{"id":60823,"date":"2024-09-12T15:02:01","date_gmt":"2024-09-12T07:02:01","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=60823"},"modified":"2024-09-12T15:02:01","modified_gmt":"2024-09-12T07:02:01","slug":"%e7%a9%ba%e9%97%b4%e8%bd%ac%e5%bd%95%e7%bb%84%e5%ad%a6%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86%e5%8f%aa%e4%be%9d%e8%b5%96r%e8%af%ad%e8%a8%80%ef%bc%9f%e8%ae%a9%e6%88%91%e4%bb%ac%e4%bb%8escanpy%e5%bc%80","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=60823","title":{"rendered":"\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u5b66\u6570\u636e\u5904\u7406\u53ea\u4f9d\u8d56R\u8bed\u8a00\uff1f\u8ba9\u6211\u4eec\u4ecescanpy\u5f00\u59cb\uff0c\u62e5\u62b1python\u5427\uff01"},"content":{"rendered":"\n

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

\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u5b66\u5206\u6790\uff0c\u662f\u8fd1\u51e0\u5e74\u7684\u4e00\u4e2a\u70ed\u70b9\u3002\u76f8\u4fe1\u5c0f\u4f19\u4f34\u4eec\u5bf9\u4e8eseurat\u518d\u719f\u6089\u4e0d\u8fc7\u4e86\uff01\u4f46\u662fseurat\u662f\u57fa\u4e8eR\u8bed\u8a00\u751f\u6001\u6765\u8fdb\u884c\u6570\u636e\u5904\u7406\u7684\uff0c\u6570\u636e\u5904\u7406\u8d77\u6765\u5b9e\u5728\u662f\u592a\u9ebb\u70e6\u4e86\uff01Python\u5728\u56fe\u50cf\u8bc6\u522b\u3001\u7a7a\u95f4\u5206\u5272\u548c\u53bb\u5377\u79ef\u7b49\u7b97\u6cd5\u9886\u57df\u62e5\u6709\u7edd\u5bf9\u7684\u4f18\u52bf\uff01Scanpy\u662f\u57fa\u4e8epython\u7684\u5355\u7ec6\u80de\u5206\u6790\u5de5\u5177\u5305\uff0c\u6c5f\u6e56\u5730\u4f4d\u76f8\u5f53\u4e8eR\u8bed\u8a00\u4e2d\u7684seurat\u3002\u91cd\u8981\u7a0b\u5ea6\u4e0d\u7528\u6211\u591a\u8bf4\u4e86\u5427\uff01\u6ca1\u63a5\u89e6\u8fc7python\u7684\u5c0f\u4f19\u4f34\u4eec\u4e5f\u4e0d\u8981\u5bb3\u6015\u54e6\uff01\u5b83\u4e0eseurat\u7684\u5206\u6790\u6d41\u7a0b\u57fa\u672c\u4e00\u81f4\uff0c\u90fd\u662f\u8d28\u63a7-\u9009\u62e9\u9ad8\u53d8\u57fa\u56e0-PCA\u964d\u7ef4-\u805a\u7c7b-UMAP(tSNE)\u964d\u7ef4\u8fd9\u4e9b\u6b65\u9aa4\u3002\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\u5c0f\u4e91\u54e6\uff01\u7ec3\u4e86\u5341\u5e74\u751f\u4fe1\u5206\u6790\u7684\u5c0f\u4e91\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

\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\u5c0f\u4e91\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\u5c0f\u4e91\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

1.\u5bfc\u5165\u76f8\u5173\u7684\u5e93<\/strong><\/strong><\/p>\n\n\n\n

import scanpy as sc<\/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 seaborn as sns<\/p>\n\n\n\n

sc.logging.print_versions()<\/p>\n\n\n\n

sc.set_figure_params(facecolor=”white”, figsize=(8, 8))<\/p>\n\n\n\n

sc.settings.verbosity = 3<\/p>\n\n\n\n

2.\u8bfb\u53d6\u6570\u636e<\/strong><\/strong><\/p>\n\n\n\n

\u6211\u4eec\u5c06\u4f7f\u7528\u4eba\u7c7b\u6dcb\u5df4\u7ed3\u7684 Visium \u7a7a\u95f4\u8f6c\u5f55\u7ec4\u5b66\u6570\u636e\u96c6\u3002\u51fd\u6570 datasets.visium_sge\uff08\uff09 \u4ece 10x Genomics \u4e0b\u8f7d\u6570\u636e\u96c6\u5e76\u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u8ba1\u6570\u3001\u56fe\u50cf\u548c\u7a7a\u95f4\u5750\u6807\u7684\u5bf9\u8c61\u3002\u4f7f\u7528\u60a8\u81ea\u5df1\u7684 Visium \u6570\u636e\u65f6\uff0c\u8bf7\u4f7f\u7528 sc.read_visium\uff08\uff09 \u51fd\u6570\u5bfc\u5165\u5b83\u3002<\/p>\n\n\n\n

