---
name: bio-proteomics-differential-abundance
description: 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.
tool_type: mixed
primary_tool: MSstats
---

## 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.

**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.

```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 stats (base)+)

```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(QFeatures)
library(proDA)

# proDA handles missing values probabilistically
fit <- proDA(protein_matrix, design = ~ condition, data = sample_info)

# Test differential abundance
results <- test_diff(fit, contrast = 'conditionTreatment')
results$adj_pval <- p.adjust(results$pval, method = 'BH')
sig_results <- results[results$adj_pval < 0.05 & abs(results$diff) > 1, ]
```

## Python: scipy/statsmodels

```python
import pandas as pd
import numpy as np
from scipy import stats
from statsmodels.stats.multitest import multipletests

def differential_test(intensities, group1_cols, group2_cols):
    results = []
    for protein in intensities.index:
        g1 = intensities.loc[protein, group1_cols].dropna()
        g2 = intensities.loc[protein, group2_cols].dropna()

        if len(g1) >= 2 and len(g2) >= 2:
            stat, pval = stats.ttest_ind(g1, g2)
            log2fc = g2.mean() - g1.mean()
            results.append({'protein': protein, 'log2FC': log2fc, 'pvalue': pval})

    df = pd.DataFrame(results)
    df['adj_pvalue'] = multipletests(df['pvalue'], method='fdr_bh')[1]
    return df

# Significance thresholds
sig = results[(results['adj_pvalue'] < 0.05) & (abs(results['log2FC']) > 1)]
```

## Visualization (R stats (base)+)

```r
# Volcano plot
library(ggplot2)

ggplot(results, aes(x = log2FC, y = -log10(adj.P.Val))) +
    geom_point(aes(color = significant), alpha = 0.6) +
    geom_hline(yintercept = -log10(0.05), linetype = 'dashed') +
    geom_vline(xintercept = c(-1, 1), linetype = 'dashed') +
    scale_color_manual(values = c('grey', 'red')) +
    theme_minimal()
```

## 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


---
Source attribution: GPTomics
Original project: https://github.com/GPTomics/bioSkills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 FridrichMethod

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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SOFTWARE.

---

NOTE ON BUNDLED CONTENT

This repository aggregates skills from multiple upstream projects (see the
README for the source list). Each `skills/<name>/` directory may carry its
own license inherited from its upstream source. The MIT license above
applies to this repository's curation and the scripts/workflows in `/`,
`/scripts/`, and `/.github/` — not to the upstream skill content itself.
Consult the upstream repository linked in the README and any LICENSE
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# Mega AI Hub resource metadata

Creator / publisher: GPTomics
Original project: https://github.com/GPTomics/bioSkills
Attribution basis: Exact package in biomedical source index and publisher source table

Fields of work: Data & Analytics, Education & Research
Roles: Business Intelligence Analyst, Data Scientist, Researcher
Specialties: API, Testing
Tools: Msstats, Limma
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 9a416d5b5a3e27ad4876f505c0e90333cf1343926686332730ffcad9e317f46f
Metadata reviewed: 2026-10-27
This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.