{"id":26923,"date":"2024-01-29T17:32:23","date_gmt":"2024-01-29T09:32:23","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26923"},"modified":"2024-01-29T17:32:25","modified_gmt":"2024-01-29T09:32:25","slug":"%e4%bb%8e%e5%b0%8f%e7%99%bd%e5%88%b0%e7%a9%ba%e8%bd%ac%e8%be%be%e4%ba%ba%ef%bc%9a%e5%b0%8f%e6%9e%9c%e6%95%99%e4%bd%a0%e7%94%a8scanpy%e5%8c%85%e7%8e%a9%e8%bd%ac%e5%89%8d%e6%b2%bf%e7%83%ad%e7%82%b9","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=26923","title":{"rendered":"\u4ece\u5c0f\u767d\u5230\u7a7a\u8f6c\u8fbe\u4eba\uff1a\u5c0f\u679c\u6559\u4f60\u7528Scanpy\u5305\u73a9\u8f6c\u524d\u6cbf\u70ed\u70b9\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u5206\u6790\uff01"},"content":{"rendered":"\n

\u5927\u5bb6\u597d\u5440\uff0c\u6211\u4eec\u53c8\u89c1\u9762\u5566\uff0c\u4eca\u5929\u548c\u5927\u5bb6\u804a\u804a\u5355\u7ec6\u80de\u548c\u7a7a\u8f6c\u5206\u6790\u4e2d\u4e00\u4e2a\u5f88\u57fa\u7840\u7684\u5305\u2014\u2014Scanpy\uff0cScanpy \u662f\u4e00\u4e2aPython\u5305,\u7528\u4e8e\u5206\u6790\u4e0e AnnData(\u4e00\u79cd\u6570\u636e\u7ed3\u6784)\u8054\u5408\u6784\u5efa\u7684\u5355\u7ec6\u80de\u5206\u6790\u6570\u636e\uff0c\u5f53\u7136\u76ee\u524d\u968f\u7740\u6280\u672f\u7684\u53d1\u5c55\uff0c\u5b83\u7684\u5f00\u53d1\u8005\u4e5f\u5728\u8ddf\u968f\u65f6\u4ee3\u70ed\u70b9\u53d8\u5316\u4e0d\u65ad\u6539\u8fdb\u5b83\uff0c\u73b0\u5728\u9664\u4e86\u5355\u7ec6\u80de\u5206\u6790\uff0c\u5b83\u4e5f\u5b9e\u73b0\u4e86\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u6570\u636e\u5206\u6790\u6d41\u7a0b\u3002\u4e4b\u524d\u5c0f\u4f19\u4f34\u5728\u540e\u53f0\u95ee\u5c0f\u679cScanpy\u548cSeurat\u5305\u7684\u533a\u522b\uff0c\u8bf4\u81ea\u5df1\u4e0d\u662f\u5f88\u61c2\u5404\u79cd\u5206\u6790\u6d41\u7a0b\u7684\u6b65\u9aa4\u548c\u539f\u7406\uff0c\u679c\u679c\u4eca\u5929\u5c31\u7ed9\u5927\u5bb6\u8be6\u7ec6\u8bb2\u8bb2python\u7684scanpy\u5305\uff0c\u5176\u5b9escanpy\u548cseurat\u7684\u529f\u80fd\u57fa\u672c\u662f\u53ef\u4ee5\u66ff\u4ee3\u7684\uff0c\u4e0d\u540c\u7684\u662f\u4e00\u4e2a\u7528\u5728python,\u4e00\u4e2a\u7528\u5728R\u3002<\/p>\n\n\n\n

\u6211\u4eec\u77e5\u9053\u5355\u7ec6\u80de\u548c\u7a7a\u8f6c\u5206\u6790\u6d41\u7a0b\u5305\u62ec\u5f88\u591a\u6b65\u9aa4\uff0c\u6bd4\u5982\u8d28\u63a7\uff08QC\uff09\uff0c\u6807\u51c6\u5316\uff08Normalization\uff09\uff0c\u964d\u7ef4\u805a\u7c7b\uff08Dimensional reduction and clustering\uff09\uff0cCluster marker genes\uff0c Spatially variable genes\u7b49\uff0c\u5c0f\u679c\u4eca\u5929\u5e76\u4e0d\u60f3\u76f4\u63a5\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u7f51\u4e0a\u90fd\u6709\u7684\u6d41\u7a0b\uff0c\u800c\u662f\u60f3\u7ed9\u5927\u5bb6\u770b\u770bScanpy\u8bfb\u53d6\u7684\u6570\u636e\u7ed3\u6784\uff0c\u4ee5\u53ca\u5982\u4f55\u53ef\u89c6\u5316\uff0c\u6559\u5927\u5bb6\u4e00\u6b65\u5165\u95e8Scanpy!<\/p>\n\n\n\n

