bio-experimental-design-multiple-testing

<!--

COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

#

This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

#

Provenance: Authenticated by MD BABU MIA

-->

---

name: bio-experimental-design-multiple-testing

description: 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.

tool_type: r

primary_tool: qvalue

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

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

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->