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Updated: Jul 2, 2025
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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
Published on: October 28, 2018
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Graph Fourier transform for spatial omics representation and analyses of complex organs
Yuzhou Chang1,2, Jixin Liu3, Yi Jiang1
1Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA.
Research Square
|February 27, 2024
Summary
We developed SpaGFT, a novel method for analyzing spatial omics data. This tool enhances understanding of tissue organization and biological functions by accurately identifying molecular signatures and improving machine learning model performance.
Area of Science:
- Spatial omics
- Graph signal processing
- Computational biology
Background:
- Spatial omics technologies offer high-resolution insights into tissue and cellular organization.
- Existing methods lack robust, interpretable, and unbiased representations for spatial omics data, hindering biological discovery.
- A theoretical framework is needed to fully leverage spatial omics data for understanding biological functions.
Conclusions:
- SpaGFT provides a powerful and interpretable method for spatial omics data analysis.
- The approach enhances machine learning applications in spatial biology, improving accuracy and biological insights.
- SpaGFT advances the theoretical understanding of tissue organization and function through explainable AI.
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