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Data & Repro
BI
bio-proteomics-differential-abundance
Maintainer FreedomIntelligence · Last updated April 1, 2026
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.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-proteomics-differential-abundance
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- 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.
- 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.
- 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').
Source Doc
Excerpt From SKILL.md
MSstats Group Comparison (R stats (base)+)
Goal: Identify differentially abundant proteins between experimental conditions using feature-level mixed models or moderated t-tests.
Approach: Define contrast matrices for pairwise comparisons, run MSstats groupComparison (or limma eBayes for protein-level data), then filter results by adjusted p-value and log2 fold change thresholds.
library(MSstats)
## Significant proteins
sig_proteins <- results$ComparisonResult[results$ComparisonResult$adj.pvalue < 0.05 &
abs(results$ComparisonResult$log2FC) > 1, ]
Log2 intensities matrix (proteins x samples)
design <- model.matrix(~ 0 + condition, data = sample_info) colnames(design) <- levels(sample_info$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, ]
Use cases
- Use when identifying proteins with significant abundance changes between experimental groups.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- quantification - Prepare normalized data for testing
- differential-expression/deseq2-basics - Similar concepts for RNA-seq
- data-visualization/specialized-omics-plots - Volcano plots, MA plots
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