SMMGCL: a novel multi-level graph contrastive learning framework for integrating spatial multi-omics data

Recent advances in spatial omics technologies have allowed various omics data to be obtained from a single tissue section. To fully explore the relationships among these different types of omics data, it is urgent to develop more effective methods for spatial multi-omics data integration. In this work, we propose a novel Multi-level Graph Contrastive Learning framework, named SMMGCL, to simultaneously mine complementary information at both spot and graph levels for integrating Spatial Multi-omics data. Specifically, to adaptively fuse multi-omics modalities, we first design a multi-modality autoencoder that integrates spatial locations with spot omic expressions to extract modality-specific embeddings. These embeddings are then fused into a consensus representation using an attention mechanism to capture spot-level cross-omics representations. Next, to explore the complex inter-omic structural information, we connect corresponding spots across different omics adjacency graphs into a heterogeneous graph. We then employ a graph convolutional network (GCN) to extract spatial correlations across the omics, learning a graph-level cross-omics global representation. Finally, SMMGCL aligns feature similarity matrixes between spot-level and graph-level representations with their pseudo-label similarity matrix, ensuring multi-level clustering consistency and leading to more accurate spatial multi-omics integration. Experimental results on simulated and real datasets from across tissues show that SMMGCL consistently outperforms other state-of-the-art methods in spatial multi-omics integration performance. The code for SMMGCL is available for download from the GitHub repository at https://github.com/cs-wangbo/SMMGCL.