{"id":61051,"date":"2024-10-11T14:48:53","date_gmt":"2024-10-11T06:48:53","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61051"},"modified":"2024-10-11T14:48:53","modified_gmt":"2024-10-11T06:48:53","slug":"%e6%b2%a1%e5%90%ac%e9%94%99%e5%90%a7%ef%bc%81%e5%8d%95%e7%bb%86%e8%83%9e%e4%b8%ad%e4%b9%9f%e5%8f%af%e4%bb%a5%e8%bf%9b%e8%a1%8c%e5%85%8d%e7%96%ab%e6%b5%b8%e6%b6%a6%e5%88%86%e6%9e%90%ef%bc%9fspatialcell","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=61051","title":{"rendered":"\u6ca1\u542c\u9519\u5427\uff01\u5355\u7ec6\u80de\u4e2d\u4e5f\u53ef\u4ee5\u8fdb\u884c\u514d\u75ab\u6d78\u6da6\u5206\u6790\uff1fSpatialCells\u5e26\u7ed9\u4f60\u65b0\u7684\u4f53\u9a8c\uff01"},"content":{"rendered":"\n

\u524d\u8a00<\/strong><\/strong><\/p>\n\n\n\n

\u8c08\u8d77\u751f\u4fe1\u5206\u6790\u5fc5\u5907\u9879\u76ee\u514d\u75ab\u6d78\u6da6\u5206\u6790\uff0c\u5c0f\u4f19\u4f34\u4eec\u518d\u719f\u6089\u4e0d\u8fc7\u4e86\uff01\u50cf\u5c0f\u4f19\u4f34\u4eec\u8033\u719f\u80fd\u8be6\u7684ImmuCellAL\u3001Cibersort\u90fd\u53ea\u80fd\u5728RNA-Seq\u548c\u82af\u7247\u6570\u636e\u4e2d\u4f7f\u7528\uff0c\u50cf\u76ee\u524d\u706b\u7206\u7684\u5355\u7ec6\u80de\u7ec4\u5b66\u6570\u636e\u8fd9\u4e24\u4e2aR\u5305\u5374\u65e0\u7528\u6b66\u4e4b\u5730\uff01\u4eca\u5929\u5927\u6d77\u54e5\u5e26\u5927\u5bb6\u5b66\u4e60\u5982\u4f55\u5728\u5355\u7ec6\u80de\u6570\u636e\u4e2d\u8fdb\u884c\u514d\u75ab\u6d78\u6da6\u5206\u6790\uff01\u5bf9\uff0c\u4f60\u6ca1\u542c\u9519\uff0c\u5355\u7ec6\u80de\u4e2d\u4e5f\u53ef\u4ee5\u8fdb\u884c\u514d\u75ab\u6d78\u6da6\u5206\u6790\u3002SpatialCells\u7684\u529f\u80fd\u6db5\u76d6\u4e86\u80bf\u7624\u5fae\u73af\u5883\u5206\u6790\u7684\u5404\u4e2a\u65b9\u9762\uff0c\u4f8b\u5982\u57fa\u4e8e\u533a\u57df\u7684\u7ec6\u80de\u7ec4\u6210\u3001\u80bf\u7624\u589e\u6b96\u6307\u6570\u3001\u80bf\u7624\u5206\u79bb\u6307\u6570\u3001\u514d\u75ab\u7ec6\u80de\u6d78\u6da6\u548c\u80bf\u7624\u514d\u75ab\u8ddd\u79bb\u7b49\u3002SpatialCells\u7684\u529f\u80fd\u5b9e\u5728\u592a\u5f3a\u5927\u4e86\uff0c\u4eca\u5929\u5927\u6d77\u54e5\u7740\u91cd\u5e26\u5927\u5bb6\u5b66\u4e60\u5355\u7ec6\u80de\u4e2d\u7684\u514d\u75ab\u6d78\u6da6\u5206\u6790\uff01\u6b64\u5916\uff0cSpatialCells \u4e5f\u6709\u52a9\u4e8e\u540e\u7eed\u7684\u5173\u8054\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u9884\u6d4b\uff0c\u4f7f\u5176\u6210\u4e3a\u63a8\u8fdb\u6211\u4eec\u5bf9\u80bf\u7624\u751f\u957f\u3001\u4fb5\u88ad\u548c\u8f6c\u79fb\u7406\u89e3\u7684\u91cd\u8981\u5de5\u5177\u3002\u751f\u4fe1\u5206\u6790\u4e0d\u719f\u6089\u7684\u5c0f\u4f19\u4f34\u4eec\u6b22\u8fce\u6765\u6ef4\u6ef4\u5927\u6d77\u54e5\uff0c\u5927\u6d77\u54e5\u5c31\u662f\u8fd9\u4e48\u7684\u5ba0\u7c89\uff0c\u6709\u4ec0\u4e48\u751f\u4fe1\u5206\u6790\u4e0a\u7684\u95ee\u9898\u5927\u5bb6\u5c3d\u7ba1\u54a8\u8be2\u5927\u6d77\u54e5\uff01\u6ca1\u6709\u65f6\u95f4\u5b66\u4e60\u7684\u5c0f\u4f19\u4f34\u4eec\u4e5f\u4e0d\u8981\u7740\u6025\u54e6\uff01\u6709\u9700\u8981\u751f\u4fe1\u5206\u6790\u7684\u5c0f\u4f19\u4f34\u4eec\u4e5f\u53ef\u4ee5\u627e\u5927\u6d77\u54e5\u54e6\uff01\u7ec3\u4e86\u5341\u5e74\u751f\u4fe1\u5206\u6790\u7684\u5927\u6d77\u54e5\u5bf9\u4e8e\u751f\u4fe1\u5206\u6790\u77e5\u8bc6\u5df2\u7ecf\u5982\u9c7c\u5f97\u6c34\u4ece\u5206\u6790\u5230\u53ef\u89c6\u5316\u76f4\u5230\u4f60\u6ee1\u610f\u4e3a\u6b62\uff01<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u751f\u4fe1\u6570\u636e\u5904\u7406\u8d77\u6765\u5360\u7528\u5185\u5b58\u5b9e\u5728\u592a\u5927\u4e86\uff0c\u653e\u8fc7\u81ea\u5df1\u7684\u7535\u8111\u5427\uff01\u5927\u6d77\u54e5\u5728\u8fd9\u91cc\u7ed9\u5927\u5bb6\u9001\u4e0a\u798f\u5229\u4e86\uff0c\u6709\u9700\u8981\u670d\u52a1\u5668\u7684\u5c0f\u4f19\u4f34\u4eec\uff0c\u6b22\u8fce\u5927\u5bb6\u8054\u7cfb\u5927\u6d77\u54e5\uff0c\u4fdd\u8bc1\u670d\u52a1\u5668\u7684\u6027\u4ef7\u6bd4\u6700\u9ad8\u54e6\uff01<\/p>\n\n\n\n

