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ChromaPert integrates chromatin accessibility data with transcriptomic measurements to predict cell-state-dependent transcriptional responses to genetic perturbations, overcoming limitations of transcriptomic-only approaches. The framework utilizes a Chromatin-Guided Response Router (CGRR) to leverage paired control RNA-ATAC features for jointly representing target identity and cellular context, while accounting for baseline differences. ChromaPert significantly outperforms existing methods in predicting all-gene and highly responsive gene correlations across unseen targets and held-out states.
Chromatin accessibility data, when properly integrated, unlocks significantly more accurate predictions of how cells will respond to genetic perturbations, even in entirely new cellular states.
Predicting transcriptional responses to genetic perturbations is central to understanding gene function. Existing predictors primarily rely on transcriptomic measurements, although chromatin accessibility provides complementary information about the cellular context in which perturbations act. Using this information requires linking chromatin context to specific perturbations and accounting for baseline differences between independently sampled control and perturbed populations. We propose ChromaPert, a chromatin-informed framework for predicting state-dependent perturbation responses. ChromaPert combines molecular and DNA-informed locus priors with paired control RNA--ATAC features to jointly represent target identity and measured cellular context. Its Chromatin-Guided Response Router (CGRR) builds a source bank of control-derived expression offsets and retrieves relevant sources using perturbation/locus or control-ATAC similarity. These offsets account for baseline differences, while conditional transport flow learns the remaining response. Across three unseen-target and held-out-state settings on K562 CAT-ATAC and Perturb-Multiome, ChromaPert achieves the highest all-gene response correlation among evaluated methods. When transferring known perturbations to held-out states, it improves correlation by 23.2% across all genes and 46.2% for the twenty most responsive genes over the strongest respective baselines. Correct RNA--ATAC pairing improves recovery of chromatin-associated response slopes over shuffled pairing, while cross-state predictions retain state-specific responses to the same perturbation.
