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')