\u4ee3\u7801\u6559\u7a0b<\/strong><\/strong><\/p>\n\n\n\n

SpatialCells \u76ee\u524d\u53ea\u80fd\u4ece\u6e90\u5b89\u88c5\u3002 \u8981\u5b89\u88c5\uff0c\u8bf7\u4ece\u5b58\u50a8\u5e93\u4e0b\u8f7d\u4ee3\u7801\u5e76\u5207\u6362\u5230\u4ee3\u7801\u6587\u4ef6\u5939\u3002<\/p>\n\n\n\n

\u4ece SpatialCells \u7684\u6839\u76ee\u5f55\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\uff1a<\/p>\n\n\n\n

pip install -r requirements.txt<\/p>\n\n\n\n

pip install .<\/p>\n\n\n\n

\u5efa\u8bae\u5728\u865a\u62df\u73af\u5883\u4e2d\u5b89\u88c5 SpatialCells\uff0c\u4f8b\u5982 conda\u3002 \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b yaml \u6587\u4ef6\u521b\u5efa conda \u73af\u5883\uff1a<\/p>\n\n\n\n

conda env create –name spatialcells –file=conda.yaml<\/p>\n\n\n\n

pip install .<\/p>\n\n\n\n

conda.yaml \u6307\u5b9a\u5982\u4e0b\uff1a<\/p>\n\n\n\n

name: spatial-cells-env<\/p>\n\n\n\n

channels:<\/p>\n\n\n\n

  – conda-forge<\/p>\n\n\n\n

  – defaults<\/p>\n\n\n\n

dependencies:<\/p>\n\n\n\n

  – python>=3.7, <3.11<\/p>\n\n\n\n

  – ipykernel<\/p>\n\n\n\n

  – matplotlib>=3.7.0<\/p>\n\n\n\n

  – pandas>=2.0.3<\/p>\n\n\n\n

  – seaborn>=0.12.2<\/p>\n\n\n\n

  – shapely>=2.0<\/p>\n\n\n\n

  – tqdm<\/p>\n\n\n\n

  – pip<\/p>\n\n\n\n

  – pip:<\/p>\n\n\n\n

      – anndata==0.9.2<\/p>\n\n\n\n

      – scanpy==1.9.4<\/p>\n\n\n\n

\u52a0\u8f7d\u76f8\u5173\u7684python\u5305<\/p>\n\n\n\n

import pandas as pd<\/p>\n\n\n\n

import matplotlib.pyplot as plt<\/p>\n\n\n\n

import anndata as ad<\/p>\n\n\n\n

import spatialcells as spc<\/p>\n\n\n\n

\u5728\u672c\u8282\u4e2d\uff0c\u6211\u4eec\u4ee5\u514d\u75ab\u7ec6\u80de\u6d78\u6da6\u5206\u6790\u4e3a\u5bfc\u5411\u7684\u5206\u6790\u7684\u7ed3\u679c\u6765\u5c55\u793a SpatialCells \u7684\u529f\u80fd\u3002\u6211\u4eec\u7684\u5206\u6790\u6d89\u53ca\u75311110585\u4e2a\u7ec6\u80de\u7ec4\u6210\u7684\u76ae\u80a4\u9ed1\u8272\u7d20\u7624\u6837\u672c\uff08MEL1\uff09\u7684\u516c\u5f00\u591a\u8def\u590d\u7528\u6210\u6570\u636e\u3002<\/p>\n\n\n\n

\u8bfb\u53d6\u548c\u9884\u5904\u7406\u6570\u636e<\/p>\n\n\n\n

adata = ad.read_h5ad(“..\/..\/data\/MEL1_adata.h5ad”)<\/p>\n\n\n\n

spc.prep.setGate(adata, “KERATIN_cellRingMask”, 6.4, debug=True)<\/p>\n\n\n\n

spc.prep.setGate(adata, “SOX10_cellRingMask”, 7.9, debug=True)<\/p>\n\n\n\n

spc.prep.setGate(adata, “CD3D_cellRingMask”, 7, debug=True)<\/p>\n\n\n\n

\u80bf\u7624\u8fb9\u7f18\u66b4\u9732\u4e8e\u6765\u81ea\u57fa\u8d28\u533a\u57df\u7684\u7ec6\u80de\u6d78\u6da6\u3001\u7269\u7406\u63a5\u89e6\u548c\u53ef\u6269\u6563\u8d8b\u5316\u68af\u5ea6\uff0c\u53cd\u4e4b\u4ea6\u7136\u3002\u56e0\u6b64\uff0c\u5b9a\u4e49\u80bf\u7624\u548c\u57fa\u8d28\u8fb9\u754c\u4f7f\u6211\u4eec\u80fd\u591f\u66f4\u5177\u4f53\u5730\u8bc4\u4f30\u4fb5\u88ad\u6027\u9ed1\u8272\u7d20\u7624\u80bf\u7624\u7ec6\u80de\u4e0e\u5fae\u73af\u5883\u4e4b\u95f4\u7684\u590d\u6742\u76f8\u4e92\u4f5c\u7528\uff0c\u5e76\u8bc6\u522b\u65b0\u7684\u6807\u8bb0\u7269\u3002<\/p>\n\n\n\n

\u5206\u79bb\u80bf\u7624\u7ec6\u80de\u7fa4\u843d\u5e76\u7ed8\u5236\u533a\u57df\u8fb9\u754c<\/p>\n\n\n\n

marker = [“SOX10_cellRingMask_positive”]<\/p>\n\n\n\n

communitycolumn = “COI_community”<\/p>\n\n\n\n

ret = spc.spatial.getCommunities(adata, marker, eps=60, newcolumn=communitycolumn)<\/p>\n\n\n\n

fig, ax = plt.subplots(figsize=(10, 8))<\/p>\n\n\n\n

spc.plt.plotCommunities(<\/p>\n\n\n\n

    adata, ret, communitycolumn, plot_first_n_clusters=10, s=2, fontsize=10, ax=ax<\/p>\n\n\n\n

