CRISPR Screen Pipeline

Pipeline Overview

FASTQ Files ──> Guide Counting ──> Count Matrix
                                        │
                                        ▼
              ┌─────────────────────────────────────────────┐
              │         crispr-screen-pipeline              │
              ├─────────────────────────────────────────────┤
              │  1. Guide Counting (MAGeCK count)           │
              │  2. QC: Library coverage, gini index        │
              │  3. Gene-level Analysis (MAGeCK RRA/MLE)    │
              │  4. Hit Calling (FDR, effect size)          │
              │  5. Visualization & Reporting               │
              └─────────────────────────────────────────────┘
                                        │
                                        ▼
                    Hit Genes + Volcano/Rank Plots

Complete Workflow

Step 1: Guide Counting

# From FASTQ files
mageck count \
    -l library.csv \
    -n experiment \
    --sample-label Day0,Day14_Rep1,Day14_Rep2,Day14_Rep3 \
    --fastq Day0.fastq.gz Day14_Rep1.fastq.gz Day14_Rep2.fastq.gz Day14_Rep3.fastq.gz \
    --trim-5 0 \
    --pdf-report

Step 2: Quality Control

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

counts = pd.read_csv('experiment.count.txt', sep='\t', index_col=0)
counts_numeric = counts.iloc[:, 1:]

qc_stats = {}
for col in counts_numeric.columns:
    total = counts_numeric[col].sum()
    zeros = (counts_numeric[col] == 0).sum()
    gini = calculate_gini(counts_numeric[col].values)
    qc_stats[col] = {'total_reads': total, 'zero_count_guides': zeros, 'gini': gini}

qc_df = pd.DataFrame(qc_stats).T
print('QC Summary:')
print(qc_df)

# Gini index function
def calculate_gini(x):
    x = np.sort(x[x > 0])
    n = len(x)
    cumsum = np.cumsum(x)
    return (2 * np.sum((np.arange(1, n+1) * x)) - (n + 1) * cumsum[-1]) / (n * cumsum[-1])

# QC thresholds
assert qc_df['zero_count_guides'].max() < len(counts) * 0.2, 'Too many zero-count guides'
assert qc_df['gini'].max() < 0.4, 'Gini index too high (uneven distribution)'
print('QC passed!')

Step 3: MAGeCK RRA Analysis (Negative Selection)

# For dropout/negative selection screens
mageck test \
    -k experiment.count.txt \
    -t Day14_Rep1,Day14_Rep2,Day14_Rep3 \
    -c Day0 \
    -n negative_screen \
    --pdf-report \
    --gene-lfc-method alphamedian

Step 4: MAGeCK MLE (Complex Designs)

# For screens with multiple conditions
# Design matrix: design.txt
# samplename,baseline,treatment
# Day0,1,0
# Day14_Ctrl,1,0
# Day14_Drug,1,1

mageck mle \
    -k experiment.count.txt \
    -d design.txt \
    -n mle_analysis \
    --threads 8

Step 5: Hit Calling

import pandas as pd

# Load MAGeCK results
gene_summary = pd.read_csv('negative_screen.gene_summary.txt', sep='\t')

# Define hits
gene_summary['neg_hit'] = (gene_summary['neg|fdr'] < 0.05) & (gene_summary['neg|lfc'] < -0.5)
gene_summary['pos_hit'] = (gene_summary['pos|fdr'] < 0.05) & (gene_summary['pos|lfc'] > 0.5)

neg_hits = gene_summary[gene_summary['neg_hit']].sort_values('neg|rank')
pos_hits = gene_summary[gene_summary['pos_hit']].sort_values('pos|rank')

print(f'Negative selection hits (dropout): {len(neg_hits)}')
print(f'Positive selection hits (enriched): {len(pos_hits)}')

