跨切片知识迁移 via 遮蔽多模态异质图对比学习用于空间基因表达推断
Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference
AI总结：
本文提出SpaHGC模型，通过多模态异质图对比学习，结合局部空间上下文和跨切片相似性，提升空间基因表达预测精度，优于现有九种方法。
摘要：
尽管空间转录组学（ST）已加深了我们对组织背景下基因表达的理解，但其高实验成本限制了大规模应用。预测ST从病理图像是一种有前景、成本效益的替代方法，但现有方法难以捕捉复杂的跨切片空间关系。为了解决这一挑战，我们提出了SpaHGC，一种基于多模态异质图的模型，能够从组织学图像中捕捉切片内和切片间的斑点-斑点关系。它整合了目标切片内的局部空间上下文和从病理基础模型提取的图像嵌入计算的跨切片相似性。这些嵌入使跨切片知识迁移成为可能，SpaHGC进一步采用遮蔽图对比学习来增强特征表示，并从参考切片向目标切片转移空间基因表达知识，使其能够建模复杂的空间依赖关系并显著提高预测精度。我们在七个匹配的组织学-ST数据集上进行了全面的基准测试，这些数据集来自不同的平台、组织和癌症亚型。结果表明，SpaHGC在所有评估指标上都显著优于现有的九种最先进方法。此外，预测结果在多个癌症相关通路中显著富集，从而突显了其强大的生物相关性和应用潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
