Gene Expression Clustermap

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 gene expression data
genes = [f'Gene_{i}' for i in range(1, 26)]
samples = [f'Sample_{i}' for i in range(1, 16)]
data = np.random.randn(25, 15)

# Add some structure (gene clusters)
data[:8, :5] += 2
data[8:16, 5:10] += 2
data[16:, 10:] += 2

df = pd.DataFrame(data, index=genes, columns=samples)

# Sample annotations (treatment groups)
treatment = ['Control']*5 + ['Treatment_A']*5 + ['Treatment_B']*5
palette = {'Control': '#27D3F5', 'Treatment_A': '#F5276C', 'Treatment_B': '#6CF527'}
col_colors = pd.Series(treatment, index=samples).map(palette)

# Custom NEON colormap
neon_cmap = LinearSegmentedColormap.from_list('neon', ['#0a0a0f', '#4927F5', '#27D3F5', '#6CF527', '#F5B027'])

plt.figure(figsize=(8, 7))
g = sns.clustermap(df, cmap=neon_cmap, col_colors=col_colors, 
                   method='ward', metric='euclidean',
                   linewidths=0.5, linecolor='#1a1a2e',
                   figsize=(8, 7), dendrogram_ratio=(0.15, 0.15),
                   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)
g.ax_heatmap.set_xlabel('Samples', color='white', fontsize=10)
g.ax_heatmap.set_ylabel('Genes', color='white', fontsize=10)

# Style dendrograms
for ax in [g.ax_row_dendrogram, g.ax_col_dendrogram]:
    ax.set_facecolor('#0a0a0f')
    
g.fig.suptitle('Gene Expression Clustering', color='white', fontsize=14, fontweight='bold', y=1.02)


plt.show()