COSMIC: Joint Representation Learning and Conditional Multi-Slice Alignment for Spatial Multi-Omics

Multi-slice spatial multi-omics integration seeks coherent tissue representations that preserve complementary molecular signals and biological heterogeneity. However, cross-modal fusion and cross-slice alignment are tightly coupled: reliable correspondence requires robust multimodal semantics, while learning such semantics is itself confounded by slice-specific shifts. Existing methods largely optimize one side first, propagating fusion noise or overcorrecting noncorresponding tissue regions. To address this limitation, we propose COSMIC, an end-to-end framework that couples target-conditioned consensus learning with cluster-guided conditional alignment in a shared latent space. Multimodal semantics guide cross-slice correspondence and are jointly refined by alignment, while target-conditioned cross-modal interaction forms a reliability-weighted consensus that preserves modality-private information. Based on the resulting representation, cluster-guided conditional alignment uses soft semantic assignments and confidence filtering to guide the alignment of well-supported slice-specific semantic centers with their global semantic counterparts. This conditional strategy avoids indiscriminate matching of entire slice distributions and reduces overcorrection of noncorresponding tissue structures. Through comprehensive benchmarks across multiple species, COSMIC achieves highly competitive performance while maintaining a favorable balance between biological structure preservation and slice-effect removal, thereby facilitating comparative analyses across heterogeneous spatial multi-omics tissue sections.