With the advances in single-cell sequencing techniques, numerous analytical methods have been developed for delineating cell development. However, most are based on Euclidean space, which would distort the complex hierarchical structure of cell differentiation. Recently, methods acting on hyperbolic space have been proposed to visualize hierarchical structures in single-cell RNA-seq (scRNA-seq) data and have been proven to be superior to methods acting on Euclidean space. However, these methods have fundamental limitations and are not optimized for the highly sparse single-cell count data. To address these limitations, we propose scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of scRNA-seq data in low-dimensional hyperbolic space. The evaluations on extensive simulation and real experiments show that scDHMap outperforms existing dimensionality-reduction methods in various common analytical tasks as needed for scRNA-seq data, including revealing trajectory branches, batch correction, and denoising the count matrix with high dropout rates. In addition, we extend scDHMap to visualize single-cell ATAC-seq data.

中文翻译：

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深度双曲流形学习揭示单细胞基因组学数据中的复杂层次结构

随着单细胞测序技术的进步，已经开发出多种分析方法来描绘细胞发育。然而，大多数都是基于欧几里得空间，这会扭曲细胞分化的复杂层次结构。最近，人们提出了作用于双曲空间的方法来可视化单细胞 RNA-seq (scRNA-seq) 数据中的层次结构，并且已被证明优于作用于欧几里得空间的方法。然而，这些方法具有根本的局限性，并且没有针对高度稀疏的单细胞计数数据进行优化。为了解决这些限制，我们提出了 scDHMap，这是一种基于模型的深度学习方法，用于可视化低维双曲空间中 scRNA-seq 数据的复杂层次结构。对大量模拟和真实实验的评估表明，scDHMap 在 scRNA-seq 数据所需的各种常见分析任务中优于现有的降维方法，包括揭示轨迹分支、批量校正和高丢失率计数矩阵去噪。此外，我们扩展了 scDHMap 以可视化单细胞 ATAC-seq 数据。