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Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention

Tongxuan Lv1,2, Yong Zhang1, Junlin Liu1

  • 1BGI Research, No. 9, Yunhua Road, Yantian District, Shenzhen 518083, China.

Briefings in Bioinformatics
|September 18, 2024
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Summary

We developed SSGATE, a novel method for integrating single-cell and spatial multi-omics data. This approach enhances understanding of biological systems by analyzing gene expression and spatial information together.

Keywords:
graph attention auto-encodermulti-omics integrationmulti-omics joint analysissingle-cell omicsspatial omics

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Area of Science:

  • Computational biology
  • Genomics
  • Proteomics

Background:

  • Single-cell multi-omics integration provides high-resolution biological insights.
  • Spatial multi-omics integration reveals cellular heterogeneity and spatial relationships.
  • Current methods often lack spatial awareness or require improvement for integrated analysis.

Purpose of the Study:

  • To develop a robust multi-omics integration method applicable to both single-cell and spatially resolved data.
  • To address limitations in existing methods by incorporating spatial information.
  • To facilitate a more comprehensive understanding of complex biological systems.

Main Methods:

  • Proposed a dual-path graph attention auto-encoder (SSGATE) for multi-omics integration.
  • SSGATE constructs neighborhood graphs using single-cell expression profiles or spatial coordinates.
  • Employs self-supervised learning via graph attention auto-encoders for data integration.

Main Results:

  • SSGATE successfully integrated transcriptomics and proteomics data from various tissues.
  • The method was applied to both single-cell and spatially resolved datasets.
  • Demonstrated superior performance and robustness compared to existing methods.

Conclusions:

  • SSGATE offers a versatile solution for multi-omics data integration, accommodating both single-cell and spatial contexts.
  • The method enhances downstream analyses by effectively leveraging combined molecular and spatial information.
  • This advancement supports more accurate and comprehensive biological system studies.