jackson, R. P., Cheng, X., Flavell, R. A., & Kluger, Y. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/711929 · Cited by 8
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Zhou, H., Xu, Z., Jiang, C., Wu, Q., Zhang, C., Liu, Z. Y., Zhang, X., Li, W., Pang, Y., Zhang, J., et al. (2025) · Cell Communication and Signaling · doi:10.1186/s12964-025-02067-5 · Cited by 7
Danesh, S., Khan, F. W., Chopko, T. C., Lee, A., Huo, R., Lu, S., Tam, V., Safi, J. P., Gao, W., Todd, A., et al. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.125.043661 · Cited by 7
Hill, R. D., Robertson, S. M., Igamberdiev, A. U., Mira, M. M., Wilkins, O., & Stasolla, C. (2026) · New Phytologist · doi:10.1111/nph.70874 · Cited by 7
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Ahn, J.-Y., Kim, S., Kim, C. R., Lee, J., Kim, J. M., Klompstra, T. M., Choi, Y. H., Jeon, Y., Na, Y., Kim, J.-S., et al. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.08.009 · Cited by 7
Hodgson, L. M., Li, Y., Iturria‐Medina, Y., Stratton, J. A., Wolf, G., Krishnaswamy, S., Bennett, D. A., & Bzdok, D. (2024) · Communications Biology · doi:10.1038/s42003-024-06273-8 · Cited by 7
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Xi, X., Li, H., Chen, S., Lv, T., Ma, T., Jiang, R., Zhang, P., Wong, W. H., & Zhang, X. (2022) · iScience · doi:10.1016/j.isci.2022.104790 · Cited by 6
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Zhang, R., Guo, S., Zhou, J., Lin, X., Wang, Y., Wang, Y., Li, M., Zhao, K., Bao, W., Shui, K., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-04367-4 · Cited by 6
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You, J.-P., Cheng, G., Zhang, X., Wang, Z., Liu, C., Ning, W., Liu, Y.-R., Hu, H.-M., Zhai, Y., Qi, Y., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-04292-x · Cited by 9
Gleitz, H. F. E., Fuchs, S. N. R., Snoeren, I., Boys, C., Nagai, J. S., Mora, H. T., Klöker, V., Pritchard, J. E., Bakker, I. A. J. M., Garasa, M. G., et al. (2025) · HemaSphere · doi:10.1002/hem3.70179 · Cited by 5
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Deng, Y., Guo, C., Liu, X., Li, X., Liu, J., Liu, W., Chen, J., Huang, Z., Zhang, Y., Bi, X., et al. (2025) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01573-3 · Cited by 6
Zhao, L., Xu, Z., Zhao, P., Pang, Z., You, Y., Wu, C., Zhu, D., Su, M., Zhang, N., Luo, J., et al. (2025) · Cell Death Discovery · doi:10.1038/s41420-025-02886-y · Cited by 5
Lin, X., Wang, X., Zhang, Y., Chu, G., Liang, J., Zhang, B., Lu, Y., Steward, O., & Luo, J. (2023) · Experimental Neurology · doi:10.1016/j.expneurol.2023.114549 · Cited by 5
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Nicol, P. B., Paulson, D., Qian, G., Liu, X. S., Irizarry, R., & Sahu, A. D. (2023) · doi:10.1101/2023.05.06.539326 · Cited by 33
Wang, H., Lyu, W., Sun, Y., Jia, X., Bi, J., Wei, R., Du, Z., Meng, F., He, J., Wang, S., et al. (2025) · Life Medicine · doi:10.1093/lifemedi/lnaf025 · Cited by 4
Xu, B., Joshi, A. S., da Silva, M. T., Liu, S., & Kumar, A. (2026) · JCI Insight · doi:10.1172/jci.insight.200396 · Cited by 5
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Zhang, J. M., Ubas, A. A., Svensson, V., Thomas, N., Subramanyam, V., Carpenter, C., Winters, A. F., de Borja, R., Teyssier, N., Thakar, N., et al. (2026) · Cell · doi:10.1016/j.cell.2026.08.035 · Cited by 10
Yu, Y., Wang, Y., Lu, J., Cao, X., Feng, Y., Tu, P., & Lu, Y. (2025) · Inflammation · doi:10.1007/s10753-025-02332-2 · Cited by 3
Yu, Q., Wu, Y., Ma, X., & Zhang, Y. (2025) · Inflammation Research · doi:10.1007/s00011-024-01965-7 · Cited by 4
