SpaFactor：轻量级空间上下文基因程序建模用于组织学-转录组推断
原标题：SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference
AI 摘要
清华大学深圳国际研究生院等机构提出 SpaFactor，一种轻量级低秩形态-程序-基因分解框架，用于从常规 H&E 染色图像推断空间转录组基因表达。该方法融合中心位点视觉表征与多尺度邻域上下文，用残差 MLP 学习组织微环境到低维潜在基因程序的非线性映射，再通过共享基因载荷解码多基因表达。在五个公开队列上取得最佳综合性能，对空间可变基因提升尤为明显，并能更忠实地恢复生物学空间模式。
正文节选
SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference Abstract 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 (H&E) images therefore offers a scalable alternative. However, conventional methods often fit high-dimensional gene outputs as independent targets, overlooking th