The rapid development of spatial transcriptomics(ST) enables the measurement
of gene expression at spatial resolution, making it possible to simultaneously
profile the gene expression, spatial locations of spots, and the matched
histopathological images. However, the cost for collecting ST data is much
higher than acquiring histopathological images, and thus several studies
attempt to predict the gene expression on ST by leveraging their corresponding
histopathological images. Most of the existing image-based gene prediction
models treat the prediction task on each spot of ST data independently, which
ignores the spatial dependency among spots. In addition, while the histology
images share phenotypic characteristics with the ST data, it is still challenge
to extract such common information to help align paired image and expression
representations. To address the above issues, we propose a Multi-view Graph
Contrastive Learning framework with HSIC-bottleneck Regularization(ST-GCHB)
aiming at learning shared representation to help impute the gene expression of
the queried imagingspots by considering their spatial dependency.

通过多视图图对比学习和 HSIC 约束正规化方法构建的空间转录组学 (ST) 框架 (ST-GCHB)，以学习共享表示来考虑空间依赖性，从而根据查询的图像点来估计相应的基因表达量。