    \n
  1. \u9996\u5148\u80af\u5b9a\u662f\u8981\u8f93\u5165\u6587\u4ef6\uff01\u5148\u6765\u6559\u6559\u5927\u5bb6h5ad\u6587\u4ef6\u7684\u8bfb\u53d6<\/li>\n<\/ol>\n\n\n\n

    h5ad\u6587\u4ef6\u5168\u79f0\u662fh5anndata\uff0c\u662fanndata\u7684h5\u683c\u5f0f\uff0canndata\u4e5f\u662fpython\u4e2d\u7684\u6570\u636e\u683c\u5f0f\uff0c\u5982\u679c\u4f60\u60f3\u7528seurat\u8bfb\uff0c\u53ef\u4ee5\u53d1\u73b0\u6700\u540e\u8bfb\u53d6\u7684\u6587\u4ef6\u683c\u5f0f\u4f1a\u53d8\u4e3ah5seurat\uff0c\u7b80\u5355\u7684\u8bf4python\u4e2d\u9002\u914dh5ad\u683c\u5f0f\u8bfb\u53d6\uff0cR\u9002\u914dh5seurat\u683c\u5f0f\u8bfb\u53d6\u3002<\/p>\n\n\n\n

    adata =anndata.read_h5ad(‘F:\/university\/research\/\u7a7a\u95f4\u8f6c\u5f55\u7ec4\/canvas\/GSM6177599_10xVisium_processed.h5ad’)<\/p>\n\n\n\n

      \n
    1. \u8bfb\u5165\u6587\u4ef6\u540e\uff0c\u8ba9\u6211\u4eec\u770b\u4e00\u4e0badata\u4e2d\u6709\u4ec0\u4e48<\/li>\n<\/ol>\n\n\n\n
      \"\"<\/figure>\n\n\n\n
        \n
      • Obsm\uff1a\u5bf9\u4e8e\u89c2\u6d4b\u7684\u591a\u7ef4\u6ce8\u91ca\uff0c\u5b83\u662f\u53ef\u53d8\u7684ndarray\uff0c\u5c31\u662f\u8bf4\u7ef4\u5ea6\u662f\u53ef\u53d8\u7684\uff01\u5c0f\u679c\u5077\u5077\u544a\u8bc9\u4f60\uff0c\u8fd9\u91cc\u7684\u7ef4\u5ea6\u4e00\u822c\u662f2\u81f3\u591a\u7ef4\u54e6\u3002\u800cObsm\u8fd9\u91cc\u7684m\u6307\u7684\u5c31\u662fmulti-dim\u591a\u4e2a\u7ef4\u5ea6\u7684\uff0cobs_m\u5bf9\u5e94\u4e8eobs\uff0c\u4f46obs\u7684\u6bcf\u4e2a\u6210\u5458\u90fd\u662f\u4e00\u7ef4\u7684\u89c2\u6d4b\u6ce8\u91ca\uff0cobs_m\u7684\u6bcf\u4e2a\u6210\u5458\uff08X_pac\u548cX_umap\uff09\u90fd\u662f\u591a\u7ef4\u7684\u89c2\u6d4b\u6ce8\u91ca\u3002\u54e6\u5bf9\u4e86\uff0c\u5fd8\u4e86\u544a\u8bc9\u4f60\uff0cobs\u5c31\u662f\u77e9\u9635\u7684\u884c\uff0c\u4e5f\u5c31\u662f\u6837\u672c\uff01<\/li>\n\n\n\n
      • Varm\uff1a\u4e0eobsm\u5bf9\u5e94\uff0c\u662f\u57fa\u56e0\u7684\u591a\u7ef4\u89c2\u6d4b~<\/li>\n\n\n\n
      • uns\uff1a\u975e\u7ed3\u6784\u5316\u6570\u636e\uff0c\u4e5f\u5c31\u662f\u5b57\u5178\u3002<\/li>\n\n\n\n
      • rank_genes_groups \u8bc6\u522b\u5dee\u5f02\u8868\u8fbe\u7684\u57fa\u56e0\uff08\u6f5c\u5728\u7684marker\uff09\uff0c\u8fd9\u4e2a\u51fd\u6570\u5c06\u83b7\u53d6\u6bcf\u7ec4\u7ec6\u80de\uff0c\u5e76\u5c06\u6bcf\u7ec4\u4e2d\u6bcf\u4e2a\u57fa\u56e0\u7684\u5206\u5e03\u4e0e\u4e0d\u5728\u8be5\u7ec4\u4e2d\u7684\u6240\u6709\u5176\u4ed6\u7ec6\u80de\u4e2d\u7684\u5206\u5e03\u8fdb\u884c\u6bd4\u8f83\u6700\u540e\u8fdb\u884c\u8bc6\u522b\u3002<\/li>\n\n\n\n
      • pca\uff1a\u4e3b\u6210\u5206\u5206\u6790\u964d\u4f4e\u6570\u636e\u7ef4\u5ea6\u3002<\/li>\n\n\n\n
      • umap\u964d\u7ef4\uff1aumap\u6bd4tsne\u4fdd\u7559\u5168\u5c40\u7ed3\u6784\uff0c\u5141\u8bb8\u5927\u6570\u636e\u8f93\u5165\uff0c\u8fd0\u884c\u65f6\u95f4\u77ed\uff0c\u8fd9\u662fumap\u548ctsne\u7684\u533a\u522b\uff0c\u4e00\u5b9a\u8981\u7262\u8bb0\u5594\uff01<\/li>\n\n\n\n
      • obsp(pair)\uff1a\u8868\u793a\u7ec6\u80de\u548c\u7ec6\u80de\u4e4b\u95f4\u7684\u8ddd\u79bb\u548c\u8fde\u901a\u6027\u3002<\/li>\n\n\n\n
      • \u987a\u4fbf\u7ed9\u5927\u5bb6\u63d0\u63d0obs\u4e2d\u7684mito\u8868\u793a\u7ebf\u7c92\u4f53\u57fa\u56e0\uff0c\u4e00\u822cmito\u90fd\u8868\u793a\u7684\u662f\u7ebf\u7c92\u4f53\u3002<\/li>\n\n\n\n
      • n_genes_by_counts`\uff1a\u6bcf\u4e2a\u7ec6\u80de\u4e2d\uff0c\u6709\u8868\u8fbe\u7684\u57fa\u56e0\u7684\u4e2a\u6570\uff1b<\/li>\n\n\n\n
      • total_counts\uff1a\u6bcf\u4e2a\u7ec6\u80de\u7684\u57fa\u56e0\u603b\u8ba1\u6570\uff08\u603b\u8868\u8fbe\u91cf\uff09<\/li>\n\n\n\n
      • pct_counts_mt\uff1a\u6bcf\u4e2a\u7ec6\u80de\u4e2d\uff0c\u7ebf\u7c92\u4f53\u57fa\u56e0\u8868\u8fbe\u91cf\u5360\u8be5\u7ec6\u80de\u6240\u6709\u57fa\u56e0\u8868\u8fbe\u91cf\u7684\u767e\u5206\u6bd4<\/li>\n<\/ul>\n\n\n\n

