Spatial multi-omics imputation and embedding with SpaMIE

SpaMIE is a deep graph neural network framework designed to tackle the challenge of multi-section integration in spatial multi-omics (SMO) datasets with systematic missing modalities. Current SMO platforms face limitations such as high cost and limited throughput, leading to many large-scale spatial atlases relying on cost-effective mono-omics measurements while only a few sections are profiled with full multi-omics technologies. This results in heterogeneous modality coverage across tissue sections. SpaMIE offers a two-stage solution. In the first stage, it performs spatially informed cross-modal imputation, enabling accurate inference of missing modalities from mono-omics data. In the second stage, it integrates measured and imputed spatial multi-omics profiles across multiple tissue sections to learn a unified embedding. Benchmarking on simulated and experimental datasets shows that SpaMIE achieves accurate cross-modal imputation, robust multi-section integration, and improved spatial domain identification, providing a flexible and scalable solution for constructing and analyzing SMO atlases.