Scalable, Generalizable, and Uncertainty-Aware Integration of Spatial Multi-Omics Across Diverse Modalities and Platforms with SCIGMA (opens in new tab)

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic, and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multi-modal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multi- omics integration. SCIGMA introduces...