Abstract: In recent years,breakthrough advancements in spatial transcriptomics(ST) technologies have opened new avenues for understanding tissue architecture and cellular heterogeneity.However,mainstream ST technologies like 10x Visium have not yet achieved single-cell resolution,posing challenges for accurate cell type deconvolution.Although various solutions have been proposed,most methods rely solely on gene expression profiles and spatial location information,often neglecting gene expression si-milarity,which limits their performance.To address this,this paper proposes a novel computational framework,STMVF,which integrates cross-attention-based multi-view feature fusion and graph contrastive learning for cell deconvolution tasks in spatial transcriptomics.STMVF first employs a multi-view feature fusion module,which introduces a gene expression-based feature adjacency matrix to provide the model with cell state information complementary to spatial location data.Subsequently,graph contrastive learning is applied to enable the model to capture complex spatial patterns of gene expression and learn robust representations.Based on the learned representations,STMVF integrates single-cell RNA sequencing data and spatial transcriptomics data by optimizing a trainable cell-spot mapping matrix,thereby reconstructing intra-tissue cell distributions with high precision.Furthermore,to further enhance model performance,STMVF implicitly introduces spatial constraints by designing a contrastive loss function to ensure the spatial continuity of reconstructed tissue structures.Benchmarked against seven state-of-the-art methods on four simulated and real datasets,STMVF consistently demonstrates superior cell deconvolution performance.

Key words: Cellular deconvolution, Spatial transcriptomics, Multi-view feature fusion, Graph convolutional network