Graph Fourier transform for spatial omics representation and analyses of complex organs

Yuzhou Chang, Jixin Liu, Yi Jiang, Anjun Ma, Yao Yu Yeo, Qi Guo, Megan McNutt, Jordan E. Krull, Scott J. Rodig, Dan H. Barouch, Garry P. Nolan, Dong Xu, Sizun Jiang, Zihai Li, Bingqiang Liu () and Qin Ma ()
Additional contact information
Yuzhou Chang: Ohio State University
Jixin Liu: Shandong University
Yi Jiang: Ohio State University
Anjun Ma: Ohio State University
Yao Yu Yeo: Beth Israel Deaconess Medical Center
Qi Guo: Ohio State University
Megan McNutt: Ohio State University
Jordan E. Krull: Ohio State University
Scott J. Rodig: Dana Farber Cancer Institute
Dan H. Barouch: Beth Israel Deaconess Medical Center
Garry P. Nolan: Stanford University School of Medicine
Dong Xu: University of Missouri
Sizun Jiang: Beth Israel Deaconess Medical Center
Zihai Li: The Ohio State University
Bingqiang Liu: Shandong University
Qin Ma: Ohio State University

Nature Communications, 2024, vol. 15, issue 1, 1-22

Abstract: Abstract Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.

Date: 2024
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DOI: 10.1038/s41467-024-51590-5

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