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