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Cell type prioritization in single-cell data.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Cell type prioritization in single-cell data.
Συγγραφείς: Skinnider, M.A., Squair, J.W., Kathe, C., Anderson, M.A., Gautier, M., Matson, KJE, Milano, M., Hutson, T.H., Barraud, Q., Phillips, A.A., Foster, L.J., La Manno, G., Levine, A.J., Courtine, G.
Έτος έκδοσης: 2021
Συλλογή: Université de Lausanne (UNIL): Serval - Serveur académique lausannois
Θεματικοί όροι: Animals, Chromatin/genetics, Chromatin/metabolism, Computational Biology/methods, Databases, Genetic, Gene Expression Profiling/methods, Machine Learning, Mice, Nerve Net/metabolism, Rats, Sequence Analysis, RNA, Single-Cell Analysis/methods, Transcriptome/genetics, Transcriptome/physiology, Walking/physiology
Περιγραφή: 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.
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
ISSN: 1546-1696
Relation: Nature Biotechnology; https://iris.unil.ch/handle/iris/82290; serval:BIB_6B2FE5BC2C8B; 000550630500001
DOI: 10.1038/s41587-020-0605-1
Διαθεσιμότητα: https://iris.unil.ch/handle/iris/82290
https://doi.org/10.1038/s41587-020-0605-1
Αριθμός Καταχώρησης: edsbas.EA31E949
Βάση Δεδομένων: BASE
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  – Url: https://iris.unil.ch/handle/iris/82290#
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  Data: Cell type prioritization in single-cell data.
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  Data: <searchLink fieldCode="AR" term="%22Skinnider%2C+M%2EA%2E%22">Skinnider, M.A.</searchLink><br /><searchLink fieldCode="AR" term="%22Squair%2C+J%2EW%2E%22">Squair, J.W.</searchLink><br /><searchLink fieldCode="AR" term="%22Kathe%2C+C%2E%22">Kathe, C.</searchLink><br /><searchLink fieldCode="AR" term="%22Anderson%2C+M%2EA%2E%22">Anderson, M.A.</searchLink><br /><searchLink fieldCode="AR" term="%22Gautier%2C+M%2E%22">Gautier, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Matson%2C+KJE%22">Matson, KJE</searchLink><br /><searchLink fieldCode="AR" term="%22Milano%2C+M%2E%22">Milano, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Hutson%2C+T%2EH%2E%22">Hutson, T.H.</searchLink><br /><searchLink fieldCode="AR" term="%22Barraud%2C+Q%2E%22">Barraud, Q.</searchLink><br /><searchLink fieldCode="AR" term="%22Phillips%2C+A%2EA%2E%22">Phillips, A.A.</searchLink><br /><searchLink fieldCode="AR" term="%22Foster%2C+L%2EJ%2E%22">Foster, L.J.</searchLink><br /><searchLink fieldCode="AR" term="%22La+Manno%2C+G%2E%22">La Manno, G.</searchLink><br /><searchLink fieldCode="AR" term="%22Levine%2C+A%2EJ%2E%22">Levine, A.J.</searchLink><br /><searchLink fieldCode="AR" term="%22Courtine%2C+G%2E%22">Courtine, G.</searchLink>
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  Data: 2021
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  Data: Université de Lausanne (UNIL): Serval - Serveur académique lausannois
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  Data: <searchLink fieldCode="DE" term="%22Animals%22">Animals</searchLink><br /><searchLink fieldCode="DE" term="%22Chromatin%2Fgenetics%22">Chromatin/genetics</searchLink><br /><searchLink fieldCode="DE" term="%22Chromatin%2Fmetabolism%22">Chromatin/metabolism</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+Biology%2Fmethods%22">Computational Biology/methods</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic%22">Genetic</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+Expression+Profiling%2Fmethods%22">Gene Expression Profiling/methods</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+Learning%22">Machine Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mice%22">Mice</searchLink><br /><searchLink fieldCode="DE" term="%22Nerve+Net%2Fmetabolism%22">Nerve Net/metabolism</searchLink><br /><searchLink fieldCode="DE" term="%22Rats%22">Rats</searchLink><br /><searchLink fieldCode="DE" term="%22Sequence+Analysis%22">Sequence Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22RNA%22">RNA</searchLink><br /><searchLink fieldCode="DE" term="%22Single-Cell+Analysis%2Fmethods%22">Single-Cell Analysis/methods</searchLink><br /><searchLink fieldCode="DE" term="%22Transcriptome%2Fgenetics%22">Transcriptome/genetics</searchLink><br /><searchLink fieldCode="DE" term="%22Transcriptome%2Fphysiology%22">Transcriptome/physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Walking%2Fphysiology%22">Walking/physiology</searchLink>
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  Data: 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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  Data: 1546-1696
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  Data: Nature Biotechnology; https://iris.unil.ch/handle/iris/82290; serval:BIB_6B2FE5BC2C8B; 000550630500001
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  Data: 10.1038/s41587-020-0605-1
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  Data: https://iris.unil.ch/handle/iris/82290<br />https://doi.org/10.1038/s41587-020-0605-1
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        Value: 10.1038/s41587-020-0605-1
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