)<\/p>\n\n\n\n

ax.invert_yaxis()<\/p>\n\n\n\n

plt.show()<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u7ed8\u5236\u80bf\u7624\u7684\u533a\u57df\u8fb9\u754c<\/p>\n\n\n\n

communityIndexList = [6, 3, 14, 51, 29, 47, 39, 44, 22]<\/p>\n\n\n\n

boundary = spc.spatial.getBoundary(<\/p>\n\n\n\n

    adata, communitycolumn, communityIndexList, alpha=130<\/p>\n\n\n\n

)<\/p>\n\n\n\n

boundary = spc.spa.pruneSmallComponents(boundary, min_edges=50, holes_min_edges=500)<\/p>\n\n\n\n

roi_boundary = spc.spa.getExtendedBoundary(boundary, offset=2000)<\/p>\n\n\n\n

markersize = 1<\/p>\n\n\n\n

fig, ax = plt.subplots(figsize=(10, 7))<\/p>\n\n\n\n

## all points<\/p>\n\n\n\n

ax.scatter(<\/p>\n\n\n\n

    *zip(*adata.obs[[“X_centroid”, “Y_centroid”]].to_numpy()),<\/p>\n\n\n\n

    s=markersize,<\/p>\n\n\n\n

    color=”grey”,<\/p>\n\n\n\n

    alpha=0.2<\/p>\n\n\n\n

)<\/p>\n\n\n\n

# Points in selected commnities<\/p>\n\n\n\n

xy = adata.obs[adata.obs[communitycolumn].isin(communityIndexList)][<\/p>\n\n\n\n

    [“X_centroid”, “Y_centroid”]<\/p>\n\n\n\n

].to_numpy()<\/p>\n\n\n\n

ax.scatter(xy[:, 0], xy[:, 1], s=markersize, color=”r”)<\/p>\n\n\n\n

# Bounds of points in selected commnities<\/p>\n\n\n\n

spc.plt.plotBoundary(boundary, ax=ax, label=”Boundary”, color=”b”)<\/p>\n\n\n\n

spc.plt.plotBoundary(roi_boundary, ax=ax, label=”ROI boundary”, color=”g”)<\/p>\n\n\n\n

ax.invert_yaxis()<\/p>\n\n\n\n

plt.show()<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5c06\u7ec6\u80de\u5206\u914d\u5230\u80bf\u7624\u533a\u57df<\/p>\n\n\n\n

spc.spatial.assignPointsToRegions(<\/p>\n\n\n\n

    adata,<\/p>\n\n\n\n

    [boundary, roi_boundary],<\/p>\n\n\n\n

    [“Tumor”, “Tumor_ROI”],<\/p>\n\n\n\n

    assigncolumn=”region”,<\/p>\n\n\n\n

    default=”BG”,<\/p>\n\n\n\n

)<\/p>\n\n\n\n

point_size = 1<\/p>\n\n\n\n

fig, ax = plt.subplots(figsize=(10, 7))<\/p>\n\n\n\n

for region in sorted(set(adata.obs[“region”])):<\/p>\n\n\n\n

    tmp = adata.obs[adata.obs.region == region]<\/p>\n\n\n\n

    ax.scatter(<\/p>\n\n\n\n

        *zip(*tmp[[“X_centroid”, “Y_centroid”]].to_numpy()),<\/p>\n\n\n\n

        s=point_size,<\/p>\n\n\n\n

        alpha=0.7,<\/p>\n\n\n\n

        label=region<\/p>\n\n\n\n

    )<\/p>\n\n\n\n

# Bounds of points in selected commnities<\/p>\n\n\n\n

spc.plt.plotBoundary(boundary, ax=ax, label=”Boundary”, color=”purple”)<\/p>\n\n\n\n

spc.plt.plotBoundary(roi_boundary, ax=ax, label=”ROI boundary”, color=”r”)<\/p>\n\n\n\n

plt.legend(loc=”upper right”)<\/p>\n\n\n\n

ax.invert_yaxis()<\/p>\n\n\n\n

plt.show()<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u6839\u636e\u73b0\u6709\u8868\u578b\u6982\u62ec\u7ec6\u80de\u7c7b\u578b<\/p>\n\n\n\n

