Authors
Yaowei Cao, Xinyu Du, Zhenhua Yu
Published in
IEEE transactions on computational biology and bioinformatics. Volume PP. Oct 14, 2026. Epub Oct 14, 2026.
Abstract
Accurate clustering of single-cell RNA sequencing (scRNA-seq) data is essential for cell type annotation and cellular heterogeneity inference, but existing methods are still limited by local gene co-expression redundancy and pairwise graph modeling, which hinder the capture of cross-module biological semantics and higher-order topological cellular relationships. To address these challenges, we propose a novel method called scHyperMod. It employs a module-aware structured masking strategy that suppresses shortcut learning driven by local co-expression redundancy and encourages the model to learn more discriminative representations from cross-module contexts. Furthermore, hypergraph modeling based on shared nearest neighbors is introduced to enable information propagation across locally connected cells, thus providing a more expressive description of higher-order topology. Extensive evaluations on 12 public benchmark datasets show that scHyperMod achieves leading overall clustering performance across varying data scales, sparsity levels, and cellular heterogeneity. Application to a glioblastoma dataset further demonstrates its ability to recover major cell populations and distinguish malignant cell states, including AC-like, OPC-like, and Cycling, while preserving cell identity-related biological differences in marker gene expression and functional enrichment.
PMID:
42873629
Bibliographic data and abstract were imported from PubMed on 15 Oct 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 8
- Comments 0