用图结构建模基因空间关系，提升病理切片预测准确性
FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction
- 基于扩散模型构建基因表达的结构化分布，融合空间图编码器保持拓扑一致
- 在10个基因上实现PCC 0.82、MSE 0.04，显著提升基因间与基因-空间关联性
- 适合生物医学图像分析、空间转录组建模方向的研究者使用
从常规H&E染色切片预测空间基因表达可实现大规模分子谱型分析，但现有模型将其视为孤立的点级任务，忽略了基因协同与空间分布等关键生物结构。为此，我们提出FLAG——一种基于扩散的框架，将该任务重构为结构化分布建模。同时，我们识别出“基因维度诅咒”问题：在高维空间中联合建模基因表达与空间交互会失效。FLAG通过引入空间图编码器确保拓扑一致性，并利用基因基础模型（GFM）对齐机制保障生成过程中的基因-基因保真度。为严格评估性能，我们设计了新型结构化评价指标，包括基因结构相关性（GSC）和空间结构相关性（SSC）。实验表明，FLAG在传统精度（PCC/MSE）上表现优异，同时显著提升了基因间及基因-空间关系的结构保真度。
原文摘要 · Abstract (English)
Predicting spatial gene expression from routine H\&E enables large-scale molecular profiling, yet current models treat this as isolated pointwise tasks, thereby overlooking essential biological structures like gene coordination and spatial distribution. To preserve these relationships, we introduce \textbf{FLAG}, a diffusion-based framework that redefines this task as structured distribution modeling. At the same time, we identify the critical \textbf{Gene Dimension Curse}, where joint modeling gene expression and their spatial interactions fail in high-dimensional spaces, and FLAG solves this challenge by integrating a spatial graph encoder for topological consistency and utilizing Gene Foundation Model (GFM) alignment for gene-gene fidelity in the generation process. To rigorously assess model performance, we propose a set of novel structural evaluation metrics, including Gene Structural Correlation (\textbf{GSC}) and Spatial Structural Correlation (\textbf{SSC}). Our experiments demonstrate that FLAG is highly competitive in traditional accuracy (PCC/MSE) while achieving significantly enhanced structural fidelity in capturing both gene-gene and gene-spatial relationships. The code is available at https://github.com/darkflash03/FLAG.
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