Using Generative Adversarial Networks to Generate and Augment Single-Cell RNA-seq Data
Nature Communications 2020 Jan 9 IF: 17.694 Introduction: GAN includes a generator that outputs realistic silicon-generated samples. This is achieved through a neural network that learns to transform a simple low-dimensional distribution into a high-dimensional distribution, which is indistinguishable from the actual training distribution. In this paper, the authors establish a single-cell GAN (scGAN) to … Read more