usage-guide.md

Multiple Testing Correction Usage Guide

Overview

This guide covers applying multiple testing corrections including FDR, Bonferroni, and q-value methods.

Prerequisites

# R/Bioconductor
install.packages('BiocManager')
BiocManager::install('qvalue')

# Python
pip install statsmodels scipy

Quick Start

Tell your AI agent what you want to do:

• "Apply FDR correction to my differential expression p-values"
• "Which multiple testing correction should I use for my GWAS results?"
• "Calculate q-values for my DE results"
• "Filter my results at FDR < 0.05"

Example Prompts

Differential Expression

"I have p-values from DESeq2 for 20,000 genes. Apply Benjamini-Hochberg correction and filter at FDR 0.05"

"Compare the number of significant genes using Bonferroni vs BH correction"

Method Selection

"I'm doing an exploratory analysis. Should I use FDR 0.05 or 0.10?"

"What's the difference between adjusted p-value and q-value?"

GWAS

"Apply genome-wide significance threshold to my GWAS results"

What the Agent Will Do

1. Identify the analysis context
2. Select appropriate correction method
3. Apply correction to p-values
4. Report number of significant results
5. Explain interpretation of corrected values

Tips

• BH/FDR is standard for most genomics analyses
• Bonferroni is appropriate for small, targeted gene sets
• q-value provides more power than BH when many true positives exist
• FDR 0.05 means 5% of significant calls are expected to be false
• For exploratory work, FDR 0.10 is acceptable
• GWAS uses genome-wide threshold of 5e-8 (Bonferroni for ~1M tests)