发表机构
Shenzhen International Graduate School, Tsinghua University; Fuzhou University; Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University; Chinese Academy of Medical Sciences and Peking Union Medical College（清华大学深圳国际研究生院; 福州大学; 清华大学附属北京清华长庚医院，清华大学临床医学院; 中国医学科学院 北京协和医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SpaFactor提出轻量级低秩分解框架，融合多尺度组织上下文与基因程序建模，从HE图像预测空间转录组，在五个公共队列中实现最佳性能并提升生物学保真度。
AI 中文摘要
空间转录组学（ST）在组织结构内剖析基因表达，但其成本和实验复杂性限制了常规使用。因此，从常规可用的苏木精-伊红（HE）图像预测空间表达提供了一种可扩展的替代方案。然而，传统方法通常将高维基因输出作为独立目标进行拟合，忽视了基因间的生物学协调，同时容易受到高维噪声和过拟合的影响。现有解决此局限性的尝试往往依赖于计算量大的图网络或复杂的辅助监督。因此，我们引入了SpaFactor，一种轻量级且高效的低秩形态-程序-基因分解框架。在输入方面，SpaFactor高效融合中心点的视觉表示与多尺度局部和区域邻域上下文，生成捕获细胞形态和微环境异质性的组织学表示。在建模方面，残差MLP稳定学习从组织微环境到低维潜在基因程序的非线性映射。这些活动通过共享基因载荷解码为协调的多基因表达预测。在五个公共队列中，SpaFactor实现了最佳总体性能，特别是在空间可变基因方面有显著改进，并能更忠实地恢复生物学组织的空间模式。这些结果表明，轻量级的组织上下文和基因程序联合建模可以提高预测准确性和生物学保真度。
英文摘要
Spatial transcriptomics (ST) profiles gene expression within tissue architecture, but its cost and experimental complexity limit routine use. Predicting spatial expression from routinely available hematoxylin and eosin (HE) images therefore offers a scalable alternative. However, conventional methods often fit high-dimensional gene outputs as independent targets, overlooking the biological coordination among genes while remaining vulnerable to high-dimensional noise and overfitting. Existing attempts to address this limitation often rely on computationally heavy graph networks or complex auxiliary supervision. We therefore introduce SpaFactor, a lightweight and efficient low-rank morphology-program-gene factorization framework. At the input, SpaFactor efficiently fuses the visual representation of the central spot with multiscale local and regional neighborhood context, yielding a histologic representation that captures cellular morphology and microenvironmental heterogeneity. For modeling, a residual MLP stably learns a nonlinear mapping from the tissue microenvironment to low-dimensional latent gene programs. These activities are decoded through shared gene loadings into coordinated multi-gene expression predictions. Across five public cohorts, SpaFactor achieves the best aggregate performance, with particularly clear improvements for spatially variable genes, and more faithfully recovers biologically organized spatial patterns. These results demonstrate that lightweight joint modeling of tissue context and gene programs can improve both predictive accuracy and biological fidelity.
