![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-1.png?resize=640%2C805\")
<\/p>\n\u5bcc\u96c6\u5206\u6790\u662f\u6211\u4eec\u5728\u505a\u751f\u4fe1\u5206\u6790\u65f6\u7ecf\u5e38\u7528\u5230\u7684\u4e00\u79cd\u65b9\u6cd5\uff0c\u4f46\u662f\u5bcc\u96c6\u5206\u6790\u6709\u5f88\u591a\u79cd\u7c7b\u578b\uff0c\u5728\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u65f6\u5019\u5e94\u8be5\u5982\u4f55\u9009\u62e9\u5462\uff1f\u8fd9\u4e2a\u95ee\u9898\u56f0\u6270\u4e86\u5c0f\u679c\u4e00\u6bb5\u65f6\u95f4\uff0c\u7ecf\u8fc7\u5c0f\u679c\u4e0d\u61c8\u7684\u52aa\u529b\uff0c\u7ec8\u4e8e\u7406\u6e05\u4e86\u5b83\u4eec\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5c0f\u679c\u8fd8\u7279\u5730\u627e\u4e86\u4e00\u4e9b\u4ee3\u7801\u6765\u8fdb\u884c\u533a\u5206\uff0c\u5982\u679c\u5c0f\u4f19\u4f34\u4eec\u4e5f\u5bf9\u5982\u4f55\u533a\u5206\u5bcc\u96c6\u5206\u6790\u611f\u5174\u8da3\uff0c\u90a3\u5c31\u63a5\u7740\u770b\u4e0b\u53bb\u5427\uff01<\/p>\n
\u9996\u5148\uff0c\u5bcc\u96c6\u5206\u6790\u662f\u7528\u4e8e\u5206\u6790\u4e00\u7ec4\u57fa\u56e0\u6216\u7269\u8d28\u5728\u67d0\u4e9b\u529f\u80fd\u8282\u70b9\u6216\u901a\u8def\u4e0a\u662f\u5426\u6bd4\u968f\u673a\u6c34\u5e73\u66f4\u591a\u5730\u51fa\u73b0\uff0c\u4ece\u800c\u63ed\u793a\u5b83\u4eec\u7684\u751f\u7269\u5b66\u610f\u4e49\u7684\u4e00\u79cd\u65b9\u6cd5\u3002\u53ef\u4ee5\u5e2e\u52a9\u7814\u7a76\u8005\u7406\u89e3\u57fa\u56e0\u8868\u8fbe\u3001\u4ee3\u8c22\u3001\u86cb\u767d\u8d28\u76f8\u4e92\u4f5c\u7528\u7b49\u6570\u636e\u80cc\u540e\u7684\u751f\u7269\u5b66\u673a\u5236\u3002\u5177\u4f53\u6765\u8bf4\uff1a<\/p>\n
GO\u5206\u6790\u662f\u4e00\u79cd\u57fa\u4e8e\u57fa\u56e0\u672c\u4f53\u8bba\uff08Gene Ontology\uff0cGO\uff09\u7684\u529f\u80fd\u6ce8\u91ca\u548c\u5bcc\u96c6\u5206\u6790\u65b9\u6cd5\uff0c\u662f\u4e00\u4e2a\u7528\u4e8e\u63cf\u8ff0\u57fa\u56e0\u548c\u86cb\u767d\u8d28\u529f\u80fd\u7684\u6807\u51c6\u5316\u8bed\u8a00\uff0c\u5305\u62ec\u4e09\u4e2a\u65b9\u9762\uff1a\u5206\u5b50\u529f\u80fd\uff08Molecular Function\uff09\uff0c\u751f\u7269\u8fc7\u7a0b\uff08Biological Process\uff09\u548c\u7ec6\u80de\u7ec4\u4ef6\uff08Cellular Component\uff09\u3002\u5206\u6790\u7684\u76ee\u7684\u662f\u627e\u51fa\u4e00\u4e2a\u57fa\u56e0\u96c6\u5408\u4e2d\u4e0e\u67d0\u4e9b\u529f\u80fd\u6216\u8fc7\u7a0b\u76f8\u5173\u7684\u57fa\u56e0\uff0c\u4ece\u800c\u63ed\u793a\u57fa\u56e0\u96c6\u5408\u7684\u751f\u7269\u5b66\u610f\u4e49\u3002<\/p>\n
