Cell type prioritization in single-cell data

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

doi:10.1038/s41587-020-0605-1

Cell type prioritization in single-cell data

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

Research output: Contribution to journal › Article › peer-review

79 Scopus citations

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.

Original language	English (US)
Pages (from-to)	30-34
Number of pages	5
Journal	Nature biotechnology
Volume	39
Issue number	1
DOIs	https://doi.org/10.1038/s41587-020-0605-1
State	Published - Jan 2021
Externally published	Yes

All Science Journal Classification (ASJC) codes

Biotechnology
Bioengineering
Applied Microbiology and Biotechnology
Molecular Medicine
Biomedical Engineering

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10.1038/s41587-020-0605-1

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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.",

author = "Skinnider, {Michael A.} and Squair, {Jordan W.} and Claudia Kathe and Anderson, {Mark A.} and Matthieu Gautier and Matson, {Kaya J.E.} and Marco Milano and Hutson, {Thomas H.} and Quentin Barraud and Phillips, {Aaron A.} and Foster, {Leonard J.} and {La Manno}, Gioele and Levine, {Ariel J.} and Gr{\'e}goire Courtine",

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doi = "10.1038/s41587-020-0605-1",

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AU - Skinnider, Michael A.

AU - Squair, Jordan W.

AU - Kathe, Claudia

AU - Anderson, Mark A.

AU - Gautier, Matthieu

AU - Matson, Kaya J.E.

AU - Milano, Marco

AU - Hutson, Thomas H.

AU - Barraud, Quentin

AU - Phillips, Aaron A.

AU - Foster, Leonard J.

AU - La Manno, Gioele

AU - Levine, Ariel J.

AU - Courtine, Grégoire

PY - 2021/1

Y1 - 2021/1

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AB - 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 in single-cell data

Abstract

All Science Journal Classification (ASJC) codes

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