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 BioTender-max/awesome-bio-agent-skills --skill multiple-testing --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.
## 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) ```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 dis
- Version Compatibility
- 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 BioTender-max/awesome-bio-agent-skills --skill multiple-testing --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.