Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS-Reference-Cited by-同舟云学术

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS

Published:2023-11-21 Issue:12 Volume:18 Page:3690-3731
ISSN:1754-2189
Container-title:Nature Protocols
language:en
Short-container-title:Nat Protoc

Author:

Johnson Jeanette A. I.^ORCID,Tsang Ashley P.,Mitchell Jacob T.^ORCID,Zhou David L.^ORCID,Bowden Julia,Davis-Marcisak Emily,Sherman Thomas,Liefeld Ted^ORCID,Loth Melanie,Goff Loyal A.^ORCID,Zimmerman Jacquelyn W.,Kinny-Köster Ben,Jaffee Elizabeth M.,Tamayo Pablo,Mesirov Jill P.^ORCID,Reich Michael^ORCID,Fertig Elana J.^ORCID,Stein-O’Brien Genevieve L.

Publisher

Springer Science and Business Media LLC

Subject

General Biochemistry, Genetics and Molecular Biology

Link

https://www.nature.com/articles/s41596-023-00892-x.pdf

Reference57 articles.

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3. Cleary, B., Cong, L., Cheung, A., Lander, E. S. & Regev, A. Efficient generation of transcriptomic profiles by random composite measurements. Cell 171, 1424–1436.e18 (2017).

4. Gaujoux, R. & Seoighe, C. A flexible R package for nonnegative matrix factorization. BMC Bioinform. 11, 367 (2010).

5. Ochs, M. F. & Fertig, E. J. Matrix factorization for transcriptional regulatory network inference. IEEE Symp. Comput. Intell. Bioinform. Comput. Biol. Proc. 2012, 387–396 (2012).

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