KEGG\u5206\u6790\u662f\u4e00\u79cd\u57fa\u4e8eKEGG\u6570\u636e\u5e93\u7684\u529f\u80fd\u6ce8\u91ca\u548c\u5bcc\u96c6\u5206\u6790\u65b9\u6cd5\u3002KEGG\u6570\u636e\u5e93\u662f\u4e00\u4e2a\u6574\u5408\u4e86\u57fa\u56e0\u7ec4\u3001\u5316\u5b66\u548c\u7cfb\u7edf\u529f\u80fd\u4fe1\u606f\u7684\u6570\u636e\u5e93\uff0c\u5305\u62ec\u4ee3\u8c22\u901a\u8def\u3001\u5206\u5c42\u5206\u7c7b\u3001\u57fa\u56e0\u3001\u57fa\u56e0\u7ec4\u7b49\u3002KEGG\u5206\u6790\u7684\u76ee\u7684\u662f\u627e\u51fa\u4e00\u4e2a\u57fa\u56e0\u96c6\u5408\u4e2d\u4e0e\u67d0\u4e9b\u4ee3\u8c22\u901a\u8def\u76f8\u5173\u7684\u57fa\u56e0\uff0c\u4ece\u800c\u63ed\u793a\u57fa\u56e0\u96c6\u5408\u7684\u751f\u7269\u5b66\u610f\u4e49\u3002KEGG\u548cGO\u7684\u533a\u522b\u4e3b\u8981\u4f53\u73b0\u5728\uff0cGO\u662f\u4e00\u6761\u6761\u7684\u7ebf\u8def\uff08GO term\uff09\uff0c\u6bcf\u4e00\u4e2a\u7ebf\u8def\u91cc\u9762\u6709\u81ea\u5df1\u7684\u57fa\u56e0\u96c6\uff0c\u7ebf\u8def\u5f7c\u6b64\u4e4b\u95f4\u662f\u6ca1\u6709\u4efb\u4f55\u8054\u7cfb\u7684\uff0c\u800cKEGG\u662f\u7f51\u72b6\u7684\uff0c\u4e0d\u4ec5\u6709\u57fa\u56e0\u96c6\uff0c\u8fd8\u5b9a\u4e49\u4e86\u57fa\u56e0\u548c\u4ee3\u8c22\u7269\u4e4b\u95f4\u7684\u590d\u6742\u7684\u76f8\u4e92\u5173\u7cfb\u3002<\/p>\n
GSEA\u5206\u6790\u662f\u4e00\u79cd\u57fa\u4e8e\u57fa\u56e0\u96c6\u7684\u5bcc\u96c6\u5206\u6790\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8bc4\u4f30\u4e00\u4e2a\u9884\u5b9a\u4e49\u7684\u57fa\u56e0\u96c6\u5728\u4e24\u79cd\u751f\u7269\u72b6\u6001\u4e4b\u95f4\u662f\u5426\u6709\u663e\u8457\u7684\u8868\u8fbe\u5dee\u5f02\u3002\u4e0eGO\uff0cKEGG\u4e0d\u540c\u7684\u662f\uff0cGO\u548cKEGG\u8ba1\u7b97\u7684p\u503c\u662f\u9700\u8981\u4eba\u4e3a\u5212\u5b9a\u9608\u503c\u7684\uff0c\u800cGSEA\u4e0d\u9700\u8981\u5212\u5b9a\u9608\u503c\uff0cGSEA\u5c06\u8f93\u5165\u6570\u636e\u4e0eGSEA\u4e2d\u6570\u636e\u5e93\u6bd4\u5bf9\uff0c\u5bf9\u57fa\u56e0\u6253\u5206\u3002<\/p>\n
SSGSEA\u662f\u4e00\u79cd\u7528\u4e8e\u8bc4\u4f30\u6bcf\u4e2a\u6837\u672c\u4e2d\u7279\u5b9a\u57fa\u56e0\u96c6\u7684\u76f8\u5bf9\u5bcc\u96c6\u7a0b\u5ea6\u7684\u65b9\u6cd5\u3002\u5728\u5f97\u5230\u6bcf\u4e2a\u6837\u672c\u548c\u57fa\u56e0\u96c6\u7684\u5bcc\u96c6\u5f97\u5206\u540e\uff0c\u53ef\u4ee5\u7528\u4e8e\u514d\u75ab\u7ec6\u80de\u6d78\u6da6\u5206\u6790\uff0c\u57fa\u56e0\u901a\u8def\u5206\u6790\uff0c\u4ee5\u53ca\u5176\u4ed6\u57fa\u4e8e\u57fa\u56e0\u96c6\u7684\u751f\u7269\u5b66\u7814\u7a76\u3002SSGSEA\u662fGSEA\u65b9\u6cd5\u7684\u5ef6\u7533\uff0c\u662f\u9488\u5bf9\u5355\u4e2a\u6837\u672c\u65e0\u6cd5\u505aGSEA\u800c\u8bbe\u8ba1\u7684\u3002<\/p>\n
