GeneFlow: Single-cell Gene Expression Translation to Histopathological Images via Rectified Flow
Mengbo Wang, Shourya Verma, Aditya Malusare, Luopin Wang, Yiyang Lu, Mario Sola, Vaneet Aggarwal, Ananth Grama, Nadia Lanman
Spatial transcriptomics technologies provide a great opportunity for biological discovery by aligning transcriptomics and histopathological morphology. We propose a novel method, "GeneFlow", the first end-to-end framework to tackle the inverse problem of translating single- and multi-cell gene expressions into paired cellular/tissue images. By combining an attention-based RNA encoder with a conditional UNet guided by rectified flow, we generate high-resolution images with various staining (e.g. H\&E, DAPI). Rectified flow with high-order ODE solvers creates a continuous, bijective mapping between expression and image manifolds, addressing the one-to-many relationship inherent in this problem. Our method provides the generation of realistic cellular morphology features and inter-cellular level spatially resolved interactions under genetic or chemical perturbations, assisting disease diagnosis with minimal invasion by revealing dysregulated patterns. Our rectified flow method outperformed diffusion baselines in all experiments.