The invention provides a spatial
transcriptome cell composition
inference method based on graph contrast learning. According to the method, a cross-
modal low-dimensional feature spatial
data simulation module is constructed, single
cell and spatial
transcriptome data are respectively mapped to a potential embedding space, feature space difference is reduced through aligned distribution, and
structural consistency of cross-
modal data is enhanced; designing a double heterogeneous graph construction and potential relation learning module, constructing a feature heterogeneous graph of two cells and spatial points based on
gene expression similarity, and capturing a high-level potential relation of spatial data in double graphs by adopting a meta-path potential relation reasoning strategy; and introducing a graph representation optimization
mechanism based on structure contrast learning, maximizing the consistency of the cross-
modal nodes in the embedding space, and obtaining a final
cell composition
inference result. The method realizes
inference of spatial
transcriptome data cell composition, can be used for
cell space positioning and tissue microenvironment analysis, and provides a reliable
computational analysis basis for related biomedical research.