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.
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| Main Authors: | , , , , , , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2020
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| Subjects: | |
| Online Access: | https://doaj.org/article/d25ce1857e63443bb60277874e79ad11 |
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