Scatter Plot (Python)

Author

Modified

2026-04-04

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A scatter plot displays values for two continuous variables as a collection of points. In biomedical research, scatter plots are widely used for visualizing correlations between gene expression levels, comparing biomarkers, and exploring relationships in multi-omics datasets. Python’s matplotlib and seaborn libraries provide flexible and publication-quality scatter plot capabilities.

Example

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming Language: Python
Dependencies: matplotlib, seaborn, pandas, numpy, scipy

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

Data Preparation

We use the classic iris dataset and simulated gene expression data for demonstration.

iris = sns.load_dataset("iris")

np.random.seed(42)
n = 200
gene_data = pd.DataFrame({
    'GeneA': np.random.normal(5, 2, n),
    'GeneB': np.random.normal(5, 2, n),
    'Group': np.random.choice(['Tumor', 'Normal'], n)
})
gene_data.loc[gene_data['Group'] == 'Tumor', 'GeneA'] += 2
gene_data.loc[gene_data['Group'] == 'Tumor', 'GeneB'] += 1.5

Visualization

Basic Scatter Plot

fig, ax = plt.subplots(figsize=(8, 6))
for species in iris['species'].unique():
    subset = iris[iris['species'] == species]
    ax.scatter(subset['sepal_length'], subset['sepal_width'],
               label=species, alpha=0.7, edgecolors='white', linewidth=0.5)
ax.set_xlabel('Sepal Length (cm)')
ax.set_ylabel('Sepal Width (cm)')
ax.set_title('Iris Scatter Plot')
ax.legend(title='Species')
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Scatter Plot with Regression Line

fig, ax = plt.subplots(figsize=(8, 6))
colors = {'Tumor': '#e63946', 'Normal': '#457b9d'}
for group in ['Tumor', 'Normal']:
    subset = gene_data[gene_data['Group'] == group]
    ax.scatter(subset['GeneA'], subset['GeneB'], c=colors[group],
               label=group, alpha=0.6, edgecolors='white', linewidth=0.5)
    slope, intercept, r, p, se = stats.linregress(subset['GeneA'], subset['GeneB'])
    x_line = np.linspace(subset['GeneA'].min(), subset['GeneA'].max(), 100)
    ax.plot(x_line, slope * x_line + intercept, color=colors[group],
            linestyle='--', linewidth=1.5)
ax.set_xlabel('Gene A Expression')
ax.set_ylabel('Gene B Expression')
ax.set_title('Gene Expression Correlation by Group')
ax.legend()
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Figure 2: Scatter Plot with Linear Regression

Seaborn Joint Plot

g = sns.jointplot(data=gene_data, x='GeneA', y='GeneB', hue='Group',
                  palette={'Tumor': '#e63946', 'Normal': '#457b9d'},
                  kind='scatter', alpha=0.6, marginal_kws=dict(fill=True, alpha=0.4))
g.set_axis_labels('Gene A Expression', 'Gene B Expression')
plt.suptitle('Joint Distribution of Gene Expression', y=1.02)
plt.tight_layout()
plt.show()

Figure 3: Joint Plot with Marginal Distributions

References

Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95.
Waskom, M. L. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021.

📋 AI Skill: Scatter Plot (Python)