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soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.

Na Yu^1,2, Wenrui Li³, Xue Sun¹

¹Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, No. 17923, Jingshi Road, Lixia District, Jinan, Shandong 250061, China.

Briefings in Bioinformatics

|September 29, 2025

Summary

This summary is machine-generated.

soFusion integrates spatial multi-omics data by learning representations, enabling automated tissue compartmentalization. This method improves spatial domain identification by capturing cross-modality relationships and preserving modality-specific features.

Keywords:

feature fusion representation learning soFusion spatial domain identification spatial multi-omics

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Spatial multi-omics technologies offer high-resolution tissue architecture analysis.
Integrating diverse omics data presents challenges due to modality disparities.

Purpose of the Study:

To develop a method for robust spatial multi-omics data integration.
To enable automated identification of tissue compartmentalization using spatial multi-omics data.

Main Methods:

soFusion utilizes a graph convolutional network (GCN) for representation learning.
Employs novel intra- and inter-omics feature learning strategies.
Incorporates modality-specific decoders to preserve unique omics information.

Main Results:

soFusion effectively delineates anatomical structures and identifies spatial domains.
Demonstrates improved continuity and reduced noise in spatial domain identification.
Outperforms existing methods across gene expression, protein expression, and epigenetic datasets.

Conclusions:

soFusion provides an effective solution for spatial multi-omics integration.
Enhances the robustness and accuracy of spatial domain identification.
Facilitates deeper understanding of tissue architecture through integrated omics analysis.