adata = sc.datasets.visium_sge(sample_id=”V1_Human_Lymph_Node”)<\/p>\n\n\n\n

adata.var_names_make_unique()<\/p>\n\n\n\n

adata.var[“mt”] = adata.var_names.str.startswith(“MT-“)<\/p>\n\n\n\n

sc.pp.calculate_qc_metrics(adata, qc_vars=[“mt”], inplace=True)<\/p>\n\n\n\n

adata<\/p>\n\n\n\n

3. \u8d28\u91cf\u63a7\u5236\u548c\u9884\u5904\u7406<\/strong><\/strong><\/p>\n\n\n\n

\u6211\u4eec\u6839\u636e\u603b\u6570\u548c\u8868\u8fbe\u57fa\u56e0\u5bf9\u6837\u672c\u8fdb\u884c\u4e00\u4e9b\u57fa\u672c\u7684\u8fc7\u6ee4<\/p>\n\n\n\n

fig, axs = plt.subplots(1, 4, figsize=(15, 4))<\/p>\n\n\n\n

sns.histplot(adata.obs[“total_counts”], kde=False, ax=axs[0])<\/p>\n\n\n\n

sns.histplot(<\/p>\n\n\n\n

    adata.obs[“total_counts”][adata.obs[“total_counts”] < 10000],<\/p>\n\n\n\n

    kde=False,<\/p>\n\n\n\n

    bins=40,<\/p>\n\n\n\n

    ax=axs[1],<\/p>\n\n\n\n

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

sns.histplot(adata.obs[“n_genes_by_counts”], kde=False, bins=60, ax=axs[2])<\/p>\n\n\n\n

sns.histplot(<\/p>\n\n\n\n

    adata.obs[“n_genes_by_counts”][adata.obs[“n_genes_by_counts”] < 4000],<\/p>\n\n\n\n

    kde=False,<\/p>\n\n\n\n

    bins=60,<\/p>\n\n\n\n

    ax=axs[3],<\/p>\n\n\n\n

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

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

sc.pp.filter_cells(adata, min_counts=5000)<\/p>\n\n\n\n

sc.pp.filter_cells(adata, max_counts=35000)<\/p>\n\n\n\n

adata = adata[adata.obs[“pct_counts_mt”] < 20].copy()<\/p>\n\n\n\n

print(f”#cells after MT filter: {adata.n_obs}”)<\/p>\n\n\n\n

sc.pp.filter_genes(adata, min_cells=10)<\/p>\n\n\n\n

\u6211\u4eec\u7ee7\u7eed\u4f7f\u7528 Scanpy \u7684\u5185\u7f6e\u65b9\u6cd5\u5bf9 Visium \u8ba1\u6570\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\uff0c\u5e76\u68c0\u6d4b\u9ad8\u5ea6\u53ef\u53d8\u7684\u57fa\u56e0<\/p>\n\n\n\n

sc.pp.normalize_total(adata, inplace=True)<\/p>\n\n\n\n

sc.pp.log1p(adata)<\/p>\n\n\n\n

sc.pp.highly_variable_genes(adata, flavor=”seurat”, n_top_genes=2000)<\/p>\n\n\n\n

4. \u57fa\u4e8e\u8f6c\u5f55\u76f8\u4f3c\u6027\u7684\u6d41\u5f62\u5d4c\u5165\u548c\u805a\u7c7b<\/strong><\/strong><\/p>\n\n\n\n

sc.pp.pca(adata)<\/p>\n\n\n\n

sc.pp.neighbors(adata)<\/p>\n\n\n\n

sc.tl.umap(adata)<\/p>\n\n\n\n

sc.tl.leiden(<\/p>\n\n\n\n

    adata, key_added=”clusters”, flavor=”igraph”, directed=False, n_iterations=2<\/p>\n\n\n\n

