\u5c0f\u4f19\u4f34\u4eec\uff0c\u8fd8\u8bb0\u4e0d\u8bb0\u5f97\u5355\u7ec6\u80de\u81ea\u52a8\u6ce8\u91ca\u5de5\u5177SingleR\uff1f\u662f\u4e0d\u662f\u7528\u8d77\u6765\u723d\u7206\u4e86\uff01\u8ba9\u539f\u672c\u5f88\u96be\u5f97\u7ec6\u80de\u6ce8\u91ca\u8f7b\u677e\u79d2\u6740\uff01\u6700\u8fd1\u6709\u5c0f\u4f19\u4f34\u4eec\u95ee\u5927\u6d77\u54e5\uff0c\u5f53\u4eca\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u7099\u624b\u53ef\u70ed\uff0c\u6709\u6ca1\u6709\u7c7b\u4f3c\u7684\u5de5\u5177\uff1f\u5c0f\u4f19\u4f34\u4eec\u771f\u7684\u5feb\u88ab\u4eba\u5de5\u6807\u6ce8\u6298\u78e8\u6b7b\u4e86\uff01\u867d\u7136\u4e5f\u6709\u4e00\u4e9b\u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u65b9\u6cd5\u5df2\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u8bc6\u522b\u7a7a\u95f4\u70b9\u7684\u7fa4\u96c6\uff0c\u5e76\u4f7f\u7528\u6807\u8bb0\u57fa\u56e0\u89e3\u91ca\u5176\u751f\u7269\u5b66\u8eab\u4efd\u3002\u4f46\u8fd9\u4e9b\u65b9\u6cd5\u901a\u5e38\u56e0\u4e3a\u7f3a\u4e4f\u4e0e\u7fa4\u96c6\u5185\u5df2\u77e5\u7ed3\u6784\u5efa\u7acb\u660e\u786e\u8054\u7cfb\u7684\u80fd\u529b\u800c\u53d7\u9650\u3002\u770b\u5230\u5c0f\u4f19\u4f34\u4eec\u7684\u56f0\u60d1\uff0c\u5927\u6d77\u54e5\u8fde\u591c\u6574\u7406\u4e86\u4efdPianno\u9526\u56ca\uff0c\u7ed9\u7c89\u4e1d\u9001\u798f\u5229\uff01Pianno\u501f\u9274\u4e86\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u7684\u300c\u8bed\u4e49\u5206\u5272\u300d\u601d\u60f3\uff0c\u63d0\u51fa\u4e86\u300c\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u8bed\u4e49\u6ce8\u91ca\u300d\u6982\u5ff5\uff0c\u5e76\u5f00\u53d1\u4e86\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u8bed\u4e49\u6ce8\u91ca\u5de5\u5177 Pianno\uff0c\u80fd\u591f\u4e3a\u7ec4\u7ec7\u5185\u7684\u7a7a\u95f4\u70b9\u81ea\u52a8\u5b9a\u4e49\u7ed3\u6784\u6216\u7ec6\u80de\u7c7b\u578b\uff0c\u4ece\u800c\u7ed3\u5408\u6765\u81ea\u591a\u4e2a\u7ef4\u5ea6\u7684\u4fe1\u606f\uff0c\u52a0\u5f3a\u5bf9\u590d\u6742\u751f\u7269\u7cfb\u7edf\u7684\u89e3\u91ca\u3002\u4e0b\u9762\u8ddf\u7740\u5927\u6d77\u54e5\u6765\u5b66\u4e60\u5427\uff01\u5bf9\u4e86\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
