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Updated: Jan 23, 2026

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Computational methods for spatial multi-omics integration.

Aoyun Geng1, Chunyan Cui1, Zhenjie Luo1

  • 1School of Computer Science and Technology, Hainan University, Haikou 570228, China.

Biotechnology Advances
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning methods integrate spatial multi-omics data, combining transcriptomic, proteomic, and epigenomic information. This review categorizes and compares these methods, aiding researchers in analyzing complex tissue microenvironments.

Keywords:
Algorithmic frameworksData integrationSpatial multi-omicsSpatial multi-omics fusion strategies

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Genomics

Background:

  • Spatial multi-omics technologies enable simultaneous acquisition of multi-modal data from single tissue sections.
  • Significant challenges exist in integrating these diverse datasets due to differing properties, dimensionality, sparsity, and noise.

Purpose of the Study:

  • To systematically review and categorize existing deep learning-based spatial multi-omics integration methods.
  • To compare these methods based on datasets used, downstream tasks supported, and current challenges.
  • To guide researchers in selecting appropriate methods for analyzing spatial multi-omics data.

Main Methods:

  • Systematic literature review of deep learning algorithms for spatial multi-omics integration.
  • Categorization of methods based on integration strategies and analytical capabilities.
  • Comparative analysis of method performance, strengths, and limitations.

Main Results:

  • A comprehensive overview of current deep learning-based spatial multi-omics integration techniques.
  • Identification of key datasets and downstream applications for these methods.
  • Summary of major challenges and limitations in the field.

Conclusions:

  • Deep learning offers promising approaches for spatial multi-omics data integration and cross-modal fusion.
  • Method selection requires careful consideration of data characteristics and research objectives.
  • Further advancements are needed to address current challenges and enhance the application of spatial multi-omics.