\u7531\u4e8eGO\u5206\u6790\u548cKEGG\u5206\u6790\u5c0f\u679c\u5df2\u7ecf\u4ecb\u7ecd\u8fc7\u4e86\uff0c\u6240\u4ee5\u4eca\u5929\u5c0f\u679c\u5c06\u5e26\u7740\u5927\u5bb6\u4e00\u8d77\u6765\u505a\u4e00\u4e0bGSEA\u5206\u6790\u4e0eSSGSEA\u5206\u6790~\u672c\u6b21\u5206\u6790\u4f7f\u7528\u7684\u6570\u636e\u6765\u81ea\u4e8e\u4e00\u7bc7\u6709\u5173\u836f\u7269\u7684\u9776\u5411\u9884\u6d4b\u7684\u6587\u7ae0\uff0c\u76ee\u7684\u662f\u627e\u5230\u4e0e\u6291\u5236\u5242TAK-243\u76f8\u5173\u7684\u4fe1\u53f7\u901a\u8def\u3002\u672c\u6b21\u590d\u73b0\u6240\u9700\u7684\u4ee3\u7801\u4e0e\u6570\u636e\u5c0f\u679c\u90fd\u5df2\u7ecf\u5e2e\u5927\u5bb6\u6574\u7406\u597d\u4e86\uff0c\u5173\u6ce8\u516c\u4f17\u53f7\u540e\u5373\u53ef\u83b7\u53d6\uff0c\u79df\u8d41\u670d\u52a1\u5668\u8fd8\u53ef\u4ee5\u83b7\u5f97\u4e13\u5c5e\u751f\u4fe1\u5206\u6790\u601d\u8def\u5b9a\u5236\u54e6\u3002<\/p>\n
\u63a5\u4e0b\u6765\u8ba9\u6211\u4eec\u6b63\u5f0f\u5f00\u59cb\u5427\uff01<\/p>\n
Step 1 GSEA\u5206\u6790<\/p>\n
\u9996\u5148\u5148\u6839\u636e GSEA \u9884\u6392\u5e8f\u5206\u6790\u7684\u663e\u7740\u6027\u548c\u76f8\u5173\u6027\u751f\u6210\u57fa\u56e0\u6392\u5e8f\uff0c\u5982\u4e0b\u56fe\u6240\u793a\u3002<\/p>\n
#\u6839\u636e GSEA \u9884\u6392\u5e8f\u5206\u6790\u7684\u663e\u7740\u6027\u548c\u76f8\u5173\u6027\u751f\u6210\u57fa\u56e0\u6392\u5e8f<\/p>\n
dir.create(“\/results\/rnks”)<\/p>\n
SCLC_rnk<-GenerateGSEA(bt_EC50, “\/results\/rnks\/SCLC1.rnk”, retRNK=T, plotRNK=F)<\/p>\n
SCLC_rnk2<-GenerateGSEA(bt_EC502, “\/results\/rnks\/SCLC2.rnk”, retRNK=T, plotRNK=F)<\/p>\n
YAP1_rnk<-GenerateGSEA(bt_EC50_YAP, “\/results\/rnks\/YAP1_1.rnk”, retRNK=T, plotRNK=F)<\/p>\n
YAP1_rnk2<-GenerateGSEA(bt_EC50_YAP2, “\/results\/rnks\/YAP1_2.rnk”, retRNK=T, plotRNK=F)<\/p>\n
ASCL1_rnk<-GenerateGSEA(bt_EC50_ASCL1, “\/results\/rnks\/ASCL1.rnk”, retRNK=T, plotRNK=F)<\/p>\n
ND1_rnk<-GenerateGSEA(bt_EC50_ND1, “\/results\/rnks\/NEUROD1.rnk”, retRNK=T, plotRNK=F)<\/p>\n
<\/p>\n
SCLC1.rnk<\/p>\n
\u6587\u4ef6\u4e2d\u5bf9\u57fa\u56e0\u4ee5\u53ca\u6392\u5e8f\u8fdb\u884c\u4e86\u660e\u786e\u7684\u6574\u7406\uff0c\u6709\u52a9\u4e8e\u6211\u4eec\u540e\u7eed\u5bfb\u627e\u4e0e\u76f8\u5e94\u7684\u9776\u70b9\u3002\u5728\u6392\u5e8f\u4e4b\u540e\uff0c\u8bbe\u7f6e\u57fa\u56e0\u96c6.<\/p>\n
#\u8bbe\u7f6e\u57fa\u56e0\u96c6<\/p>\n
gmt<-“\/data\/GSEA_gmt.gmt”<\/p>\n
