SpaVGN: A hybrid deep learning framework for high-resolution spatial transcriptomics data reconstruction and spatial domain identification

Abstract

Spatial transcriptomics has revolutionized the analysis of gene expression while preserving tissue spatial information, which provides novel insights into the cellular composition and function of complex biological tissues. However, current technologies are constrained by limited resolution and data sparsity, compromising the accuracy of downstream analyses. To address these challenges, we developed SpaVGN, a deep learning framework integrating convolutional neural networks, vision transformer, and graph neural networks for high-fidelity gene expression imputation and spatial domain identification. By combining local feature extraction, global attention mechanisms, and spatial graph-based modeling, SpaVGN effectively reconstructs missing transcriptomic data while preserving spatial tissue architecture. Evaluated on melanoma and sagittal posterior mouse brain datasets, SpaVGN outperformed existing methods in gene expression prediction, achieving Pearson correlation coefficients of 0.609 (melanoma) and 0.682 (mouse brain). It clearly delineated tumor regions and lymphoid niches in melanoma tissue, achieving fine-grained resolution of hippocampal subfields, including Cornu Ammonis and Dentate Gyrus, with a Silhouette Score of 0.43 and a Davies-Bouldin Index of 0.86. Validation through UMAP dimensionality reduction and PAGA network analysis demonstrated that SpaVGN significantly mitigates the negative impact of data sparsity in spatial transcriptomics, improving data completeness and spatial continuity. This study presents an innovative solution that enhances the resolution of spatial transcriptomics data, offering cross-tissue applicability and providing a valuable tool for research in biological development, disease, and tumor heterogeneity.

Fig 1. Framework for SpaVGN.

Fig 2. Algorithm performance comparison.

(a). Pearson correlation coefficients (PCC) between predicted and true expression patterns for three representative genes (RPS25, TPT1, and MS4A1) across methods. Genes were selected from the top 3 genes with highest median PCC shared by all methods. (b). Violin plots showing gene-wise PCC distributions for all predicted genes in melanoma (left) and mouse brain (right) datasets.

Fig 3. Performance evaluation of SpaVGN on melanoma ST dataset.

(a). Microscopic image of melanoma tissue sections showing three main tissue types: melanoma (black arrows), stroma (red arrows), and lymphoid tissue (green arrows). Scale bar: 100 μm. (b-c). Spatial expression patterns of representative genes (CD37 and DLL3) before and after imputation. Color scale indicates normalized expression levels. (d). Performance comparison of five computational methods in tissue region segmentation. Color-coded regions correspond to different tissue domains. (e). Uniform Manifold Approximation and Projection (UMAP) visualization of cell distributions generated by five different algorithms. (f). Spatial trajectory analysis results from five methods, showing inferred developmental paths between tissue regions. Arrows indicate trajectory directions.

Fig 4. Performance evaluation of SpaVGN on Mouse Brain dataset.

(a). Microscopic image of the tissue section and its annotated regions, showing the distribution of different anatomical areas. (b). Performance comparison of different methods in tissue region segmentation. The left panel shows the Separation Score, and the right panel shows the Davies-Bouldin Score. (c). Visualization of spatial domain identification results by different methods. The clustering results of each method are represented by different colors. (d). UMAP analysis results of different methods. (e). Partition-based Graph Abstraction analysis results of different methods.