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, 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
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.
Conflict of interest statement
Competing interests
G.C. is a founder and shareholder of GTXmedical, a company with no direct relationships with the present work. M.A.S., J.W.S., and G.C. are named as co-inventors on a patent application related to this work.
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References
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Source: PubMed