Gene Expression Level KDE

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

np.random.seed(789)
BG_COLOR = '#0d1117'
TEXT_COLOR = 'white'

# Expression levels (log2 TPM)
normal = np.random.normal(5, 1.5, 600)
tumor = np.random.normal(7, 2.0, 600)

fig, ax = plt.subplots(figsize=(10, 6), facecolor=BG_COLOR)
ax.set_facecolor(BG_COLOR)

x_range = np.linspace(0, 14, 500)

for data, color, label in [(normal, '#27F5B0', 'Normal Tissue'), 
                            (tumor, '#F5276C', 'Tumor Tissue')]:
    kde = gaussian_kde(data)
    cdf = np.array([kde.integrate_box_1d(-np.inf, x) for x in x_range])
    ax.plot(x_range, cdf, color=color, linewidth=3, label=label)

ax.axhline(0.5, color='#888888', linestyle=':', alpha=0.7)
ax.text(12, 0.52, 'Median', color='#888888', fontsize=10)

ax.set_xlabel('Expression Level (log2 TPM)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Cumulative Probability', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Gene Expression: Cumulative Distribution', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)

ax.tick_params(colors='#888888', labelsize=10)
for spine in ax.spines.values():
    spine.set_color('#333333')
ax.legend(facecolor=BG_COLOR, edgecolor='#333333', labelcolor=TEXT_COLOR, fontsize=10, loc='lower right')

plt.tight_layout()
plt.show()