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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-14018-z |