CFM-GP: Unified Conditional Flow Matching to Learn Gene Perturbation Across Cell Types

• URL: http://arxiv.org/abs/2508.08312v2
• Date: Tue, 28 Oct 2025 02:55:43 GMT
• Title: CFM-GP: Unified Conditional Flow Matching to Learn Gene Perturbation Across Cell Types
• Authors: Abrar Rahman Abir, Sajib Acharjee Dip, Liqing Zhang,
• Abstract summary: We present CFM-GP, a method for cell type-agnostic gene perturbation prediction.<n>CFM-GP learns a continuous, time-dependent transformation between unperturbed and perturbed gene expression distributions.<n>We evaluate on five datasets: SARS-CoV-2 infection, IFN-beta stimulated PBMCs, treated with glioblastoma Panobinostat, lupus under IFN-beta stimulation, and Statefate progenitor fate mapping.
• Score: 7.197454335008732
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding gene perturbation effects across diverse cellular contexts is a central challenge in functional genomics, with important implications for therapeutic discovery and precision medicine. Single-cell technologies enable high-resolution measurement of transcriptional responses, but collecting such data is costly and time-consuming, especially when repeated for each cell type. Existing computational methods often require separate models per cell type, limiting scalability and generalization. We present CFM-GP, a method for cell type-agnostic gene perturbation prediction. CFM-GP learns a continuous, time-dependent transformation between unperturbed and perturbed gene expression distributions, conditioned on cell type, allowing a single model to predict across all cell types. Unlike prior approaches that use discrete modeling, CFM-GP employs a flow matching objective to capture perturbation dynamics in a scalable manner. We evaluate on five datasets: SARS-CoV-2 infection, IFN-beta stimulated PBMCs, glioblastoma treated with Panobinostat, lupus under IFN-beta stimulation, and Statefate progenitor fate mapping. CFM-GP consistently outperforms state-of-the-art baselines in R-squared and Spearman correlation, and pathway enrichment analysis confirms recovery of key biological pathways. These results demonstrate the robustness and biological fidelity of CFM-GP as a scalable solution for cross-cell type gene perturbation prediction.

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