Bioinformatics Visualizations for Publication

Sample code / prompt

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
n_genes = 5000
log2fc = np.random.normal(0, 1.5, n_genes)
pvalues = 10 ** (-np.abs(log2fc) * np.random.uniform(0.5, 3, n_genes))
neg_log10p = -np.log10(pvalues)

fc_thresh = 1.0

Sample code / prompt

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

# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
    [1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
    [0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],