ArticleGenome research2023
Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning.
Article in Genome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.Communications biology · 2026Article
- GAHIB: graph attention VAE with a hyperbolic information bottleneck for biologically structured single-cell representations.Frontiers in genetics · 2026Article
- LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference.Frontiers in genetics · 2026Article
- SMOPCA: spatially aware dimension reduction integrating multi-omics improves the efficiency of spatial domain detection.Genome biology · 2025Article
- scFocus: Detecting branching probabilities in single-cell data with SAC.Computational and structural biotechnology journal · 2025Article
- Lorentz-regularized interpretable VAE for multi-scale single-cell transcriptomic and epigenomic embeddings.Frontiers in genetics · 2025Article
- Complex hierarchical structures analysis in single-cell data with Poincaré deep manifold transformation.Briefings in bioinformatics · 2024Article
- Computational single cell oncology: state of the art.Frontiers in genetics · 2023Review
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Authors and funding
5 authors.
Funding
Abstract
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.
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