        \u5148\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u8fd9\u4e48\u591a\u4e86\uff0c\u5177\u4f53\u7684\u8fd8\u662f\u9700\u8981\u540e\u7eed\u81ea\u5df1\u67e5\u4e00\u4e0b\u54e6~<\/p>\n\n\n\n

          \n
        1. \u53ef\u89c6\u5316\u4e00\u4e0b<\/li>\n<\/ol>\n\n\n\n

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

          from matplotlib import rc_context<\/p>\n\n\n\n

          # rc_context\u7528\u4e8e\u6307\u5b9afigure\u5927\u5c0f<\/p>\n\n\n\n

          with rc_context({‘figure.figsize’: (4, 4)}):<\/p>\n\n\n\n

          # vmax\u6539\u53d8\u989c\u8272\u6700\u5927\u503c\uff0cframeon\u4e3a\u53bb\u6389\u8fb9\u6846<\/p>\n\n\n\n

          sc.pl.umap(Myadata, color=[‘Rp1′,’clusters’,’pct_counts_mito’,’total_counts’],s=50,ncols=4,frameon=False,vmax=’p99′)<\/p>\n\n\n\n

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

          \u4e0a\u56fe\u4e2d\u6211\u4eec\u4f7f\u7528umap\u7ed8\u5236\u4e86\u5bf9\u5e94\u7684cluster\uff0cmetadata\u7684\u503c\uff0cmetadata\u5c31\u662f\u6bcf\u4e2a\u7ec6\u80de\u7684\u4fe1\u606f\u5594\uff0c\u8fd8\u7ed8\u5236\u4e86Rp1\uff0c\u8fd9\u4e48\u591a\u56fe\u53ea\u9700\u8981\u7b80\u5355\u7684\u4e24\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u7ed8\u5236\uff01\u662f\u4e0d\u662f\u89c9\u5f97\u5199\u8fd9\u4e2a\u5305\u7684\u4eba\u5f88\u725b\uff01<\/p>\n\n\n\n

          \u4f46\u662f\u5176\u5b9e\u5c0f\u679c\u8fd8\u662f\u60f3\u77e5\u9053\u8fd9\u4e2a\u5750\u6807\u662f\u54ea\u91cc\u6765\u7684\uff0c\u6240\u4ee5\u5c31\u53bb\u8bd5\u4e86\u4e00\u4e0badata\u4e2d\u7684obsm\u4e2d\u7684umap\u7684\u503c\uff0c\u60f3\u770b\u770b\u7a76\u7adf\u5728\u8fd9\u4e2a\u5305\u4e2d\u662f\u4e0d\u662fX_umap\u7684\u5750\u6807\u7ed8\u5236\u4e86\u4e0a\u9762\u8fd9\u6837\u7684\u56fe\uff0c\u4e8e\u662f\u5c31\u8fdb\u884c\u4e86\u4e0b\u8ff0\u64cd\u4f5c\uff1a<\/p>\n\n\n\n

