# Combined Exon and Gene Methylation Analysis

This document presents a comprehensive analysis of exon and gene methylation, including:
- Histograms and heatmaps of exon positions by methylation level
- Exon and gene methylation density by individual and season
- Correlation plots of gene expression and methylation
- Coefficient of variation (CV) of expression vs. mean methylation (gene and exon)

All code and plots are included for reproducibility and blog post preparation.

## Import Required Libraries
We will use pandas, numpy, matplotlib, seaborn, and re for data analysis and visualization.

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
```

## Load and Prepare Data
We will load the annotated exon methylation data and extract exon positions for downstream analysis.

```python
# Load annotated methylation table
exon_df = pd.read_csv('annotated_exon_methylation.csv', index_col=0)

def get_exon_pos(exon_name):
    if pd.isnull(exon_name):
        return None
    m = re.search(r'-(\\d+)$', exon_name)
    return int(m.group(1)) if m else None

if 'exon_position' not in exon_df.columns:
    exon_df['exon_position'] = exon_df['exon_name'].apply(get_exon_pos)

exon_df = exon_df.dropna(subset=['exon_position'])
```

## Histogram of Exon Positions by Methylation Level (Log Scale)
Visualize the distribution of exon positions for each methylation level using log-transformed counts.

```python
levels = ['low', 'moderate', 'high']
fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharey=True)
for ax, level in zip(axes, levels):
    sub = exon_df[exon_df['methylation_level'] == level]
    ax.hist(sub['exon_position'], bins=range(1, int(exon_df['exon_position'].max())+2), align='left', edgecolor='black', log=True)
    ax.set_title(f'{level.capitalize()} Methylation')
    ax.set_xlabel('Exon Position')
    if ax is axes[0]:
        ax.set_ylabel('Log10(Count of Exons)')
plt.suptitle('Distribution of Exon Positions by Methylation Level (Log Scale)')
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
```

## Heatmap of Exon Positions by Methylation Level
This heatmap shows the log10(count) of exons at each position and methylation level. Methylation levels are ordered and labeled with their percentage ranges.

```python
level_order = ['low', 'moderate', 'high']
exon_df['methylation_level'] = pd.Categorical(exon_df['methylation_level'], categories=level_order, ordered=True)
heatmap_data = exon_df.groupby(['exon_position', 'methylation_level']).size().unstack(fill_value=0)[level_order]
heatmap_data_log = np.log10(heatmap_data + 1)
plt.figure(figsize=(8, 6))
ax = sns.heatmap(heatmap_data_log, cmap='viridis', annot=False)
plt.title('Log10(Count) of Exons by Position and Methylation Level')
ax.set_xlabel('Methylation Level')
ax.set_ylabel('Exon Position')
ax.set_xticklabels([
    'low (<=10%)',
    'moderate (11-79%)',
    'high (>=80%)'
])
plt.tight_layout()
plt.show()
```