tooluniverse-crispr-screen-analysis

ToolUniverse CRISPR Screen Analysis

Comprehensive skill for analyzing CRISPR-Cas9 genetic screens to identify essential genes, synthetic lethal interactions, and therapeutic targets through robust statistical analysis and pathway enrichment.

Overview

CRISPR screens enable genome-wide functional genomics by systematically perturbing genes and measuring fitness effects. This skill provides an 8-phase workflow for:

• Processing sgRNA count matrices
• Quality control and normalization
• Gene-level essentiality scoring (MAGeCK-like and BAGEL-like approaches)
• Synthetic lethality detection
• Pathway enrichment analysis
• Drug target prioritization with DepMap integration
• Integration with expression and mutation data

Core Workflow

Phase 1: Data Import & sgRNA Count Processing

Load sgRNA Count Matrix

import pandas as pd
import numpy as np

def load_sgrna_counts(counts_file):
    """
    Load sgRNA count matrix from MAGeCK format or generic TSV.

    Expected format:
    sgRNA | Gene | Sample1 | Sample2 | Sample3 | ...
    sgRNA_1 | BRCA1 | 1500 | 1200 | 1100 | ...
    sgRNA_2 | BRCA1 | 1800 | 1500 | 1400 | ...
    """
    counts = pd.read_csv(counts_file, sep='\t')

    # Validate required columns
    required_cols = ['sgRNA', 'Gene']
    if not all(col in counts.columns for col in required_cols):
        raise ValueError(f"Missing required columns: {required_cols}")

    # Extract sample columns
    sample_cols = [col for col in counts.columns if col not in ['sgRNA', 'Gene']]

    # Create count matrix
    count_matrix = counts[sample_cols].copy()
    count_matrix.index = counts['sgRNA']

    # Gene mapping
    sgrna_to_gene = dict(zip(counts['sgRNA'], counts['Gene']))

    metadata = {
        'n_sgrnas': len(counts),
        'n_genes': counts['Gene'].nunique(),
        'n_samples': len(sample_cols),
        'sample_names': sample_cols,
        'sgrna_to_gene': sgrna_to_gene
    }

    return count_matrix, metadata

# Load counts
counts, meta = load_sgrna_counts("sgrna_counts.txt")
print(f"Loaded {meta['n_sgrnas']} sgRNAs targeting {meta['n_genes']} genes across {meta['n_samples']} samples")