散点图（Python）

作者

修改于

2026-04-04

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散点图以点的集合展示两个连续变量的值。在生物医学研究中，散点图广泛用于可视化基因表达水平之间的相关性、比较生物标志物，以及探索多组学数据集中的关系。Python 的 matplotlib 和 seaborn 库提供了灵活且可用于发表的散点图功能。

示例

环境配置

系统要求：跨平台（Linux/MacOS/Windows）
编程语言：Python
依赖包：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

数据准备

我们使用经典的 iris 数据集和模拟的基因表达数据进行演示。

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

可视化

基础散点图

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()

带回归线的散点图

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()

Seaborn 联合图

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()

参考文献

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: 散点图（Python）

示例