Open AccessCell type prioritization in single-cell data
Author(s) -
Michael A. Skinnider,
Jordan W. Squair,
Claudia Kathe,
Mark 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
Publication year - 2020
Publication title -
nature biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 15.358
H-Index - 445
eISSN - 1546-1696
pISSN - 1087-0156
DOI - 10.1038/s41587-020-0605-1
Subject(s) - chromatin , prioritization , cell type , cell , computer science , computational biology , artificial intelligence , neuroscience , biology , gene , engineering , genetics , management science
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.