通过跨视图注意力融合空间与基因信息，提升空间转录组聚类效果。
A Multi-scale Fused Graph Neural Network with Inter-view Contrastive Learning for Spatial Transcriptomics Data Clustering
- 在每一层卷积后动态融合空间与基因特征，实现多尺度交互。
- 在大脑和乳腺癌数据集上，聚类准确率最高提升14%（ARI）。
- 适合研究空间转录组的生物学家与算法开发者参考。
空间转录组技术可在组织原生背景下进行全基因组表达分析，但识别空间区域仍面临挑战，主要源于复杂的基因-空间相互作用。现有方法通常分别处理空间和特征视图，仅在输出阶段融合——即“分开展示、后期融合”范式，限制了多尺度语义捕捉与跨视图交互能力。为此，本文提出stMFG，一种多尺度交互融合图神经网络，引入逐层跨视图注意力机制，在每次卷积后动态整合空间与基因特征。模型结合跨视图对比学习与空间约束，增强类别可区分性的同时保持空间连续性。在DLPFC与乳腺癌数据集上，stMFG优于现有先进方法，部分切片上ARI提升高达14%。
原文摘要 · Abstract (English)
Spatial transcriptomics enables genome-wide expression analysis within native tissue context, yet identifying spatial domains remains challenging due to complex gene-spatial interactions. Existing methods typically process spatial and feature views separately, fusing only at output level - an "encode-separately, fuse-late" paradigm that limits multi-scale semantic capture and cross-view interaction. Accordingly, stMFG is proposed, a multi-scale interactive fusion graph network that introduces layer-wise cross-view attention to dynamically integrate spatial and gene features after each convolution. The model combines cross-view contrastive learning with spatial constraints to enhance discriminability while maintaining spatial continuity. On DLPFC and breast cancer datasets, stMFG outperforms state-of-the-art methods, achieving up to 14% ARI improvement on certain slices.
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