Recent advances in single-cell RNA sequencing have driven the simultaneous measurement of the expression of thousands of genes in thousands of single cells. These growing datasets allow us to model gene sets in biological networks at an unprecedented level of detail, in spite of heterogeneous cell populations. Here, we propose a deep neural network model that is a hybrid of matrix factorization and variational autoencoders, which we call restricted latent variational autoencoder (resVAE). The model uses weights as factorized matrices to obtain gene sets, while class-specific inputs to the latent variable space facilitate a plausible identification of cell types. This artificial neural network model seamlessly integrates functional gene set inference, experimental covariate effect isolation, and static gene identification, which we conceptually demonstrate here for four single-cell RNA sequencing datasets.

单细胞RNA测序的最新进展已经推动了同时测量数千个单细胞中数千个基因的表达。这些不断增长的数据集使我们能够以前所未有的详细程度对生物网络中的基因集进行建模，尽管存在异质细胞群体。在这里，我们提出了一个深度神经网络模型，该模型是矩阵分解和变分自动编码器的混合体，我们称其为受限隐性变分自动编码器（resVAE）。该模型使用权重作为因子矩阵来获取基因集，而对潜在变量空间的类特定输入则有助于对细胞类型的合理识别。这种人工神经网络模型无缝集成了功能基因集推断，实验协变量效应隔离和静态基因识别，