The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Cell type prioritization in single-cell data
Open accessPublished version of a preprint
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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 measuredThe cited source says what the claim attributes to it.
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
“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
“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]
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
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
Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159
Single-cell transcriptomic analysis of Alzheimer’s disease
Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019) · Nature · doi:10.1038/s41586-019-1195-2 · Cited by 2,568
(2012) · doi:10.1016/j.jmoldx.2011.08.002 · Cited by 2,567
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279
Astrocyte scar formation aids central nervous system axon regeneration
Anderson, M. A., Burda, J. E., Ren, Y., Ao, Y., O’Shea, T. M., Kawaguchi, R., Coppola, G., Khakh, B. S., Deming, T. J., & Sofroniew, M. V. (2016) · Nature · doi:10.1038/nature17623 · Cited by 1,851
Self-citationA single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750
Three-dimensional intact-tissue sequencing of single-cell transcriptional states
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620
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
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422
Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis
Smillie, C. S., Biton, M., Ordovás-Montañés, J., Sullivan, K. M., Burgin, G., Graham, D. B., Herbst, R. H., Rogel, N., Slyper, M., Waldman, J., et al. (2019) · Cell · doi:10.1016/j.cell.2019.06.029 · Cited by 1,417
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402
Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner
Jaitin, D. A., Adlung, L., Thaiss, C. A., Weiner, A., Li, B., Descamps, H., Lundgren, P., Bleriot, C., Liu, Z., Deczkowska, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.05.054 · Cited by 1,377
Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., Rubinstein, N. D., Hao, J., Regev, A., Dulac, C., et al. (2018) · Science · doi:10.1126/science.aau5324 · Cited by 1,365
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 4×
“Initial simulation parameters were estimated from the Kang et al. dataset 4 using the ‘splatEstimate’ function, and populations of 100–1,000 cells from two experimental conditions were generated, in increments of 100.”
BackgroundMethodInfluentialTargeted neurotechnology restores walking in humans with spinal cord injury
Wagner, F. B., Mignardot, J.-B., Le Goff-Mignardot, C. G., Demesmaeker, R., Komi, S., Capogrosso, M., Rowald, A., Seáñez, I., Caban, M., Pirondini, E., et al. (2018) · Nature · doi:10.1038/s41586-018-0649-2 · Cited by 1,087
Self-citationSingle-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations
van den Brink, S. C., Sage, F., Vértesy, Á., Spanjaard, B., Peterson-Maduro, J., Baron, C. S., Robin, C., & van Oudenaarden, A. (2017) · Nature Methods · doi:10.1038/nmeth.4437 · Cited by 1,072
Clustering trees: a visualization for evaluating clusterings at multiple resolutions
Zappia, L., & Oshlack, A. (2018) · GigaScience · doi:10.1093/gigascience/giy083 · Cited by 1,064
Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., Olsen, B. N., Mumbach, M. R., Pierce, S. E., Corces, M. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0206-z · Cited by 980
Accounting for technical noise in single-cell RNA-seq experiments
Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A., Marioni, J. C., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2645 · Cited by 979
Grubman, A., Chew, G., Ouyang, J. F., Sun, G., Choo, X. Y., McLean, C., Simmons, R. K., Buckberry, S., Vargas-Landin, D. B., Poppe, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0539-4 · Cited by 956
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945
Splatter: simulation of single-cell RNA sequencing data
Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905
Wirka, R. C., Wagh, D. A., Paik, D. T., Pjanic, M., Nguyen, T., Miller, C. L., Kundu, R., Nagao, M., Coller, J., Koyano, T. K., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0512-5 · Cited by 874
Eigengene networks for studying the relationships between co-expression modules
