Genomic Variant Effect

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

np.random.seed(42)

# Genomic regions and variant types
regions = ['Promoter', 'UTR5', 'Exon1', 'Intron1', 'Exon2', 'Intron2', 
           'Exon3', 'UTR3', 'Enhancer', 'Silencer']
variants = ['Missense', 'Nonsense', 'Frameshift', 'Splice', 'Synonymous', 'Regulatory']

# Effect scores (0 = no effect, 1 = severe)
effects = np.random.rand(len(regions), len(variants)) * 0.3
# Exons have higher effect for coding variants
effects[2, 0:4] += 0.5
effects[4, 0:4] += 0.5
effects[6, 0:4] += 0.4
# Regulatory regions for regulatory variants
effects[0, 5] += 0.6
effects[8, 5] += 0.5
effects[9, 5] += 0.4

df = pd.DataFrame(effects, index=regions, columns=variants)

# NEON fire colormap
neon_fire = LinearSegmentedColormap.from_list('neon_fire', ['#0a0a0f', '#4927F5', '#F54927', '#F5B027'])

g = sns.clustermap(df, cmap=neon_fire, 
                   method='average', metric='euclidean',
                   linewidths=0.5, linecolor='#1a1a2e',
                   figsize=(8, 7), dendrogram_ratio=(0.12, 0.12),
                   cbar_pos=(0.01, 0.08, 0.008, 0.12),
                   tree_kws={'linewidths': 1.5, 'colors': '#27D3F5'})

g.fig.patch.set_facecolor('#0a0a0f')
g.ax_heatmap.set_facecolor('#0a0a0f')
g.ax_heatmap.tick_params(colors='white', labelsize=9)

for ax in [g.ax_row_dendrogram, g.ax_col_dendrogram]:
    ax.set_facecolor('#0a0a0f')

g.fig.suptitle('Variant Effect by Genomic Region', color='white', fontsize=14, fontweight='bold', y=1.02)


plt.show()