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Cell type prioritization in single-cell data

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.

ORGANISM(S): Mus musculus

PROVIDER: GSE142245 | GEO | 2020/06/05

REPOSITORIES: GEO

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