\u63a5\u7740\u8fdb\u884c\u57fa\u56e0\u96c6\u5bcc\u96c6\u5206\u6790GSEA\uff0c\u7ed8\u5236\u6392\u5e8f\u524d\u6cbf\u7684\u62b5\u6297\u57fa\u56e0\u548c\u654f\u611f\u57fa\u56e0\u7684\u8def\u5f84\uff0c\u5982\u4e0b\u56fe\u6240\u793a\uff0c\u7ed3\u679c\u56fe\u4e2d\u7684\u7ea2\u8272\u548c\u84dd\u8272\u5206\u522b\u8868\u793a\u4e24\u79cd\u751f\u7269\u72b6\u6001\u7684\u57fa\u56e0\u96c6\u5bcc\u96c6\u5f97\u5206\uff08Enrichment Score\uff09\u3002\u7ea2\u8272\u8868\u793a\u6b63\u7684\u5bcc\u96c6\u5f97\u5206\uff0c\u8bf4\u660e\u8be5\u57fa\u56e0\u96c6\u5728\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u7684\u9876\u7aef\u5bcc\u96c6\uff0c\u4e0e\u7b2c\u4e00\u79cd\u72b6\u6001\u76f8\u5173\u3002\u84dd\u8272\u8868\u793a\u8d1f\u7684\u5bcc\u96c6\u5f97\u5206\uff0c\u8bf4\u660e\u8be5\u57fa\u56e0\u96c6\u5728\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u7684\u5e95\u7aef\u5bcc\u96c6\uff0c\u4e0e\u7b2c\u4e8c\u79cd\u72b6\u6001\u76f8\u5173\u3002\u5bcc\u96c6\u5f97\u5206\u7684\u7edd\u5bf9\u503c\u8d8a\u5927\uff0c\u8868\u793a\u5bcc\u96c6\u7a0b\u5ea6\u8d8a\u9ad8\uff0c\u4e5f\u5c31\u662f\u57fa\u56e0\u96c6\u4e0e\u751f\u7269\u72b6\u6001\u7684\u76f8\u5173\u6027\u8d8a\u5f3a\u3002<\/p>\n
#\u4f7f\u7528 Bader Lab \u9014\u5f84\u7684\u6570\u636e\u5bf9\u6bcf\u4e2a\u9884\u6392\u5e8f\u5206\u6790\u8fdb\u884c\u57fa\u56e0\u96c6\u5bcc\u96c6\u5206\u6790 (GSEA)<\/p>\n
pdf(“\/results\/TAK243_GSEA_.pdf”)<\/p>\n
SCLC_gsea<-GSEA(SCLC_rnk, gmt, pval=1)<\/p>\n
SCLC_gsea2<-GSEA(SCLC_rnk2, gmt, pval=1)<\/p>\n
YAP1_gsea<-GSEA(YAP1_rnk, gmt, pval=1)<\/p>\n
YAP1_gsea2<-GSEA(YAP1_rnk2, gmt, pval=1)<\/p>\n
ASCL1_gsea<-GSEA(ASCL1_rnk, gmt, pval=1)<\/p>\n
ND1_gsea<-GSEA(ND1_rnk, gmt, pval=1)<\/p>\n
dev.off()<\/p>\n
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-2.png?resize=640%2C640\")
<\/p>\n
TAK243_GSEA_<\/p>\n
\u7136\u540e\u7ed8\u5236\u6392\u5e8f\u524d\u6cbf\u7684\u62b5\u6297\u57fa\u56e0\u548c\u654f\u611f\u57fa\u56e0\u7684\u8def\u5f84\uff0c\u5982\u56fe\u6240\u793a\uff0c\u662fMYC_TARGETS(\u654f\u611f)\u548cAEROBIC_RESPIRATION(\u62b5\u6297)\u7684\u7ed8\u56fe\u3002\u6211\u4eec\u901a\u8fc7GSEA\u9009\u62e9\u524d\u6cbf\u57fa\u56e0\uff0c\u4ece\u800c\u8bc6\u522bSCLC\u7ec6\u80de\u7cfb\u4ee5\u53ca\u4e9a\u578b\u4e2d\u7684\u654f\u611f(\u5de6)\u548c\u62b5\u6297(\u53f3)\u9014\u5f84\u7684\u4ee3\u8868\u793a\u4f8b\u3002\u524d\u6cbf\u57fa\u56e0(\u7a81\u51fa\u663e\u793a\u7684\u6a59\u8272\u6761)\u53ef\u4ee5\u7528\u4f5cTAK-243\u53cd\u5e94\u7684\u6f5c\u5728\u751f\u7269\u6807\u5fd7\u7269\u3002<\/p>\n
#\u7ed8\u5236\u6392\u5e8f\u524d\u6cbf\u7684\u62b5\u6297\u57fa\u56e0\u548c\u654f\u611f\u57fa\u56e0\u7684\u8def\u5f84<\/p>\n
png(“\/results\/enPlot_AerobicRespiration.png”)<\/p>\n
enPlot(ASCL1_gsea[which(ASCL1_gsea$pathway %in% “AEROBIC RESPIRATION%GOBP%GO:0009060”),], rnk=ASCL1_rnk, gmt=gmt)<\/p>\n
