Spatial Multi-Omics Integration Via Information-Aware Multi-View Contrastive Learning

Résumé fourni par la source

The rapid advancement of spatial multi-omics technology enables the simultaneous acquisition of diverse expression data from the same tissue or slice. Different omics offer unique and critical information about the biological system. However, most existing methods are unable to fully utilize this information for downstream tasks such as spatial domain identification. To integrate this information effectively for downstream analysis, we introduce a novel Spatial Multi-omics data integration method based on Information-Aware Multi-view Contrastive Learning (SM-IAMCL). It optimizes the spatial and feature neighborhood graphs for each omics by the specific graph learner and fused graph learner, and learns the fused graph of spatial and feature neighborhood graphs at the same time. Then, to make fused graph of each omics integrate both shared and unique information of spatial and feature neighborhood graphs, we incorporate graph-level contrastive learning between different views in each omics. Finally, the learned fused representation of each omics is then integrated via a weighted fusion strategy to generate an integrated low-dimensional latent representation of spatial multiomics. This integrated representation is used for a variety of downstream analysis tasks. The experimental results show that SM-IAMCL outperforms other seven existing methods in the downstream tasks such as spatial domain identification.

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