bio-proteomics-differential-abundance
Statistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-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: R stats (base), ggplot2 3.5+, limma 3.58+, 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 quantified protein intensities to identify proteins with significant abundance changes between experimental groups. - R: `MSstats::groupComparison()` for feature-level mixed models - R: `limma::eBayes()` for empirical Bayes moderated t-tests on protein-level data - Python: `scipy.stats.ttest_ind()` with `statsmodels` FDR correction ## MSstats Group Comparison (R stats (base)+) **Goal:** Identify differentially abundant proteins between experimental conditions using feature-level mixed models or moderated t-tests
- Version Compatibility
- MSstats Group Comparison (R stats (base)+)
- limma for Proteomics (R stats (base)+)
- QFeatures/proDA (Modern Alternative)
- Python: scipy/statsmodels
- Visualization (R stats (base)+)
- Related Skills
What does the bio-proteomics-differential-abundance skill do?
Statistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.