SpatialFuser: A Unified Deep Learning Framework for Spatial Multi-Omics Data Integrative Analysis

Recent advances in spatial multi-omics technologies provide unprecedented opportunities to integrate and interpret molecular features within the tissue microenvironment. Here we present SpatialFuser, the first unified deep learning framework for detailed molecular profiling within individual tissue sections and multiomics integrative analysis of cross-modality spatial data. SpatialFuser offers comprehensive tools and models for accurate spatial interpretation, robust cross-modality integration, and effective cross-slice alignment of spatial epigenomics, transcriptomics, proteomics, and metabolomics. Benchmarking results demonstrate SpatialFusers superior performance and reliability in spatial domain detection and consecutive slice alignment task compared to existing state-of-the-art methods. Applications to diverse datasets spanning various resolution and omics types further highlight SpatialFusers ability to capture precise molecular patterns, reveal developmental dynamics, and uncover fine-grained biological variation from complementary perspectives, offering a holistic view of cellular and tissue properties. The SpatialFuser framework is open-source and available at https://github.com/liwz-lab/SpatialFuser.