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SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi-Omics Using Multimodal Mixture of Disentangled

Xubin Zheng1,2, Xinlei Huang3,4, Xiang Zhou5

  • 1Dongguan Key Laboratory for AI and Dynamical Systems, School of Computing and Information Technology, Great Bay University, Dongguan, China.

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PubMed
Summary
This summary is machine-generated.

SpaMode is a new framework for integrating spatial multi-omic data, including transcriptomics, proteomics, epigenomics, and metabolomics. It offers a trustworthy solution for analyzing complex tissue data in situ.

Keywords:
Mixture of ExpertsMosaic IntegrationMultimodal DisentanglingSpatial multi‐omics

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Area of Science:

  • Multi-omic spatial biology
  • Computational biology
  • Bioinformatics

Background:

  • Spatial multi-omic technologies provide insights into biological processes and disease within native tissue context.
  • Increasing complexity in tissue spatial architecture necessitates broadly applicable integration models for vertical, horizontal, and mosaic analyses.
  • Existing methods often lack the versatility to handle diverse integration modes and omic types.

Purpose of the Study:

  • To introduce SpaMode, a versatile framework for spatial multi-omic data integration.
  • To accommodate vertical, horizontal, and mosaic integration across transcriptomics, proteomics, epigenomics, and metabolomics.
  • To provide an interpretable alternative to black-box models for spatial multi-omic analysis.

Main Methods:

  • SpaMode disentangles omics modalities into modality-invariant and modality-variant distributions.
  • Hierarchical aggregation of distributions resolves spatial heterogeneity.
  • Multi-slice joint regularization and missing modality translation unify horizontal and mosaic integration.

Main Results:

  • SpaMode outperforms existing targeted approaches in vertical, horizontal, and mosaic integration settings.
  • Demonstrates novel insights into invariant and variant multi-level biomolecular features contributing to tissue spatial context.
  • Provides a general and trustworthy solution for spatial multi-omic data analysis.

Conclusions:

  • SpaMode offers a robust and interpretable framework for spatial multi-omic data integration.
  • Enables systematic decoding of complex cellular states and disease mechanisms in situ.
  • Paves the way for advanced in situ studies of tissue biology and pathology.