Protein Interaction Matrix

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)

# Simulated protein interaction data
proteins = [f'Protein_{chr(65+i)}' for i in range(18)]
n = len(proteins)

# Create symmetric interaction matrix
interactions = np.zeros((n, n))
for i in range(n):
    for j in range(i+1, n):
        if np.random.rand() > 0.7:  # 30% interaction rate
            strength = np.random.rand()
            interactions[i, j] = strength
            interactions[j, i] = strength

df = pd.DataFrame(interactions, index=proteins, columns=proteins)

# Protein families
families = ['Kinase']*6 + ['Receptor']*6 + ['Enzyme']*6
fam_palette = {'Kinase': '#F5276C', 'Receptor': '#27D3F5', 'Enzyme': '#6CF527'}
colors = pd.Series(families, index=proteins).map(fam_palette)

# NEON purple colormap
neon_purple = LinearSegmentedColormap.from_list('neon_p', ['#0a0a0f', '#4927F5', '#F527B0'])

g = sns.clustermap(df, cmap=neon_purple, row_colors=colors, col_colors=colors,
                   method='ward', linewidths=0.2, linecolor='#1a1a2e',
                   figsize=(8, 7), dendrogram_ratio=(0.1, 0.1),
                   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=8)

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

g.fig.suptitle('Protein Interaction Network', color='white', fontsize=14, fontweight='bold', y=1.02)


plt.show()