simCRISPR: Modeling experimental complexity in pooled CRISPR screens

Abstract

Pooled CRISPR screens are widely used to investigate gene function and uncover genetic interactions. However, benchmarking computational methods for detecting gene-by-environment (GxE) interactions remains difficult because ground truth is rarely available and existing simulation tools are not designed for GxE screening contexts. To address this, we developed simCRISPR, a flexible simulation framework for generating pooled CRISPR screen data under complex experimental designs. Using simulated datasets informed by empirical CRISPR screen designs, we evaluated commonly used analysis methods, comparing normalization strategies based on safe-harbor versus non-targeting sgRNAs and assessing empirical log2FC thresholds as an additional effect-size criterion. We found that safe-harbor-based normalization improved interaction detection when DNA damage-related effects were present, particularly when combined with empirical log2FC thresholding for DESeq2. Application of this workflow to a doxorubicin GxE screen further showed that safe-harbor-based normalization reduced bias in log2FC distributions and identified additional biologically relevant candidates. simCRISPR is available at https://github.com/bachergroup/simCRISPR.