"SMGC: Spatial Multi-omics Analysis with Granular-ball Contrastive Learning Framework"

"These datasets include: spatial multi-omics simulation dataset, human lymph node datasets, spatial epigenome-transcriptome mouse brain dataset and mouse embryo datasets. These datasets will be used in the following. Advances in spatial transcriptomics have promoted spatial multi-omics technologies, and the integration of multi-omics data is a key strategy to analyze complex biological systems. Current methods incorporate spatial context yet often fail to fully leverage local spatial structures during feature fusion, limiting their ability to capture complex spatial patterns. Moreover, in discrete tissue regions, unsupervised integration faces interference from false negative samples, compromising integration robustness and the granularity of spatial domain identification. Here, we introduce SMGC, a spatial domain analysis method that integrates spatial multiple omics data using multi-view autoencoders and granular-ball contrastive learning. SMGC proposes a strategy to generate granular balls from high-level features, which defines each ball as a cluster to naturally partition the sample set into multiple local clusters, thereby enabling subsequent feature fusion to fully exploit the underlying local topological structures. To mitigate the interference from false negative samples, our method achieves feature alignment and contrastive learning at the local structural level by constructing intra-view and cross-view associative relationships based on the granular balls of each view."