def merge_pheno(row):<\/p>\n\n\n\n

    if row[“phenotype_large_cohort”] in [<\/p>\n\n\n\n

        “T cells”,<\/p>\n\n\n\n

        “Cytotoxic T cells”,<\/p>\n\n\n\n

        “Exhausted T cells”,<\/p>\n\n\n\n

    ]:<\/p>\n\n\n\n

        return “T cells”<\/p>\n\n\n\n

    elif row[“phenotype_large_cohort”] in [“Melanocytes”]:<\/p>\n\n\n\n

        return “Tumor cells”<\/p>\n\n\n\n

    else:<\/p>\n\n\n\n

        return “Other cells”<\/p>\n\n\n\n

def cell_type(row):<\/p>\n\n\n\n

    if row[“SOX10_cellRingMask_positive”]:<\/p>\n\n\n\n

        return “SOX10+”<\/p>\n\n\n\n

    elif row[“CD3D_cellRingMask_positive”]:<\/p>\n\n\n\n

        return “CD3D+”<\/p>\n\n\n\n

    else:<\/p>\n\n\n\n

        return “Other cells”<\/p>\n\n\n\n

# Applying the function to create the new columns<\/p>\n\n\n\n

adata.obs[“pheno1”] = pd.Categorical(adata.obs.apply(merge_pheno, axis=1))<\/p>\n\n\n\n

adata.obs[“Cell Types”] = pd.Categorical(adata.obs.apply(cell_type, axis=1))<\/p>\n\n\n\n

spc.msmt.getRegionComposition(adata, “pheno1”)<\/p>\n\n\n\n

\u5728\u80bf\u7624ROI\u533a\u57df\u627e\u5230\u514d\u75ab\u7ec6\u80de\u6d78\u6da6\u533a\u57df<\/p>\n\n\n\n

melano = adata[<\/p>\n\n\n\n

    (adata.obs.SOX10_cellRingMask_positive) & (adata.obs.region.isin([“Tumor_ROI”, “Tumor”]))<\/p>\n\n\n\n

]<\/p>\n\n\n\n

tcells = adata[<\/p>\n\n\n\n

    (adata.obs.CD3D_cellRingMask_positive)<\/p>\n\n\n\n

    & (adata.obs.region.isin([“Tumor_ROI”, “Tumor”]))<\/p>\n\n\n\n

]<\/p>\n\n\n\n

fig, ax = plt.subplots(figsize=(10, 7))<\/p>\n\n\n\n

ax.invert_yaxis()<\/p>\n\n\n\n

ax.set_aspect(“equal”)<\/p>\n\n\n\n

plt.scatter(<\/p>\n\n\n\n

    tcells.obs[“X_centroid”],<\/p>\n\n\n\n

    tcells.obs[“Y_centroid”],<\/p>\n\n\n\n

    s=0.5,<\/p>\n\n\n\n

    label=”T cells”,<\/p>\n\n\n\n

    color=”green”,<\/p>\n\n\n\n

    alpha=0.5,<\/p>\n\n\n\n

)<\/p>\n\n\n\n

spc.plt.plotBoundary(roi_boundary, ax=ax, label=”ROI boundary”, color=”r”)<\/p>\n\n\n\n

plt.legend(loc=”upper right”, markerscale=5)<\/p>\n\n\n\n

plt.show()<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

tumor = adata[adata.obs.region.isin([“Tumor_ROI”, “Tumor”])]<\/p>\n\n\n\n

communitycolumn = “CD3D_cellRingMask_positive”<\/p>\n\n\n\n

communityIndexList = [True]<\/p>\n\n\n\n

immune_boundary = spc.spatial.getBoundary(<\/p>\n\n\n\n

    tumor, communitycolumn, communityIndexList, alpha=130<\/p>\n\n\n\n

)<\/p>\n\n\n\n

immune_boundary = spc.spa.pruneSmallComponents(<\/p>\n\n\n\n

    immune_boundary, min_edges=25, holes_min_edges=30, min_area=30000<\/p>\n\n\n\n

)<\/p>\n\n\n\n

markersize = 0.1<\/p>\n\n\n\n

fig, ax = plt.subplots(figsize=(10, 7))<\/p>\n\n\n\n

## all points<\/p>\n\n\n\n

ax.scatter(<\/p>\n\n\n\n

    *zip(*adata.obs[[“X_centroid”, “Y_centroid”]].to_numpy()),<\/p>\n\n\n\n

    s=markersize,<\/p>\n\n\n\n

    color=”grey”,<\/p>\n\n\n\n

    alpha=0.2<\/p>\n\n\n\n

)<\/p>\n\n\n\n

# Points in selected commnities<\/p>\n\n\n\n

xy = tumor.obs[tumor.obs[communitycolumn].isin(communityIndexList)][<\/p>\n\n\n\n