Langfelder, P., & Horvath, S. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-54 · Cited by 867
Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo
Wagner, D. E., Weinreb, C., Collins, Z. M., Briggs, J. A., Megason, S. G., & Klein, A. M. (2018) · Science · doi:10.1126/science.aar4362 · Cited by 849
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770
Restoring Voluntary Control of Locomotion after Paralyzing Spinal Cord Injury
van den Brand, R., Heutschi, J., Barraud, Q., DiGiovanna, J., Bartholdi, K., Huerlimann, M., Friedli, L., Vollenweider, I., Moraud, E. M., Duis, S., et al. (2012) · Science · doi:10.1126/science.1217416 · Cited by 767
Self-citationXia, C., Fan, J., Emanuel, G., Hao, J., & Zhuang, X. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912459116 · Cited by 755
Single-cell transcriptomic profiling of the aging mouse brain
Ximerakis, M., Lipnick, S. L., Innes, B. T., Simmons, S. K., Adiconis, X., Dionne, D., Mayweather, B. A., Nguyen, L., Niziolek, Z., Ozek, C., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0491-3 · Cited by 736
Validation of noise models for single-cell transcriptomics
Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721
Phipson, B., & Smyth, G. K. (2010) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1585 · Cited by 712
Transformation of nonfunctional spinal circuits into functional states after the loss of brain input
Courtine, G., Gerasimenko, Y., van den Brand, R., Yew, A., Musienko, P., Zhong, H., Song, B., Ao, Y., Ichiyama, R. M., Lavrov, I., et al. (2009) · Nature Neuroscience · doi:10.1038/nn.2401 · Cited by 706
Self-citationGrimm, D., Lee, J. S., Wang, L., Desai, T., Akache, B., Storm, T. A., & Kay, M. A. (2008) · Journal of Virology · doi:10.1128/jvi.00254-08 · Cited by 696
Recovery of Over-Ground Walking after Chronic Motor Complete Spinal Cord Injury
Angeli, C. A., Boakye, M., Morton, R. A., Vogt, J., Benton, K., Chen, Y., Ferreira, C. K., & Harkema, S. J. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1803588 · Cited by 670
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643
Neuronal vulnerability and multilineage diversity in multiple sclerosis
Schirmer, L., Velmeshev, D., Holmqvist, S., Kaufmann, M., Werneburg, S., Jung, D., Vistnes, S., Stockley, J. H., Young, A., Steindel, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1404-z · Cited by 638
Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity
Chen, R., Wu, X., Jiang, L., & Zhang, Y. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.03.004 · Cited by 592
Gill, M. L., Grahn, P. J., Calvert, J. S., Linde, M. B., Lavrov, I. A., Strommen, J. A., Beck, L. A., Sayenko, D. G., Van Straaten, M. G., Drubach, D. I., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0175-7 · Cited by 574 · cited 1×
“In published datasets, we confirmed that this bias led DE methods to prioritize highly abundant cell”
MethodPower analysis of single-cell RNA-sequencing experiments
Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., & Teichmann, S. A. (2017) · Nature Methods · doi:10.1038/nmeth.4220 · Cited by 568
Experimental Modeling of Spinal Cord Injury: Characterization of a Force-Defined Injury Device
Scheff, S. W., Rabchevsky, A. G., Fugaccia, I., Main, J. A., & Lumpp, J. E. (2003) · Journal of Neurotrauma · doi:10.1089/08977150360547099 · Cited by 564
Required growth facilitators propel axon regeneration across complete spinal cord injury
Anderson, M. A., O’Shea, T. M., Burda, J. E., Ao, Y., Barlatey, S. L., Bernstein, A. M., Kim, J. H., James, N. D., Rogers, A. D., Kato, B., et al. (2018) · Nature · doi:10.1038/s41586-018-0467-6 · Cited by 528
Self-citationCrowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515
Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
Hrvatin, S., Hochbaum, D. R., Nagy, M. A., Cicconet, M., Robertson, K., Cheadle, L., Zilionis, R., Ratner, A., Borges-Monroy, R., Klein, A. M., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0029-5 · Cited by 497
Häring, M., Zeisel, A., Hochgerner, H., Rinwa, P., Jakobsson, J. E. T., Lönnerberg, P., La Manno, G., Sharma, N., Borgius, L., Kiehn, O., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0141-1 · Cited by 470
High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer
Grosselin, K., Durand, A., Marsolier, J., Poitou, A., Marangoni, E., Nemati, F., Dahmani, A., Lameiras, S., Reyal, F., Frenoy, O., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0424-9 · Cited by 466
Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility
Lareau, C. A., Duarte, F. M., Chew, J. G., Kartha, V. K., Burkett, Z. D., Kohlway, A. S., Pokholok, D., Aryee, M. J., Steemers, F. J., Lebofsky, R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0147-6 · Cited by 444
Detecting Activated Cell Populations Using Single-Cell RNA-Seq
Wu, Y. E., Pan, L., Zuo, Y., Li, X., & Hong, W. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.026 · Cited by 438
Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments
McDavid, A., Finak, G., Chattopadyay, P. K., Dominguez, M., Lamoreaux, L., Ma, S. S., Roederer, M., & Gottardo, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts714 · Cited by 395
Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems
Zhang, X., Li, T., Liu, F., Chen, Y., Yao, J., Li, Z., Huang, Y., & Wang, J. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.10.020 · Cited by 395
Ketone Body Signaling Mediates Intestinal Stem Cell Homeostasis and Adaptation to Diet
Cheng, C., Biton, M., Haber, A. L., Gündüz, N., Eng, G. M., Gaynor, L. T., Tripathi, S. M., Calibasi‐Kocal, G., Rickelt, S., Butty, V. L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.048 · Cited by 379
Sathyamurthy, A., Johnson, K. R., Matson, K. J. E., Dobrott, C. I., Li, L., Ryba, A. R., Bergman, T. B., Kelly, M. C., Kelley, M. W., & Levine, A. J. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.02.003 · Cited by 370
Self-citationFormento, E., Minassian, K., Wagner, F., Mignardot, J. B., Le Goff-Mignardot, C. G., Rowald, A., Bloch, J., Micera, S., Capogrosso, M., & Courtine, G. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0262-6 · Cited by 370
Self-citationWenger, N., Moraud, E. M., Gandar, J., Musienko, P., Capogrosso, M., Baud, L., Le Goff, C. G., Barraud, Q., Pavlova, N., Dominici, N., et al. (2016) · Nature Medicine · doi:10.1038/nm.4025 · Cited by 369
Self-citationCrone, S. A., Quinlan, K. A., Zagoraiou, L., Droho, S., Restrepo, C. E., Lundfald, L., Endo, T., Setlak, J., Jessell, T. M., Kiehn, O., et al. (2008) · Neuron · doi:10.1016/j.neuron.2008.08.009 · Cited by 358
Droplet scRNA-seq is not zero-inflated
Svensson, V. (2019) · doi:10.1101/582064 · Cited by 356
Parsing the interferon transcriptional network and its disease associations
Mostafavi, S., Yoshida, H., Moodley, D., LeBoité, H., Rothamel, K., Raj, T., Ye, C. J., Chevrier, N., Zhang, S.-Y., Feng, T., et al. (2016) · Cell · doi:10.1016/j.cell.2015.12.032 · Cited by 337
Asboth, L., Friedli, L., Beauparlant, J., Martinez-Gonzalez, C., Anil, S., Rey, E., Baud, L., Pidpruzhnykova, G., Anderson, M. A., Shkorbatova, P., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0093-5 · Cited by 324
Self-citationDetection 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
Sensory lesioning induces microglial synapse elimination via ADAM10 and fractalkine signaling
Gunner, G., Cheadle, L., Johnson, K. M., Ayata, P., Badimon, A., Mondo, E., Nagy, M. A., Liu, L., Bemiller, S. M., Kim, K.-W., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0419-y · Cited by 318
Takeoka, A., Vollenweider, I., Courtine, G., & Arber, S. (2014) · Cell · doi:10.1016/j.cell.2014.11.019 · Cited by 318
Self-citationHighly scalable generation of DNA methylation profiles in single cells
Mulqueen, R. M., Pokholok, D., Norberg, S. J., Torkenczy, K. A., Fields, A. J., Sun, D., Sinnamon, J. R., Shendure, J., Trapnell, C., O'Roak, B. J., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4112 · Cited by 289
Multimodal analysis of cell types in a hypothalamic node controlling social behavior
Kim, D.-W., Yao, Z., Graybuck, L. T., Kim, T. K., Nguyen, T. N., Smith, K. A., Fong, O., Yi, L., Koulena, N., Pierson, N., et al. (2019) · Cell · doi:10.1016/j.cell.2019.09.020 · Cited by 259
Hafemeister, C., & Satija, R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/576827 · Cited by 238
Zhang, J., Lanuza, G. M., Britz, O., Wang, Z., Siembab, V. C., Zhang, Y., Velasquez, T., Alvarez, F. J., Frank, E., & Goulding, M. (2014) · Neuron · doi:10.1016/j.neuron.2014.02.013 · Cited by 233
scSLAM-seq reveals core features of transcription dynamics in single cells
Erhard, F., Baptista, M. A. P., Krammer, T., Hennig, T., Lange, M., Arampatzi, P., Jürges, C. S., Theis, F. J., Saliba, A.-E., & Dölken, L. (2019) · Nature · doi:10.1038/s41586-019-1369-y · Cited by 233
Gene expression variability across cells and species shapes innate immunity
