Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

Abstract

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.