Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
Low sample numbers often limit the robustness of analyses in biomedical research. Here, the authors introduce a method to generate realistic scRNA-seq data using GANs that learn gene expression dependencies from complex samples, and show that augmenting spare cell populations improves downstream ana...
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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/54eea9701f364c569d5dd410717e6a68 |
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