Metabolomics Pathway Heatmap

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(123)

metabolites = ['Glucose', 'Lactate', 'Pyruvate', 'Citrate', 'Succinate', 
               'Fumarate', 'Malate', 'Glutamate', 'Alanine', 'Serine',
               'Glycine', 'Leucine', 'Valine', 'Isoleucine', 'Proline']
conditions = ['Ctrl_1', 'Ctrl_2', 'Ctrl_3', 'Drug_1', 'Drug_2', 'Drug_3', 'Fast_1', 'Fast_2', 'Fast_3']

data = np.random.randn(15, 9)
data[:5, 3:6] += 2  # Drug effect on glycolysis
data[5:10, 6:] -= 1.5  # Fasting effect on TCA

df = pd.DataFrame(data, index=metabolites, columns=conditions)

# Condition colors
cond_type = ['Control']*3 + ['Drug']*3 + ['Fasting']*3
cond_palette = {'Control': '#27D3F5', 'Drug': '#F5276C', 'Fasting': '#F5B027'}
col_colors = pd.Series(cond_type, index=conditions).map(cond_palette)

neon_cmap = LinearSegmentedColormap.from_list('neon', ['#27D3F5', '#0a0a0f', '#F5276C'])

g = sns.clustermap(df, cmap=neon_cmap, center=0, col_colors=col_colors,
                   method='average', metric='euclidean',
                   linewidths=0.3, linecolor='#1a1a2e',
                   figsize=(9, 8), dendrogram_ratio=(0.12, 0.12),
                   cbar_pos=(0.01, 0.08, 0.008, 0.12),
                   tree_kws={'linewidths': 1.5, 'colors': '#6CF527'})

g.fig.patch.set_facecolor('#0a0a0f')
g.ax_heatmap.set_facecolor('#0a0a0f')
g.ax_heatmap.tick_params(colors='white', labelsize=8)
g.ax_row_dendrogram.set_facecolor('#0a0a0f')
g.ax_col_dendrogram.set_facecolor('#0a0a0f')
g.cax.tick_params(colors='white', labelsize=7)

g.fig.suptitle('Metabolomics Analysis', color='white', fontsize=13, fontweight='bold', y=1.02)
plt.show()