Bishal

PertEMA is a post-hoc, model-agnostic reliability layer for single-cell perturbation-effect predictors. Deep models that predict how a CRISPR perturbation reshapes gene expression are used to nominate experimental targets, but on unseen perturbations they rarely beat a trivial mean baseline. Instead of building a better predictor, I ask which individual predictions can be trusted: a gradient-boosted-tree meta-model estimates each prediction's error from prediction-time features only, with isotonic calibration and split-conformal intervals. On the genome-scale Gladstone CD4 T cell Perturb-seq screen, the reliability score ranks realized error above the effect-magnitude and training-similarity heuristics, calibrates to near-nominal coverage, and builds a validation shortlist that is measurably more reproducible than ranking by effect size. I also characterized where reliability cannot help: per-instance model routing is infeasible, because candidate predictors co-fail on the same hard, noise-dominated perturbations. I quantified this with a measured noise ceiling and a break-even condition, and I pre-registered a routing test on an independent screen that failed exactly as predicted. Why it matters: it gives perturbation-prediction users a calibrated, honest abstention signal on real primary data, plus a transferable, reproducible finding about the limits of per-instance model selection, shipped as a self-hostable tool and an open reliability benchmark.