          #\u6d4b\u8bd5\u4e00\u4e0bumap \u7b26\u5408\u9884\u671f\uff0c\u786e\u5b9e\u662f\u6570\u636e\u4e3a\u7ec6\u80de\u7684\u5750\u6807<\/p>\n\n\n\n

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

          # \u5c06\u4e8c\u7ef4\u6570\u7ec4\u8f6c\u6362\u4e3a\u4e24\u4e2a\u4e00\u7ef4\u6570\u7ec4x\u548cy<\/p>\n\n\n\n

          data=Myadata.obsm[‘X_umap’]<\/p>\n\n\n\n

          x = [d[0] for d in data]<\/p>\n\n\n\n

          y = [d[1] for d in data]<\/p>\n\n\n\n

          # \u7ed8\u5236\u6563\u70b9\u56fe<\/p>\n\n\n\n

          plt.scatter(x, y)<\/p>\n\n\n\n

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

          \u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u786e\u5b9e\u662f\u8fd9\u6837\u7684\uff01\u6240\u4ee5\u5927\u5bb6\u4e4b\u540e\u8981\u662f\u4e0d\u61c2\u5305\u5185\u90e8\u7684\u539f\u7406\u7684\u8bdd\u4e00\u5b9a\u8981\u591a\u5c1d\u8bd5\uff0c\u591a\u8bd5\u8bd5\u603b\u5f52\u662f\u9a97\u4e0d\u4e86\u4eba\u7684\uff01<\/p>\n\n\n\n

          with rc_context({‘figure.figsize’: (4.5, 3)}):<\/p>\n\n\n\n

          ”’stripplot=False\u7528\u4e8e\u5220\u9664internal dots, inner=’box’\u7528\u4e8e\u5728\u5c0f\u63d0\u7434\u5185\u90e8\u52a0\u65b9\u6846\u56fe”’<\/p>\n\n\n\n

          sc.pl.violin(Myadata, [‘pct_counts_in_top_50_genes’, ‘total_counts_mito’], groupby=’clusters’, stripplot=False, inner=’box’)<\/p>\n\n\n\n

          ”’mino\u8868\u793a\u7ebf\u7c92\u4f53\u57fa\u56e0”’<\/p>\n\n\n\n

          \u53ef\u4ee5\u518d\u6765\u8bd5\u8bd5\u5c0f\u63d0\u7434\u56fe\uff01<\/p>\n\n\n\n

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

          \u8fd9\u662f11\u4e2acluster\u4e2d\u6bcf\u4e2acluster\u5bf9\u5e94\u7684\u4e0d\u540c\u7c7b\u7684\u57fa\u56e0\u7684\u5c0f\u63d0\u7434\u56fe\u3002<\/p>\n\n\n\n

          \u603b\u7ed3\u4e00\u4e0b\u53ed\uff0cpl.umap\u53ef\u4ee5\u753bumap\u964d\u7ef4\u56fe\uff0cpl.violin\u53ef\u4ee5\u753b\u5c0f\u63d0\u7434\u56fe\uff0c\u90a3\u4f60\u77e5\u9053\u70ed\u56fe\u600e\u4e48\u753b\u5417\uff0c\u8fd8\u6709\u5806\u53e0\u5c0f\u63d0\u7434\u56fe\uff0c\u6bd4\u5982\u4e0b\u9762\u8fd9\u79cd\u56fe\u6240\u793a\uff0c\u662f\u4e0d\u662f\u5f88\u597d\u770b\u54c8\u54c8\u54c8\uff0c\u5c0f\u4f19\u4f34\u4eec\u53ef\u4ee5\u4e4b\u540e\u8bd5\u8bd5\u54e6\uff0c\u5982\u679c\u6709\u9700\u8981\u7684\u8bdd\uff0c\u5c0f\u679c\u4e5f\u4f1a\u4e13\u95e8\u7ed9\u5927\u5bb6\u51fa\u4e00\u671f\u7ee7\u7eed\u8bb2\u4e00\u4e0b\u6211\u4eec\u8fd9\u6b21\u6240\u7528\u6570\u636e\u7684\u540e\u7eed\u5206\u6790\u7684\uff0c\u5c31\u770b\u5927\u5bb6\u7684\u8d5e\u7684\u4e2a\u6570\u4e86\u54e6<\/p>\n\n\n\n

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

          \u63a8\u8350\u5927\u5bb6\u4f7f\u7528\u4e91\u751f\u4fe1- \u5b66\u751f\u7269\u4fe1\u606f\u5b66\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\uff0c\u91cc\u9762\u4e5f\u6709\u5f88\u591a0\u4ee3\u7801\u5206\u6790\u8fc7\u7a0b\uff0c\u4f7f\u7528\u5f88\u65b9\u4fbf~<\/p>\n\n\n