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

\u6211\u4eec\u7ed8\u5236\u4e86\u4e00\u4e9b\u534f\u53d8\u91cf\uff0c\u4ee5\u68c0\u67e5 UMAP \u4e2d\u662f\u5426\u5b58\u5728\u4e0e\u603b\u6570\u548c\u68c0\u6d4b\u5230\u7684\u57fa\u56e0\u76f8\u5173\u7684\u4efb\u4f55\u7279\u5b9a\u7ed3\u6784\u3002<\/p>\n\n\n\n

plt.rcParams[“figure.figsize”] = (4, 4)<\/p>\n\n\n\n

sc.pl.umap(adata, color=[“total_counts”, “n_genes_by_counts”, “clusters”], wspace=0.4)<\/p>\n\n\n\n

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

5. \u7a7a\u95f4\u5750\u6807\u4e2d\u7684\u53ef\u89c6\u5316<\/strong><\/strong><\/p>\n\n\n\n

plt.rcParams[“figure.figsize”] = (8, 8)<\/p>\n\n\n\n

sc.pl.spatial(adata, img_key=”hires”, color=[“total_counts”, “n_genes_by_counts”])<\/p>\n\n\n\n

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

\u51fd\u6570 sc.pl.spatial \u63a5\u53d7 4 \u4e2a\u9644\u52a0\u53c2\u6570\uff1a<\/p>\n\n\n\n

    \n
  • img_key\uff1a\u5176\u4e2d IMG \u5b58\u50a8\u5728\u5143\u7d20\u4e2dadata.uns<\/li>\n\n\n\n
  • crop_coord\uff1a\u7528\u4e8e\u88c1\u526a\u7684\u5750\u6807\uff08\u5de6\u3001\u53f3\u3001\u4e0a\u3001\u4e0b\uff09<\/li>\n\n\n\n
  • alpha_img\uff1a\u56fe\u50cf\u900f\u660e\u5ea6\u7684 alpha \u503c<\/li>\n\n\n\n
  • bw\uff1a\u6807\u5fd7\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6<\/li>\n<\/ul>\n\n\n\n

    \u4e4b\u524d\uff0c\u6211\u4eec\u5728\u57fa\u56e0\u8868\u8fbe\u7a7a\u95f4\u4e2d\u8fdb\u884c\u4e86\u805a\u7c7b\uff0c\u5e76\u7528UMAP\u53ef\u89c6\u5316\u4e86\u7ed3\u679c\u3002\u901a\u8fc7\u5728\u7a7a\u95f4\u7ef4\u5ea6\u4e0a\u53ef\u89c6\u5316\u805a\u7c7b\u6837\u672c\uff0c\u6211\u4eec\u53ef\u4ee5\u6df1\u5165\u4e86\u89e3\u7ec4\u7ec7\u7ec4\u7ec7\uff0c\u5e76\u53ef\u80fd\u6df1\u5165\u4e86\u89e3\u7ec6\u80de\u95f4\u901a\u8baf\u3002<\/p>\n\n\n\n

    sc.pl.spatial(adata, img_key=”hires”, color=”clusters”, size=1.5)<\/p>\n\n\n\n

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

    \u57fa\u56e0\u8868\u8fbe\u7a7a\u95f4\u4e2d\u5c5e\u4e8e\u540c\u4e00\u7c07\u7684\u6591\u70b9\u901a\u5e38\u5728\u7a7a\u95f4\u7ef4\u5ea6\u4e0a\u5171\u73b0\u3002\u4f8b\u5982\uff0c\u5c5e\u4e8e\u805a\u7c7b 5 \u7684\u6591\u70b9\u901a\u5e38\u88ab\u5c5e\u4e8e\u805a\u7c7b 0 \u7684\u70b9\u5305\u56f4\u3002<\/p>\n\n\n\n

