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 E. McNutt, Jordan E. Krull, Scott J. Rodig, Dan H. Barouch, Garry P. Nolan, Dong Xu, Sizun Jiang, Zihai Li, Bingqiang Liu, Qin Ma
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. SpaGFT is a spatial omics representation method. It identifies spatially variable genes, enhances gene imputation, detects immunological regions, and characterises variations in secondary lymphoid organs. This method improves predictive power in wide downstream machine-learning tasks.