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

Computational single-cell RNA-seq analyses often face challenges in scalability, model interpretability, and confounders. Here, we show a new model to address these challenges by learning meaningful embeddings from the data that simultaneously refine gene signatures and cell functions in diverse con...

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Autores principales: Yifan Zhao, Huiyu Cai, Zuobai Zhang, Jian Tang, Yue Li
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/7990959eb2ba4fc09e678054e84e1c54
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