代码分享 | 空间数据肿瘤交界区扩展--绘图

import pandas as pd
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

# 定义函数
def plot_spatial_distance_distribution(
    adata, 
    celltype_col='celltype', 
    distance_col='distance', 
    target_celltypes=[],
    bin_width=20,
    x_range=(-240, 200)
):
    # 1. 准备数据
    df = adata.obs[[celltype_col, distance_col]].copy()
    df = df[df[celltype_col].isin(target_celltypes)]
    # 2. 定义分箱 (Bins), 创建从 x_range[0] 到 x_range[1] 的区间
    bins = np.arange(x_range[0], x_range[1] + bin_width, bin_width)
    df['bin'] = pd.cut(df[distance_col], bins=bins, include_lowest=True, right=False)
    # 3. 计算上方柱状图数据 (Total Cell Count per bin)
    count_data = df.groupby('bin', observed=False).size()
    # 4. 计算下方堆叠图数据 (Proportions), 交叉表：行是bin，列是celltype
    pivot_df = pd.crosstab(df['bin'], df[celltype_col])
    prop_df = pivot_df.div(pivot_df.sum(axis=1), axis=0).fillna(0)  # 归一化：将每行的计数转换为比例 (行和为1)
    prop_df = prop_df[target_celltypes]
    # 5. 准备绘图用的 X 轴坐标和标签, 获取每个bin的中心点用于绘图
    x_centers = [b.mid for b in count_data.index]
    x_labels = [f"[{int(b.left)}-{int(b.right)}]"for b in count_data.index]
    # 6. 设置颜色
    colors = sns.color_palette("tab20", len(target_celltypes))

    # ================= 绘图开始 =================
    fig = plt.figure(figsize=(8, 8))
    gs = gridspec.GridSpec(2, 1, height_ratios=[1, 4], hspace=0.05)
    # --- 上图：Cell Count (柱状图) ---
    ax0 = plt.subplot(gs[0])
    ax0.bar(x_centers, count_data.values, width=bin_width*0.9, color='#6096BA', align='center')
    # 样式调整
    ax0.set_ylabel("Cell count", fontsize=12)
    ax0.axvline(x=0, color='gray', linestyle='--', linewidth=1.5) # 0点虚线
    ax0.set_xticks([]) # 隐藏X轴刻度
    ax0.spines['top'].set_visible(False)
    ax0.spines['right'].set_visible(False)
    ax0.spines['bottom'].set_visible(False)
    ax0.set_xlim(x_range[0], x_range[1])
    # --- 下图：Proportion (堆叠面积图) ---
    ax1 = plt.subplot(gs[1])
    # 绘制堆叠图 (Stackplot), transpose() 是因为 stackplot 需要 y 轴数据为 (M, N)
    ax1.stackplot(x_centers, prop_df.T.values, labels=target_celltypes, colors=colors, alpha=0.9)
    # 样式调整
    ax1.set_ylabel("Proportion", fontsize=16)
    ax1.set_ylim(0, 1)
    ax1.set_xlim(x_range[0], x_range[1])
    ax1.axvline(x=0, color='gray', linestyle='--', linewidth=1.5) # 0点虚线
    # 设置X轴标签
    ax1.set_xticks(x_centers)
    ax1.set_xticklabels(x_labels, rotation=90, fontsize=10)
    ax1.set_xlabel("Unit: µm", loc='left', fontsize=12)
    # 添加底部箭头和文字 (Inside / Outside), 这里需要手动调整位置坐标
    y_arrow = -0.35 # 箭头在X轴下方的位置
    ax1.text(-120, y_arrow+0.05, "Inside", ha='center', fontsize=14)
    ax1.arrow(-60, y_arrow, -100, 0, head_width=0.05, head_length=15, fc='k', ec='k', clip_on=False)
    ax1.text(120, y_arrow+0.05, "Outside", ha='center', fontsize=14)
    ax1.arrow(60, y_arrow, 100, 0, head_width=0.05, head_length=15, fc='k', ec='k', clip_on=False)
    ax1.text(0, y_arrow, "Tumor border", ha='center', fontsize=16, clip_on=False)
    # Legend (图例), 将图例放在底部
    handles, labels = ax1.get_legend_handles_labels()
    ax1.legend(
        handles, labels, 
        loc='center left',           # 图例盒子的锚点在"左中"
        bbox_to_anchor=(1.02, 0.5),  # 将锚点固定在坐标轴右边缘外侧 (X=1.02 稍微留点空隙, Y=0.5 垂直居中)
        ncol=1,                      # 设为单列，垂直排列
        frameon=False,               # 去除图例边框
        fontsize=14,
        borderaxespad=0              # 减少内边距
    )
    # 边框清理
    ax1.spines['top'].set_visible(False)
    ax1.spines['right'].set_visible(False)
    plt.show()

plot_spatial_distance_distribution(
    adata,
    celltype_col='celltype', 
    distance_col='is_fitted_boundary_dist_', 
    target_celltypes=adata.obs['celltype'].drop_duplicates(),
    bin_width=20,
    x_range=(-240, 240),
)