PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modelling

2026-04-15

AI Summary

Modeling cellular responses to perturbations is highly challenging due to the heterogeneity of single-cell gene expression and complex implicit dependencies among genes. To address this, this work introduces, for the first time, a flow matching framework into the field, proposing an end-to-end method that directly models the effects of genetic and small-molecule perturbations on cell states within the native gene expression space. The approach employs a U-Net architecture to parameterize the velocity field and achieves high-fidelity predictions by fitting single-cell expression distributions. Evaluated on the PerturBench benchmark, the model demonstrates superior performance and was awarded first place in the general track of the inaugural ARC Virtual Cell Challenge, confirming its effectiveness and state-of-the-art capability in capturing both expression heterogeneity and perturbation-induced changes.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: User model development and evaluation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Predicting the effects of perturbations in-silico on cell state can identify drivers of cell behavior at scale and accelerate drug discovery. However, modeling challenges remain due to the inherent heterogeneity of single cell gene expression and the complex, latent gene dependencies. Here, we present PRiMeFlow, an end-to-end flow matching based approach to directly model the effects of genetic and small molecule perturbations in the gene expression space. The distribution-fitting approach taken by PRiMeFlow enables it to accurately approximate the empirical distribution of single-cell gene expression, which we demonstrate through extensive benchmarking inside PerturBench. Through ablation studies, we also validate important model design choices such as operating in gene expression space and parameterizing the velocity field with a U-Net architecture. The PRiMeFlow architecture was used as the basis for the model that won the Generalist Prize in the first ARC Virtual Cell Challenge.

Problem

• perturbation response
• expression heterogeneity
• single-cell gene expression
• gene dependencies
• cell state modeling

Innovation

• flow matching
• perturbation response modelling
• single-cell gene expression
• U-Net architecture
• expression heterogeneity