Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation

As single-cell RNA sequencing technologies mature, massive gene expression profiles can be obtained. Consequently, cell clustering and annotation become two crucial and fundamental procedures affecting other specific downstream analyses. Most existing single-cell RNA-seq (scRNA-seq) data clustering...

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Detalles Bibliográficos
Autores principales: Chen, Liang, Zhai, Yuyao, He, Qiuyan, Wang, Weinan, Deng, Minghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397036/
https://www.ncbi.nlm.nih.gov/pubmed/32674393
http://dx.doi.org/10.3390/genes11070792

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397036/
https://www.ncbi.nlm.nih.gov/pubmed/32674393
http://dx.doi.org/10.3390/genes11070792

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