          \u4ece\u5c0f\u767d\u5230\u7a7a\u8f6c\u8fbe\u4eba\uff1a\u5c0f\u679c\u6559\u4f60\u7528Scanpy\u5305\u73a9\u8f6c\u524d\u6cbf\u70ed\u70b9\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u5206\u6790\uff01<\/strong><\/p>\n

          \u5927\u5bb6\u597d\u5440\uff0c\u6211\u4eec\u53c8\u89c1\u9762\u5566\uff0c\u4eca\u5929\u548c\u5927\u5bb6\u804a\u804a\u5355\u7ec6\u80de\u548c\u7a7a\u8f6c\u5206\u6790\u4e2d\u4e00\u4e2a\u5f88\u57fa\u7840\u7684\u5305\u2014\u2014Scanpy\uff0cScanpy \u662f\u4e00\u4e2aPython\u5305,\u7528\u4e8e\u5206\u6790\u4e0e AnnData(\u4e00\u79cd\u6570\u636e\u7ed3\u6784)\u8054\u5408\u6784\u5efa\u7684\u5355\u7ec6\u80de\u5206\u6790\u6570\u636e\uff0c\u5f53\u7136\u76ee\u524d\u968f\u7740\u6280\u672f\u7684\u53d1\u5c55\uff0c\u5b83\u7684\u5f00\u53d1\u8005\u4e5f\u5728\u8ddf\u968f\u65f6\u4ee3\u70ed\u70b9\u53d8\u5316\u4e0d\u65ad\u6539\u8fdb\u5b83\uff0c\u73b0\u5728\u9664\u4e86\u5355\u7ec6\u80de\u5206\u6790\uff0c\u5b83\u4e5f\u5b9e\u73b0\u4e86\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u6570\u636e\u5206\u6790\u6d41\u7a0b\u3002\u4e4b\u524d\u5c0f\u4f19\u4f34\u5728\u540e\u53f0\u95ee\u5c0f\u679cScanpy\u548cSeurat\u5305\u7684\u533a\u522b\uff0c\u8bf4\u81ea\u5df1\u4e0d\u662f\u5f88\u61c2\u5404\u79cd\u5206\u6790\u6d41\u7a0b\u7684\u6b65\u9aa4\u548c\u539f\u7406\uff0c\u679c\u679c\u4eca\u5929\u5c31\u7ed9\u5927\u5bb6\u8be6\u7ec6\u8bb2\u8bb2python\u7684scanpy\u5305\uff0c\u5176\u5b9escanpy\u548cseurat\u7684\u529f\u80fd\u57fa\u672c\u662f\u53ef\u4ee5\u66ff\u4ee3\u7684\uff0c\u4e0d\u540c\u7684\u662f\u4e00\u4e2a\u7528\u5728python,\u4e00\u4e2a\u7528\u5728R\u3002<\/p>\n

          \u6211\u4eec\u77e5\u9053\u5355\u7ec6\u80de\u548c\u7a7a\u8f6c\u5206\u6790\u6d41\u7a0b\u5305\u62ec\u5f88\u591a\u6b65\u9aa4\uff0c\u6bd4\u5982\u8d28\u63a7\uff08QC\uff09\uff0c\u6807\u51c6\u5316\uff08Normalization\uff09\uff0c\u964d\u7ef4\u805a\u7c7b\uff08Dimensional reduction and clustering\uff09\uff0cCluster marker genes\uff0c Spatially variable genes\u7b49\uff0c\u5c0f\u679c\u4eca\u5929\u5e76\u4e0d\u60f3\u76f4\u63a5\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u7f51\u4e0a\u90fd\u6709\u7684\u6d41\u7a0b\uff0c\u800c\u662f\u60f3\u7ed9\u5927\u5bb6\u770b\u770bScanpy\u8bfb\u53d6\u7684\u6570\u636e\u7ed3\u6784\uff0c\u4ee5\u53ca\u5982\u4f55\u53ef\u89c6\u5316\uff0c\u6559\u5927\u5bb6\u4e00\u6b65\u5165\u95e8Scanpy!<\/p>\n