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Lang, N. J., Gote-Schniering, J., Porras-Gonzalez, D., Yang, L., Sadeleer, L. D., Jentzsch, R. C., Shitov, V. A., Zhou, S., Ansari, M., Agami, A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.16.524219 · Cited by 4
Cober, N. D., McCourt, E. R., Godoy, R. S., Deng, Y., Schlosser, K., Situ, A., Cook, D. P., Lemay, S.-E. S., Klouda, T. M., Yuan, K., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.06.555321 · Cited by 4
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Ko, B. S., Han, M., Kwon, M. J., Gon, D., Ji, Y., Park, E. S., Jeon, M. J., Kim, S., Lee, K., Choi, Y. H., et al. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01169-3 · Cited by 3
Xie, Y., Yang, J., Ouyang, J. F., & Petretto, E. G. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae482 · Cited by 5
Korah, M. M., Reveron‐Thornton, R. F., Fallah, M., Xie, P. Y., Gonçalves, A. C., Guo, C., Agolia, J. P., Delitto, A. E., Flojo, R. A. B., Reddy, B., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116347 · Cited by 4
Sonti, S., Cantu, A., Guttierez, M. C., Leek, C. C., Phinizy, P. A., Jensen, E. A., & Lingappan, K. (2026) · Redox Biology · doi:10.1016/j.redox.2026.104057 · Cited by 4
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Ceto, S., Elshove, S. A., Wu, M., Ramadi, K. B., Tondera, C., Minev, I. R., Srinivasan, S. S., Lee, K., Gora, M. J., Rogers, J. A., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71188-3 · Cited by 3
Self-citationHou, J., Chen, K.-X., Xiao, Q.-N., Hu, C., Wei, J., Jiang, Y.-Z., Liu, L., He, C., Huang, M., Jiao, Y., et al. (2026) · Nature Aging · doi:10.1038/s43587-026-01130-1 · Cited by 4
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Yang, L., Liu, F., Hahm, H., Okuda, T., Li, X., Zhang, Y., Kalyanaraman, V., Heitmeier, M. R., & Samineni, V. K. (2025) · Nature Communications · doi:10.1038/s41467-025-60360-w · Cited by 3
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Lin, Y., Qi, Y., Yao, Y.-T., Tong, H., Chen, L., Song, W., Li, G., Xu, H., & He, X. (2025) · The FASEB Journal · doi:10.1096/fj.202500116r · Cited by 5
cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data
Ghosh, T., Baxter, R. M., Seal, S., Lui, V. G., Rudra, P., Vu, T., Hsieh, E. W. Y., & Ghosh, D. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf399 · Cited by 3
A 3-step acne system containing solubilized benzoyl peroxide versus clindamycin-benzoyl peroxide.
Thiboutot, D. M., Eichenfield, L. F., Shalita, A. R., Rosso, J. Q. D., Swinyer, L. J., Tanghetti, E. A., Tschen, E. H., & Parr, L. (2009) · PubMed · Cited by 3
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Zholudeva, L. V., & Lane, M. A. (2022) · Neuroscience Insights · doi:10.1177/26331055221101607 · Cited by 3
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Fages, A., Luxey, M., Ronco, F., Huyghe, C. E. T., Menon, P. N. K., Indermaur, A., Salzburger, W., & Tschopp, P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.28.625862 · Cited by 3
Schuler, H., Zhou, D., Savignac, C., Cvetkovska, V., Tse, Y.-C., Meccia, J., Lopez, J., Harutyunyan, A. S., Ragoussis, J., Bzdok, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631940 · Cited by 5
Neotenic transcriptomic features in the adult turquoise killifish brainPreprint
Burgos-Ruiz, A. M., Naranjo, S., Hernández-Pérez, M. D. V., Ardila‐García, L., Moreno-Oñate, M., Fernández‐Miñán, A., Reyes-Benitez, A. D. L., López-Rı́os, J., Nusspaumer, G., & Tena, J. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.24.645095 · Cited by 5
Direct interoceptive input to the insular cortex shapes learned feeding behaviorPreprint
