Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data

BACKGROUND: A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to auto...

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Detalles Bibliográficos
Autores principales: Yu, Lijia, Cao, Yue, Yang, Jean Y. H., Yang, Pengyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8822786/
https://www.ncbi.nlm.nih.gov/pubmed/35135612
http://dx.doi.org/10.1186/s13059-022-02622-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8822786/
https://www.ncbi.nlm.nih.gov/pubmed/35135612
http://dx.doi.org/10.1186/s13059-022-02622-0

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