Pianno\uff1a\u521b\u65b0\u7684\u81ea\u52a8\u5316\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u8bed\u4e49\u6ce8\u91ca\u65b0\u5de5\u5177<\/strong><\/strong><\/p>\n\n\n\n \u7814\u7a76\u56e2\u961f\u63d0\u51fa\u4e86\u4e00\u4e2a\u57fa\u4e8e\u8d1d\u53f6\u65af\u6846\u67b6\u7684\u65b0\u5de5\u5177 Pianno\uff0c\u8be5\u5de5\u5177\u7ed3\u5408\u4e86\u9a6c\u5c14\u53ef\u592b\u968f\u673a\u573a (MRF) \u4e0e\u7a7a\u95f4\u6cca\u677e\u70b9\u8fc7\u7a0b (sPPP)\uff0c\u5145\u5206\u5229\u7528\u4e86 sPPP \u5efa\u6a21 RNA-seq \u8ba1\u6570\u6570\u636e\u5206\u5e03\u7684\u80fd\u529b\uff0c\u540c\u65f6\u8003\u8651\u4e86\u7a7a\u95f4\u70b9\u7684\u4f4d\u7f6e\u4fe1\u606f\uff0c\u53ef\u4ee5\u4f7f\u7528\u9884\u5b9a\u4e49\u7684\u6807\u8bb0\u57fa\u56e0\u5217\u8868\uff0c\u81ea\u52a8\u5316\u5730\u6ce8\u91ca\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u6570\u636e\u4e2d\u6bcf\u4e2a\u70b9\u7684\u751f\u7269\u8eab\u4efd\u3002<\/p>\n\n\n\n Pianno \u63d0\u4f9b\u4e24\u79cd\u66f4\u65b0\u6ce8\u91ca\u7684\u65b9\u6cd5\uff1a<\/p>\n\n\n\n \u603b\u7684\u6765\u8bf4\uff0cPianno \u7b80\u5316\u4e86\u6ce8\u91ca\u8fc7\u7a0b\uff0c\u540c\u65f6\u91c7\u7528\u542f\u53d1\u5f0f\u65b9\u6cd5 (heuristic approach) \u4f7f\u7528\u521d\u59cb\u5355\u4e2a\u6807\u8bb0\u57fa\u56e0\u6765\u8bc6\u522b\u989d\u5916\u7684\u6807\u8bb0\u57fa\u56e0\uff0c\u53ef\u4ee5\u6700\u5927\u9650\u5ea6\u51cf\u5c11\u5bf9\u5df2\u77e5\u6807\u8bb0\u6570\u91cf\u7684\u8f93\u5165\u3002<\/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\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\u5b9e\u73b0<\/strong><\/strong><\/p>\n\n\n\n \u5b89\u88c5\u6307\u5357\uff1a<\/strong>\u4ece PyPi \u5b89\u88c5<\/strong><\/strong><\/p>\n\n\n\n # \u521b\u9020\u4e00\u4e2aPianno\u73af\u5883<\/p>\n\n\n\n conda create -n Pianno python=3.9.10<\/p>\n\n\n\n # \u6fc0\u6d3b\u73af\u5883<\/p>\n\n\n\n conda activate Pianno<\/p>\n\n\n\n # \u5728piano\u73af\u5883\u4e2d\u5b89\u88c5R<\/p>\n\n\n\n conda install -c conda-forge r-base=4.1.1<\/p>\n\n\n\n #\u5b89\u88c5tensorflow\u548ctensorflow-probability<\/p>\n\n\n\n #\u786e\u4fddtensorflow2\u7248\u672c<\/p>\n\n\n\n pip install tensorflow-gpu==2.6.0<\/p>\n\n\n\n pip install tensorflow-probability==0.14.0<\/p>\n\n\n\n # install pianno from PyPi<\/p>\n\n\n\n pip install pianno<\/p>\n\n\n\n \u52a0\u8f7d\u6570\u636e\u96c6<\/strong><\/strong><\/p>\n\n\n\n \u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u4eba\u7c7b\u80cc\u5916\u4fa7\u524d\u989d\u53f6\u76ae\u5c42 \uff08dlPFC\uff09 \u6570\u636e\u96c6\u6837\u672c\u7684\u6ce8\u91ca\u8fc7\u7a0b\u3002\u6b64\u6f14\u793a\u6f14\u793a\u4e86\u5982\u4f55\u4f7f\u7528 Pianno \u4ee5 10x Visium \u6570\u636e\u96c6\u4e3a\u4f8b\u8fdb\u884c\u8bed\u4e49\u6ce8\u91ca\u3002\u60a8\u53ef\u4ee5\u6269\u5c55\u5230\u5176\u4ed6\u5e73\u53f0\u751f\u6210\u7684\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u6570\u636e\u6216\u8005\u81ea\u5df1\u7684\u6d4b\u5e8f\u6570\u636e\u3002<\/p>\n\n\n\n \u5bfc\u5165\u6a21\u5757<\/p>\n\n\n\n import pianno as po<\/p>\n\n\n\n \u9884\u5904\u7406<\/p>\n\n\n\n Step1\uff1a\u521b\u5efaPianno\u5bf9\u8c61<\/p>\n\n\n\n Pianno \u5bf9\u8c61\u57fa\u4e8e\u539f\u59cb\u57fa\u56e0\u8ba1\u6570\u77e9\u9635\u548c\u7a7a\u95f4\u6591\u70b9\u7684\u5750\u6807\u8fdb\u884c\u521d\u59cb\u5316\u3002\u8fd8\u8fdb\u884c\u9884\u5904\u7406\uff0c\u5305\u62ec\u5c3a\u5bf8\u7cfb\u6570\u7684\u8ba1\u7b97\u548c\u8d28\u91cf\u63a7\u5236\u3002<\/p>\n\n\n\n data_path = “~\/data\/dlPFC-151674\/”<\/p>\n\n\n\n count_file = “filtered_feature_bc_matrix.h5”<\/p>\n\n\n\n adata = po.CreatePiannoObject(data_path=data_path, count_file=count_file, min_spots_prop=0.01)<\/p>\n\n\n\n \u5bf9\u4e8e\u4e0d\u662f\u6765\u81ea 10x Visium \u7684\u6570\u636e\u96c6\uff0cPianno \u5bf9\u8c61\u4e5f\u53ef\u4ee5\u76f4\u63a5\u4ece\u9010\u4e2a\u57fa\u56e0\u7684\u539f\u59cb\u8ba1\u6570\u77e9\u9635 A \u548c\u7a7a\u95f4\u5750\u6807\u6570\u636e\u5e27 B \u6784\u5efa\uff1a<\/p>\n\n\n\n adata = po.CreatePiannoObject(count_matrix=A, coordinates=B, min_spots_prop=0.01)<\/p>\n\n\n\n \u901a\u8fc7SAVER\u53bb\u9664\u566a\u58f0<\/p>\n\n\n\n adata = po.SAVER(adata, layer_key=’DenoisedX’)<\/p>\n\n\n\n \u6ce8\u610f\uff1a\u77ed\u77ed\u4e00\u884c\u4ee3\u7801\u5927\u6d77\u54e5\u8fd0\u884c\u4e862\u5c0f\u65f6 17\u5206 23\u79d2<\/p>\n\n\n\n \u8fd9\u4e00\u6b65\u9700\u8981\u5f88\u957f\u65f6\u95f4\u3002\u6211\u4eec\u63d0\u4f9b\u4e86\u4e00\u4e2a\u53bb\u566a\u5bf9\u8c61\uff0c\u5982\u679c\u60a8\u4e0d\u60f3\u7b49\u5f85\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u52a0\u8f7d\u8be5\u5bf9\u8c61\uff1a<\/p>\n\n\n\n adata = sc.read(“~\/data\/dlPFC-151674\/adata.h5ad”)<\/p>\n\n\n\n \u60a8\u8fd8\u53ef\u4ee5\u9009\u62e9\u5176\u4ed6\u964d\u566a\u65b9\u6cd5\uff0c\u53ea\u9700\u5c06\u9010\u4e2a\u57fa\u56e0\u53bb\u566a\u7684\u57fa\u8d28\u5b58\u50a8\u5230adata.layers[‘DenoisedX’] \u3002\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u6570\u636e\u7684\u964d\u566a\u5bf9\u4e8e\u6ce8\u91ca\u662f\u5fc5\u8981\u7684\u3002\u964d\u566a\u7684\u7ed3\u679c\u76f4\u63a5\u5f71\u54cd\u6ce8\u91ca\u3002<\/p>\n\n\n\n Step2\uff1a\u521b\u5efa\u8499\u7248\u56fe\u50cf<\/p>\n\n\n\n \u4e8c\u8fdb\u5236\u8499\u7248\u662f\u901a\u8fc7\u5728\u7a7a\u95f4\u70b9\u548c\u56fe\u50cf\u50cf\u7d20\u4e4b\u95f4\u5efa\u7acb\u6620\u5c04\u6765\u521b\u5efa\u7684\u3002<\/p>\n\n\n\n # \u6839\u636e\u8499\u7248\u56fe\u50cf\u8c03\u6574scale_factor\u7684\u5927\u5c0f<\/p>\n\n\n\n