Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics-Reference-Cited by-同舟云学术

Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics

Published:2021-10-21 Issue: Volume: Page:
ISSN:1087-0156
Container-title:Nature Biotechnology
language:en
Short-container-title:Nat Biotechnol

Author:

Reshef Yakir A.,Rumker Laurie,Kang Joyce B.,Nathan Aparna,Korsunsky Ilya,Asgari Samira^ORCID,Murray Megan B.,Moody D. Branch,Raychaudhuri Soumya^ORCID

Funder

U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute

U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Foundation for the National Institutes of Health

U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases

Publisher

Springer Science and Business Media LLC

Subject

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

Link

https://www.nature.com/articles/s41587-021-01066-4.pdf

Reference33 articles.

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3. Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

4. Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

5. Burkhardt, D. B. et al. Quantifying the effect of experimental perturbations at single-cell resolution. Nat. Biotechnol. 39, 619–629 (2021).

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