ReddRadar
Agent skill
bio-experimental-design-multiple-testing
Applies multiple testing correction methods including FDR, Bonferroni, and q-value for genomics data. Use when filtering differential expression results, setting significance thresholds, or choosing between correction methods for different study designs.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/multiple-testing
SKILL.md
Multiple Testing Correction
The Problem
Testing 20,000 genes at p < 0.05 yields ~1,000 false positives by chance. Correction is essential.
Common Methods
Bonferroni (Most Conservative)
# Strict family-wise error rate control
p_adj <- p.adjust(pvalues, method = 'bonferroni')
# Threshold: alpha / n_tests
# Use for: small gene sets, confirmatory studies
Benjamini-Hochberg FDR (Standard)
# Controls false discovery rate
p_adj <- p.adjust(pvalues, method = 'BH')
# Most common for genomics
# FDR 0.05 = expect 5% of significant results to be false
q-value (Recommended for Large-Scale)
library(qvalue)
qobj <- qvalue(pvalues)
qvalues <- qobj$qvalues
pi0 <- qobj$pi0 # Estimated proportion of true nulls
# q-value directly estimates FDR for each gene
# More powerful than BH when many true positives exist
Method Selection Guide
| Scenario | Recommended Method | Threshold |
|---|---|---|
| Genome-wide DE | BH or q-value | FDR < 0.05 |
| Candidate genes | Bonferroni | p < 0.05/n |
| Exploratory | BH | FDR < 0.10 |
| Validation study | Bonferroni | p < 0.05/n |
| GWAS | Bonferroni | p < 5e-8 |
Python Equivalent
from statsmodels.stats.multitest import multipletests
# Benjamini-Hochberg
rejected, pvals_corrected, _, _ = multipletests(pvalues, method='fdr_bh')
# Bonferroni
rejected, pvals_corrected, _, _ = multipletests(pvalues, method='bonferroni')
Interpreting Results
- FDR 0.05: Among genes called significant, ~5% are false positives
- FDR 0.01: More stringent, fewer false positives but more false negatives
- padj vs qvalue: Both estimate FDR; q-value is slightly more powerful
Related Skills
- differential-expression/de-results - Applying corrections to DE output
- population-genetics/association-testing - GWAS significance thresholds
- pathway-analysis/go-enrichment - Correcting enrichment p-values
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