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name: bio-crispr-screens-screen-qc description: Quality control for pooled CRISPR screens. Covers library representation, read distribution, replicate correlation, and essential gene recovery. Use when assessing screen quality before hit calling or diagnosing poor screen performance. tool_type: python primary_tool: pandas measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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CRISPR Screen Quality Control

Load Count Data

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

# Load MAGeCK count output
counts = pd.read_csv('screen.count.txt', sep='\t', index_col=0)
genes = counts['Gene']
count_matrix = counts.drop('Gene', axis=1)

print(f'sgRNAs: {len(count_matrix)}')
print(f'Genes: {genes.nunique()}')
print(f'Samples: {count_matrix.columns.tolist()}')

Library Representation

# Zero-count sgRNAs per sample
zero_counts = (count_matrix == 0).sum()
zero_pct = zero_counts / len(count_matrix) * 100

print('Zero-count sgRNAs per sample:')
for sample, pct in zero_pct.items():
    status = 'OK' if pct < 1 else 'WARNING' if pct < 5 else 'FAIL'
    print(f'  {sample}: {pct:.2f}% [{status}]')

# Low-count sgRNAs (<30 reads)
low_counts = (count_matrix < 30).sum()
low_pct = low_counts / len(count_matrix) * 100
print('\nLow-count sgRNAs (<30 reads):')
for sample, pct in low_pct.items():
    print(f'  {sample}: {pct:.2f}%')

Read Distribution (Gini Index)

def gini_index(x):
    '''Calculate Gini index (0=perfect equality, 1=complete inequality)'''
    x = np.sort(x[x > 0])
    n = len(x)
    cumx = np.cumsum(x)
    return (n + 1 - 2 * np.sum(cumx) / cumx[-1]) / n

gini_values = count_matrix.apply(gini_index)
print('\nGini index per sample (lower is better, <0.2 ideal):')
for sample, gini in gini_values.items():
    status = 'OK' if gini < 0.2 else 'WARNING' if gini < 0.3 else 'FAIL'
    print(f'  {sample}: {gini:.3f} [{status}]')

Read Count Distribution

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Log read count distribution
for sample in count_matrix.columns:
    log_counts = np.log10(count_matrix[sample] + 1)
    axes[0].hist(log_counts, bins=50, alpha=0.5, label=sample)
axes[0].set_xlabel('Log10(counts + 1)')
axes[0].set_ylabel('sgRNAs')
axes[0].set_title('Read Count Distribution')
axes[0].legend()

# Cumulative distribution
for sample in count_matrix.columns:
    sorted_counts = np.sort(count_matrix[sample])[::-1]
    cumsum = np.cumsum(sorted_counts) / sorted_counts.sum()
    axes[1].plot(np.arange(len(cumsum)) / len(cumsum) * 100, cumsum * 100, label=sample)
axes[1].set_xlabel('% of sgRNAs (ranked)')
axes[1].set_ylabel('% of total reads')
axes[1].set_title('Cumulative Read Distribution')
axes[1].legend()

plt.tight_layout()
plt.savefig('qc_distribution.png', dpi=150)

Replicate Correlation

# Correlation matrix
log_counts = np.log10(count_matrix + 1)
corr_matrix = log_counts.corr()

plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, cmap='RdYlBu_r', vmin=0.5, vmax=1,
            square=True, fmt='.2f')
plt.title('Replicate Correlation (log10 counts)')
plt.tight_layout()
plt.savefig('qc_correlation.png', dpi=150)

# Check replicate pairs
print('\nReplicate correlations:')
for i, col1 in enumerate(count_matrix.columns):
    for col2 in count_matrix.columns[i+1:]:
        r = corr_matrix.loc[col1, col2]
        status = 'OK' if r > 0.8 else 'WARNING' if r > 0.6 else 'FAIL'
        print(f'  {col1} vs {col2}: r={r:.3f} [{status}]')

Essential Gene Recovery

# Load known essential genes (e.g., from Hart et al. or DepMap)
essential_genes = set(pd.read_csv('essential_genes.txt', header=None)[0])
nonessential_genes = set(pd.read_csv('nonessential_genes.txt', header=None)[0])

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

# Check recovery in T0 vs later timepoint
present_essential = results[results['id'].isin(essential_genes)]
present_nonessential = results[results['id'].isin(nonessential_genes)]

# ROC-like analysis
from sklearn.metrics import roc_auc_score

y_true = results['id'].isin(essential_genes).astype(int)
y_score = -results['neg|score']  # More negative = more essential

if y_true.sum() > 0:
    auc = roc_auc_score(y_true, y_score)
    print(f'\nEssential gene recovery AUC: {auc:.3f}')
    status = 'EXCELLENT' if auc > 0.9 else 'GOOD' if auc > 0.8 else 'FAIR' if auc > 0.7 else 'POOR'
    print(f'Status: {status}')

sgRNA Performance

# sgRNAs per gene
sgrnas_per_gene = genes.value_counts()
print(f'\nsgRNAs per gene: mean={sgrnas_per_gene.mean():.1f}, min={sgrnas_per_gene.min()}, max={sgrnas_per_gene.max()}')

# Check for genes with few sgRNAs
few_sgrnas = sgrnas_per_gene[sgrnas_per_gene < 3]
if len(few_sgrnas) > 0:
    print(f'WARNING: {len(few_sgrnas)} genes have <3 sgRNAs')

Sample Normalization Check

# Total reads per sample
total_reads = count_matrix.sum()
print('\nTotal reads per sample:')
for sample, total in total_reads.items():
    print(f'  {sample}: {total:,}')

# Check for major imbalances
cv = total_reads.std() / total_reads.mean()
print(f'\nCoefficient of variation: {cv:.3f}')
if cv > 0.5:
    print('WARNING: Large variation in sequencing depth')

QC Summary Report

def generate_qc_report(count_matrix, genes):
    report = {
        'total_sgrnas': len(count_matrix),
        'total_genes': genes.nunique(),
        'samples': len(count_matrix.columns),
        'zero_count_pct': (count_matrix == 0).sum().mean() / len(count_matrix) * 100,
        'gini_mean': count_matrix.apply(gini_index).mean(),
        'replicate_corr_min': np.log10(count_matrix + 1).corr().min().min(),
    }

    print('=== QC Summary ===')
    for key, value in report.items():
        if isinstance(value, float):
            print(f'{key}: {value:.3f}')
        else:
            print(f'{key}: {value}')

    # Overall status
    passes = []
    passes.append(report['zero_count_pct'] < 5)
    passes.append(report['gini_mean'] < 0.25)
    passes.append(report['replicate_corr_min'] > 0.7)

    status = 'PASS' if all(passes) else 'FAIL'
    print(f'\nOverall QC: {status}')

    return report

report = generate_qc_report(count_matrix, genes)

Related Skills

• mageck-analysis - Run MAGeCK after QC
• hit-calling - Downstream analysis
• read-qc/quality-reports - General NGS QC

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