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 |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/7990959eb2ba4fc09e678054e84e1c54 |
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