| name | crispr-screen-analysis |
| description | Analyze CRISPR pooled screens using MAGeCK. Accepts a raw count table or FASTQ files. Runs MAGeCK RRA (pairwise test) or MAGeCK MLE (multi-condition). Produces gene-level hit rankings, volcano plots, sgRNA rank plots, and hit summary figures for both positive and negative selection. Supports genome-wide KO screens, CRISPRi, and CRISPRa experiments. |
CRISPR Screen Analysis (MAGeCK)
Purpose
Run a complete CRISPR pooled screen analysis pipeline using MAGeCK. Starting from either a raw count matrix or FASTQ files, identify positively and negatively selected genes, and generate publication-quality figures.
Use when
- the user has a CRISPR screen count table and wants to identify hits
- the user wants to find essential genes (negative selection) or enriched genes (positive selection)
- the user wants to compare treatment vs control conditions in a pooled screen
- the user has multiple conditions or time points (use MLE mode)
- the user has raw FASTQ files from a pooled screen and wants to go from reads to hits
- the user mentions MAGeCK, RRA, beta score, sgRNA count, or pooled screen analysis
- the user wants a volcano plot, rank plot, or gene score plot from screen data
Do not use when
- the user wants arrayed screen analysis (not pooled)
- the user wants single-cell CRISPR screen analysis (Perturb-seq / CROP-seq)
- the user wants to design sgRNAs (use design-sgrnas-by-gene instead)
- the user has only a gene hit list without raw count data
Supported modes
| Mode | Input | When to use |
|---|
test | count table TSV | Pairwise comparison (treatment vs control); standard RRA |
mle | count table TSV + design matrix | Multiple conditions, time-course, or complex designs |
count | FASTQ files + sgRNA library | Convert raw reads to count table |
all | FASTQ files + library | Full pipeline: count → test |
Inputs
--count-table — TSV with columns: sgRNA, gene, sample1, sample2, ... (required for test/mle)--treatment — comma-separated sample column names for the treatment group--control — comma-separated sample column names for the control group--mode — test (default), mle, count, or all--fastq — space-separated FASTQ paths (for count/all mode)--library — sgRNA library file: TSV with columns sgRNA_id, sequence, gene (for count mode)--design-matrix — tab-separated design matrix file (for mle mode)--norm-method — median (default), total, control, or none--fdr-cutoff — FDR significance threshold (default 0.25)--lfc-cutoff — |log2FC| threshold for volcano plot labeling (default 1.0)--gene-lfc-method — median or alphamedian (default median)--top-n — number of top hits to label in plots (default 20)--prefix — output file prefix (default screen)--outdir — output directory (default ./crispr_screen_results/)
Outputs
| File | Description |
|---|
{prefix}.gene_summary.txt | MAGeCK gene-level results (RRA scores, p-values, FDR) |
{prefix}.sgrna_summary.txt | sgRNA-level results (LFC, p-value per guide) |
{prefix}_volcano.png | Gene-level volcano plot (neg/pos selection) |
{prefix}_rank_neg.png | Negative selection rank plot (essential genes) |
{prefix}_rank_pos.png | Positive selection rank plot |
{prefix}_hit_summary.png | Top hit genes bar chart |
{prefix}_sgrna_scatter.png | sgRNA-level LFC scatter (treatment vs control) |
{prefix}_qc.png | Read count distribution and Gini index QC |
{prefix}_hits.tsv | Filtered hit gene table (FDR < cutoff) |
{prefix}_count_table.txt | (count mode) Normalized count table |
Execution
python scripts/crispr_screen_analysis.py \
--count-table counts.txt \
--treatment Day14_rep1,Day14_rep2 \
--control Day0_rep1,Day0_rep2 \
--mode test \
--outdir results/
python scripts/crispr_screen_analysis.py \
--count-table counts.txt \
--mode mle \
--design-matrix design.txt \
--outdir results/
python scripts/crispr_screen_analysis.py \
--fastq sample1.fastq.gz sample2.fastq.gz \
--library sgrna_library.txt \
--treatment sample1 \
--control sample2 \
--mode all \
--outdir results/
Parameter decision guide
| Signal in user request | Parameter to set |
|---|
| "pairwise", "treatment vs control", "two groups", "KO screen" | --mode test |
| "multiple conditions", "time course", "complex design", "beta score" | --mode mle |
| "from FASTQ", "raw reads", "sequencing data" | --mode count or --mode all |
| "essential genes", "negative selection", "dropout" | --mode test; focus on neg.score column |
| "enriched genes", "positive selection", "gain of function", "CRISPRa" | --mode test; focus on pos.score column |
| "more stringent", "high confidence hits only" | --fdr-cutoff 0.05 |
| "permissive", "screening", "exploratory" | --fdr-cutoff 0.25 (default) |
| "median normalization" or no mention | --norm-method median (default) |
| "spike-in controls", "non-targeting controls for normalization" | --norm-method control |
| "label top N hits" | --top-n N |
| replicate columns named Day0_rep1, Day0_rep2 | --control Day0_rep1,Day0_rep2 |
| replicate columns named treated_A, treated_B | --treatment treated_A,treated_B |
| "strong effect size only" | --lfc-cutoff 1.5 or --lfc-cutoff 2.0 |
MAGeCK installation note
MAGeCK must be installed separately. The recommended method is conda:
conda install -c bioconda mageck
Or via pip (Python-only implementation):
pip install mageck
This skill will automatically detect whether MAGeCK is available and fall back to a Python re-implementation of the core RRA algorithm if it is not installed.
