Deep generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), have achieved remarkable successes in generating and manipulating high-dimensional images. VAEs excel at learning disentangled image representations, while GANs excel at generating realistic images. Here, we systematically assess disentanglement and generation performance on single-cell gene expression data and find that these strengths and weaknesses of VAEs and GANs apply to single-cell gene expression data in a similar way. We also develop MichiGAN, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of two large single-cell RNA-seq datasets and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment.

中文翻译：

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使用生成对抗网络从单细胞数据的解缠结表示中采样

包括变分自动编码器（VAE）和生成对抗网络（GAN）在内的深度生成模型在生成和处理高维图像方面取得了显著成功。VAE擅长学习纠缠的图像表示，而GAN擅长生成逼真的图像。在这里，我们系统地评估了单细胞基因表达数据的解缠和生成性能，发现VAE和GAN的这些优缺点以类似的方式适用于单细胞基因表达数据。我们还开发了MichiGAN，这是一种新颖的神经网络，结合了VAE和GAN的优势，可以从纠缠的表示中进行采样而不会牺牲数据生成质量。我们学习了两个大型单细胞RNA-seq数据集的解缠表示，并使用MichiGAN从这些表示中进行采样。