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Skill: Heatmap (Python)

Category

Python

When to Use

A heatmap is a data visualization technique that uses color to represent values in a matrix. In biomedical research, heatmaps are essential for visualizing gene expression profiles, correlation matrices, methylation data, and drug response panels. Python's seaborn and matplotlib libraries offer powerful heatmap capabilities with built-in clustering support.

Required Python Packages

  • matplotlib
  • numpy
  • pandas
  • scipy
  • seaborn

Minimal Reproducible Code

# Load packages
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from scipy.cluster.hierarchy import linkage

# Prepare data
np.random.seed(42)
n_genes = 30
n_samples = 12
gene_names = [f'Gene_{i+1}' for i in range(n_genes)]
sample_names = [f'Sample_{i+1}' for i in range(n_samples)]
groups = ['Tumor'] * 6 + ['Normal'] * 6

expr_matrix = np.random.randn(n_genes, n_samples)
expr_matrix[:10, :6] += 2.5
expr_matrix[10:20, 6:] += 2.0

expr_df = pd.DataFrame(expr_matrix, index=gene_names, columns=sample_names)

# Create visualization
fig, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(expr_df, cmap='RdBu_r', center=0, xticklabels=True,
            yticklabels=True, linewidths=0.5, ax=ax)
ax.set_title('Gene Expression Heatmap')
ax.set_xlabel('Samples')
ax.set_ylabel('Genes')
plt.tight_layout()
plt.show()

Key Parameters

  • figsize: Figure dimensions as (width, height) in inches
  • cmap: Colormap for continuous color mapping
  • annot: Whether to annotate cells with values (True/False)

Tips

  • Call plt.tight_layout() to prevent label overlap
  • Seaborn integrates with pandas DataFrames for convenient column-based plotting
  • See the full tutorial for additional customization options and advanced examples

Full Tutorial

https://openbiox.github.io/Bizard/Python/Heatmap.html