Integrative deep learning of spatial multi-omics with SWITCH

Advancements in spatial omics permit spatially resolved measurements across several biological modalities. The high cost of acquiring co-profiled multimodal data limits the analysis. This underscores the necessity for computational methods to integrate unpaired spatial multi-omics data and perform cross-modal predictions on single-modality data. The integration of spatial omics is challenging due to typically low signal-to-noise ratios. Here we introduce SWITCH (Spatially Weighted Multi-omics Integration and Cross-modal Translation with Cycle-mapping Harmonization), a deep generative model for spatial multi-omics integration. SWITCH presents a cycle-mapping mechanism that produces dependable cross-modal translations without requiring additional paired data. These cross-modal translations function as pseudo-pairs to provide supplementary signals. Systematic evaluations demonstrate that SWITCH outperforms existing methods in terms of integration accuracy and achieves more precise spatial domain delineation, resolving brain cortical structures at higher resolution. The reliability of cross-modal translations was validated, facilitating various downstream analyses such as differential analysis, trajectory inference and gene regulatory network inference.