FlowMOS: Continuous Latent Transport for Spatial Multi-Omics Integration

Spatial multi-omics integration aims to combine molecular measurements from different modalities with spatial information to learn a unified representation that can be used for downstream biological analysis. However, significant differences among modalities in terms of dimensionality, sparsity, noise, and biological semantics make it difficult to obtain stable and discriminative unified representations, while the transformation from modality specific representations to the shared space is typically modeled implicitly. To address these challenges, we proposes FlowMOS, a flow matching representation learning framework for spatial multi-omics integration. FlowMOS introduces shared space flow matching to explicitly model the continuous transfer process from each modality specific representation to a shared latent space. To further improve the robustness of representation learning against incomplete and noisy measurements, mask consistency regularization is employed to simulate local feature missingness and sequencing noise in the input data. Extensive experiments on several real world spatial multi-omics datasets demonstrate that FlowMOS effectively captures modality to shared transformations, while the unified representation achieve robust performance in downstream task.