Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
oleh: Mohamed Marouf, Pierre Machart, Vikas Bansal, Christoph Kilian, Daniel S. Magruder, Christian F. Krebs, Stefan Bonn
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2020-01-01 |
Deskripsi
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 analyses.