Unveiling Tissue Structure and Tumor Microenvironment from Spatially Resolved Transcriptomics by Hypergraph Learning

Abstract Spatially resolved transcriptomics (SRT) technologies acquire gene expressions and spatial information simultaneously, reshaping the perspectives of life sciences. Identifying spatial patterns is essential for exploring organ development and tumor microenvironment. Nevertheless, emerging SRT technologies have also introduced diverse spatial resolutions, posing challenges in characterizing spatial domains with finer resolutions. Here we propose a hypergraph-based method, termed HyperSTAR to precisely recognize spatial domains across varying spatial resolutions by utilizing higher-order relationships among spatially adjacent tissue programs. Specifically, a gene expression-guided hyperedge decomposition module is incorporated to refine the structure of the hypergraph to precisely delineate the boundaries of spatial domains. A hypergraph attention convolutional neural network is designed to adaptively learn the significance of each hyperedge. With the power of capturing intricate higher-order relationships within spatially neighboring multi-spots/cells, HyperSTAR demonstrates superior performance across different technologies with various resolutions compared to existing advanced graph neural network models in multiple tasks including uncovering tissue sub-structure, inferring spatiotemporal patterns, and denoising spatially resolved gene expressions. It successfully reveals spatial heterogeneity in breast cancer section and its findings are further validated through functional and survival analyses of independent clinical data. Notably, HyperSTAR performs well with diverse spatial omics data types and seamlessly extends to large-scale datasets.