bio-proteomics-differential-abundance
Statistical testing for differentially abundant proteins between conditions. Covers preprocessing (log2 transformation, normalization), limma and DEqMS workflows with empirical Bayes moderation, fold change shrinkage for accurate effect size estimation, and Python alternatives. Use when identifying proteins with significant abundance changes between experimental groups.
npx skills add BioTender-max/awesome-bio-agent-skills --skill bioskills__proteomics__differential-abundance --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: limma 3.58+, DEqMS 1.20+, ashr 2.2+, proDA 1.20+, numpy 1.26+, pandas 2.2+, scipy 1.12+, 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. # Differential Protein Abundance **"Find differentially abundant proteins between my conditions"** -> Perform statistical testing on protein intensities to identify significant abundance changes. - R: `limma::eBayes()` for empirical Bayes moderated t-tests (preferred for small n) - R: `DEqMS::spectraCounteBayes()` when PSM/peptide count metadata is available - R: `proDA::test_diff()` when missing values are extensive (label-free) - Python: `scipy.stats.ttest_ind(equal_var=False)` with `statsmodels` BH correction ## Preprocessing Pipeline Raw mass spectrometry intensities require log2 transformation and normalization before statistical
- Version Compatibility
- Preprocessing Pipeline
- Log2 Transformation
- Normalization
- Method Selection
- limma Workflow (R)
- DEqMS Workflow (R)
- proDA Workflow (R)
- Python Workflow
- Fold Change Reporting
- When to report raw fold changes
- When to apply fold change shrinkage
- Minimum fold change testing
- Visualization
What does the bio-proteomics-differential-abundance skill do?
Statistical testing for differentially abundant proteins between conditions. Covers preprocessing (log2 transformation, normalization), limma and DEqMS workflows with empirical Bayes moderation, fold change shrinkage for accurate effect size estimation, and Python alternatives. Use when identifying proteins with significant abundance changes between experimental groups.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill bioskills__proteomics__differential-abundance --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.