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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.
Nature Communications
|August 29, 2024
Summary
Spatial Graph Fourier Transform (SpaGFT) offers a new graph signal processing method for spatial omics data. It enhances gene identification and imputation, outperforming existing tools for tissue biology exploration.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Spatial omics technologies reveal cellular and subcellular details in complex organs.
- Interpretable representations are crucial for analyzing complex spatial omics data.
Purpose of the Study:
- To introduce Spatial Graph Fourier Transform (SpaGFT) for analyzing diverse spatial omics platforms.
- To enhance spatially variable gene identification and gene expression imputation.
- To provide an explainable graph representation for tissue biology exploration.
Main Methods:
- Applied graph signal processing to spatial omics data.
- Developed Spatial Graph Fourier Transform (SpaGFT) for interpretable data representation.
- Integrated SpaGFT with machine learning frameworks.
Main Results:
- SpaGFT outperforms existing tools in human and mouse spatial transcriptomics data analysis.
- Identified immunological regions for B cell maturation and characterized secondary follicles.
- Improved spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%.
- Detected rare subcellular organelles in spatial proteomics data.
Conclusions:
- SpaGFT provides an effective and explainable method for spatial omics data analysis.
- The approach enhances the understanding of tissue biology and function at multiple resolutions.
- SpaGFT is a versatile tool applicable to various spatial omics profiling platforms.
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