            \n
          1. \u9996\u5148\u80af\u5b9a\u662f\u8981\u8f93\u5165\u6587\u4ef6\uff01\u5148\u6765\u6559\u6559\u5927\u5bb6h5ad\u6587\u4ef6\u7684\u8bfb\u53d6<\/li>\n<\/ol>\n

            h5ad\u6587\u4ef6\u5168\u79f0\u662fh5anndata\uff0c\u662fanndata\u7684h5\u683c\u5f0f\uff0canndata\u4e5f\u662fpython\u4e2d\u7684\u6570\u636e\u683c\u5f0f\uff0c\u5982\u679c\u4f60\u60f3\u7528seurat\u8bfb\uff0c\u53ef\u4ee5\u53d1\u73b0\u6700\u540e\u8bfb\u53d6\u7684\u6587\u4ef6\u683c\u5f0f\u4f1a\u53d8\u4e3ah5seurat\uff0c\u7b80\u5355\u7684\u8bf4python\u4e2d\u9002\u914dh5ad\u683c\u5f0f\u8bfb\u53d6\uff0cR\u9002\u914dh5seurat\u683c\u5f0f\u8bfb\u53d6\u3002<\/p>\n

            adata =anndata.read_h5ad(‘F:\/university\/research\/\u7a7a\u95f4\u8f6c\u5f55\u7ec4\/canvas\/GSM6177599_10xVisium_processed.h5ad’)<\/p>\n

              \n
            1. \u8bfb\u5165\u6587\u4ef6\u540e\uff0c\u8ba9\u6211\u4eec\u770b\u4e00\u4e0badata\u4e2d\u6709\u4ec0\u4e48<\/li>\n<\/ol>\n

              <\/p>\n

                \n
              • Obsm\uff1a\u5bf9\u4e8e\u89c2\u6d4b\u7684\u591a\u7ef4\u6ce8\u91ca\uff0c\u5b83\u662f\u53ef\u53d8\u7684ndarray\uff0c\u5c31\u662f\u8bf4\u7ef4\u5ea6\u662f\u53ef\u53d8\u7684\uff01\u5c0f\u679c\u5077\u5077\u544a\u8bc9\u4f60\uff0c\u8fd9\u91cc\u7684\u7ef4\u5ea6\u4e00\u822c\u662f2\u81f3\u591a\u7ef4\u54e6\u3002\u800cObsm\u8fd9\u91cc\u7684m\u6307\u7684\u5c31\u662fmulti-dim\u591a\u4e2a\u7ef4\u5ea6\u7684\uff0cobs_m\u5bf9\u5e94\u4e8eobs\uff0c\u4f46obs\u7684\u6bcf\u4e2a\u6210\u5458\u90fd\u662f\u4e00\u7ef4\u7684\u89c2\u6d4b\u6ce8\u91ca\uff0cobs_m\u7684\u6bcf\u4e2a\u6210\u5458\uff08X_pac\u548cX_umap\uff09\u90fd\u662f\u591a\u7ef4\u7684\u89c2\u6d4b\u6ce8\u91ca\u3002\u54e6\u5bf9\u4e86\uff0c\u5fd8\u4e86\u544a\u8bc9\u4f60\uff0cobs\u5c31\u662f\u77e9\u9635\u7684\u884c\uff0c\u4e5f\u5c31\u662f\u6837\u672c\uff01<\/li>\n
              • Varm\uff1a\u4e0eobsm\u5bf9\u5e94\uff0c\u662f\u57fa\u56e0\u7684\u591a\u7ef4\u89c2\u6d4b~<\/li>\n
              • uns\uff1a\u975e\u7ed3\u6784\u5316\u6570\u636e\uff0c\u4e5f\u5c31\u662f\u5b57\u5178\u3002<\/li>\n
              • rank_genes_groups \u8bc6\u522b\u5dee\u5f02\u8868\u8fbe\u7684\u57fa\u56e0\uff08\u6f5c\u5728\u7684marker\uff09\uff0c\u8fd9\u4e2a\u51fd\u6570\u5c06\u83b7\u53d6\u6bcf\u7ec4\u7ec6\u80de\uff0c\u5e76\u5c06\u6bcf\u7ec4\u4e2d\u6bcf\u4e2a\u57fa\u56e0\u7684\u5206\u5e03\u4e0e\u4e0d\u5728\u8be5\u7ec4\u4e2d\u7684\u6240\u6709\u5176\u4ed6\u7ec6\u80de\u4e2d\u7684\u5206\u5e03\u8fdb\u884c\u6bd4\u8f83\u6700\u540e\u8fdb\u884c\u8bc6\u522b\u3002<\/li>\n
              • pca\uff1a\u4e3b\u6210\u5206\u5206\u6790\u964d\u4f4e\u6570\u636e\u7ef4\u5ea6\u3002<\/li>\n
              • umap\u964d\u7ef4\uff1aumap\u6bd4tsne\u4fdd\u7559\u5168\u5c40\u7ed3\u6784\uff0c\u5141\u8bb8\u5927\u6570\u636e\u8f93\u5165\uff0c\u8fd0\u884c\u65f6\u95f4\u77ed\uff0c\u8fd9\u662fumap\u548ctsne\u7684\u533a\u522b\uff0c\u4e00\u5b9a\u8981\u7262\u8bb0\u5594\uff01<\/li>\n
              • obsp(pair)\uff1a\u8868\u793a\u7ec6\u80de\u548c\u7ec6\u80de\u4e4b\u95f4\u7684\u8ddd\u79bb\u548c\u8fde\u901a\u6027\u3002<\/li>\n
              • \u987a\u4fbf\u7ed9\u5927\u5bb6\u63d0\u63d0obs\u4e2d\u7684mito\u8868\u793a\u7ebf\u7c92\u4f53\u57fa\u56e0\uff0c\u4e00\u822cmito\u90fd\u8868\u793a\u7684\u662f\u7ebf\u7c92\u4f53\u3002<\/li>\n
              • n_genes_by_counts`\uff1a\u6bcf\u4e2a\u7ec6\u80de\u4e2d\uff0c\u6709\u8868\u8fbe\u7684\u57fa\u56e0\u7684\u4e2a\u6570\uff1b<\/li>\n
              • total_counts\uff1a\u6bcf\u4e2a\u7ec6\u80de\u7684\u57fa\u56e0\u603b\u8ba1\u6570\uff08\u603b\u8868\u8fbe\u91cf\uff09<\/li>\n
              • pct_counts_mt\uff1a\u6bcf\u4e2a\u7ec6\u80de\u4e2d\uff0c\u7ebf\u7c92\u4f53\u57fa\u56e0\u8868\u8fbe\u91cf\u5360\u8be5\u7ec6\u80de\u6240\u6709\u57fa\u56e0\u8868\u8fbe\u91cf\u7684\u767e\u5206\u6bd4<\/li>\n<\/ul>\n

