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Related Experiment Videos

Multimodal spatial omics: From data acquisition to computational integration.

Esra Busra Isik1, Yusuf Hakan Usta1, Maryam Riazi1

  • 1Division of Informatics, Imaging & Data Sciences, School of Health Sciences, University of Manchester, Manchester, UK.

Patterns (New York, N.Y.)
|July 15, 2026
PubMed
Summary

Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Spatial omics technologies generate complex multimodal data. This review overviews computational methods for integrating spatial transcriptomics, proteomics, and imaging data, highlighting deep learning approaches.

Area of Science:

  • Biotechnology
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial omics technologies enable high-dimensional molecular data generation within tissue context.
  • Multimodal datasets combine transcriptomics, proteomics, epigenomics, and imaging for comprehensive tissue analysis.
  • Integrating these diverse datasets presents significant computational challenges.

Purpose of the Study:

  • To provide a comprehensive overview of computational methods for multimodal spatial omics and imaging data integration.
  • To highlight the algorithmic principles behind these integration methods.
  • To discuss the application of various computational approaches, including deep learning.

Main Methods:

  • Review of existing literature on computational methods for multimodal spatial omics data integration.
Keywords:
computational methodsdeep learningmatrix factorizationmultimodal integrationoptimal transportspatial omics

Related Experiment Videos

  • Categorization of methods based on underlying algorithmic principles (probabilistic, deep learning).
  • Analysis of challenges and advancements in data integration techniques.

    • Main Results:

    • Identification of a wide range of computational methods for integrating spatial omics and imaging data.
    • Discussion of the strengths and weaknesses of different approaches.
    • Emphasis on the growing role of deep learning in addressing integration complexity.

    Conclusions:

    • Effective integration of multimodal spatial omics data is crucial for understanding tissue complexity.
    • Advancements in computational methods, particularly deep learning, are key to overcoming integration challenges.
    • This review serves as a resource for researchers navigating the computational landscape of spatial omics data integration.