    [“X_centroid”, “Y_centroid”]<\/p>\n\n\n\n

].to_numpy()<\/p>\n\n\n\n

ax.scatter(xy[:, 0], xy[:, 1], s=markersize, color=”green”, alpha=1, label=”T cells”)<\/p>\n\n\n\n

# Bounds of points in selected commnities<\/p>\n\n\n\n

spc.plt.plotBoundary(<\/p>\n\n\n\n

    immune_boundary, ax=ax, label=”Immune Cell Region Boundary”, color=”k”, linewidth=1<\/p>\n\n\n\n

)<\/p>\n\n\n\n

spc.plt.plotBoundary(roi_boundary, ax=ax, label=”ROI boundary”, color=”r”)<\/p>\n\n\n\n

# ax.set_xlim(0, 20000)<\/p>\n\n\n\n

# ax.set_ylim(0, 13000)<\/p>\n\n\n\n

ax.invert_yaxis()<\/p>\n\n\n\n

ax.set_aspect(“equal”)<\/p>\n\n\n\n

ax.set_axis_off()<\/p>\n\n\n\n

# plt.legend(loc=”upper right”, markerscale=5, fontsize=13.5)<\/p>\n\n\n\n

# plt.savefig(“immune_cell_region1.png”, dpi=400)<\/p>\n\n\n\n

plt.show()<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

spc.spatial.assignPointsToRegions(<\/p>\n\n\n\n

    melano, [immune_boundary], [“T”], assigncolumn=”tumor_isolated_region”, default=”F”<\/p>\n\n\n\n

)<\/p>\n\n\n\n

\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5c06\u5df2\u8bc6\u522b\u7684\u514d\u75ab\u6d78\u6da6\u533a\u57df\u4e0e\u6240\u6709\u80bf\u7624\u7ec6\u80de\u91cd\u53e0\u6765\u4f30\u8ba1\u514d\u75ab\u6d78\u6da6\u7684\u89c4\u6a21\u3002<\/p>\n\n\n\n

point_size = 0.5<\/p>\n\n\n\n

fig, ax = plt.subplots(figsize=(10, 7))<\/p>\n\n\n\n

ax.scatter(<\/p>\n\n\n\n

    *zip(*adata.obs[[“X_centroid”, “Y_centroid”]].to_numpy()),<\/p>\n\n\n\n

    s=markersize,<\/p>\n\n\n\n

    color=”grey”,<\/p>\n\n\n\n

    alpha=0.2<\/p>\n\n\n\n

)<\/p>\n\n\n\n

colors = [“red”, “orange”]<\/p>\n\n\n\n

labels = [“Immune-isolated Tumor Cells”, “Immune-rich Tumor Cells”]<\/p>\n\n\n\n

for i, region in enumerate(sorted(set(melano.obs[“tumor_isolated_region”]))):<\/p>\n\n\n\n

    tmp = melano.obs[melano.obs.tumor_isolated_region == region]<\/p>\n\n\n\n

    ax.scatter(<\/p>\n\n\n\n

        *zip(*tmp[[“X_centroid”, “Y_centroid”]].to_numpy()),<\/p>\n\n\n\n

        s=point_size,<\/p>\n\n\n\n

        alpha=0.5,<\/p>\n\n\n\n

        color=colors[i],<\/p>\n\n\n\n

        label=labels[i]<\/p>\n\n\n\n

    )<\/p>\n\n\n\n

# Bounds of points in selected commnities<\/p>\n\n\n\n

spc.plt.plotBoundary(<\/p>\n\n\n\n

    immune_boundary, ax=ax, label=”Immune Cell Region Boundary”, color=”k”, linewidth=1<\/p>\n\n\n\n

