Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
Increasingly large scRNA-seq datasets demand better and more scalable analysis tools. Here, the authors introduce a scalable unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function and enables removal of batch effects.
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-15851-3 |