Auditing Confidence-Calibrated Benchmarking of Single-Cell Perturbation Models
- 1Algoverse AI
Abstract
Single-cell perturbation models are increasingly assessed for both predictive performance and calibrated uncertainty under distribution shift. We constructed such an evaluation harness (two datasets, Norman 2019 and Replogle 2022; four split types over six dataset-split combinations; three models, scVI, CPA and a linear Ridge baseline; three uncertainty quantification methods, MC dropout, quantile regression and split conformal prediction) and then audited it against null baselines it had never included. Most of its headline results measure something other than what their metric names claim. The harness supplied each test cell to the model as input and then scored the model on reproducing that same cell's expression, so an identity predictor returning the input unchanged attains a perturbation-effect correlation of 1.0 on all six splits: any model given the target cell is measured against this echo ceiling rather than against a null. Against a constant train-mean predictor, scVI wins on 99.7% of Norman cells but only 0.2% of Replogle cells. One split held out sequencing batches rather than perturbations; another scores no genuinely novel perturbation after label collapse; unseen perturbations are silently replaced with a negative-control sentinel at inference; and the most convention-sensitive decision-value entry moves 29.2 percentage points under a library-size convention that changes that row's all-gene MSE by 0.01%. Because a validation budget is spent on a ranked shortlist rather than on a marginal interval, we also score each uncertainty signal by the DE-gene error it avoids in the top k: the worst-calibrated method is the best ranker, and a benchmark reporting only coverage orders the two backwards. We document six failure modes, each with its mechanism, the diagnostic that exposes it, and a detection procedure transferable to other harnesses. One result survives, with a boundary condition: split-conformal coverage calibrated on K562 residuals under-covers on RPE1 (0.900 to 0.847), and marginal coverage at the nominal 0.900 is compatible with covering 0.271 to 0.342 of the DE-gene intervals a prioritization decision would rest on. We continue to recommend conformal prediction, recalibrated per cellular context and reported per gene set rather than marginally.
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BibTeX
@misc{gallone2026auditing,
title = {{Auditing Confidence-Calibrated Benchmarking of Single-Cell Perturbation Models}},
author = {M. Gallone and S. Ravidath and S. Madireddy},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.12.758706},
url = {https://www.biorxiv.org/content/10.64898/2026.10.12.758706v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Auditing Confidence-Calibrated Benchmarking of Single-Cell Perturbation Models
AU - M. Gallone
AU - S. Ravidath
AU - S. Madireddy
PY - 2026
DA - 2026/10/12
PB - bioRxiv
DO - 10.64898/2026.10.12.758706
UR - https://www.biorxiv.org/content/10.64898/2026.10.12.758706v1
AB - Single-cell perturbation models are increasingly assessed for both predictive performance and calibrated uncertainty under distribution shift. We constructed such an evaluation harness (two datasets, Norman 2019 and Replogle 2022; four split types over six dataset-split combinations; three models, scVI, CPA and a linear Ridge baseline; three uncertainty quantification methods, MC dropout, quantile regression and split conformal prediction) and then audited it against null baselines it had never included. Most of its headline results measure something other than what their metric names claim. The harness supplied each test cell to the model as input and then scored the model on reproducing that same cell's expression, so an identity predictor returning the input unchanged attains a perturbation-effect correlation of 1.0 on all six splits: any model given the target cell is measured against this echo ceiling rather than against a null. Against a constant train-mean predictor, scVI wins on 99.7% of Norman cells but only 0.2% of Replogle cells. One split held out sequencing batches rather than perturbations; another scores no genuinely novel perturbation after label collapse; unseen perturbations are silently replaced with a negative-control sentinel at inference; and the most convention-sensitive decision-value entry moves 29.2 percentage points under a library-size convention that changes that row's all-gene MSE by 0.01%. Because a validation budget is spent on a ranked shortlist rather than on a marginal interval, we also score each uncertainty signal by the DE-gene error it avoids in the top k: the worst-calibrated method is the best ranker, and a benchmark reporting only coverage orders the two backwards. We document six failure modes, each with its mechanism, the diagnostic that exposes it, and a detection procedure transferable to other harnesses. One result survives, with a boundary condition: split-conformal coverage calibrated on K562 residuals under-covers on RPE1 (0.900 to 0.847), and marginal coverage at the nominal 0.900 is compatible with covering 0.271 to 0.342 of the DE-gene intervals a prioritization decision would rest on. We continue to recommend conformal prediction, recalibrated per cellular context and reported per gene set rather than marginally.
ER -