Zhao, Z., Xu, B., Anthony, S. A., Subramanian, S., Granger, B., Von-Walter, C., Mizrachi, E., Bajpai, D., Tyagi, P., Kidd, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.13.653896 · Cited by 3
Hou, X., Li, Z., Lin, J., Lin, W., Li, L., Zheng, X., Lai, X., Zhu, L., Guo, P., Cai, F., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-96434-4 · Cited by 3
Li, L., Wu, G., Xu, X., Yang, J., Yi, L., Yang, Z., Mo, Z., Li, X., Shan, Y., Yu, Z., et al. (2024) · eLife · doi:10.7554/elife.100346.4 · Cited by 2
Tang, Q., Zhang, Y., Wu, Z.-Y., Zhang, T., Song, X., Zhu, Y., Yang, Z., Zhao, C., Zhao, J.-W., Wang, H.-K., et al. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003883 · Cited by 3
Zhāng, P., Qiao, Z., Zhao, S., & Wu, K. (2025) · Transplant Immunology · doi:10.1016/j.trim.2025.102326 · Cited by 2
Developmental stage-dependent transcriptomic responses to neonatal intraventricular hemorrhage
Wallace-Anthony, E., Zamorano, M., Vekaria, H. J., Oldham, B. B., Umpornpun, K. P., Olson, S. D., Berto, S., & Miller, B. A. (2025) · Journal of Neuroinflammation · doi:10.1186/s12974-025-03599-w · Cited by 2
Di, Y., Ji, W., Xiong, W., Song, W., Chen, G., Li, D., & Qi, F. (2025) · Journal of Inflammation Research · doi:10.2147/jir.s554975 · Cited by 2
Integrated human and mouse single-cell profiling reveals immune-stromal niche driving silicosis
Begka, C., Macowan, M. G., Cardwell, B., Dang, A. T., Daunt, C., Chatzis, R., Barnes, H., Pilcher, D., Glaspole, I., Hoy, R. F., et al. (2026) · Mucosal Immunology · doi:10.1016/j.mucimm.2026.01.003 · Cited by 2
Sicim, H., Ela, A. S. A. A. A. E., Lu, S., Karunasumetta, C., Danesh, S., Knop, G. L., Azzam, F. A., Gao, W., Wang, Y., Zhu, W., et al. (2026) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2026.03.014 · Cited by 2
Samejima, S., Hanna, R., Cariappa, B. K., Arvizu, R. R., Nimbalkar, S., Montgomery‐Walsh, R., Galindo, S. L., Henderson, R. P., Khorasani, A., Moritz, C. T., et al. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3229200 · Cited by 2
Ranking of cell clusters in a single-cell RNA-sequencing analysis framework using prior knowledge
Oulas, A., Savva, K., Karathanasis, N., & Spyrou, G. M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011550 · Cited by 2
Reverse engineering spinal-cord injury
(2024) · Nature · doi:10.1038/d41586-024-02307-7 · Cited by 2
Anomaly detection in spatial transcriptomics via spatially localized density comparison
Hu, G., Gold, J., Chitra, U., Joshi, S., & Raphael, B. J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf242 · Cited by 1
MSR1+ macrophages passivate antitumor immunity by inducing ITM2A+ CD4T exhaustion differentiation
Wang, S., Wu, P., Xu, N., Xiao, Z., Liu, Y., Bian, W., Meng, L., Guo, R., Xu, Y., & Ding, H. (2025) · Hepatology · doi:10.1097/hep.0000000000001578 · Cited by 2
Lu, R. J., Chen, S., Kim, M., Sampathkumar, N. K., Lee, E. H., Christensen, A., Wang, E., Lau, I. C. Y., Parihar, S., Ravikumar, C. K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.11.659200 · Cited by 2
Targeting RUNX1 in Macrophages Facilitates Cardiac RecoveryPreprint
Amrute, J., Zhu, A., Pai, Y.-L., Hector-Greene, M., An, Y., Kim, K. R., Sheth, M. U., Padmanabhan, A., Lee, C. Y., Yamawaki, T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.25.665779 · Cited by 4
Zhao, M., Lei, Y., Zhou, Y., Sun, M.-A., Li, X., Zhou, Z., Huang, J., Li, X., & Zhao, B. (2023) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2023.1165647 · Cited by 1
ADM: adaptive graph diffusion for meta-dimension reduction
Feng, J., Liang, Y. F., & Yu, T. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae612 · Cited by 1
He, L., Gong, X., Guo, H., Zhou, K., Lan, Y., Lv, M., Liu, X., Lin, S., Hua, Y., Guo, J., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1528446 · Cited by 1
