空间代谢组学（成像质谱）¶

# === 空间代谢组学数据分析（Python） ===
# 使用pyimzML读取imzML格式数据
# pip install pyimzml

from pyimzml.ImzMLParser import ImzMLParser
import numpy as np
import matplotlib.pyplot as plt

# 1. 读取imzML文件
parser = ImzMLParser("tissue_section.imzML")

# 获取数据基本信息
n_pixels = len(parser.coordinates)  # 总像素数
print(f"总像素数: {n_pixels}")
print(f"坐标范围: x={min(c[0] for c in parser.coordinates)}-{max(c[0] for c in parser.coordinates)}")

# 2. 提取特定m/z的离子图像
target_mz = 885.55  # 例如PI 38:4（磷脂酰肌醇）
tolerance = 0.1     # m/z容忍度 ±0.1 Da

# 获取坐标网格
x_coords = [c[0] for c in parser.coordinates]
y_coords = [c[1] for c in parser.coordinates]
x_range = max(x_coords) - min(x_coords) + 1
y_range = max(y_coords) - min(y_coords) + 1

# 创建空白图像
ion_image = np.zeros((y_range, x_range))

# 逐像素提取目标m/z的强度
for idx, (x, y, z) in enumerate(parser.coordinates):
    mzs, intensities = parser.getspectrum(idx)  # 获取该像素的质谱
    # 找到目标m/z范围内的最大强度
    mask = (mzs >= target_mz - tolerance) & (mzs <= target_mz + tolerance)
    if mask.any():
        ion_image[y - min(y_coords), x - min(x_coords)] = intensities[mask].max()

# 3. 可视化离子图像
plt.figure(figsize=(10, 8))
plt.imshow(ion_image, cmap="viridis", interpolation="nearest")
plt.colorbar(label="Intensity")
plt.title(f"Ion Image: m/z {target_mz}")
plt.xlabel("X (pixels)")
plt.ylabel("Y (pixels)")
plt.savefig("ion_image.png", dpi=150, bbox_inches="tight")

# 4. 提取多个代谢物的离子图像对比
target_mzs = {
    "PC 34:1": 760.58,    # 磷脂酰胆碱
    "PE 36:2": 742.54,    # 磷脂酰乙醇胺
    "SM 34:1": 703.57,    # 鞘磷脂
    "Cholesterol": 369.35  # 胆固醇（正离子脱水形式）
}

fig, axes = plt.subplots(2, 2, figsize=(14, 12))
for ax, (name, mz) in zip(axes.flat, target_mzs.items()):
    img = np.zeros((y_range, x_range))
    for idx, (x, y, z) in enumerate(parser.coordinates):
        mzs, intensities = parser.getspectrum(idx)
        mask = (mzs >= mz - tolerance) & (mzs <= mz + tolerance)
        if mask.any():
            img[y - min(y_coords), x - min(x_coords)] = intensities[mask].max()
    ax.imshow(img, cmap="hot", interpolation="nearest")
    ax.set_title(f"{name} (m/z {mz})")
    ax.axis("off")

plt.tight_layout()
plt.savefig("multi_ion_images.png", dpi=150)