COHERE: Coherent Response Transfer for Cross-Context Perturbation Prediction

Predicting perturbation responses across cellular contexts requires capturing more than average transcriptional changes. Populations with similar means can differ in expressing fractions and positive-expression distributions, yielding distinct differential-expression (DE) evidence. We introduce COHERE, a framework that jointly transfers response means and marginal shape while enforcing mean–shape compatibility. To predict unobserved perturbation–context responses, COHERE uses target controls and responses to other training perturbations in the target context to adapt source mean changes, expressing fractions, and positive-expression quantiles. A shared low-rank transformation updates these statistics while preserving source components outside the response subspace. Constrained generation constructs finite, nonnegative cell populations that preserve feasible predicted gene-wise means. Across six genetic, chemical, and cytokine perturbation benchmarks, COHERE demonstrates broad performance advantages over evaluated baselines in mean-response prediction, perturbation discrimination, and DE recovery. Under the benchmark DE scoring protocol, learned shape improves the area under the precision–recall curve (AUPRC) at fixed predicted means on all six datasets, including a relative 32.5% gain on Nadig. Matched retraining shows that preserving source components outside the response subspace improves DE recovery and marginal fidelity on PBMC and Nadig. These findings support coherent transfer of marginal response shape as a complement to accurate mean prediction.