Wang, X., Xiao, H., Wu, J., Lin, Y., Ao, Y., Ye, Z., Tan, X., Kong, F., Chen, X., Chai, R., et al. (2025) · Cell Proliferation · doi:10.1111/cpr.70081 · Cited by 1
Guo, Y., Ma, J., Ma, X., Ye, K., Jiang, C., Xu, J., Huang, Y., Yang, X., Ge, Q., Zhang, J., et al. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.147708 · Cited by 2
Nguyen, N. M. P., Mermin-Bunnell, A. M., de Mattos, K., Cleverdon, J., Kano, M., Chauvin, M. Y., Godin, P., Kashiwagi, A., Duong, T., Nagykery, N. G., et al. (2025) · Biology of Reproduction · doi:10.1093/biolre/ioaf233 · Cited by 1
The functional landscape of alternative splicing in hematopoietic lineage commitment
Hu, X., Wang, J., Chen, L., Yang, Q., Tardáguila, M., Mao, B., 牛胜辉, Xu, Z., Wang, G., Zhang, D., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68811-8 · Cited by 1
Sun, X., Geng, S., Wang, Z., & Chen, Q. (2026) · International Immunopharmacology · doi:10.1016/j.intimp.2026.116265 · Cited by 1
Phong, K. T., Song, S., Kim, E. A., Conrad, D. N., & Gartner, Z. J. (2026) · Journal of Mammary Gland Biology and Neoplasia · doi:10.1007/s10911-026-09605-y · Cited by 1
Characterizing gene perturbations in single cells via network divergence analysis
Huang, C., Li, Y., Fa, B., Zhu, J., Liu, Z., Ma, Y., Zhang, Z., Xu, Y., Xu, Q., & Xiao, Z. (2026) · Nature Communications · doi:10.1038/s41467-026-71507-8 · Cited by 1
Ouyang, S., Wang, J., Du, X., Zhang, S., Han, S., Xu, X., Ren, B., & Yu, W. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1827122 · Cited by 1
Zhang, X., Yang, Y., Zhang, M., Kaisi, F., Huang, Z., Zhang, Y., Kong, L., Chen, Q., Tan, R., Zhang, S., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-08296-7 · Cited by 2
Metadata
- Authors
- Skinnider, M. A., Squair, J. W., Kathe, C., Anderson, M. A., Gautier, M., Matson, K. J. E., Milano, M., Hutson, T. H., Barraud, Q., Phillips, A. A., Foster, L. J., La Manno, G., Levine, A. J., & Courtine, G.
- Affiliations
- University of British Columbia (CA)
- Centre Hospitalier Universitaire Vaudois (CH)
- National Institute of Neurological Disorders and Stroke (US)
- University of Calgary (CA)
- Corresponding author
- Skinnider, Michael A.
- Squair, Jordan W.
- Courtine, Grégoire
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2020-07-20
- Volume
- 39
- Issue
- 1
- Pages
- 30-34
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC7610525
- OpenAlex
- W3043086101
- Cited by
- 274
- References
- 92
- Citations by year
- 2026: 64 · 2025: 83 · 2024: 45 · 2023: 33 · 2022: 29 · 2021: 16 · 2020: 2
- FWCI
- 6.46
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- SERVAL (Université de Lausanne) (acceptedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- IRIS (submittedVersion)
- Funders
- National Science Foundation
- Genome Canada (214PRO)
- Fondation Brain Canada
- Compute Canada
- Western Canada Research Grid
- Genome British Columbia (214PRO, project 214PRO)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (185214, 310030)
- Killam Trusts
- Alberta Innovates
- National Institutes of Health
- Canadian Institutes of Health Research
- National Institute of Neurological Disorders and Stroke
- 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 Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell data, Augur, differential gene expression, chromatin accessibility, imaging transcriptomics, locomotor recovery, neural circuits
- MeSH terms
- Machine Learning, Animals, Chromatin, Nerve Net, Walking, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Databases, Genetic, Mice, Rats, Single-Cell Analysis, Transcriptome
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