Multimodal spatial omics integration offers a powerful paradigm to decipher the hierarchical regulatory mechanisms underlying cellular function and tissue architecture. In this study, a novel method of multimodal spatial omics fusion, named mmspao, is proposed to obtain cross-modal interactive features and combine them with the features of each modality to obtain fusion results at the spatial resolution. This method integrates the data of spatial transcriptomics, epigenomics, and proteomics. It further integrates information from each modality using adjacency graph modeling and latent spatial representation. We demonstrate the effectiveness of this method on simulated and real multiomics data. By leveraging adjacency graph modeling and latent spatial representation, mmspao effectively aligns multiomics modalities within shared spatial domains, preserving individual gene expression profiles while generating globally integrated fusion maps that advance the decoding of tissue architecture and cellular regulatory hierarchies.
Spatial transcriptomics facilitates tissue microenvironment analysis by retaining gene expression alongside spatial context, with spatial domain detection being crucial. Conventional clustering or graph-based approaches often fail to capture global spatial dependencies and low-dimensional features due to complex nonlinear patterns and intricate neighborhood structures, limiting both accuracy and generalizability. We introduce stKAN, a novel framework integrating Kolmogorov-Arnold Network with variational autoencoder to effectively model spatially resolved gene expression with graph attention network. StKAN fuses spatial information, gene expression, and optional morphological features, and applies contrastive learning to identify biologically coherent domains. Leveraging explicit function decomposition, it ensures flexible adaptation to diverse data scales. Evaluated on seven spatial transcriptomics datasets, stKAN outperforms existing methods in domain detection accuracy and robustness. It shows strong potential for downstream analyses, offering deeper insights into disease pathology and tumor invasion. By bridging deep learning and spatial context, stKAN advances spatial biology with enhanced generalizability.
Jing Lin, Aijing Feng, Yankun Cao et al.· Computational biology and ch...· 0 citations