Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing methods fail to fully leverage the interactions between different tissue locations, which are crucial for accurate joint prediction. To address this, we introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a multi-faceted hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from WSIs. By clustering tissue image patches based on both spatial and morphological features, and incorporating intra- and inter-cluster edges, our approach fosters interactions between distant tissue locations during GNN learning. As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections. We advocate for adopting gene-aware smoothing methods that are more biologically justified. Experimental results on gene expression prediction show that our GNN method outperforms state-of-the-art techniques across multiple metrics.

翻译：空间转录组学（ST）的最新进展将组织学图像与空间分辨的基因表达谱相结合，使得能够基于图像块预测不同组织位置的基因表达。这为利用局部基因表达增强全切片图像（WSI）预测任务开辟了新的可能性。然而，现有方法未能充分利用不同组织位置之间的相互作用，而这种相互作用对于准确的联合预测至关重要。为解决这一问题，我们提出了MERGE（用于基因表达的多层面层次化图），该方法结合了多层面层次化图构建策略与图神经网络（GNN），以改进从WSI进行的基因表达预测。通过基于空间和形态学特征对组织图像块进行聚类，并纳入簇内和簇间边，我们的方法在GNN学习过程中促进了远距离组织位置之间的交互。作为另一项贡献，我们评估了不同的数据平滑技术，这些技术对于缓解ST数据中常由技术缺陷引起的伪影是必要的。我们主张采用更具生物学合理性的基因感知平滑方法。基因表达预测的实验结果表明，我们的GNN方法在多项指标上均优于现有最先进技术。