Cell type prioritization in single-cell data

We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs 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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610525/
https://www.ncbi.nlm.nih.gov/pubmed/32690972
http://dx.doi.org/10.1038/s41587-020-0605-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610525/
https://www.ncbi.nlm.nih.gov/pubmed/32690972
http://dx.doi.org/10.1038/s41587-020-0605-1

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