Drug Sensitivity Profile

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)

# Cell lines and drugs
cell_lines = [f'CL_{i:02d}' for i in range(1, 21)]
drugs = ['Drug_A', 'Drug_B', 'Drug_C', 'Drug_D', 'Drug_E', 
         'Drug_F', 'Drug_G', 'Drug_H', 'Drug_I', 'Drug_J']

# IC50 values (log-transformed, lower = more sensitive)
ic50 = np.random.randn(len(cell_lines), len(drugs))
# Add drug class sensitivity patterns
ic50[:7, :3] -= 1.5  # Group 1 sensitive to drugs A-C
ic50[7:14, 3:6] -= 1.5  # Group 2 sensitive to drugs D-F
ic50[14:, 6:] -= 1  # Group 3 sensitive to drugs G-J

df = pd.DataFrame(ic50, index=cell_lines, columns=drugs)

# Cancer type annotations
cancer_type = ['Breast']*7 + ['Lung']*7 + ['Colon']*6
type_palette = {'Breast': '#F5276C', 'Lung': '#27D3F5', 'Colon': '#6CF527'}
row_colors = pd.Series(cancer_type, index=cell_lines).map(type_palette)

# NEON sensitivity colormap (red=resistant, blue=sensitive)
neon_sens = LinearSegmentedColormap.from_list('neon_sens', ['#F5276C', '#0a0a0f', '#27D3F5'])

g = sns.clustermap(df, cmap=neon_sens, center=0, row_colors=row_colors,
                   method='average', metric='euclidean',
                   linewidths=0.3, linecolor='#1a1a2e',
                   figsize=(9, 7), dendrogram_ratio=(0.1, 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)

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

g.fig.suptitle('Drug Sensitivity Profiling', color='white', fontsize=14, fontweight='bold', y=1.02)


plt.show()