🤖 AI Summary
This study addresses the challenge of limited observational data for specific perturbations in target cellular contexts within single-cell perturbation atlases, where direct transfer of responses from source environments often introduces bias. To mitigate this, the authors propose a path reliability–weighted transfer mechanism that integrates a local low-rank basis from the target environment with a source-to-target ridge regression expert network. The reliability of transfer paths is evaluated using validation anchors, and existing perturbation responses are retrieved and aggregated via cosine similarity–based weighting. This approach effectively incorporates cross-environment information while preserving perturbation specificity. Evaluated on the Perturb-CITE-seq melanoma dataset, the method reduces mean squared error by 4.1% and improves top-10 in-context perturbation retrieval accuracy from 74.5% to 80.5% compared to the local low-rank baseline.
📝 Abstract
Single-cell perturbation atlases rarely measure every intervention in every cellular context: a query perturbation is often observed in one or more source contexts but missing in the recipient context where its effect is needed. Ignoring those measured responses discards query-specific experimental evidence, whereas copying or weakly calibrating them across contexts risks transferring the wrong signal. We propose PerturbMap, which predicts a missing recipient-context effect by combining a recipient-local low-rank base with accepted proposals that transport the same perturbation's measured source responses through source-to-recipient ridge experts fit on paired training perturbations, with proposal weights determined by route reliability estimated on validation anchors. On the Perturb-CITE-seq melanoma cohort, PerturbMap improves full-effect MSE by 4.1\% over a recipient-local low-rank base and achieves lower MSE than FedAvg, zero-response, raw-copy, calibrated-copy, and identity-shuffled affine controls. It remains within $2.82\times10^{-6}$ MSE of our centralized token-matched pooled reference, which uses a stronger training interface. A condition-mean specificity diagnostic shows the same direction: same-recipient top-10 counterpart retrieval by cosine increases from 74.5\% for the low-rank base to 80.5\% for PerturbMap.