MCFST: spatial domain identification method based on multi-view graph convolutional network and graph fusion network

The emergence of spatial transcriptomics, which integrates spatial and gene expression information, has greatly advanced research in disease mechanisms and developmental biology. A core task in this field is spatial domain identification, which reveals regions with shared molecular signatures and histological features, thereby facilitating the study of tissue function and pathology. Although existing methods have achieved promising performance, many of them still face limitations in effectively integrating heterogeneous information from multiple views, such as gene expression, spatial coordinates, and spatially informed expression profiles. In particular, discrepancies across views may lead to inconsistent representations and distorted similarity relationships, which can reduce the accuracy and robustness of spatial domain recognition.

To address these limitations, we propose MCFST, a graph neural network framework that integrates multi-view graph convolution with a fusion module guided by mutual information maximization. By incorporating diverse views of spatial data and aligning their representations, MCFST effectively captures latent patterns and achieves robust domain recognition. We evaluated MCFST against state-of-the-art methods on two simulated datasets with varying sparsity and noise levels, as well as three real spatial transcriptomics datasets. Results show that MCFST consistently outperforms baselines in spatial domain identification, highlighting its robustness and efficiency. Moreover, spatially variable genes detected from MCFST-derived domains exhibited clear spatial expression patterns, further confirming the accuracy and utility of MCFST.

The code implementation of the MCFST algorithm is publicly available at https://github.com/dw666666/MCFST.