Cell type prioritization in single-cell data

Michael A. Skinnider, Jordan W. Squair, Claudia Kathe, Mark A. 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, Grégoire Courtine

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)30-34
Number of pages5
JournalNature biotechnology
Issue number1
StatePublished - Jan 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Applied Microbiology and Biotechnology
  • Bioengineering
  • Molecular Medicine
  • Biotechnology
  • Biomedical Engineering


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