Cell type prioritization in single-cell data

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.

doi:10.1101/2019.12.20.884916

bioRxiv · 10.1101/2019.12.20.884916

Cell type prioritization in single-cell data

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.

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Abstract

We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.

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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.. 2019-12-23. Cell type prioritization in single-cell data. https://doi.org/10.1101/2019.12.20.884916

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