Finite-Measurement Transport of Perturbation Reliability Rankings

Predictions validated in one biological context are often considered for use in another, yet perturbations with low prediction error in one context need not retain their relative accuracy after a context change. We study this ordering of perturbation-level realized error while the predictor is held fixed. Because finite cell counts can themselves induce apparent rank changes, we define a tie-aware discrepancy that compares pairwise ordering distributions and subtracts expected within-context ordering disagreement. The resulting finite-measurement estimand is zero when the two pair-state laws agree, while its unbiased estimator may be negative at finite sample size. In the Frangieh Perturb-CITE-seq dataset, reliability rankings change across control, co-culture, and IFN-gamma conditions under three complementary risk metrics. The changes exceed same-context sampling baselines, become increasingly resolvable with cell budget, and are reproduced in independent HepG2–Jurkat and iPSC–neuron raw-cell analyses. Matched source-only RBF, direct-MLP, latent-MLP, and strict source-frozen GEARS sensitivities preserve positive transport signs on their respective eligible Frangieh surfaces, although their within-metric profiles are not identical. Reliability reordering also changes finite top-k experimental shortlists. In the tested controls, however, the adjusted discrepancy did not improve held-out regret prediction beyond metric identity and mean-risk shift. A known-truth comparator audit shows why the finite-measurement correction matters: uncorrected rank statistics can declare transport under a same-context null, whereas matched-null corrections restore calibration. The framework therefore supports measurement-aware auditing of cross-context changes in model-error ordering.