                \u5148\u7ed9\u5927\u5bb6\u4ecb\u7ecd\u8fd9\u4e48\u591a\u4e86\uff0c\u5177\u4f53\u7684\u8fd8\u662f\u9700\u8981\u540e\u7eed\u81ea\u5df1\u67e5\u4e00\u4e0b\u54e6~<\/p>\n

                  \n
                1. \u53ef\u89c6\u5316\u4e00\u4e0b<\/li>\n<\/ol>\n

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

                  from matplotlib import rc_context<\/p>\n

                  # rc_context\u7528\u4e8e\u6307\u5b9afigure\u5927\u5c0f<\/p>\n

                  with rc_context({‘figure.figsize’: (4, 4)}):<\/p>\n

                  # vmax\u6539\u53d8\u989c\u8272\u6700\u5927\u503c\uff0cframeon\u4e3a\u53bb\u6389\u8fb9\u6846<\/p>\n

                  sc.pl.umap(Myadata, color=[‘Rp1′,’clusters’,’pct_counts_mito’,’total_counts’],s=50,ncols=4,frameon=False,vmax=’p99′)<\/p>\n

                  <\/p>\n

                  \u4e0a\u56fe\u4e2d\u6211\u4eec\u4f7f\u7528umap\u7ed8\u5236\u4e86\u5bf9\u5e94\u7684cluster\uff0cmetadata\u7684\u503c\uff0cmetadata\u5c31\u662f\u6bcf\u4e2a\u7ec6\u80de\u7684\u4fe1\u606f\u5594\uff0c\u8fd8\u7ed8\u5236\u4e86Rp1\uff0c\u8fd9\u4e48\u591a\u56fe\u53ea\u9700\u8981\u7b80\u5355\u7684\u4e24\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u7ed8\u5236\uff01\u662f\u4e0d\u662f\u89c9\u5f97\u5199\u8fd9\u4e2a\u5305\u7684\u4eba\u5f88\u725b\uff01<\/p>\n

                  \u4f46\u662f\u5176\u5b9e\u5c0f\u679c\u8fd8\u662f\u60f3\u77e5\u9053\u8fd9\u4e2a\u5750\u6807\u662f\u54ea\u91cc\u6765\u7684\uff0c\u6240\u4ee5\u5c31\u53bb\u8bd5\u4e86\u4e00\u4e0badata\u4e2d\u7684obsm\u4e2d\u7684umap\u7684\u503c\uff0c\u60f3\u770b\u770b\u7a76\u7adf\u5728\u8fd9\u4e2a\u5305\u4e2d\u662f\u4e0d\u662fX_umap\u7684\u5750\u6807\u7ed8\u5236\u4e86\u4e0a\u9762\u8fd9\u6837\u7684\u56fe\uff0c\u4e8e\u662f\u5c31\u8fdb\u884c\u4e86\u4e0b\u8ff0\u64cd\u4f5c\uff1a<\/p>\n

                  #\u6d4b\u8bd5\u4e00\u4e0bumap \u7b26\u5408\u9884\u671f\uff0c\u786e\u5b9e\u662f\u6570\u636e\u4e3a\u7ec6\u80de\u7684\u5750\u6807<\/p>\n

