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
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
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.
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