CRISPRScreenEngine: MAGeCK-Style Genome-Wide CRISPR Knockout Screen Analysis with Robust Rank Aggregation
Introduction
Genome-wide CRISPR knockout screens using pooled sgRNA libraries enable unbiased identification of genes required for cell viability, drug resistance, or other phenotypes. The MAGeCK algorithm uses negative binomial models and robust rank aggregation to identify significant hits from sgRNA count data.
Methods
sgRNA Normalization
Median ratio normalization corrects for library size differences.
Gene Score Aggregation
Robust Rank Aggregation (RRA): mean log2FC of top 3 sgRNAs per gene.
Screen Quality
Gini index measures count distribution evenness. ROC analysis uses known essential genes as positive controls.
Results
202 depleted genes, 57 enriched genes (FDR<0.05). Essential recovery: 200/200 (100%). ROC AUC=1.000. Gini index=0.203.
Code Availability
https://github.com/BioTender-max/CRISPRScreenEngine
Key Results
- 20,000 sgRNAs, 4000 genes, 4 samples
- Depleted: 202, Enriched: 57
- Essential recovery: 100%
- ROC AUC: 1.000
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