用基因表达数据生成高分辨率组织图像，还原细胞形态与空间关系。
GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
- 结合注意力编码器与修正流引导的条件UNet生成图像。
- 通过高阶常微分方程实现转录组到图像的连续双射映射。
- 可生成不同染色方式图像，适合病理诊断与药物扰动研究。
空间转录组学技术可将转录组信息与组织病理形态对齐，为生物分子发现带来新机遇。基于配对的单细胞基因表达与细胞图像数据，我们构建了GeneFlow框架，将转录组映射到图像空间。该方法结合基于注意力的RNA编码器与由修正流引导的条件UNet，生成不同染色方式（如H&E、DAPI）的高分辨率图像，突出显示各类细胞/组织结构。利用高阶常微分方程求解器的修正流，在转录组与图像流形间建立连续且双射的映射，解决该问题固有的多对一关系。本方法可从观测基因表达中生成真实细胞形态特征与空间解析的细胞间相互作用，具备引入遗传或化学扰动的潜力，并可通过揭示影像表型中的异常模式实现疾病诊断。在所有实验中，基于修正流的方法均优于扩散基线模型。代码见：https://github.com/wangmengbo/GeneFlow。
原文摘要 · Abstract (English)
Spatial transcriptomics (ST) technologies can be used to align transcriptomes with histopathological morphology, presenting exciting new opportunities for biomolecular discovery. Using ST data, we construct a novel framework, GeneFlow, to map transcriptomics onto paired cellular images. By combining an attention-based RNA encoder with a conditional UNet guided by rectified flow, we generate high-resolution images with different staining methods (e.g. H&E, DAPI) to highlight various cellular/tissue structures. Rectified flow with high-order ODE solvers creates a continuous, bijective mapping between transcriptomics and image manifolds, addressing the many-to-one relationship inherent in this problem. Our method enables the generation of realistic cellular morphology features and spatially resolved intercellular interactions from observational gene expression profiles, provides potential to incorporate genetic/chemical perturbations, and enables disease diagnosis by revealing dysregulated patterns in imaging phenotypes. Our rectified flow-based method outperforms diffusion-based baseline method in all experiments. Code can be found at https://github.com/wangmengbo/GeneFlow.
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