Protein Expression by Cell Line

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

np.random.seed(444)

cell_lines = ['HeLa', 'HEK293', 'MCF7', 'A549']
data = []
for cell in cell_lines:
    n = 38
    stimulus = np.random.uniform(0, 100, n)
    responses = {'HeLa': 0.45, 'HEK293': 0.55, 'MCF7': 0.38, 'A549': 0.42}
    expression = responses[cell] * np.log(stimulus + 1) + np.random.normal(0, 0.2, n)
    for s, e in zip(stimulus, expression):
        data.append({'Stimulus (nM)': s, 'Expression (AU)': e, 'Cell Line': cell})

df = pd.DataFrame(data)

# Light theme
sns.set_style("whitegrid", {
    'axes.facecolor': '#ffffff',
    'figure.facecolor': '#ffffff',
    'grid.color': '#eeeeee',
    'axes.edgecolor': '#cccccc',
    'axes.labelcolor': '#333333',
    'text.color': '#333333',
    'xtick.color': '#666666',
    'ytick.color': '#666666'
})

palette = ['#F5276C', '#4927F5', '#6CF527', '#F5B027']

g = sns.lmplot(
    data=df,
    x='Stimulus (nM)',
    y='Expression (AU)',
    hue='Cell Line',
    col='Cell Line',
    col_wrap=2,
    height=3.5,
    aspect=1.2,
    palette=palette,
    scatter_kws={'alpha': 0.75, 's': 55, 'edgecolor': 'white', 'linewidths': 0.8},
    line_kws={'linewidth': 2.5},
    ci=95
)

g.fig.set_facecolor('#ffffff')
for ax in g.axes.flat:
    ax.set_facecolor('#ffffff')
    for spine in ax.spines.values():
        spine.set_color('#dddddd')

g.fig.suptitle('Stimulus-Response by Cell Model', fontsize=14, fontweight='bold', color='#1a1a1a', y=1.02)
plt.tight_layout()
plt.show()