Nature BiotechnologyGenomicsPeer-reviewed journal article
Cell type prioritization in single-cell data
Michael A. SkinnideriD, Jordan W. SquairiD, Claudia KatheiD, Mark A. AndersoniD, Matthieu GautieriD, Kaya J.E. MatsoniD, Marco MilanoiD, Thomas Haynes HutsoniD, Quentin BarraudiD, Aaron Alexander PhillipsiD, Leonard J. FosteriD, Gioele La MannoiD, Ariel J. LevineiD, Grégoire CourtineiD
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.
- assay: scRNA-seq
- assay: ATAC-seq
- assay: imaging
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- approach: machine learning (classical)
- Citations
- 274
- FWCI
- 6.49
- Field percentile
- top 2%
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