ArticleBriefings in bioinformatics2025
scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Bioinformatics Analysis Based on Omics and Clinical Data for Graph-Based Patient Stratification in Hepatocellular Carcinoma.Biology · 2026Article
- scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.Briefings in bioinformatics · 2026Article
- Network clustering algorithms and preprocessing pipelines for robust cell type identification in single-cell RNA sequencing data.Scientific reports · 2026Article
- From cellular heterogeneity to precision medicine: single-cell multi-omics in CNS disease research.Frontiers in cellular neuroscience · 2026Review
- ArchetypeShift: An R Package Integrating KEGG-Informed Pathway Analysis and IPA-Derived Functional Predictions for Validation of Single-Cell Archetypes.Bioinformatics and biology insights · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
Single-cell multi-omics clustering confronts noise and heterogeneity barriers. Current multi-view anchor graph approaches, though successful in noise reduction, inadequately model higher order feature interactions. To address this issue, we propose scAGCI, a cell clustering method based on anchor graphs that integrates both scRNA-seq and scATAC-seq data. Our method captures specific and shared anchor graphs representing the properties of omics data in the process of dynamic anchor unification, and mines high-order shared information to complete the omics representation. Subsequently, clustering results are obtained by integrating the specific and shared omics representation. Benchmarking against 13 state-of-the-art methods confirms scAGCI's superior clustering performance and computational efficiency in cell-type identification and subtype resolution. The method preserves biologically meaningful omics patterns, as evidenced by marker gene enrichment and functional analyses, establishing it as a robust tool for elucidating cellular heterogeneity in single-cell multi-omics data.
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Registered trials
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