如何基于Pandas DataFrame绘制Shell Plot（壳图）？

我有一个记录配体-受体对相互作用数量的DataFrame，数据如下：

import pandas as pd

d = {'ligand': ['B cells','CAFs','Malignant cells','T cells','TAMs','TAMs','TAMs','TAMs','TAMs','TAMs','TAMs','TECs','unclassified'],
     'receptor': ['TAMs','TAMs','TAMs','TAMs','B cells','CAFs','Malignant cells','T cells','TAMs','TECs','unclassified','TAMs','TAMs'],
    'interactions': [18, 29, 89, 22, 17, 12, 48, 34, 56, 27, 14, 53, 24]}
df = pd.DataFrame(d)

我需要将其可视化为Shell Plot（壳图），样式为内外双圈结构，通过连线粗细表示相互作用数量，但找不到相关实现资料，请问如何用Python模块实现？

方法1：使用pycirclize库（推荐） #

pycirclize是专门用于环形可视化的Python库，能快速实现壳图效果，无需复杂的坐标计算。

步骤1：安装依赖库

pip install pycirclize

步骤2：编写实现代码

import pandas as pd
from pycirclize import Circos

# 加载数据
d = {'ligand': ['B cells','CAFs','Malignant cells','T cells','TAMs','TAMs','TAMs','TAMs','TAMs','TAMs','TAMs','TECs','unclassified'],
     'receptor': ['TAMs','TAMs','TAMs','TAMs','B cells','CAFs','Malignant cells','T cells','TAMs','TECs','unclassified','TAMs','TAMs'],
    'interactions': [18, 29, 89, 22, 17, 12, 48, 34, 56, 27, 14, 53, 24]}
df = pd.DataFrame(d)

# 获取所有唯一细胞类型，作为环形节点
cell_types = sorted(list(set(df["ligand"].tolist() + df["receptor"].tolist())))
n_cells = len(cell_types)
cell_to_idx = {cell: idx for idx, cell in enumerate(cell_types)}

# 创建Circos对象，设置环形间距
circos = Circos(n_sectors=n_cells, space=5)
circos.text("配体-受体相互作用", size=12, loc=(100, 0))

# 配置内外圈：外圈为配体，内圈为受体
for sector in circos.sectors:
    cell_name = cell_types[sector.idx]
    # 外圈添加细胞类型标签
    sector.text(cell_name, size=10, orientation="vertical")
    # 内圈绘制灰色背景弧
    sector.arc(inner_radius=80, outer_radius=90, color="#e0e0e0")

# 绘制相互作用连线，线宽与作用数量成正比
max_interaction = df["interactions"].max()
for _, row in df.iterrows():
    ligand_idx = cell_to_idx[row["ligand"]]
    receptor_idx = cell_to_idx[row["receptor"]]
    line_width = (row["interactions"] / max_interaction) * 5
    
    # 连接内外圈对应节点
    circos.link(
        circos.sectors[ligand_idx], 0.5,
        circos.sectors[receptor_idx], 0.5,
        color="#4a90e2",
        alpha=0.7,
        linewidth=line_width,
    )

# 保存并显示图像
circos.savefig("ligand_receptor_shell_plot.png", dpi=300)
circos.show()

方法2：使用matplotlib手动绘制 #

如果不想额外安装库，可通过matplotlib结合numpy手动实现基础壳图：

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

# 加载数据
d = {'ligand': ['B cells','CAFs','Malignant cells','T cells','TAMs','TAMs','TAMs','TAMs','TAMs','TAMs','TAMs','TECs','unclassified'],
     'receptor': ['TAMs','TAMs','TAMs','TAMs','B cells','CAFs','Malignant cells','T cells','TAMs','TECs','unclassified','TAMs','TAMs'],
    'interactions': [18, 29, 89, 22, 17, 12, 48, 34, 56, 27, 14, 53, 24]}
df = pd.DataFrame(d)

# 获取所有唯一细胞类型并映射为角度
cell_types = sorted(list(set(df["ligand"].tolist() + df["receptor"].tolist())))
n_cells = len(cell_types)
cell_to_angle = {cell: 2 * np.pi * idx / n_cells for idx, cell in enumerate(cell_types)}

# 创建极坐标画布
fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={'projection': 'polar'})
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_xticks([cell_to_angle[cell] for cell in cell_types])
ax.set_xticklabels(cell_types, fontsize=10)
ax.set_ylim(0, 2)
ax.set_yticks([])

# 绘制内外圈轮廓
outer_radius = 2
inner_radius = 1.5
ax.plot(np.linspace(0, 2*np.pi, 100), [outer_radius]*100, color="#333333")
ax.plot(np.linspace(0, 2*np.pi, 100), [inner_radius]*100, color="#333333")

# 绘制相互作用连线
max_interaction = df["interactions"].max()
for _, row in df.iterrows():
    ligand_angle = cell_to_angle[row["ligand"]]
    receptor_angle = cell_to_angle[row["receptor"]]
    line_width = (row["interactions"] / max_interaction) * 3
    
    # 绘制配体（外圈）到受体（内圈）的连线
    ax.plot([ligand_angle, receptor_angle], [outer_radius, inner_radius],
            color="#4a90e2", alpha=0.7, linewidth=line_width)

plt.title("配体-受体相互作用", y=1.1, fontsize=12)
plt.savefig("ligand_receptor_shell_plot_matplotlib.png", dpi=300)
plt.show()

内容的提问来源于stack exchange，提问作者Yulia Kentieva