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sxSNF: Similarity Network Fusion-Guided Deep Graph Learning for Single-Cell Multimodal Integration.

Hongyu Duan, Yang Wang, Qianwen Chen

IEEE Transactions on Computational Biology and Bioinformatics

|June 1, 2026

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We developed sxSNF, a novel framework for single-cell multimodal data integration. It enhances cell type identification by fusing similarity networks before graph learning, improving accuracy and biological interpretation.

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Single-cell multimodal data analysis is crucial for understanding cellular heterogeneity.
Integrating diverse data modalities (e.g., RNA-seq, ATAC-seq) presents challenges due to sparsity and noise.
Existing methods often struggle to preserve biological neighborhood structures.

Purpose of the Study:

To present sxSNF, a single-cell multimodal integration framework.
To improve cell type identification and biological interpretation from multimodal single-cell data.
To address sparsity and noise while preserving biological structure.

Main Methods:

sxSNF constructs modality-specific cell-cell similarity graphs.
It employs iterative Similarity Network Fusion (SNF) followed by self-supervised graph representation learning.

Related Experiment Videos

A masked-edge reconstruction objective with negative sampling refines the fused graph.

Main Results:

sxSNF achieved high Adjusted Rand Index (ARI) scores on PBMC-10k (0.694) and SHARE-seq (0.589) benchmarks.
It outperformed baseline methods in Normalized Mutual Information (NMI) and Adjusted Mutual Information (AMI).
On the Chen-2019 SNARE-seq dataset, sxSNF accurately identified cell types and refined oligodendrocyte subpopulations.

Conclusions:

Combining SNF-based structural denoising with graph learning enhances multimodal single-cell clustering.
sxSNF improves downstream biological interpretation of single-cell multimodal data.
The framework offers a robust approach for integrating and analyzing complex single-cell datasets.