dev.off()<\/p>\n
png(“\/results\/enPlot_MYCTargets.png”)<\/p>\n
enPlot(SCLC_gsea2[which(SCLC_gsea2$pathway %in% “HALLMARK_MYC_TARGETS_V1%MSIGDB_C2%HALLMARK_MYC_TARGETS_V1”),], rnk=SCLC_rnk2, gmt=gmt)<\/p>\n
dev.off()<\/p>\n
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-3.png?resize=480%2C480\")
<\/p>\n
enPlot_AerobicRespiration<\/p>\n
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-4.png?resize=480%2C480\")
<\/p>\n
enPlot_MYCTargets<\/p>\n
\u5bf9GSEA\u7684\u7ed3\u679c\u8fdb\u884c\u6bd4\u8f83\uff0c\u5982\u56fe\u6240\u793a\uff0c\u662f\u4e00\u7cfb\u5217\u7684\u57fa\u56e0\u96c6\u5bcc\u96c6\u5206\u6790\uff08GSEA\uff09\u7684\u6bd4\u8f83\u56fe\uff0c\u53ef\u4ee5\u663e\u793a\u4e0d\u540c\u7684\u751f\u7269\u72b6\u6001\u4e4b\u95f4\u7684\u57fa\u56e0\u96c6\u5bcc\u96c6\u5f97\u5206\uff08Enrichment Score\uff09\u7684\u76f8\u5173\u6027\u3002\u6a2a\u8f74\u548c\u7eb5\u8f74\u5206\u522b\u8868\u793a\u4e24\u4e2a\u751f\u7269\u72b6\u6001\u7684\u57fa\u56e0\u96c6\u5bcc\u96c6\u5f97\u5206\uff0c\u6bcf\u4e2a\u70b9\u8868\u793a\u4e00\u4e2a\u57fa\u56e0\u96c6\uff0c\u989c\u8272\u8868\u793a\u8be5\u57fa\u56e0\u96c6\u7684p\u503c\u6216q\u503c\uff0c\u5927\u5c0f\u8868\u793a\u8be5\u57fa\u56e0\u96c6\u7684\u57fa\u56e0\u6570\u76ee\uff0c\u53ef\u4ee5\u53d1\u73b0\u4e0d\u540c\u751f\u7269\u72b6\u6001\u4e4b\u95f4\u7684\u5171\u540c\u6216\u7279\u5f02\u7684\u529f\u80fd\u6216\u901a\u8def\u3002<\/p>\n
\u7ed3\u679c\u56fe\u4e0a\u65b9\u7684trend\u662f\u6307\u4e24\u4e2a\u751f\u7269\u72b6\u6001\u7684\u57fa\u56e0\u96c6\u5bcc\u96c6\u5f97\u5206\uff08Enrichment Score\uff09\u4e4b\u95f4\u7684\u7ebf\u6027\u76f8\u5173\u6027\u3002trend\u7684\u503c\u8d8a\u63a5\u8fd11\u6216-1\uff0c\u8868\u793a\u4e24\u4e2a\u751f\u7269\u72b6\u6001\u7684\u57fa\u56e0\u96c6\u5bcc\u96c6\u5f97\u5206\u8d8a\u6b63\u76f8\u5173\u6216\u8d1f\u76f8\u5173\uff0c\u5373\u8d8a\u6709\u76f8\u540c\u6216\u76f8\u53cd\u7684\u529f\u80fd\u6216\u901a\u8def\u3002trend\u7684\u503c\u8d8a\u63a5\u8fd10\uff0c\u8868\u793a\u4e24\u4e2a\u751f\u7269\u72b6\u6001\u7684\u57fa\u56e0\u96c6\u5bcc\u96c6\u5f97\u5206\u8d8a\u65e0\u5173\u3002<\/p>\n
#GSEA \u7ed3\u679c\u7684\u6bd4\u8f83<\/p>\n
pdf(“\/results\/GSEA_comparisons.pdf”)<\/p>\n
comp1<-compGSEA(SCLC_gsea, YAP1_gsea, title=”SCLC vs YAP1″, xlab=”SCLC”, ylab=”YAP1″, p=0.05)<\/p>\n
comp2<-compGSEA(SCLC_gsea, ASCL1_gsea, title=”SCLC vs ASCL1″, xlab=”SCLC”, ylab=”ASCL1″, p=0.05)<\/p>\n
comp3<-compGSEA(SCLC_gsea, ND1_gsea, title=”SCLC vs NEUROD1″, xlab=”SCLC”, ylab=”NEUROD1″, p=0.05)<\/p>\n