)<\/p>\n\n\n\n

spc.plt.plotBoundary(roi_boundary, ax=ax, label=”ROI boundary”, color=”b”)<\/p>\n\n\n\n

ax.invert_yaxis()<\/p>\n\n\n\n

ax.set_axis_off()<\/p>\n\n\n\n

# plt.savefig(“roi_region1.png”, dpi=400)<\/p>\n\n\n\n

plt.show()<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

print(“Percentage of tumor cells in immune-isolated regions: “)<\/p>\n\n\n\n

melano.obs[“tumor_isolated_region”].value_counts() \/ len(melano.obs)<\/p>\n\n\n\n

\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7\u6bd4\u8f83\u80bf\u7624\u533a\u57df\u548c\u514d\u75ab\u7ec6\u80de\u533a\u57df\u4e4b\u95f4\u7684\u533a\u57df\u91cd\u53e0\u6765\u89c2\u5bdf\u514d\u75ab\u6d78\u6da6\u7684\u89c4\u6a21\u3002<\/p>\n\n\n\n

roi_area = spc.msmt.getRegionArea(roi_boundary)<\/p>\n\n\n\n

tumor_area = spc.msmt.getRegionArea(boundary)<\/p>\n\n\n\n

immune_area = spc.msmt.getRegionArea(immune_boundary)<\/p>\n\n\n\n

tumor_immune_overlap = boundary.intersection(immune_boundary)<\/p>\n\n\n\n

overlap_area = spc.msmt.getRegionArea(tumor_immune_overlap)<\/p>\n\n\n\n

print(f”Area of ROI: {roi_area:.2f}”)<\/p>\n\n\n\n

print(f”Area of main tumor cell region: {tumor_area:.2f}”)<\/p>\n\n\n\n

print(f”Area of immune cell region: {immune_area}”)<\/p>\n\n\n\n

print(f”Area of overlap between tumor and immune cell regions: {overlap_area:.2f}”)<\/p>\n\n\n\n

print(<\/p>\n\n\n\n

    f”Percentage of tumor region that has overlap with “<\/p>\n\n\n\n

    f”immune cell region: {overlap_area \/ tumor_area:.3f}”<\/p>\n\n\n\n

)<\/p>\n\n\n\n

\u8bc6\u522b\u4e0e\u514d\u75ab\u7ec6\u80de\u76f8\u90bb\u7684\u80bf\u7624\u7ec6\u80de\u3002<\/p>\n\n\n\n

dists = spc.msmt.getMinCellTypesDistance(melano, tcells)<\/p>\n\n\n\n

adata.obs.loc[<\/p>\n\n\n\n

    (adata.obs.SOX10_cellRingMask_positive) & (adata.obs.region.isin([“Tumor_ROI”, “Tumor”])), “dist”<\/p>\n\n\n\n

] = dists<\/p>\n\n\n\n

threshold = 20<\/p>\n\n\n\n

adata.obs[“dist_binned”] = adata.obs[“dist”] <= threshold<\/p>\n\n\n\n

infiltrated = adata.obs[<\/p>\n\n\n\n

    (adata.obs.SOX10_cellRingMask_positive)<\/p>\n\n\n\n

    & (adata.obs.region == “Tumor”)<\/p>\n\n\n\n

    & (adata.obs.dist_binned == True)<\/p>\n\n\n\n

]<\/p>\n\n\n\n

non_infiltrated = adata.obs[<\/p>\n\n\n\n

    (adata.obs.SOX10_cellRingMask_positive)<\/p>\n\n\n\n

    & (adata.obs.region == “Tumor”)<\/p>\n\n\n\n

    & (adata.obs.dist_binned == False)<\/p>\n\n\n\n

]<\/p>\n\n\n\n

fig, ax = plt.subplots(figsize=(10, 6))<\/p>\n\n\n\n

ax.invert_yaxis()<\/p>\n\n\n\n

region = adata.obs[(adata.obs.region == “Tumor”)]<\/p>\n\n\n\n

plt.scatter(region[“X_centroid”], region[“Y_centroid”], s=1, alpha=0.2, color=”grey”)<\/p>\n\n\n\n

plt.scatter(<\/p>\n\n\n\n

    infiltrated[“X_centroid”],<\/p>\n\n\n\n

    infiltrated[“Y_centroid”],<\/p>\n\n\n\n

    s=1,<\/p>\n\n\n\n

    alpha=0.5,<\/p>\n\n\n\n

    color=”green”,<\/p>\n\n\n\n

    label=f”Infiltrated (distance to t-cells <= {threshold}um)”,<\/p>\n\n\n\n

)<\/p>\n\n\n\n

plt.legend(markerscale=10)<\/p>\n\n\n\n

plt.show()<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u5c0f\u7ed3<\/strong><\/strong><\/p>\n\n\n\n

