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 majiayu000/claude-skill-registry --skill 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.
# Differential Protein Abundance ## MSstats Group Comparison ```r library(MSstats) # After dataProcess() comparison_matrix <- matrix(c(1, -1, 0, 0, 1, 0, -1, 0, 0, 1, -1, 0), nrow = 3, byrow = TRUE) rownames(comparison_matrix) <- c('Treatment1-Control', 'Treatment2-Control', 'Treatment1-Treatment2') colnames(comparison_matrix) <- c('Control', 'Treatment1', 'Treatment2', 'Treatment3') results <- groupComparison(contrast.matrix = comparison_matrix, data = processed) # Significant proteins sig_proteins <- results$ComparisonResult[results$ComparisonResult$adj.pvalue < 0.05 & abs(results$ComparisonResult$log2FC) > 1, ] ``` ## limma for Proteomics ```r library(limma) # Log2 intensities matrix (proteins x samples) design <- model.matrix(~ 0 + condition, data = sample_info) colnames(design) <- levels(sample_info$condition) fit <- lmFit(protein_matrix, design) contrast_matrix <- makeContrasts(Treatment - Control, levels = design) fit2 <- contrasts.fit(fit, contrast_matrix) fit2 <- eBayes(fit2) results <- topTable(fit2, number = Inf, adjust.method = 'BH') sig_results <- results[results$adj.P.Val < 0.05 & abs(results$logFC) > 1, ] ``` ## QFeatures/proDA (Modern Alternative) ```r library(QFeat
- MSstats Group Comparison
- limma for Proteomics
- QFeatures/proDA (Modern Alternative)
- Python: scipy/statsmodels
- Visualization
- 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 majiayu000/claude-skill-registry --skill 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 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.