CELLKAFC: KNOWLEDGE-AWARE SINGLE-CELL PERTURBATION PREDICTION WITH FLOW CORRECTION

Single-cell perturbation-response modeling is essential for decoding cellular mechanisms and accelerating drug discovery. However, predicting perturbation-induced gene expression distributions remains a formidable machine learning challenge: the data is inherently high-dimensional, sparse, and heterogeneous, and target conditions frequently exhibit substantial distribution shifts from cell distributions. While existing methods improve conditional prediction, they often underutilize gene-level biological priors and lack explicit mechanisms to correct condition-dependent distribution shifts. In this work, we introduce CellKAFC, a Knowledge-Aware Flow Correction framework for single-cell perturbation modeling. CellKAFC integrates biological priors into a conditional transport formulation and couples this process with an explicit correction for distributional mismatch across varying conditions. Through extensive evaluation on public benchmarks—including Norman, RPE1, and ComboSciPlex—CellKAFC consistently outperforms prior state-of-the-art methods. Notably, it reduces mean squared error by 12.5–40.5% relative to corresponding uncorrected baselines. Furthermore, an orthogonal decomposition analysis reveals that 21.7–42.4% of this net improvement stems from components orthogonal to the decoded dynamic space, validating the efficacy of our correction mechanism. These results demonstrate that knowledge-aware transport coupled with explicit shift correction provides a principled and highly transferable representation for single-cell perturbation modeling.