bioRxivBioinformaticsPreprint
Averaging in gene perturbation benchmarks hides perturbation-strength variation: Consequences for model evaluation
Yash SharmaiD, Madhav Subramaniyam, Pio Passariello, Dylan Jay, David Render, Márió Szegedy
Abstract
Perturbation-response benchmarks usually represent each genetic intervention by averaging all cells assigned that perturbation label. We inquire what perturbation-strength information is lost by this averaging, and whether current model predictions preserve enough perturbation-specific information for this perturbation-strength variation to be evaluated. Using X-Atlas/Orion CRISPRi screens, we use depth-corrected guide abundance as a cell-level proxy for perturbation strength and examine how transcriptional responses vary within a nominal perturbation. Higher perturbation strength is associated with greater response reproducibility. The effect is present in two human cell lines, HCT116 and HEK293T, but is larger in HCT116. For model evaluation, HCT116 retrieval tests show that observed responses preserve perturbation identity, whereas the evaluated STATE and Cell2Sentence predictions do not reliably recover it, hence the question of within-perturbation strength information cannot be meaningfully evaluated.
- organism: human
- assay: CRISPR screen
- research focus: benchmarking
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: perturbation response prediction
- approach: statistical model
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Towards Principled Evaluation of Single-Cell Perturbation Prediction Models
Philipp Sven Lars Schäfer, Kendall A. Reid, … Julio Sáez-Rodríguez·bioRxiv·Jul 27, 2026
- A Systematic Comparison of Single-Cell Perturbation Response Prediction Models
Lanxiang Li, Yue You, … Luyi Tian·Science AdvancesPeer-reviewed·Sep 9, 2026
- Perturbation Curve models continuous transcriptional response trajectories and improves prediction of genetic modulations
yunhua Zhong, Lifei Wang, … Haibo Jiang·bioRxiv·Jun 19, 2026
- Reliable single-cell perturbations explain and improve model performance
Xi Wang, Jack Kuipers, … Niko Beerenwinkel·bioRxiv·Aug 12, 2026
- COMPASS: Component-Wise Inference of Shared and Gene-Specific Perturbation Response
Huan Liang, Rohit Singh·bioRxiv·Aug 6, 2026
- Evaluating Single-Cell Perturbation Response Models Is Far from Straightforward
Mahshid Heidari, Mina Karimpour, … Hesam Montazeri·bioRxiv·Feb 17, 2026