Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization.

Zhu, Xun; Ching, Travers; Pan, Xinghua; Weissman, Sherman M; Garmire, Lana

Zhu, Xun; Ching, Travers; Pan, Xinghua; Weissman, Sherman M; Garmire, Lana.

Afiliación

Zhu X; Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States; Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, United States.
Ching T; Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States; Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, United States.
Pan X; Department of Genetics, Yale University , New Haven , CT , United States.
Weissman SM; Department of Genetics, Yale University , New Haven , CT , United States.
Garmire L; Epidemiology Program, University of Hawaii Cancer Center , Honolulu , HI , United States.

PeerJ ; 5: e2888, 2017.

Article en En | MEDLINE | ID: mdl-28133571

Palabras clave

Clustering; Feature gene; Heterogeneity; Modularity; Non-negative matrix factorization; RNA-Seq; Single cell; Single cell sequencing; Single-cell; Subpopulation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: PeerJ Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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