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

                  # \u5c06\u4e8c\u7ef4\u6570\u7ec4\u8f6c\u6362\u4e3a\u4e24\u4e2a\u4e00\u7ef4\u6570\u7ec4x\u548cy<\/p>\n

                  data=Myadata.obsm[‘X_umap’]<\/p>\n

                  x = [d[0] for d in data]<\/p>\n

                  y = [d[1] for d in data]<\/p>\n

                  # \u7ed8\u5236\u6563\u70b9\u56fe<\/p>\n

                  plt.scatter(x, y)<\/p>\n

                  <\/p>\n

                  \u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u786e\u5b9e\u662f\u8fd9\u6837\u7684\uff01\u6240\u4ee5\u5927\u5bb6\u4e4b\u540e\u8981\u662f\u4e0d\u61c2\u5305\u5185\u90e8\u7684\u539f\u7406\u7684\u8bdd\u4e00\u5b9a\u8981\u591a\u5c1d\u8bd5\uff0c\u591a\u8bd5\u8bd5\u603b\u5f52\u662f\u9a97\u4e0d\u4e86\u4eba\u7684\uff01<\/p>\n

                  with rc_context({‘figure.figsize’: (4.5, 3)}):<\/p>\n

                  ”’stripplot=False\u7528\u4e8e\u5220\u9664internal dots, inner=’box’\u7528\u4e8e\u5728\u5c0f\u63d0\u7434\u5185\u90e8\u52a0\u65b9\u6846\u56fe”’<\/p>\n

                  sc.pl.violin(Myadata, [‘pct_counts_in_top_50_genes’, ‘total_counts_mito’], groupby=’clusters’, stripplot=False, inner=’box’)<\/p>\n

                  ”’mino\u8868\u793a\u7ebf\u7c92\u4f53\u57fa\u56e0”’<\/p>\n

                  \u53ef\u4ee5\u518d\u6765\u8bd5\u8bd5\u5c0f\u63d0\u7434\u56fe\uff01<\/p>\n

                  <\/p>\n

                  \u8fd9\u662f11\u4e2acluster\u4e2d\u6bcf\u4e2acluster\u5bf9\u5e94\u7684\u4e0d\u540c\u7c7b\u7684\u57fa\u56e0\u7684\u5c0f\u63d0\u7434\u56fe\u3002<\/p>\n

                  \u603b\u7ed3\u4e00\u4e0b\u53ed\uff0cpl.umap\u53ef\u4ee5\u753bumap\u964d\u7ef4\u56fe\uff0cpl.violin\u53ef\u4ee5\u753b\u5c0f\u63d0\u7434\u56fe\uff0c\u90a3\u4f60\u77e5\u9053\u70ed\u56fe\u600e\u4e48\u753b\u5417\uff0c\u8fd8\u6709\u5806\u53e0\u5c0f\u63d0\u7434\u56fe\uff0c\u6bd4\u5982\u4e0b\u9762\u8fd9\u79cd\u56fe\u6240\u793a\uff0c\u662f\u4e0d\u662f\u5f88\u597d\u770b\u54c8\u54c8\u54c8\uff0c\u5c0f\u4f19\u4f34\u4eec\u53ef\u4ee5\u4e4b\u540e\u8bd5\u8bd5\u54e6\uff0c\u5982\u679c\u6709\u9700\u8981\u7684\u8bdd\uff0c\u5c0f\u679c\u4e5f\u4f1a\u4e13\u95e8\u7ed9\u5927\u5bb6\u51fa\u4e00\u671f\u7ee7\u7eed\u8bb2\u4e00\u4e0b\u6211\u4eec\u8fd9\u6b21\u6240\u7528\u6570\u636e\u7684\u540e\u7eed\u5206\u6790\u7684\uff0c\u5c31\u770b\u5927\u5bb6\u7684\u8d5e\u7684\u4e2a\u6570\u4e86\u54e6<\/p>\n

                  <\/p>\n

                  \u63a8\u8350\u5927\u5bb6\u4f7f\u7528\u4e91\u751f\u4fe1- \u5b66\u751f\u7269\u4fe1\u606f\u5b66\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\uff0c\u91cc\u9762\u4e5f\u6709\u5f88\u591a0\u4ee3\u7801\u5206\u6790\u8fc7\u7a0b\uff0c\u4f7f\u7528\u5f88\u65b9\u4fbf~<\/p>","protected":false},"excerpt":{"rendered":"

                  \u5927\u5bb6\u597d\u5440\uff0c\u6211\u4eec\u53c8\u89c1\u9762\u5566\uff0c\u4eca\u5929\u548c\u5927\u5bb6\u804a\u804a\u5355\u7ec6\u80de\u548c\u7a7a\u8f6c\u5206\u6790\u4e2d\u4e00\u4e2a\u5f88\u57fa\u7840\u7684\u5305\u2014\u2014Scanpy\uff0cScanpy \u662f\u4e00\u4e2aP […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","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\/26923"}],"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=26923"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/26923\/revisions"}],"predecessor-version":[{"id":26929,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/26923\/revisions\/26929"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=26923"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=26923"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=26923"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}