bio-proteomics-proteomics-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)

## 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.

## R: QC with limma

```r
library(limma)
library(ggplot2)