Cited by 278Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisGene expression and cancer classification

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Cell type prioritization in single-cell data

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Skinnider, Michael A., Squair, Jordan W., Kathe, Claudia, Anderson, Mark A., Gautier, Matthieu, Matson, Kaya J. E., Milano, Marco, Hutson, Thomas H., Barraud, Quentin, Phillips, Aaron A., Foster, Leonard J., La Manno, Gioele, Levine, Ariel J., Courtine, Grégoire·2021·Nature Biotechnology

DOI 10.1038/s41587-020-0605-1PMC PMC7610525

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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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Additional file 2 of Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE
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Additional file 3 of Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE
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Additional file 6 of Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE
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Additional file 7 of Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE
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Additional file 5 of Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE
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Additional file 1 of Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE
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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: Skinnider, Michael A.
Squair, Jordan W.
Kathe, Claudia
Anderson, Mark A.
Gautier, Matthieu
Matson, Kaya J. E.
Milano, Marco
Hutson, Thomas H.
Barraud, Quentin
Phillips, Aaron A.
Foster, Leonard J.
Levine, Ariel J.
Courtine, Grégoire
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2020-07-20
Volume: 39
Issue: 1
Pages: 30-34
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-020-0605-1
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: cc-by-nc-nd
www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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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