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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-09-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-25534-2 |