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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.
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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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Self-citationTriglyceride-derived fatty acids reduce autophagy in a model of retinal angiomatous proliferation
Heckel, É., Cagnone, G., Agnihotri, T., Cakir, B., Das, A., Kim, J. S., Kim, N., Lavoie, G., Situ, A., Pundir, S., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.154174 · Cited by 27
A Single-Cell Transcriptomic Analysis of the Mouse Hippocampus After Voluntary Exercise
Methi, A., Islam, M. R., Kaurani, L., Sakib, M. S., Krüger, D. M., Pena, T., Burkhardt, S., Liebetanz, D., & Fischer, A. (2024) · Molecular Neurobiology · doi:10.1007/s12035-023-03869-9 · Cited by 33
Neural ensembles that encode nocifensive mechanical and heat pain in mouse spinal cord
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Immunotherapy in the Treatment of Urothelial Bladder Cancer: Insights From Single-Cell Analysis
Zang, J., Ye, K., Yang, F., Zhang, R., Chen, H., & Zhuang, G. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.696716 · Cited by 25
Identification of a human hematopoietic stem cell subset that retains memory of inflammatory stressPreprint
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Estimating the impact of single-cell RNA sequencing of human tissues on drug target validationPreprint
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Weakly supervised learning uncovers phenotypic signatures in single-cell dataPreprint
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ARID5A orchestrates cardiac aging and inflammation through MAVS mRNA stabilization
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Optimal distance metrics for single-cell RNA-seq populationsPreprint
Ji, Y., Green, T. D., Peidli, S., Bahrami, M., Liu, M., Zappia, L., Hrovatin, K., Sander, C., & Theis, F. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.26.572833 · Cited by 22
Best practices for differential accessibility analysis in single-cell epigenomics
Teo, A. Y. Y., Squair, J. W., Courtine, G., & Skinnider, M. A. (2024) · Nature Communications · doi:10.1038/s41467-024-53089-5 · Cited by 19
Self-citationEnabling reproducible re-analysis of single-cell data
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Self-citationSingle-nucleus transcriptomics reveals a gatekeeper role for FOXP1 in primate cardiac aging
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Machine learning to dissect perturbations in complex cellular systems
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Single‐cell transcriptomic analysis of PB and BM NK cells from severe aplastic anaemia patients
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Sensitive cluster-free differential expression testing.Preprint
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Human haematopoietic stem cells remember inflammatory stress
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Decoding aging in the heart via single cell dual omics of non-cardiomyocytes
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Self-citationCellDrift: inferring perturbation responses in temporally sampled single-cell data
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Analysis of multi-condition single-cell data with latent embedding multivariate regression
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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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