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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.
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| 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 |
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:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Find differentially abundant proteins between my conditions" → Perform statistical testing on quantified protein intensities to identify proteins with significant abundance changes between experimental groups.
MSstats::groupComparison() for feature-level mixed modelslimma::eBayes() for empirical Bayes moderated t-tests on protein-level datascipy.stats.ttest_ind() with statsmodels FDR correctionGoal: 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)
# 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$log2FC) > 1, ]
library(limma)
# Log2 intensities matrix (proteins x samples)
design <- model.matrix(~ 0 + condition, data = sample_info)
colnames(design) <- levels(sample_info$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, ]
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, ]
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)]
# 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()