Cross-Cell-Line Perturbation Prediction Needs Controls

Predicting genetic perturbation responses across cell lines is challenging not only for models, but also for the metrics used to evaluate them. We introduce a strict cross-cell-line CRISPRi stress test on Replogle Perturb-seq, training on K562 and RPE1 (or additionally HepG2) and evaluating on held-out Jurkat. We pair this setting with two necessary controls: target-control-copy, which predicts no perturbation effect, and shifted-control baselines, which translate target-line controls by a source-derived mean shift. Across classical baselines, recent deep models, CellFlow, and context-conditioned flow-matching variants with five frozen single-cell foundation-model encoders plus a PCA/null context, we find that aggregate distributional metrics can be strongly driven by target-line cell-state variability rather than perturbation-specific response. Target-control-copy is competitive on aggregate distributional metrics, while shifted-control approaches CellFlow on Energy and substantially outperforms it on perturbation-specific Pearson. Used as fixed per-cell context for a shared downstream flow head, none of five frozen scFM encoders matches a PCA/null encoder in this strict cross-line setting. Finally, anchored mean-preserving residual-flow probes reveal a metric-specific trade-off: full-space residual flows improve $W_2$ but degrade per-gene moment matching, whereas low-rank constrained flows preserve moments but lose most $W_2$ gain. No tested method simultaneously dominates perturbation-specific response and biologically relevant distributional fidelity. These results argue for perturbation-aware cell-level evaluation in cross-cell-line virtual-cell benchmarks.