GeneFlowV2: RNA-Controllable Histology Generation and Counterfactual Editing

Spatial transcriptomics (ST) aligns gene expression with histopathological morphology, enabling the investigation of how molecular states translate into observable visual phenotypes. However, RNA-conditioned generative models may produce visually realistic tissue images while underutilizing transcriptomic inputs, limiting their conditional fidelity and controllability. We introduce GeneFlowV2, a rectified-flow framework for generating cellular-level histopathological images from single-cell gene expression profiles. GeneFlowV2 integrates gene-aware RNA encoding with global and spatial conditioning, and further introduces a matched-versus-shuffled RNA ranking objective to enhance the model's sensitivity to transcriptomic variation. The model generates high-quality H&E histopathology images and supports RNA-conditioned visual counterfactual generation by editing an observed single-cell RNA profile toward a target RNA profile. Under the same spatially guarded evaluation protocol, across three 100K-scale image–gene paired datasets, GeneFlowV2 consistently outperforms GeneFlow in all experiments, demonstrating improved image generation quality, and stronger responsiveness to RNA conditioning.