BARCS: beta-binomial regression for multivariate CRISPR screen design
- 1Baylor College of Medicine
Abstract
Pooled CRISPR screens increasingly use longitudinal, donor-adjusted, and factorial designs, but beta-binomial screen methods have largely remained limited to pairwise comparisons. BARCS extends the library-total-conditional beta-binomial model to guide-level regression with an arbitrary design matrix, enabling direct estimation of time, covariate, and interaction effects. In four replicate-complete Cas13 screens, adding the intermediate time point modestly improved essential-gene recovery. Applying the same non-targeting-control scaling rule to BARCS, MAGeCK-MLE, edgeR-QL, DESeq2, and limma--voom produced similar calibration across all five methods, while the four alternatives ranked essential genes more strongly than BARCS. In an ordered-bin IL2RA screen, donor-adjusted BARCS recovered more validated regulators with fewer total calls than the matched four-bin MAGeCK-MLE fit, and cross-fitted controls exposed excess guide-level significance. Simulations showed gains from dispersion moderation and control-based denominators, but seed-specific results exposed denominator sensitivity and a null grid localized substantial gene-level error to correlated-guide aggregation rather than dispersion alone. Aggregation-matched control scaling reduced but did not eliminate this error. An external audit prompted by concerns about beta-binomial false discoveries showed that the reported CB2 null-discovery count disappeared when full-library totals were restored. This corrected one denominator-dependent result but did not refute the broader calibration concern; nominal-level calibration remained unresolved. BARCS therefore contributes a multivariable extension of the library-total-conditional beta-binomial model together with an explicit account of where its inference is valid: guide-level coefficients are supported by independent biological libraries, whereas gene-level summaries and partitioned-bin designs require correlation-aware aggregation or joint modelling that the present implementation provides diagnostically rather than generatively. We report this boundary because complex pooled designs make it consequential, not because it is unique to the beta-binomial model.
Related
- A CRISPR/Cas9 screen reveals convergent dysregulation of neurological pathways within and across glycosylation pathways
- 699 A CRISPR-Cas9 screen identifies negative regulators of CXCL9 expression by dendritic cells
- CRISPR knockout screen identifies MYD88/IRAK4 signaling as a mediator of deoxynivalenol-induced oxidative damage
- An improved CRISPR-LICHT screen of 120 Tdark genes illuminates new biologically relevant SOX2 targets in human brain organoids
- A genome-wide CRISPR screen in human prostate cancer cells reveals drivers of macrophage-mediated cell killing and positions AR as a tumor-intrinsic immunomodulator
BibTeX
@misc{lee2026barcs,
title = {{BARCS: beta-binomial regression for multivariate CRISPR screen design}},
author = {K.-W. Lee and H.-H. Jeong},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.31.748412},
url = {https://www.biorxiv.org/content/10.64898/2026.08.31.748412v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - BARCS: beta-binomial regression for multivariate CRISPR screen design
AU - K.-W. Lee
AU - H.-H. Jeong
PY - 2026
DA - 2026/08/31
PB - bioRxiv
DO - 10.64898/2026.08.31.748412
UR - https://www.biorxiv.org/content/10.64898/2026.08.31.748412v1
AB - Pooled CRISPR screens increasingly use longitudinal, donor-adjusted, and factorial designs, but beta-binomial screen methods have largely remained limited to pairwise comparisons. BARCS extends the library-total-conditional beta-binomial model to guide-level regression with an arbitrary design matrix, enabling direct estimation of time, covariate, and interaction effects. In four replicate-complete Cas13 screens, adding the intermediate time point modestly improved essential-gene recovery. Applying the same non-targeting-control scaling rule to BARCS, MAGeCK-MLE, edgeR-QL, DESeq2, and limma--voom produced similar calibration across all five methods, while the four alternatives ranked essential genes more strongly than BARCS. In an ordered-bin IL2RA screen, donor-adjusted BARCS recovered more validated regulators with fewer total calls than the matched four-bin MAGeCK-MLE fit, and cross-fitted controls exposed excess guide-level significance. Simulations showed gains from dispersion moderation and control-based denominators, but seed-specific results exposed denominator sensitivity and a null grid localized substantial gene-level error to correlated-guide aggregation rather than dispersion alone. Aggregation-matched control scaling reduced but did not eliminate this error. An external audit prompted by concerns about beta-binomial false discoveries showed that the reported CB2 null-discovery count disappeared when full-library totals were restored. This corrected one denominator-dependent result but did not refute the broader calibration concern; nominal-level calibration remained unresolved. BARCS therefore contributes a multivariable extension of the library-total-conditional beta-binomial model together with an explicit account of where its inference is valid: guide-level coefficients are supported by independent biological libraries, whereas gene-level summaries and partitioned-bin designs require correlation-aware aggregation or joint modelling that the present implementation provides diagnostically rather than generatively. We report this boundary because complex pooled designs make it consequential, not because it is unique to the beta-binomial model.
ER -