ArticleNature communications2025
Topological identification and interpretation for single-cell epigenetic regulation elucidation in multi-tasks using scAGDE.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Insights into Microbiota-Vaccine Crosstalk in Humans: Mechanisms, Modulators, and Translational Horizons.Vaccines · 2026Review
- Multi-omics-driven precision medicine.iMeta · 2026Review
- ARISE: RNA-anchored shared-edge topology and hierarchical fusion for spatial multi-omics integration.Bioinformatics (Oxford, England) · 2026Article
- Compact and informative representation learning for scRNA-seq data clustering with masked information bottleneck.BMC biology · 2026Article
- Genetic Architecture of N-Terminal Pro-B-Type Natriuretic Peptide in a Multiancestry Study Population.Circulation. Genomic and precision medicine · 2026Article
- scVMAP: a comprehensive platform for integrating single-cell chromatin accessibility regions with causal variants.Nucleic acids research · 2026Article
- Dynamic Synthesis of Multi-Modal Representations for CITE-seq Data Integration and Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- DeepNanoHi-C: deep learning enables accurate single-cell nanopore long-read data analysis and 3D genome interpretation.Nucleic acids research · 2025Article
- Topological identification and interpretation for single-cell epigenetic regulation elucidation in multi-tasks using scAGDE.Nature communications · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
Single-cell ATAC-seq technology advances our understanding of single-cell heterogeneity in gene regulation by enabling exploration of epigenetic landscapes and regulatory elements. However, low sequencing depth per cell leads to data sparsity and high dimensionality, limiting the characterization of gene regulatory elements. Here, we develop scAGDE, a single-cell chromatin accessibility model-based deep graph representation learning method that simultaneously learns representation and clustering through explicit modeling of data generation. Our evaluations demonstrated that scAGDE outperforms existing methods in cell segregation, key marker identification, and visualization across diverse datasets while mitigating dropout events and unveiling hidden chromatin-accessible regions. We find that scAGDE preferentially identifies enhancer-like regions and elucidates complex regulatory landscapes, pinpointing putative enhancers regulating the constitutive expression of CTLA4 and the transcriptional dynamics of CD8A in immune cells. When applied to human brain tissue, scAGDE successfully annotated cis-regulatory element-specified cell types and revealed functional diversity and regulatory mechanisms of glutamatergic neurons.
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Registered trials
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