Version Compatibility

Reference examples tested with: ggplot2 3.5+, limma 3.58+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scikit-learn 1.4+, scipy 1.12+, seaborn 0.13+

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.

Proteomics Quality Control

"Check the quality of my proteomics data" → Assess data quality through identification rates, missing value patterns, replicate correlation, intensity distributions, and batch effect detection before downstream analysis.

• Python: pandas + matplotlib/seaborn for QC metrics and visualization
• R: limma::plotMDS(), correlation heatmaps, CV distributions

Sample Quality Metrics

python
import pandas as pd
import numpy as np

def sample_qc_metrics(intensity_matrix):
    '''Calculate per-sample QC metrics'''
    metrics = pd.DataFrame(index=intensity_matrix.columns)
    metrics['n_proteins'] = intensity_matrix.notna().sum()
    metrics['median_intensity'] = intensity_matrix.median()
    metrics['mean_intensity'] = intensity_matrix.mean()
    metrics['cv'] = intensity_matrix.std() / intensity_matrix.mean()
    metrics['missing_pct'] = 100 * intensity_matrix.isna().sum() / len(intensity_matrix)
    return metrics

qc = sample_qc_metrics(log2_intensities)
print(qc)

Replicate Correlation

python
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import pearsonr

def replicate_correlation(intensity_matrix, sample_groups):
    '''Calculate within-group correlations'''
    corr_matrix = intensity_matrix.corr(method='pearson')

    # Mask for within-group comparisons
    results = []
    for group in sample_groups.unique():
        group_samples = sample_groups[sample_groups == group].index
        for i, s1 in enumerate(group_samples):
            for s2 in group_samples[i+1:]:
                r = corr_matrix.loc[s1, s2]
                results.append({'group': group, 'sample1': s1, 'sample2': s2, 'correlation': r})

    return pd.DataFrame(results)

# Heatmap
sns.clustermap(intensity_matrix.corr(), cmap='RdBu_r', center=0, vmin=-1, vmax=1,
               figsize=(10, 10), annot=False)
plt.savefig('correlation_heatmap.pdf')

Missing Value Patterns

python
import missingno as msno

def analyze_missing_patterns(intensity_matrix):
    '''Analyze missing value patterns'''
    # Missing value matrix visualization
    msno.matrix(intensity_matrix, figsize=(12, 8))
    plt.savefig('missing_pattern.pdf')

    # Missing by sample
    missing_per_sample = intensity_matrix.isna().sum() / len(intensity_matrix) * 100

    # Missing by protein
    missing_per_protein = intensity_matrix.isna().sum(axis=1) / intensity_matrix.shape[1] * 100

    # Check for systematic patterns
    return {'per_sample': missing_per_sample, 'per_protein': missing_per_protein}

Batch Effect Detection with PCA

Goal: Detect batch effects in proteomics data by testing whether processing batches explain significant variance in the principal components.

Approach: Impute missing values, scale the intensity matrix, run PCA, then test the association of each top PC with batch labels using one-way ANOVA.

python
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

def detect_batch_effects(intensity_matrix, sample_info, batch_col='batch'):
    '''PCA to detect batch effects'''
    # Impute for PCA (temporary)
    imputed = intensity_matrix.fillna(intensity_matrix.median())
    scaled = StandardScaler().fit_transform(imputed.T)

    pca = PCA(n_components=5)
    pcs = pca.fit_transform(scaled)
    pc_df = pd.DataFrame(pcs, columns=[f'PC{i+1}' for i in range(5)], index=intensity_matrix.columns)
    pc_df = pc_df.join(sample_info)

    # Check batch association with PCs
    from scipy.stats import f_oneway
    for pc in ['PC1', 'PC2', 'PC3']:
        groups = [pc_df[pc_df[batch_col] == b][pc] for b in pc_df[batch_col].unique()]
        stat, pval = f_oneway(*groups)
        print(f'{pc} ~ {batch_col}: F={stat:.2f}, p={pval:.4f}')

    return pc_df, pca.explained_variance_ratio_

R: QC with limma

r
library(limma)
library(ggplot2)

# Intensity distribution
plotDensities(protein_matrix, legend = FALSE, main = 'Intensity Distributions')

# MA plots between samples
for (i in 2:ncol(protein_matrix)) {
    plotMA(protein_matrix[, c(1, i)], main = paste('MA:', colnames(protein_matrix)[i]))
}

# MDS plot (similar to PCA)
plotMDS(protein_matrix, col = as.numeric(sample_info$condition))

Coefficient of Variation

python
def calculate_cv(intensity_matrix, sample_groups):
    '''Calculate CV within groups'''
    cv_results = []
    for group in sample_groups.unique():
        group_samples = sample_groups[sample_groups == group].index
        group_data = intensity_matrix[group_samples]

        # CV per protein
        cv = group_data.std(axis=1) / group_data.mean(axis=1) * 100
        cv_results.append({'group': group, 'median_cv': cv.median(), 'mean_cv': cv.mean()})

    return pd.DataFrame(cv_results)

# Technical replicates should have CV < 20%
# Biological replicates typically 20-40%

Digestion Efficiency

python
def check_digestion(evidence_df):
    '''Check digestion efficiency from MaxQuant evidence.txt'''
    # Missed cleavages distribution
    mc_dist = evidence_df['Missed cleavages'].value_counts(normalize=True) * 100
    print('Missed cleavage distribution:')
    print(mc_dist)

    # Good digestion: >80% with 0 missed cleavages
    if mc_dist.get(0, 0) < 80:
        print('Warning: Poor digestion efficiency (<80% fully cleaved)')

    return mc_dist

QC Report Summary

python
def generate_qc_report(intensity_matrix, sample_info):
    '''Generate comprehensive QC summary'''
    report = {
        'n_samples': intensity_matrix.shape[1],
        'n_proteins': intensity_matrix.shape[0],
        'median_proteins_per_sample': intensity_matrix.notna().sum().median(),
        'overall_missing_pct': 100 * intensity_matrix.isna().sum().sum() / intensity_matrix.size,
        'median_correlation': intensity_matrix.corr().values[np.triu_indices_from(intensity_matrix.corr(), k=1)].mean(),
    }

    # Flags
    report['flags'] = []
    if report['overall_missing_pct'] > 30:
        report['flags'].append('High missing values (>30%)')
    if report['median_correlation'] < 0.9:
        report['flags'].append('Low replicate correlation (<0.9)')

    return report

Related Skills

• data-import - Load data before QC
• quantification - Normalization after QC
• differential-abundance - Analysis after QC passes
• data-visualization/heatmaps-clustering - QC heatmaps