scFNSA: A Factorized Node-Set Attentive Framework for Single-Cell Multi-Omics Integration

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not quantified in abstract

• Primary Outcome: Improved integrative representation quality and cell type resolution

• Nnt Or Sensitivity: Consistent improvement across multiple datasets claimed but not quantified

• Confidence Interval: Not reported

Clinical Application

High - computational method requiring standard bioinformatics infrastructure Relevant to Australian research institutions conducting single-cell genomics research, particularly in cancer biology and developmental biology programs Researchers conducting single-cell multi-omics studies across various biological contexts

Abstract

Single-cell multi-omics technologies enable simultaneous interrogation of transcriptional and epigenomic states at single-cell resolution, providing powerful means to dissect cellular heterogeneity and regulatory mechanisms. However, effective integration of single-cell multi-omics data remains challenging due to extreme sparsity, high dimensionality, and cross-modal heterogeneity, which often lead to distorted similarity structures, information loss, and limited biological interpretability in existing methods. Here, we propose scFNSA, a factorized node-set attentive framework for integrative analysis of single-cell multi-omics data that explicitly decouples modality-specific feature learning from graph-based relational modeling. scFNSA first learns aligned low-dimensional representations for individual modalities through multi-view variational inference and subsequently models complex cellular dependencies via attention-guided graph learning with structured node masking. This design enables robust cross-modal structural alignment while effectively mitigating noise propagation and oversmoothing commonly encountered in deep graph neural networks. Across multiple single-cell multi-omics datasets, scFNSA consistently improves integrative representation quality and cell type resolution compared with state-of-the-art approaches. By decoupling feature extraction from relational inference, scFNSA provides a robust and interpretable framework for single-cell multi-omics integration, facilitating more accurate characterization of cellular states and underlying regulatory landscapes.

References

1. 1.Jiang, S., Shang, J., Qiu, D., Wang, H., & Liu, J. X. (2026). scFNSA: A Factorized Node-Set Attentive Framework for Single-Cell Multi-Omics Integration. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.6c00448