comp4<-compGSEA(YAP1_gsea, ASCL1_gsea, title=”YAP1 vs ASCL1″, xlab=”YAP1″, ylab=”ASCL1″, p=0.05)<\/p>\n
comp5<-compGSEA(YAP1_gsea, ND1_gsea, title=”YAP1 vs NEUROD1″, xlab=”YAP1″, ylab=”NEUROD1″, p=0.05)<\/p>\n
comp6<-compGSEA(ASCL1_gsea, ND1_gsea, title=”ASCL1 vs NEUROD1″, xlab=”ASCL1″, ylab=”NEUROD1″, p=0.05)<\/p>\n
comp7<-compGSEA(SCLC_gsea2, YAP1_gsea2, title=”SCLC vs YAP1″, xlab=”SCLC”, ylab=”YAP1″, p=0.05)<\/p>\n
comp8<-compGSEA(SCLC_gsea2, ASCL1_gsea, title=”SCLC vs ASCL1″, xlab=”SCLC”, ylab=”ASCL1″, p=0.05)<\/p>\n
comp9<-compGSEA(SCLC_gsea2, ND1_gsea, title=”SCLC vs NEUROD1″, xlab=”SCLC”, ylab=”NEUROD1″, p=0.05)<\/p>\n
comp10<-compGSEA(YAP1_gsea2, ASCL1_gsea, title=”YAP1 vs ASCL1″, xlab=”YAP1″, ylab=”ASCL1″, p=0.05)<\/p>\n
comp11<-compGSEA(YAP1_gsea2, ND1_gsea, title=”YAP1 vs NEUROD1″, xlab=”YAP1″, ylab=”NEUROD1″, p=0.05)<\/p>\n
comp12<-compGSEA(SCLC_gsea, SCLC_gsea2, xlab=”SCLC”, ylab=”SCLC without NCIH196″, p=0.05)<\/p>\n
comp13<-compGSEA(YAP1_gsea, YAP1_gsea2, xlab=”YAP1″, ylab=”YAP1 without NCIH196″, p=0.05)<\/p>\n
comp13<-comp13[order(comp13$x, decreasing=T),]<\/p>\n
dev.off()<\/p>\n
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-5.png?resize=632%2C600\")
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-6.png?resize=640%2C632\")
<\/p>\n
GSEA_comparisons.pdf<\/p>\n
\u6839\u636e\u6bd4\u8f83\u7684\u7ed3\u679c\uff0c\u67e5\u627e\u5e76\u6bd4\u5bf9\u53cd\u76f8\u5173\u7684\u8def\u5f84\uff0c\u5e76\u4ece\u6bcf\u6b21\u5206\u6790\u4e2d\u63d0\u53d6\u524d\u6cbf\u57fa\u56e0\u3002<\/p>\n
#\u67e5\u627e\u5e76\u6bd4\u5bf9\u53cd\u76f8\u5173\u8def\u5f84\uff0c\u5e76\u4ece\u6bcf\u6b21\u5206\u6790\u4e2d\u63d0\u53d6\u524d\u6cbf\u57fa\u56e0<\/p>\n
YAP1_res_bioM2<-tryCatch(gsea_gmt(rownames(subset(comp7, Direction==”Negative” & y>0)), gmt=YAP1_gsea2, leadingEdge = T), error=function(x) return(list(c(“”))))<\/p>\n
YAP1_sen_bioM2<-tryCatch(gsea_gmt(rownames(subset(comp7, Direction==”Negative” & y<0)), gmt=YAP1_gsea2, leadingEdge=T), error=function(x) return(list(c(“”))))<\/p>\n
try(ASCL1_res_terms<-plotVenn(list(ASCL1_1=rownames(subset(comp2, Direction==”Negative” & y>0)),<\/p>\n
ASCL1_2=rownames(subset(comp8, Direction==”Negative” & y>0))), retVals=T)$Only_1)<\/p>\n
ASCL1_res_bioM<-tryCatch(gsea_gmt(ASCL1_res_terms, gmt=ASCL1_gsea, leadingEdge = T), error=function(x) return(list(c(“”))))<\/p>\n
try(ND1_res_terms<-plotVenn(list(ND1_1=rownames(subset(comp3, Direction==”Negative” & y>0)),<\/p>\n
