[Objective]Increasing the spatial resolution of transcriptome gene expression can improve the accuracy of cell lineage and type changes in genetic development and disease studies,providing more detailed molecular phenotypic information.[Method]Using image segmentation to simulate the spatial distribution of cells in a spatial transcriptomics grid,the high-resolution spatial gene expression using linear interpolation was reconstructed.Graph clustering methods were used to reveal the spatial preferences of cell distribution within tissues.[Result]Application of SpaGMM on the 10X Visium dataset of the mouse posterior brain helped to accurately identify various spatial domains of the mouse brain.Compared with several commonly used methods for spatial transcriptome clustering,the results showed that the clustering results by SpaGMM were more consistent with the annotated histological regions,which were supported by the spatial expression of many marker genes.SpaGMM also recognized layers corresponding to Purkinje cells and Bergmann glial cells from mouse cerebellum,revealing complementary gene expression patterns in different cell layers.[Conclusion]SpaGMM may reveal the fine structure of tissue domains by improving the spatial resolution of spots.
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