Hagai, T., Chen, X., Miragaia, R. J., Rostom, R., Gomes, T., Kunowska, N., Henriksson, J., Park, J.-E., Proserpio, V., Donati, G., et al. (2018) · Nature · doi:10.1038/s41586-018-0657-2 · Cited by 220
Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data
Yip, S. H., Sham, P. C., & Wang, J. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby011 · Cited by 201
Single cell molecular alterations reveal target cells and pathways of concussive brain injury
Arneson, D., Zhang, G., Ying, Z., Zhuang, Y., Byun, H. R., Ahn, I. S., Gomez-Pinilla, F., & Yang, X. (2018) · Nature Communications · doi:10.1038/s41467-018-06222-0 · Cited by 193
Petukhov, V., Guo, J., Baryawno, N., Sévère, N., Scadden, D. T., Samsonova, M. G., & Kharchenko, P. V. (2018) · Genome biology · doi:10.1186/s13059-018-1449-6 · Cited by 176
Obesity remodels activity and transcriptional state of a lateral hypothalamic brake on feeding
Rossi, M. A., Basiri, M. L., McHenry, J. A., Kosyk, O., Otis, J. M., van den Munkhof, H. E., Bryois, J., Hübel, C., Breen, G. D., Guo, W., et al. (2019) · Science · doi:10.1126/science.aax1184 · Cited by 172
Hu, P., Fabyanic, E., Kwon, D. Y., Tang, S., Zhou, Z., & Wu, H. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.11.017 · Cited by 162
Heimberg, G., Bhatnagar, R., El-Samad, H., & Thomson, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.001 · Cited by 156
A discriminative learning approach to differential expression analysis for single-cell RNA-seq
Ntranos, V., Yi, L., Melsted, P., & Pachter, L. S. (2019) · Nature Methods · doi:10.1038/s41592-018-0303-9 · Cited by 152
Evaluating measures of association for single-cell transcriptomics
Skinnider, M. A., Squair, J. W., & Foster, L. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0372-4 · Cited by 145
Self-citationSingle-cell RNA-seq uncovers a robust transcriptional response to morphine by glia
Avey, D., Sankararaman, S., Yim, A. K. Y., Barve, R., Milbrandt, J., & Mitra, R. D. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.08.080 · Cited by 143
Identification of a regeneration-organizing cell in the Xenopus tail
Aztekin, C., Hiscock, T. W., Marioni, J. C., Gurdon, J. B., Simons, B. D., & Jullien, J. (2019) · Science · doi:10.1126/science.aav9996 · Cited by 142
Dominici, N., Keller, U., Vallery, H., Friedli, L., van den Brand, R., Starkey, M. L., Евгеньевич, М. П., Riener, R., & Courtine, G. (2012) · Nature Medicine · doi:10.1038/nm.2845 · Cited by 105
Self-citationSingle-cell proteomic and transcriptomic analysis of macrophage heterogeneityPreprint
Specht, H., Emmott, E., Petelski, A. A., Huffman, R. G., Perlman, D. H., Serra, M., Kharchenko, P. V., Koller, A. A., & Slavov, N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/665307 · Cited by 54
Crowell, H. L., Soneson, C., Germain, P.-L., et al. (2019) · bioRxiv · doi:10.1101/713412 · Cited by 40
Squair, J. W., Tigchelaar, S., Moon, K., Liu, J., Tetzlaff, W., Kwon, B. K., Krassioukov, A. V., West, C. R., Foster, L. J., & Skinnider, M. A. (2018) · eLife · doi:10.7554/elife.39188 · Cited by 38
Self-citationReyes, M., Vickers, D. A. L., Billman, K., Eisenhaure, T., Hoover, P., Browne, E. P., Rao, D. A., Hacohen, N., & Blainey, P. C. (2019) · Science Advances · doi:10.1126/sciadv.aau9223 · Cited by 35
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
Sorted by citations
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
“3).”
Confronting false discoveries in single-cell differential expression
Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Hudelle, R., Qaiser, T., Matson, K. J. E., Barraud, Q., et al. (2021) · doi:10.1101/2021.03.12.435024 · Cited by 1,086
Self-citationIanevski, A., Giri, A. K., & Aittokallio, T. A. (2022) · Nature Communications · doi:10.1038/s41467-022-28803-w · Cited by 787
The neurons that restore walking after paralysis
Kathe, C., Skinnider, M. A., Hutson, T. H., Regazzi, N., Gautier, M., Demesmaeker, R., Komi, S., Ceto, S., James, N. D., Cho, N., et al. (2022) · Nature · doi:10.1038/s41586-022-05385-7 · Cited by 304
Self-citationBrain endothelial GSDMD activation mediates inflammatory BBB breakdown
Wei, C., Jiang, W., Wang, R., Zhong, H., He, H., Gao, X., Zhong, S., Yu, F., Guo, Q., Zhang, L., et al. (2024) · Nature · doi:10.1038/s41586-024-07314-2 · Cited by 296
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
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
