ARCADE: Cross-Section Multimodal Learning for Mosaic Spatial Deconvolution

Spatial multi-omics profiles tissues across multiple molecular modalities, but real-world experiments often exhibit heterogeneous modality coverage across sections, giving rise to mosaic spatial multi-omics, where different sections contain different subsets of molecular modalities. Deconvolving such data creates an incomplete multi-view latent mixture inference problem, where cell-type compositions must be recovered under heterogeneous modality coverage. Existing spatial deconvolution methods typically require RNA measurements, whereas multimodal integration methods mainly focus on representation alignment without explicitly modeling cell-type compositions. We propose ARCADE, a unified framework for mosaic spatial deconvolution that integrates cross-section multimodal information, spatial structure, and single-cell RNA references through heterogeneous graph learning and reference-guided mixture inference. A directional–magnitude factorization further separates directional information for cross-modal alignment from abundance-related information for composition inference. Experiments on real spatial multi-omics datasets demonstrate consistent improvements over widely used spatial deconvolution methods, with ARI gains over the best competing method averaging 18% across RNA-observed datasets and reaching 98% in the section where RNA is entirely unavailable. Reproducible code is available at https://anonymous.4open.science/r/submission-D956.