ND1_2=rownames(subset(comp9, Direction==”Negative” & y>0))), retVals=T)$Only_1)<\/p>\n
try(ND1_sen_terms<-plotVenn(list(ND1_1=rownames(subset(comp3, Direction==”Negative” & y<0)),<\/p>\n
ND1_2=rownames(subset(comp9, Direction==”Negative” & y<0))), retVals=T)$Only_2)<\/p>\n
ND1_res_bioM<-tryCatch(gsea_gmt(ND1_res_terms, ND1_gsea, leadingEdge=T), error=function(x) return(list(c(“”))))<\/p>\n
ND1_sen_bioM<-tryCatch(gsea_gmt(ND1_sen_terms, ND1_gsea, leadingEdge=T), error=function(x) return(list(c(“”))))<\/p>\n
dev.off()<\/p>\n
pdf(“\/results\/GSEA_Venns.pdf”)<\/p>\n
SCLC_res_terms1<-plotVenn(list(YAP1=rownames(subset(comp1, Direction==”Positive” & x>0)),<\/p>\n
ASCL1=rownames(subset(comp2, Direction==”Positive” & x>0)),<\/p>\n
ND1=rownames(subset(comp3, Direction==”Positive” & x>0))),”Resistors”, retVals=T, scale=F)$n123<\/p>\n
SCLC_sen_terms1<-plotVenn(list(YAP1=rownames(subset(comp1, Direction==”Positive” & x<0)),<\/p>\n
ASCL1=rownames(subset(comp2, Direction==”Positive” & x<0)),<\/p>\n
ND1=rownames(subset(comp3, Direction==”Positive” & x<0))),”Sensitizers”, retVals=T, scale=F)$n123<\/p>\n
SCLC_res_terms2<-plotVenn(list(YAP1=rownames(subset(comp7, Direction==”Positive” & x>0)),<\/p>\n
ASCL1=rownames(subset(comp8, Direction==”Positive” & x>0)),<\/p>\n
ND1=rownames(subset(comp9, Direction==”Positive” & x>0))),”Resistors – no H196″, retVals=T, scale=F)$n123<\/p>\n
SCLC_sen_terms2<-plotVenn(list(YAP1=rownames(subset(comp7, Direction==”Positive” & x<0)),<\/p>\n
ASCL1=rownames(subset(comp8, Direction==”Positive” & x<0)),<\/p>\n
ND1=rownames(subset(comp9, Direction==”Positive” & x<0))), “Sensitizers – no H196”, retVals=T, scale=F)$n123<\/p>\n
SCLC_sen_terms<-plotVenn(list(SCLC_1=SCLC_sen_terms1, SCLC_2=SCLC_sen_terms2), “SCLC Sensitizers”, retVals=T, scale=F)$n12<\/p>\n
SCLC_res_terms<-plotVenn(list(SCLC_1=SCLC_res_terms1, SCLC_2=SCLC_res_terms2), “SCLC Resistors”, retVals=T, scale=F)$n12<\/p>\n
dev.off()<\/p>\n
SCLC_res_bioM<-tryCatch(gsea_gmt(SCLC_res_terms, SCLC_gsea, T), error=function(x) return(list(c(“”))))<\/p>\n
SCLC_sen_bioM<-tryCatch(gsea_gmt(SCLC_sen_terms, SCLC_gsea2, T), error=function(x) return(list(c(“”))))<\/p>\n
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-7.png?resize=640%2C590\")
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-8.png?resize=640%2C637\")
<\/p>\n
GSEA_Venns<\/p>\n
\u6700\u540e\u4ece\u5df2\u6709\u7684\u57fa\u56e0\u96c6\uff0c\u7b5b\u9009\u51fa\u751f\u7269\u6807\u8bb0\u57fa\u56e0\u96c6\u7684\u91cd\u53e0\u90e8\u5206\uff0c\u5982\u4e0b\u56fe\u3002<\/p>\n
