面向空间基因表达推断的掩码多模态异构图对比学习跨切片知识迁移
Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference
把病理基础模型提取的图像嵌入当作跨切片相似度度量，再用掩码图对比学习在切片间迁移知识——这套「基础模型嵌入 + 异构图对比」的范式可直接迁移到其他需要跨样本/跨域迁移的病理与医学图像预测任务。
原文依据 Masked Graph Contrastive Learning to enhance feature representation and transfer spatial gene expression knowledge from reference to target slides
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管空间转录组学（ST）加深了我们对组织背景下基因表达的理解，但其高昂的实验成本限制了大规模应用。从病理图像预测 ST 是一种有前景且低成本的替代方案，但现有方法难以捕捉复杂的跨切片空间关系。为应对这一挑战，我们提出 SpaHGC，一种基于多模态异构图模型的模型，可从组织学图像中同时捕捉切片内与切片间的位点—位点关系。它融合了目标切片内的局部空间上下文，以及由病理基础模型提取的图像嵌入所计算出的跨切片相似度。这些嵌入实现了切片间知识迁移，SpaHGC 进一步引入掩码图对比学习以增强特征表示，并将空间基因表达知识从参考切片迁移至目标切片，从而能够建模复杂的空间依赖关系并显著提升预测精度。我们在来自不同平台、组织与癌症亚型的七个配对组织学—ST 数据集上开展了全面基准评测。结果表明，SpaHGC 在所有评价指标上均显著优于现有的九种最优方法。此外，预测结果在多个癌症相关通路上显著富集，凸显了其较强的生物学相关性与应用潜力。代码与数据见 https://github.com/wenwenmin/SpaHGC。
英文原文摘要 1523 字符
While spatial transcriptomics (ST) has advanced our understanding of gene expression in tissue context, its high experimental cost limits its large-scale application. Predicting ST from pathology images is a promising, cost-effective alternative, but existing methods struggle to capture complex cross-slide spatial relationships. To address the challenge, we propose SpaHGC, a multi-modal heterogeneous graph-based model that captures both intra-slice and inter-slice spot-spot relationships from histology images. It integrates local spatial context within the target slide and cross-slide similarities computed from image embeddings extracted by a pathology foundation model. These embeddings enable inter-slice knowledge transfer, and SpaHGC further incorporates Masked Graph Contrastive Learning to enhance feature representation and transfer spatial gene expression knowledge from reference to target slides, enabling it to model complex spatial dependencies and significantly improve prediction accuracy.We conducted comprehensive benchmarking on seven matched histology-ST datasets from different platforms, tissues, and cancer subtypes. The results demonstrate that SpaHGC significantly outperforms the existing nine state-of-the-art methods across all evaluation metrics. Additionally, the predictions are significantly enriched in multiple cancer-related pathways, thereby highlighting its strong biological relevance and application potential. Code and data are available at https://github.com/wenwenmin/SpaHGC.