PerturbRIFT: Response-Informed Flow Matching for Predicting Single-Cell Responses to Unseen Genetic Perturbations

Predicting single-cell responses to unseen genetic perturbations requires modeling both population-level expression changes and cell-to-cell variability. Here we introduce PerturbRIFT, a generative framework that incorporates measured response statistics into a perturbation-specific source distribution for conditional flow matching. Gene embeddings and knowledge graphs guide the learned aggregation of gene-wise means, zero probabilities, and nonzero-expression variances from related training perturbations. These statistics parameterize a factorized zero-inflated normal (ZINormal) prior, providing a query-specific starting point for generation. Residual latent flow matching then transforms samples from this prior into predicted single-cell response populations, with a latent maximum mean discrepancy objective aligning transported and observed populations during training. Across four within-dataset unseen-perturbation benchmarks in K562, RPE1, HepG2, and Jurkat, PerturbRIFT improves mean-response accuracy and multiple distributional metrics relative to the published prediction baselines evaluated here. K562 ablations show that biologically guided prior estimation contributes the largest gains in mean-response accuracy, while residual flow matching with latent distribution alignment improves multiple measures of single-cell distributional fidelity. Together, our findings support using measured response statistics to define generative source distributions for predicting single-cell responses to unseen perturbations.