scPertWGF：Wasserstein Gradient Flow for Single-Cell Perturbation Prediction

Predicting how an unseen genetic or chemical perturbation reshapes a cell population is a central problem in functional genomics. Due to the destructive nature of single-cell sequencing, control and perturbed populations are strictly unpaired. While recent deep generative models strive to capture complex cellular heterogeneity, they often fail to outperform simple linear baselines on mean expression shifts. We propose **scPertWGF**, a **W**asserstein **G**radient **F**low framework for **s**ingle-**c**ell perturbation prediction that explicitly models **pert**urbation effects through changes in mean expression and distributional shape. To guide residual generation, a smoothed Wasserstein gradient flow objective aligns generated and observed populations, with flow matching providing complementary supervision of transport velocities. Among the evaluated baselines, scPertWGF achieves the lowest $L_2$ prediction error and highest Pearson delta correlation on four genetic perturbation screens and one drug screen, with the lowest energy distance on four, demonstrating its superior capability to capture both mean shifts and complex population shapes.