CHARM-Flow: Cellular Hierarchy-Aware Response Modeling for Single-Cell Perturbations

Predicting single-cell perturbation responses from unpaired observations requires capturing both population-level expression changes and variation among responding cells. We introduce CHARM-Flow, a cellular hierarchy-aware generative framework that models a population-level response together with heterogeneous cellular variation around that response. A shared control-derived population–state–cell hierarchy coordinates response prediction, conditional source composition, and cell-level residual generation. Condition-dependent weights organize empirical control cells into a state-structured source, while each sampled source cell remains available as a persistent reference during generation. A control-derived gene partition parameterizes the source and evolving residual representations, and conditional flow matching provides the residual update mechanism. Across Replogle, PBMC, and Tahoe, CHARM-Flow captures perturbation-level population responses, achieving PDCorr values of 0.477, 0.870, and 0.959, respectively. Controlled analyses on Replogle show that state structured empirical sources and persistent source access improve agreement with observed cellular distributions within perturbations. The shared hierarchy connects population-level response prediction, empirical source construction, and heterogeneous cell-level generation.