Shared clones make random cell halves look reproducible in pooled screens
Abstract
Single-cell perturbation screens are used to train and evaluate models of perturbation effects, and model scores are judged against the reproducibility of the measured effects. Reproducibility is usually judged by splitting each perturbation's cells into two random halves, assumed to be independent replicates. In pooled screens such as Perturb-seq, the cells carrying a guide RNA descend from a limited number of transduced cells, so two random halves contain the same clones: pseudoreplication whose unit is not marked in the data. Using null perturbations (no expected effect) as controls, we compared random halves with independent replicates from separate transductions, and tested directly with halves that differ only in their clones. In a genome-scale screen of primary T cells, null perturbations agreed between random halves almost always and between independent replicates only half the time, the rate expected from noise alone; more cells did not remove it. With halves that share no clone, split by the T-cell receptor constant region or by a clone barcode, the excess agreement largely disappeared. Under a simple model, consistent with a screen that recorded clones, its size equals the share of expression differences set by clones divided by the effective number of clones per perturbation; in cell lines an upper bound on it was small. In the T-cell screen, shared clones inflated benchmark ceilings and reliability labels, the more so in donors with fewer effective clones, and when a model's input and scoring answer come from one transduction, published models that predict effects in a new cell state scored well even on perturbations with no expected effect; for the model tested (STATE), the gain vanished with the same guide from another transduction as answer. Reproducibility is better judged across separate transductions than between random halves.
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BibTeX
@misc{huang2026shared,
title = {{Shared clones make random cell halves look reproducible in pooled screens}},
author = {Xuanrui Huang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.32962},
eprint = {2610.32962},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.32962},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Shared clones make random cell halves look reproducible in pooled screens
AU - Xuanrui Huang
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.32962
UR - https://arxiv.org/abs/2610.32962
AB - Single-cell perturbation screens are used to train and evaluate models of perturbation effects, and model scores are judged against the reproducibility of the measured effects. Reproducibility is usually judged by splitting each perturbation's cells into two random halves, assumed to be independent replicates. In pooled screens such as Perturb-seq, the cells carrying a guide RNA descend from a limited number of transduced cells, so two random halves contain the same clones: pseudoreplication whose unit is not marked in the data. Using null perturbations (no expected effect) as controls, we compared random halves with independent replicates from separate transductions, and tested directly with halves that differ only in their clones. In a genome-scale screen of primary T cells, null perturbations agreed between random halves almost always and between independent replicates only half the time, the rate expected from noise alone; more cells did not remove it. With halves that share no clone, split by the T-cell receptor constant region or by a clone barcode, the excess agreement largely disappeared. Under a simple model, consistent with a screen that recorded clones, its size equals the share of expression differences set by clones divided by the effective number of clones per perturbation; in cell lines an upper bound on it was small. In the T-cell screen, shared clones inflated benchmark ceilings and reliability labels, the more so in donors with fewer effective clones, and when a model's input and scoring answer come from one transduction, published models that predict effects in a new cell state scored well even on perturbations with no expected effect; for the model tested (STATE), the gain vanished with the same guide from another transduction as answer. Reproducibility is better judged across separate transductions than between random halves.
ER -