Clinical Trial Biomarkers

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(111)

biomarkers = ['CRP', 'IL-6', 'TNF-a', 'WBC', 'Platelets', 'Hemoglobin',
              'Creatinine', 'ALT', 'AST', 'Albumin', 'Glucose', 'HbA1c']
patients = [f'P{i:02d}' for i in range(1, 17)]

data = np.random.randn(12, 16)
data[:4, :6] -= 1.5
data[:3, 10:] += 2

df = pd.DataFrame(data, index=biomarkers, columns=patients)

response = ['Responder']*6 + ['Partial']*4 + ['Non-responder']*6
resp_palette = {'Responder': '#22c55e', 'Partial': '#f59e0b', 'Non-responder': '#ef4444'}
col_colors = pd.Series(response, index=patients).map(resp_palette)

light_cmap = LinearSegmentedColormap.from_list('clinical', ['#3b82f6', '#f8fafc', '#ef4444'])

g = sns.clustermap(df, cmap=light_cmap, center=0, col_colors=col_colors,
                   method='ward', metric='euclidean',
                   linewidths=0.5, linecolor='#e2e8f0',
                   figsize=(10, 8), dendrogram_ratio=(0.12, 0.12),
                   cbar_pos=(0.01, 0.08, 0.008, 0.12))

g.fig.patch.set_facecolor('#ffffff')
g.ax_heatmap.set_facecolor('#ffffff')
g.ax_heatmap.tick_params(colors='#1f2937', labelsize=8)
g.ax_row_dendrogram.set_facecolor('#ffffff')
g.ax_col_dendrogram.set_facecolor('#ffffff')
g.cax.tick_params(colors='#1f2937', labelsize=7)

g.fig.suptitle('Clinical Trial Biomarkers', color='#1f2937', fontsize=13, fontweight='bold', y=1.02)
plt.show()