Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized transcriptomic studies. However, large-scale integrative analysis of scRNA-seq data remains a challenge largely due to unwanted batch effects and the limited transferabilty, interpretability, and scalability of the...

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Detalles Bibliográficos
Autores principales: Zhao, Yifan, Cai, Huiyu, Zhang, Zuobai, Tang, Jian, Li, Yue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421403/
https://www.ncbi.nlm.nih.gov/pubmed/34489404
http://dx.doi.org/10.1038/s41467-021-25534-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421403/
https://www.ncbi.nlm.nih.gov/pubmed/34489404
http://dx.doi.org/10.1038/s41467-021-25534-2

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