Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis

Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding algorithm that clusters scRNA-seq data by iterative...

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Detalles Bibliográficos
Autores principales: Li, Xiangjie, Wang, Kui, Lyu, Yafei, Pan, Huize, Zhang, Jingxiao, Stambolian, Dwight, Susztak, Katalin, Reilly, Muredach P., Hu, Gang, Li, Mingyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214470/
https://www.ncbi.nlm.nih.gov/pubmed/32393754
http://dx.doi.org/10.1038/s41467-020-15851-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214470/
https://www.ncbi.nlm.nih.gov/pubmed/32393754
http://dx.doi.org/10.1038/s41467-020-15851-3

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