sc2Flow: Mitigating Mean Prediction Bias in Single-Cell Perturbation with Dual-Stage Flow Matching

Abstract

Predicting single-cell gene responses to chemical perturbations is vital for personalized therapy, yet existing deep learning models face significant hurdles. Standard regression-based approaches suffer from "mean prediction bias," failing to capture cellular heterogeneity, while current Flow Matching (FM) methods struggle with the extreme sparsity of single-cell data and the dimensionality mismatches inherent in transformer-based generation. To address these challenges, we introduce sc2Flow, a dual-stage framework that unifies discrete and continuous flow matching. sc2Flow first predicts the binary mask of expressed genes and subsequently models their quantitative levels using a scalable Transformer. This decoupling effectively resolves dimensionality conflicts and eliminates parameter redundancy. Extensive benchmarks on Sci-Plex3 and other datasets demonstrate that sc2Flow significantly outperforms state-of-the-art baselines on distribution matching, successfully mitigating mean bias to preserve critical biological heterogeneity.