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Bio Experimental Design Multiple Testing by tomevault-io

Name: Bio Experimental Design Multiple Testing
Author: tomevault-io

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. Use when this capability is needed.

tomevault-io 01b0626 2 files · 4.2 KB Updated Sep 17, 2026

File contents

Version Compatibility

Reference examples tested with: R stats (base), statsmodels 0.14+

Before using code patterns, verify installed versions match. If versions differ:

Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Multiple Testing Correction

"Correct p-values for multiple testing" → Adjust raw p-values from thousands of simultaneous tests to control false discovery rate or family-wise error rate.

R: p.adjust(pvalues, method = 'BH'), qvalue::qvalue()
Python: statsmodels.stats.multitest.multipletests()

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)

Goal: Estimate the false discovery rate for each gene in a genome-wide test while maximizing detection power by estimating the proportion of true nulls.

Approach: Fit the q-value model to the p-value distribution, which estimates pi0 (fraction of true null hypotheses) and converts each p-value to a q-value representing the minimum FDR at which that gene would be called significant.

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

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

---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. Use when this capability is needed.---## Version CompatibilityReference examples tested with: R stats (base), statsmodels 0.14+Before using code patterns, verify installed versions match. If versions differ:- Python: `pip show <package>` then `help(module.function)` to check signatures- R: `packageVersion('<pkg>')` then `?function_name` to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installedpackage and adapt the example to match the actual API rather than retrying.# Multiple Testing Correction**"Correct p-values for multiple testing"** → Adjust raw p-values from thousands of simultaneous tests to control false discovery rate or family-wise error rate.- R: `p.adjust(pvalues, method = 'BH')`, `qvalue::qvalue()`- Python: `statsmodels.stats.multitest.multipletests()`## 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)**Goal:** Estimate the false discovery rate for each gene in a genome-wide test while maximizing detection power by estimating the proportion of true nulls.**Approach:** Fit the q-value model to the p-value distribution, which estimates pi0 (fraction of true null hypotheses) and converts each p-value to a q-value representing the minimum FDR at which that gene would be called significant.```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---> Converted and distributed by [TomeVault](https://tomevault.io/claim/gptomics) — claim your Tome and manage your conversions.

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