MultiFlow: coupled flow matching for predicting single-cell multiomic perturbation responses in unseen cellular contexts

Authors

Wang, H., Zhang, C., Zhang, M., Nie, X., Liu, Q.

Abstract

Predicting cellular responses to perturbation requires resolving coordinated changes across molecular layers, yet most single-cell perturbation models focus on transcriptional responses alone. Here we present MultiFlow, a coupled flow-matching framework that unifies generation and perturbation prediction of paired gene expression and chromatin accessibility. By learning coupled RNA-ATAC flows conditioned on perturbation and control-derived cellular-state representation, MultiFlow enables prediction of coordinated multiomic responses in unseen cellular contexts. Across multiomic generation benchmarks, MultiFlow accurately reproduced paired RNA-ATAC states and their population distributions. In multiomic perturbation benchmarks, MultiFlow achieved the strongest overall performance in predicting both gene-expression and chromatin-accessibility responses, outperforming competing modality-specific perturbation-prediction methods. Joint multiomic modeling further preserved perturbation-induced RNA-ATAC coordination, including concordant peak-gene effects and cross-modal cellular neighborhood structure. These results establish coupled flow matching as a unified generative framework for modeling paired multiomic states and predicting coordinated perturbation responses across cellular contexts. Code and tutorial for MultiFlow are available at https://github.com/liuq-lab/MultiFlow.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 26 Aug 2026.

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