Single-cell transcriptomics offers a tool to study the diversity of cell phenotypes through snapshots of the abundance of mRNA in individual cells. Often there is additional information available besides the single-cell gene expression counts, such as bulk transcriptome data from the same tissue, or quantification of surface protein levels from the same cells. In this study, we propose models based on the Bayesian deep learning approach, where protein quantification, available as CITE-seq counts, from the same cells is used to constrain the learning process, thus forming a SemI-SUpervised generative Autoencoder (SISUA) model. The generative model is based on the deep variational autoencoder (VAE) neural network architecture.

中文翻译：

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用于单细胞数据的半监督生成自编码器。

单细胞转录组学提供了一种工具，可以通过单个细胞中 mRNA 丰度的快照来研究细胞表型的多样性。除了单细胞基因表达计数外，通常还有其他信息可用，例如来自同一组织的大量转录组数据，或来自同一细胞的表面蛋白水平的量化。在这项研究中，我们提出了基于贝叶斯深度学习方法的模型，其中来自相同细胞的蛋白质量化，可用作 CITE-seq 计数，用于约束学习过程，从而形成半监督生成自动编码器 (SISUA) 模型. 生成模型基于深度变分自编码器 (VAE) 神经网络架构。