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

Area of Science:

• Computational Biology
• Genomics
• Single-cell Analysis

Background:

• Single-cell multi-omics technologies offer deep insights into cellular heterogeneity and regulatory mechanisms.
• Integrating these complex datasets is challenging due to sparsity, high dimensionality, and cross-modal differences, leading to information loss and poor interpretability in current methods.

Purpose of the Study:

• To develop a robust and interpretable framework for the integrative analysis of single-cell multi-omics data.
• To improve the quality of integrated representations and cell type resolution compared to existing state-of-the-art methods.

Main Methods:

• Proposed scFNSA (factorized node-set attentive framework) that decouples modality-specific feature learning from graph-based relational modeling.
• Employed multi-view variational inference for aligned low-dimensional representations of individual modalities.
• Utilized attention-guided graph learning with structured node masking to model cellular dependencies, mitigating noise and oversmoothing.

Main Results:

• scFNSA demonstrated superior integrative representation quality and enhanced cell type resolution across multiple single-cell multi-omics datasets.
• The framework achieved robust cross-modal structural alignment.
• Effectively mitigated noise propagation and oversmoothing issues common in deep graph neural networks.

Conclusions:

• scFNSA provides a robust and interpretable framework for single-cell multi-omics integration by decoupling feature extraction from relational inference.
• Facilitates more accurate characterization of cellular states and underlying regulatory landscapes.
• Represents a significant advancement in analyzing complex single-cell multi-omics data.