Cell type prioritization in single-cell data.

Nature Biotechnology
Michael A SkinniderGrégoire 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.

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Citations

May 19, 2021·Proceedings of the National Academy of Sciences of the United States of America·Jun ZhaoYuval Kluger
Jun 4, 2021·Nature·Min Kyu YumBenjamin D Simons
Jun 5, 2021·Journal for Immunotherapy of Cancer·Shakoora A SabreeGeorge J Weiner
Jun 18, 2021·Cell Systems·Yuge JiFabian J Theis
Jul 28, 2021·Genome Biology·Michael A SkinniderGrégoire Courtine
Jul 31, 2021·Signal Transduction and Targeted Therapy·Ge ChenKezhen Li
Aug 13, 2021·Frontiers in Cellular Neuroscience·Aaron J MoulsonPeggy Assinck
Sep 30, 2021·Nature Communications·Jordan W SquairGrégoire Courtine

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Datasets Mentioned

BETA
AB32849

Methods Mentioned

BETA
fluorescence-activated cell sorting
scRNA-seq
FACS
RNA-seq
Smart-seq
Smart-seq2
inDrops
Drop-seq

Software Mentioned

ImageJ
velocyto
CellRanger
STAR
TESS
R
ZEN
mclapply
SRA
DropletUtils ’

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