    \u6211\u4eec\u53ef\u4ee5\u653e\u5927\u7279\u5b9a\u7684\u611f\u5174\u8da3\u533a\u57df\u4ee5\u83b7\u5f97\u5b9a\u6027\u89c1\u89e3\u3002\u6b64\u5916\uff0c\u901a\u8fc7\u6539\u53d8\u6591\u70b9\u7684 alpha \u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u4ece H&E \u56fe\u50cf\u4e2d\u66f4\u597d\u5730\u53ef\u89c6\u5316\u4e0b\u9762\u7684\u7ec4\u7ec7\u5f62\u6001\u3002<\/p>\n\n\n\n

    sc.pl.spatial(<\/p>\n\n\n\n

        adata,<\/p>\n\n\n\n

        img_key=”hires”,<\/p>\n\n\n\n

        color=”clusters”,<\/p>\n\n\n\n

        groups=[“5”, “9”],<\/p>\n\n\n\n

        crop_coord=[7000, 10000, 0, 6000],<\/p>\n\n\n\n

        alpha=0.5,<\/p>\n\n\n\n

        size=1.3,<\/p>\n\n\n\n

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

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

    6. \u4e9a\u7fa4\u6807\u8bb0\u57fa\u56e0<\/strong><\/strong><\/p>\n\n\n\n

    \u8ba1\u7b97\u6807\u8bb0\u57fa\u56e0\u5e76\u7ed8\u5236\u70ed\u56fe\uff0c\u5176\u4e2d\u524d 10 \u4e2a\u6807\u8bb0\u57fa\u56e0\u7684\u8868\u8fbe\u6c34\u5e73\u3002<\/p>\n\n\n\n

    sc.tl.rank_genes_groups(adata, “clusters”, method=”t-test”)<\/p>\n\n\n\n

    sc.pl.rank_genes_groups_heatmap(adata, groups=”9″, n_genes=10, groupby=”clusters”)<\/p>\n\n\n\n

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

    ##\u6211\u4eec\u770b\u5230 CR2 \u6982\u62ec\u4e86\u7a7a\u95f4\u7ed3\u6784\u3002<\/p>\n\n\n\n

    sc.pl.spatial(adata, img_key=”hires”, color=[“clusters”, “CR2”])<\/p>\n\n\n\n

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

    sc.pl.spatial(adata, img_key=”hires”, color=[“COL1A2”, “SYPL1”], alpha=0.7)<\/p>\n\n\n\n

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

    7.<\/strong> MERFISH\u793a\u4f8b<\/strong><\/strong><\/p>\n\n\n\n

    \u5982\u679c\u60a8\u4f7f\u7528\u57fa\u4e8e FISH \u7684\u6280\u672f\u751f\u6210\u7a7a\u95f4\u6570\u636e\uff0c\u53ea\u9700\u9605\u8bfb cordinate \u8868\u5e76\u5c06\u5176\u5206\u914d\u7ed9\u5143\u7d20\u5373\u53ef\u3002<\/p>\n\n\n\n

    # If needed:<\/p>\n\n\n\n

    # %pip install openpyxl<\/p>\n\n\n\n

    coordinates = pd.read_excel(“.\/data\/pnas.1912459116.sd15.xlsx”, index_col=0)<\/p>\n\n\n\n

    counts = sc.read_csv(“.\/data\/pnas.1912459116.sd12.csv”).transpose()<\/p>\n\n\n\n

    adata_merfish = counts[coordinates.index, :].copy()<\/p>\n\n\n\n

    adata_merfish.obsm[“spatial”] = coordinates.to_numpy()<\/p>\n\n\n\n

    #\u9884\u5904\u7406\u548c\u964d\u7ef4<\/p>\n\n\n\n

    sc.pp.normalize_per_cell(adata_merfish, counts_per_cell_after=1e6)<\/p>\n\n\n\n

    sc.pp.log1p(adata_merfish)<\/p>\n\n\n\n

    sc.pp.pca(adata_merfish, n_comps=15)<\/p>\n\n\n\n

    sc.pp.neighbors(adata_merfish)<\/p>\n\n\n\n

    sc.tl.umap(adata_merfish)<\/p>\n\n\n\n

    sc.tl.leiden(<\/p>\n\n\n\n

        adata_merfish,<\/p>\n\n\n\n

        key_added=”clusters”,<\/p>\n\n\n\n

        resolution=0.5,<\/p>\n\n\n\n

        n_iterations=2,<\/p>\n\n\n\n

        flavor=”igraph”,<\/p>\n\n\n\n

        directed=False,<\/p>\n\n\n\n

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

    adata_merfish<\/p>\n\n\n\n

    sc.pl.umap(adata_merfish, color=”clusters”)<\/p>\n\n\n\n

    sc.pl.embedding(adata_merfish, basis=”spatial”, color=”clusters”)<\/p>\n\n\n\n

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

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

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