adata = po.CreateMaskImage(adata, scale_factor=1)<\/p>\n\n\n\n \u6b64\u8fc7\u7a0b\u7b49\u6548\u4e8e\u5bf9\u7a7a\u95f4\u70b9\u8fdb\u884c\u5206\u7bb1\u3002\u5bf9\u4e8e\u7a7a\u95f4\u5750\u6807\u4e3a\u6570\u7ec4\u72b6\u7684\u6570\u636e\u96c6\uff0c\u6bd4\u4f8b\u56e0\u5b50\u53ef\u4ee5\u8bbe\u7f6e\u4e3a 1\u3002\u5bf9\u4e8e\u7a7a\u95f4\u5750\u6807\u4e0d\u662f\u6570\u7ec4\u5f0f\u7684\u6570\u636e\u96c6\uff0c\u4f8b\u5982 SlidseqV2 \u548c stereo-seq\uff0c\u53ef\u4ee5\u589e\u52a0\u6bd4\u4f8b\u56e0\u5b50\uff0c\u76f4\u5230\u8f93\u51fa\u5408\u9002\u7684\u8499\u7248\u56fe\u50cf\u3002<\/p>\n\n\n\n \u6ce8\u91ca<\/strong><\/strong><\/p>\n\n\n\n \u7814\u7a76\u4e2d\uff0c\u4e3a\u907f\u514d\u964d\u566a\u3001\u5e73\u6ed1\u3001\u9510\u5316\u7b49\u56fe\u50cf\u5904\u7406\u6280\u672f\u5bf9\u751f\u7269\u539f\u59cb\u7279\u5f81\u9020\u6210\u7834\u574f\uff0c\u7814\u7a76\u56e2\u961f\u57fa\u4e8e\u539f\u59cb\u8ba1\u6570\u6784\u5efa\u4e86\u8d1d\u53f6\u65af\u5206\u7c7b\u5668 (Bayesian classifier) \u4ee5\u5fae\u8c03\u521d\u59cb\u6ce8\u91ca\u3002\u540c\u65f6\uff0c\u7814\u7a76\u56e2\u961f\u5e94\u7528\u4e86\u9ad8\u9636\u9a6c\u5c14\u79d1\u592b\u968f\u673a\u573a (Markov random field, MRF) \u5148\u9a8c\u6a21\u578b\u3002\u5728\u7a7a\u95f4\u8f6c\u5f55\u7ec4\u5b66\u80cc\u666f\u4e0b\uff0c\u7531\u4e8e\u5fc5\u987b\u5171\u540c\u8003\u8651\u6bcf\u4e2a\u4f4d\u70b9\u7684\u57fa\u56e0\u8868\u8fbe\u548c\u7a7a\u95f4\u4f4d\u7f6e\uff0c\u7814\u7a76\u56e2\u961f\u8fd8\u91c7\u7528\u4e86\u7a7a\u95f4\u6cca\u677e\u70b9\u8fc7\u7a0b (spatial Poisson point process, sPPP) \u6a21\u578b\u3002<\/p>\n\n\n\n #\u8bbe\u7f6e\u6570\u636e\u548c\u914d\u7f6e\u6587\u4ef6\u7684\u5b58\u653e\u8def\u5f84\u3002<\/p>\n\n\n\n sample_name = “GT151674”<\/p>\n\n\n\n config_path = “.\/Tutorials\/” + sample_name<\/p>\n\n\n\n Step1\uff1a\u81ea\u52a8\u8d85\u53c2\u6570\u9009\u62e9<\/p>\n\n\n\n #\u4e3a\u6bcf\u4e2a\u6a21\u5f0f\u6307\u5b9a\u4e00\u4e2a\u5df2\u77e5\u7684\u6807\u8bb0\u57fa\u56e0<\/p>\n\n\n\n Patterndict = dict(L1 = [‘CXCL14’],<\/p>\n\n\n\n L2 = [‘HPCAL1’],<\/p>\n\n\n\n L3 = [‘CALB1’],<\/p>\n\n\n\n L4 = [‘NEFH’],<\/p>\n\n\n\n L5 = [‘PCP4’],<\/p>\n\n\n\n L6 = [‘KRT17’],<\/p>\n\n\n\n WM = [‘MOBP’])<\/p>\n\n\n\n \u5982\u679c\u8fde\u63a5\u5931\u8d25\uff0c\u8bf7\u518d\u5c1d\u8bd5\u51e0\u6b21\u3002<\/p>\n\n\n\n \u6253\u5f00Web UI url\u4ee5\u53ef\u89c6\u5316\u8d85\u53c2\u6570\u8c03\u4f18\u8fc7\u7a0b\u3002<\/p>\n\n\n\n \u9ed8\u8ba4\u5b9e\u9a8c\u6700\u591a\u6301\u7eed10\u5206\u949f\uff0c<\/p>\n\n\n\n \u53ef\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u8fdb\u884c\u4fee\u6539\u3002<\/p>\n\n\n\n adata = po.AutoPatternRecognition(adata,<\/p>\n\n\n\n Patterndict=Patterndict,<\/p>\n\n\n\n config_path=config_path,<\/p>\n\n\n\n param_tuning=True,<\/p>\n\n\n\n max_experiment_duration=’10m’)<\/p>\n\n\n\n #\u8f93\u51fa\u4e0a\u4e00\u6b65\u4fdd\u5b58\u7684\u6700\u4f18\u53c2\u6570\u3002<\/p>\n\n\n\n with open(join(config_path, “best_params.json”),’r’) as f:<\/p>\n\n\n\n best_params_dict = json.load(f)<\/p>\n\n\n\n for key in best_params_dict:<\/p>\n\n\n\n best_params = best_params_dict[key] <\/p>\n\n\n\n best_params<\/p>\n\n\n\n Step2\uff1a\u6807\u8bb0\u9009\u62e9<\/p>\n\n\n\n \u5c06\u524d10\u4e2adeg\u4f5c\u4e3a\u5019\u9009\u6807\u8bb0\u57fa\u56e0\uff0c\u63d0\u51fa\u4e00\u79cd\u6a21\u5f0f\u9884\u6d4b<\/p>\n\n\n\n Patterndict = po.ProposedPatterndict(adata, top_n=10)<\/p>\n\n\n\n # \u5019\u9009\u6807\u8bb0\u57fa\u56e0\u7684\u53ef\u89c6\u5316<\/p>\n\n\n\n for k, v in Patterndict.items():<\/p>\n\n\n\n print(k)<\/p>\n\n\n\n print(v)<\/p>\n\n\n\n with mpl.rc_context({‘axes.facecolor’: ‘black’,<\/p>\n\n\n\n ‘figure.figsize’: [4.5, 5]}):<\/p>\n\n\n\n sc.pl.spatial(adata, #cmap=’magma’,<\/p>\n\n\n\n layer=’DenoisedX’,<\/p>\n\n\n\n color=v,<\/p>\n\n\n\n ncols=5, size=5,<\/p>\n\n\n\n spot_size=25,<\/p>\n\n\n\n vmin=0, vmax=’p99′<\/p>\n\n\n\n )<\/p>\n\n\n\n Step3\uff1a\u521d\u59cb\u6a21\u5f0f\u8bc6\u522b<\/p>\n\n\n\n \u901a\u8fc7\u4e3a\u6bcf\u4e2a\u6a21\u5f0f\u9009\u62e91-3\u4e2a\u9ad8\u8d28\u91cf\u57fa\u56e0\u6765\u6784\u5efa\u6807\u8bb0\u5217\u8868,\u5e76\u4ece\u4e0a\u8ff0\u5019\u9009\u6807\u8bb0\u57fa\u56e0\u3002<\/p>\n\n\n\n Patterndict = dict(L1 = [‘CXCL14′,’MT1G’,’FABP7′],<\/p>\n\n\n\n L2 = [‘HPCAL1′,’C1QL2’],<\/p>\n\n\n\n L3 = [‘CALB1′,’LINC01007’],<\/p>\n\n\n\n L4 = [‘NEFH’,’SYT2′,’NEFM’],<\/p>\n\n\n\n L5 = [‘PCP4′,’TMSB10’],<\/p>\n\n\n\n L6 = [‘KRT17’],<\/p>\n\n\n\n WM = [‘MOBP’,’MBP’])<\/p>\n\n\n\n Patterndict\u4e5f\u53ef\u4ee5\u662f\u4e00\u4e2a\u6574\u6570N\u3002piano\u5c06\u9009\u62e9\u524dN\u4e2a\u5019\u9009\u6807\u8bb0\u57fa\u56e0\u6765\u6784\u5efa\u6807\u8bb0\u5217\u8868\u3002<\/p>\n\n\n\n adata = po.AutoPatternRecognition(adata,<\/p>\n\n\n\n Patterndict=Patterndict,<\/p>\n\n\n\n config_path=config_path,<\/p>\n\n\n\n param_tuning=False)<\/p>\n\n\n\n Step4\uff1a\u6ce8\u91ca\u6539\u8fdb<\/p>\n\n\n\n adata = po.AnnotationImprovement(adata)<\/p>\n\n\n\n \u5c0f\u7ed3<\/strong><\/strong><\/p>\n\n\n\n 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