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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Cell type prioritization using Augur indicated that endothelial cells underwent the most profound transcriptional changes in the spinal cord below the injury.
“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]
The paper’s Abstract describes applying Augur to lumbar spinal-cord cells after injury, but its spinal-cord prioritization focuses on interneuron subtypes. The Abstract reports profound changes in endothelial cells in Alzheimer’s disease datasets, not in the spinal cord below an injury. It therefore supports the use of Augur in a spinal-cord injury setting, but not the attributed endothelial-cell finding. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAugur is versatile, accommodating various molecular feature modalities such as scRNA-seq, proteomics, epigenomics, and imaging transcriptomics datasets.
“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]
Augur employs a Random Forest classifier, making it robust to different molecular measurements and pre-processing steps, surpassing the Wilcoxon test usually used for differential expression analysis.
“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]
Augur prioritises cell types based on their molecular response to perturbations in highly multidimensional single-cell data.
“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]
The taxonomy recapitulated the known hierarchical organization of neuronal subpopulations in the spinal cord.
“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]
For five acute comparisons, Augur was applied to a matrix of RNA velocity because the total transcriptional output of each cell may not yet fully reflect the impact of the perturbation at the time of snRNA-seq.
“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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Best practices for single-cell analysis across modalities
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- 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 · 2019: 1
- 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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