Proteomics Quality Control

Sample Quality Metrics

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

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

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

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

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

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

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

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