Hit Calling - Usage Guide

Hit Calling - Usage Guide

Overview

Multiple methods exist for calling hits in CRISPR screens. The choice depends on screen design, reference data availability, and desired stringency.

Prerequisites

# MAGeCK
pip install mageck
# BAGEL2
pip install bagel
# Or via conda
conda install -c bioconda mageck bagel2

Quick Start

Tell your AI agent what you want to do:

• "Call significant hits from my CRISPR screen results"
• "Identify essential genes using multiple methods"
• "Find consensus hits across MAGeCK and BAGEL2"

Example Prompts

Single Method Analysis

"Run MAGeCK RRA on my screen and call hits at FDR < 0.1."

"Use BAGEL2 to identify essential genes. I have reference essential and non-essential gene lists."

"Apply drugZ analysis to my drug screen data to find resistance genes."

Threshold Selection

"What's the appropriate FDR threshold for my exploratory CRISPR screen?"

"I need high-confidence hits. What BAGEL2 Bayes Factor cutoff should I use?"

Consensus Approach

"Run MAGeCK, BAGEL2, and drugZ on my data and find genes called by at least 2 methods."

"Compare hit lists between different analysis methods. Which genes are consistent?"

Selection Direction

"Find genes that drop out (negative selection) in my essentiality screen."

"Identify resistance genes (positive selection) from my drug treatment screen."

What the Agent Will Do

1. Select appropriate hit-calling method(s)
2. Run statistical analysis
3. Apply significance thresholds
4. Separate positive and negative selection hits
5. Generate ranked gene lists
6. Optionally find consensus across methods

Tips

• Use multiple methods and take consensus for robust hits
• FDR < 0.1 for discovery, FDR < 0.05 for high confidence
• BAGEL2 Bayes Factor > 5 indicates strong evidence
• Z-score |Z| > 3 corresponds to approximately p < 0.003
• Consider both positive and negative selection directions

Method Comparison

Significance Thresholds

References

• BAGEL2: doi:10.1186/s13059-019-1749-z
• drugZ: doi:10.1186/s13073-019-0665-3