A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics

Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving human tissues. Most existing methods remove batch ef...

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Detalles Bibliográficos
Autores principales: Lakkis, Justin, Wang, David, Zhang, Yuanchao, Hu, Gang, Wang, Kui, Pan, Huize, Ungar, Lyle, Reilly, Muredach P., Li, Xiangjie, Li, Mingyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494213/
https://www.ncbi.nlm.nih.gov/pubmed/34035047
http://dx.doi.org/10.1101/gr.271874.120

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494213/
https://www.ncbi.nlm.nih.gov/pubmed/34035047
http://dx.doi.org/10.1101/gr.271874.120

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