SoFusion: facilitating tissue structure identification via spatial multi-omics data fusion.

ABSTRACT: The rapid advancement of spatial multi-omics technologies has opened new avenues for dissecting tissue architecture with unprecedented resolution. However, inherent disparities across omics modalities, such as differences in biological hierarchy and resolution, pose significant challenges for integrative analysis. To address this, we present soFusion, a method for representation learning on spatial multi-omics data that enables automated identification of tissue compartmentalization. soFusion employs a graph convolutional network (GCN) to extract latent embeddings from spatial omics profiles. To simultaneously capture both cross-modality relationships and modality-specific features, we introduce a novel strategy for intra- and inter-omics feature learning. Moreover, modality-specific decod

SUBMITTER: Yu N 

PROVIDER: S-EPMC12477611 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature