How do I install the Bio Experimental Design Multiple Testing skill?

Run npx skillmds@latest add bg-szy/bio-experimental-design-multiple-testing in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Bio Experimental Design Multiple Testing safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Bio Experimental Design Multiple Testing?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Bio Experimental Design Multiple Testing free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Bio Experimental Design Multiple Testing?

bg-szy (@bg-szy) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bio Experimental Design Multiple Testing by bg-szy

Name: Bio Experimental Design Multiple Testing
Author: bg-szy

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.

bg-szy Updated Sep 17, 2026

File contents

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

---name: bio-experimental-design-multiple-testingdescription: 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.---# Multiple Testing Correction## The ProblemTesting 20,000 genes at p < 0.05 yields ~1,000 false positives by chance. Correction is essential.## Common Methods### Bonferroni (Most Conservative)```r# Strict family-wise error rate controlp_adj <- p.adjust(pvalues, method = 'bonferroni')# Threshold: alpha / n_tests# Use for: small gene sets, confirmatory studies```### Benjamini-Hochberg FDR (Standard)```r# Controls false discovery ratep_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)```rlibrary(qvalue)qobj <- qvalue(pvalues)qvalues <- qobj$qvaluespi0 <- qobj$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```pythonfrom statsmodels.stats.multitest import multipletests# Benjamini-Hochbergrejected, pvals_corrected, _, _ = multipletests(pvalues, method='fdr_bh')# Bonferronirejected, 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

Frequently asked questions