crispr-screen-qc

To perform QC on a pooled CRISPR screen, input a tab-delimited count matrix with sgRNA and Gene columns to evaluate library representation, read depth, and replicate consistency, generating a comprehensive QC report with plots.

The Gini index in CRISPR screen QC indicates library distribution inequality by analyzing sgRNA read counts across samples to detect under-represented guides and monitor library bias.

You can check replicate correlation by applying QC analysis to your sgRNA read count matrix, which calculates replicate consistency across samples and generates plots for quick visual assessment.

Sample metadata is optional for running CRISPR screen quality control, but a tab-delimited count matrix with sgRNA, Gene, and sample columns is required to compute statistics and generate visuals.

Computing CRISPR QC statistics and generating plots requires Python libraries including pandas, numpy, matplotlib, and scipy to analyze the sgRNA count matrix and produce visualizations.

You can analyze control vs targeting guides within your CRISPR screen to monitor library bias and evaluate data quality before proceeding to downstream analyses.