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.
npx skills add majiayu000/claude-skill-registry --skill multiple-testing-gptomics-bioskills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```p
- The Problem
- Common Methods
- Bonferroni (Most Conservative)
- Benjamini-Hochberg FDR (Standard)
- q-value (Recommended for Large-Scale)
- Method Selection Guide
- Python Equivalent
- Interpreting Results
- Related Skills
What does the bio-experimental-design-multiple-testing skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill multiple-testing-gptomics-bioskills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.