ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating indivi...

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Detalles Bibliográficos
Autores principales: Iida, Keita, Kondo, Jumpei, Wibisana, Johannes Nicolaus, Inoue, Masahiro, Okada, Mariko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477531/
https://www.ncbi.nlm.nih.gov/pubmed/35924984
http://dx.doi.org/10.1093/bioinformatics/btac541

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477531/
https://www.ncbi.nlm.nih.gov/pubmed/35924984
http://dx.doi.org/10.1093/bioinformatics/btac541

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