Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics
<|reference_start|>Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics: The recent advancement of spatial transcriptomics (ST) allows to characterize spatial gene expression within tissue for discovery research. However, current ST platforms suffer from low resolution, hindering in-depth understanding of spatial gene expression. Super-resolution approaches promise to enhance ST maps by integrating histology images with gene expressions of profiled tissue spots. However, current super-resolution methods are limited by restoration uncertainty and mode collapse. Although diffusion models have shown promise in capturing complex interactions between multi-modal conditions, it remains a challenge to integrate histology images and gene expression for super-resolved ST maps. This paper proposes a cross-modal conditional diffusion model for super-resolving ST maps with the guidance of histology images. Specifically, we design a multi-modal disentangling network with cross-modal adaptive modulation to utilize complementary information from histology images and spatial gene expression. Moreover, we propose a dynamic cross-attention modelling strategy to extract hierarchical cell-to-tissue information from histology images. Lastly, we propose a co-expression-based gene-correlation graph network to model the co-expression relationship of multiple genes. Experiments show that our method outperforms other state-of-the-art methods in ST super-resolution on three public datasets.<|reference_end|>
@article{wang2024cross-modal,
    title={Cross-modal Diffusion Modelling for Super-resolved Spatial
  Transcriptomics},
    author={Xiaofei Wang, Xingxu Huang, Stephen J. Price, Chao Li},
    journal={arXiv preprint arXiv:2404.12973},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12973},
    primaryClass={eess.IV cs.CV cs.LG q-bio.QM}
}