SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial...

Traditional spatial transcriptomics methods typically rely on the direct relationship between spatial location and gene expression data, but they often fail to capture the intricate structures embedded in spatial data. To address this limitation, we introduce SpatioFreq, an innovative approach that includes two fundamental tasks: spatial domain identification and cell type deconvolution. In the spatial domain identification task, the goal is to identify biologically meaningful functional regions through spatial clustering, thereby revealing the spatial organization of cells within tissues. In the first task, SpatioFreq utilizes the Laplacian matrix to extract frequency domain features, enabling detection of subtle structures and dynamic patterns within spatial data, thereby enhancing the accuracy of spatial clustering. Additionally, by incorporating graph self-supervised contrastive learning, SpatioFreq optimizes long-range dependencies within the spatial data, further improving spatial structure modeling. Contrastive learning is used in cell type deconvolution to refine the relationship between spatial position and single-cell embeddings, enhancing the accuracy of cell type distributions. The dual-task design of SpatioFreq enables information sharing between tasks and has been validated across various datasets. Comparative analysis with current mainstream methods demonstrates that SpatioFreq significantly improves both the accuracy and efficiency of spatial transcriptomics analysis. Notably, in the DCIS breast cancer dataset, SpatioFreq's spatial heterogeneity analysis uncovers complex interactions between tumor cells and their microenvironment. These findings provide new insights into potential therapeutic targets and offer valuable guidance for precision oncology.