Recent advances in single-cell technologies, including single-cell ATAC-seq (scATAC-seq), have enabled large-scale profiling of the chromatin accessibility landscape at the single-cell level. However, the characteristics of scATAC-seq data, including high sparsity and high dimensionality, have greatly complicated the computational analysis. Here, we propose scDEC, a computational tool for scATAC-seq analysis with deep generative neural networks. scDEC is built on a pair of generative adversarial networks, and is capable of simultaneously learning the latent representation and inferring cell labels. In a series of experiments, scDEC demonstrates superior performance over other tools in scATAC-seq analysis across multiple datasets and experimental settings. In downstream applications, we demonstrate that the generative power of scDEC helps to infer the trajectory and intermediate state of cells during differentiation and the latent features learned by scDEC can potentially reveal both biological cell types and within-cell-type variations. We also show that it is possible to extend scDEC for the integrative analysis of multi-modal single cell data.

单细胞技术的最新进展，包括单细胞 ATAC-seq (scATAC-seq)，已经能够在单细胞水平上大规模分析染色质可及性景观。然而，scATAC-seq数据的高稀疏性和高维度的特点使得计算分析变得非常复杂。在这里，我们提出了 scDEC，一种使用深度生成神经网络进行 scATAC-seq 分析的计算工具。 scDEC 建立在一对生成对抗网络之上，能够同时学习潜在表示和推断细胞标签。在一系列实验中，scDEC 在跨多个数据集和实验设置的 scATAC-seq 分析中表现出了优于其他工具的性能。在下游应用中，我们证明 scDEC 的生成能力有助于推断细胞在分化过程中的轨迹和中间状态，并且 scDEC 学习的潜在特征可以潜在地揭示生物细胞类型和细胞内类型变异。我们还表明，可以扩展 scDEC 以进行多模式单细胞数据的综合分析。