# Save hit lists
neg_hits.to_csv('negative_hits.csv', index=False)
pos_hits.to_csv('positive_hits.csv', index=False)

Step 6: Visualization

import matplotlib.pyplot as plt
import numpy as np

# Volcano plot
fig, ax = plt.subplots(figsize=(10, 8))
x = gene_summary['neg|lfc']
y = -np.log10(gene_summary['neg|fdr'] + 1e-10)

colors = ['red' if h else 'blue' if p else 'gray'
          for h, p in zip(gene_summary['neg_hit'], gene_summary['pos_hit'])]
ax.scatter(x, y, c=colors, alpha=0.5, s=20)

ax.axhline(-np.log10(0.05), linestyle='--', color='black', alpha=0.5)
ax.axvline(-0.5, linestyle='--', color='black', alpha=0.5)
ax.axvline(0.5, linestyle='--', color='black', alpha=0.5)

ax.set_xlabel('Log2 Fold Change')
ax.set_ylabel('-Log10(FDR)')
ax.set_title('CRISPR Screen Volcano Plot')
plt.tight_layout()
plt.savefig('volcano_plot.png', dpi=150)

Complete R Workflow

library(MAGeCKFlute)
library(ggplot2)

# Load MAGeCK results
gene_summary <- read.delim('negative_screen.gene_summary.txt')
sgrna_summary <- read.delim('negative_screen.sgrna_summary.txt')

# QC with MAGeCKFlute
FluteMLE(mle_output = 'mle_analysis.gene_summary.txt',
         treatname = 'treatment',
         proj = 'crispr_screen',
         pathview.top = 10)

# Or for RRA results
FluteRRA(gene_summary = gene_summary,
         sgrna_summary = sgrna_summary,
         proj = 'rra_analysis')

# Custom rank plot
gene_summary$rank <- rank(gene_summary$`neg.score`)
gene_summary$is_hit <- gene_summary$`neg.fdr` < 0.05

ggplot(gene_summary, aes(x = rank, y = -log10(`neg.fdr` + 1e-10), color = is_hit)) +
    geom_point(alpha = 0.5) +
    geom_hline(yintercept = -log10(0.05), linetype = 'dashed') +
    scale_color_manual(values = c('gray', 'red')) +
    theme_bw() +
    labs(title = 'Gene Rank Plot', x = 'Rank', y = '-Log10(FDR)')
ggsave('rank_plot.png', width = 10, height = 6)

BAGEL2 Alternative (Essential Genes)

# Calculate Bayes Factor for essentiality
BAGEL.py bf \
    -i experiment.count.txt \
    -o bagel_output \
    -e CEGv2.txt \
    -n NEGv1.txt \
    -c Day0 \
    -s Day14_Rep1,Day14_Rep2,Day14_Rep3

# Precision-recall analysis
BAGEL.py pr \
    -i bagel_output.bf \
    -o bagel_pr \
    -e CEGv2.txt \
    -n NEGv1.txt

QC Checkpoints

Workflow Variants

Positive Selection Screen

# For enrichment screens (e.g., drug resistance)
mageck test \
    -k counts.txt \
    -t Resistant_Rep1,Resistant_Rep2 \
    -c Sensitive \
    -n positive_screen \
    --gene-lfc-method alphamedian

CRISPRi/CRISPRa

# Same workflow, different interpretation
# CRISPRi: negative LFC = gene promotes phenotype
# CRISPRa: positive LFC = gene promotes phenotype
mageck test -k counts.txt -t Treated -c Control -n crispri_screen

Related Skills

• crispr-screens/screen-qc - Detailed QC metrics
• crispr-screens/mageck-analysis - MAGeCK parameters
• crispr-screens/hit-calling - Hit calling methods
• crispr-screens/crispresso-editing - Individual editing analysis
• crispr-screens/library-design - sgRNA selection and library design
• crispr-screens/batch-correction - Multi-batch normalization
• pathway-analysis/enrichment-analysis - Pathway enrichment of hits