SpatialCells \u662f\u4e00\u4e2a\u7528\u4e8e\u7a7a\u95f4\u5206\u6790\u591a\u8def\u590d\u7528\u5355\u7ec6\u80de\u6210\u50cf\u6570\u636e\u7684\u8f6f\u4ef6\u5305\uff0c\u5176\u7279\u70b9\u662f\u80fd\u591f\u6839\u636e\u4efb\u4f55\u7ec6\u80de\u7ec4\u5b9a\u4e49\u611f\u5174\u8da3\u533a\u57df\uff0c\u7136\u540e\u8fdb\u884c\u57fa\u4e8e\u533a\u57df\u7684\u5206\u6790\u3002SpatialCells\u7684\u529f\u80fd\u6db5\u76d6\u4e86\u80bf\u7624\u5fae\u73af\u5883\u5206\u6790\u7684\u5404\u4e2a\u65b9\u9762\uff0c\u4f8b\u5982\u57fa\u4e8e\u533a\u57df\u7684\u7ec6\u80de\u7ec4\u6210\u3001\u80bf\u7624\u589e\u6b96\u6307\u6570\u3001\u80bf\u7624\u5206\u79bb\u6307\u6570\u3001\u514d\u75ab\u7ec6\u80de\u6d78\u6da6\u548c\u80bf\u7624\u514d\u75ab\u8ddd\u79bb\u3002SpatialCells \u5141\u8bb8\u4f7f\u7528\u7528\u6237\u5b9a\u4e49\u7684\u53c2\u6570\u4ee5\u6807\u51c6\u5316\u65b9\u5f0f\u9884\u5904\u7406\u548c\u5206\u6790\u6570\u636e\uff0c\u5e76\u4e14\u53ef\u4ee5\u5904\u7406\u5305\u542b\u6570\u767e\u4e07\u4e2a\u7ec6\u80de\u7684\u6837\u672c\u3002\u6700\u540e\u5927\u6d77\u54e5\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a\u4e91\u5de5\u5177\uff01\u540c\u5b66\u4eec\u5982\u679c\u89c9\u5f97\u81ea\u5df1\u7684\u4ee3\u7801\u6c34\u5e73\u4e00\u822c\uff0c\u5bf9\u4e8e\u5f88\u591a\u7684\u53c2\u6570\u4e0d\u77e5\u9053\u600e\u4e48\u6539\uff0c\u53ef\u4ee5\u4f53\u9a8c\u4e00\u4e0b\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\uff0c\u53ea\u9700\u8f93\u5165\u6570\u636e\uff0c\u5373\u53ef\u8f7b\u677e\u751f\u6210\u6240\u9700\u56fe\u8868\uff0c\u5b57\u4f53\u5927\u5c0f\u3001\u6807\u9898\u7b49\u4e5f\u53ef\u4e00\u952e\u66f4\u6539\u3002\u611f\u5174\u8da3\u7684\u5c0f\u4f19\u4f34\u53bb\u4e91\u751f\u4fe1\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\u4f53\u9a8c\u4e00\u4e0b\u5427\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"

\u524d\u8a00 \u8c08\u8d77\u751f\u4fe1\u5206\u6790\u5fc5\u5907\u9879\u76ee\u514d\u75ab\u6d78\u6da6\u5206\u6790\uff0c\u5c0f\u4f19\u4f34\u4eec\u518d\u719f\u6089\u4e0d\u8fc7\u4e86\uff01\u50cf\u5c0f\u4f19\u4f34\u4eec\u8033\u719f\u80fd\u8be6\u7684ImmuCellAL\u3001Cib […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61051"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=61051"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61051\/revisions"}],"predecessor-version":[{"id":61060,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/61051\/revisions\/61060"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=61051"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=61051"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=61051"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}