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majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bio Experimental Design Multiple Testing by majiayu000

Name: Bio Experimental Design Multiple Testing
Rating: 4.5 (4 reviews)
Author: majiayu000

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.

majiayu000 ebca733 2 files · 2.9 KB Updated Sep 9, 2026 567 repo stars

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 Problem Testing 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 control p_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 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) ```r 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 ```python 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

Frequently asked questions