#\u6839\u636e\u5df2\u6709\u7684\u57fa\u56e0\u96c6\uff0c\u67e5\u770b\u751f\u7269\u6807\u8bb0\u57fa\u56e0\u96c6\u7684\u91cd\u53e0\u90e8\u5206<\/p>\n
pdf(“\/results\/Pathway_Biomarker_Venns.pdf”)<\/p>\n
try(plotVenn(list(SCLC=unique(unlist(SCLC_res_bioM)),<\/p>\n
YAP1=unique(unlist(YAP1_res_bioM2)),<\/p>\n
ASCL1=unique(unlist(ASCL1_res_bioM)),<\/p>\n
NEUROD1=unique(unlist(ND1_res_bioM))), title=”Leading Edge Resistors”))<\/p>\n
try(plotVenn(list(SCLC=unique(unlist(SCLC_sen_bioM)),<\/p>\n
YAP1=unique(unlist(YAP1_sen_bioM2)),<\/p>\n
#ASCL1=unique(unlist(ASCL1_sen_bioM)),<\/p>\n
NEUROD1=unique(unlist(ND1_sen_bioM))), title=”Leading Edge Sensitizers”))<\/p>\n
try(finalbioM<-list(SCLC.res=SCLC_res_bioM, SCLC.sen=SCLC_sen_bioM,<\/p>\n
YAP1.res=YAP1_res_bioM2, YAP1.sen=YAP1_sen_bioM2,<\/p>\n
ASCL1.res=ASCL1_res_bioM, NEUROD1.res=ND1_res_bioM,<\/p>\n
NEUROD1.sen=ND1_sen_bioM))<\/p>\n
dev.off()<\/p>\n
#\u8f93\u51fa GSEA \u5206\u6790\u7684\u57fa\u56e0\u96c6<\/p>\n
write.gmt(unlist(finalbioM, recursive = F), “\/results\/gmts\/Final_BioMarker_List.gmt”)<\/p>\n
save.image(“\/results\/TAK243_Biomarker.RData”)<\/p>\n
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-9.png?resize=640%2C634\")
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-10.png?resize=640%2C639\")
<\/p>\n
Pathway_Biomarker_Venns<\/p>\n
\u53ef\u4ee5\u975e\u5e38\u6e05\u695a\u7684\u770b\u5230\u5904\u4e8e\u524d\u6cbf\u7684\u91cd\u53e0\u57fa\u56e0\u3002\u73b0\u5728\u5c0f\u679c\u5df2\u7ecf\u5e26\u7740\u5927\u5bb6\u5c06\u53ef\u80fd\u7684\u57fa\u56e0\u7b5b\u9009\u51fa\u6765\u4e86\uff0c\u63a5\u4e0b\u6765\u8981\u505a\u7684\u5c31\u662f\u9884\u6d4bSCLC\u7ec6\u80de\u7cfb\u7684TAK\u53cd\u5e94\u4e86\u3002<\/p>\n
Step 2 \u9884\u6d4b SCLC \u7ec6\u80de\u7cfb\u7684 TAK \u53cd\u5e94\uff08<\/p>\n
\u5728\u8fd9\u4e00\u6b65\uff0c\u6211\u4eec\u7684\u76ee\u7684\u662f\u5bf9SCLC\u4e2d\u53ef\u80fd\u5bf9TAK\u8fdb\u884c\u53cd\u5e94\u7684\u901a\u8def\u8fdb\u884c\u9884\u6d4b\u3002\u9996\u5148\u5c06\u57fa\u56e0\u96c6\u5bfc\u5165\uff0c\u5c06\u57fa\u56e0\u7ec4\u7ec4\u5408\u4e3a\u4e00\u4e2a\u5bf9\u8c61\uff0c\u7136\u540e\u5220\u9664\u5c11\u4e8e\u4e24\u4e2a\u57fa\u56e0\u7684\u57fa\u56e0\u96c6\uff0c\u4ee5\u53ca\u6765\u81ea\u56de\u5f52\u5206\u6790\u7684\u91cd\u53e0\u7684\u57fa\u56e0\u96c6\u3002\u63a5\u7740\u4f7f\u7528\u7ec4\u5408\u7684\u57fa\u56e0\u96c6\u5bf9\u6240\u6709 SCLC \u7ec6\u80de\u7cfb\u8868\u8fbe\u6570\u636e\u8fd0\u884c ssgsea\uff0c\u5c06 TAK243 \u54cd\u5e94\u6570\u636e\uff08EC50 \u548c AUC\uff09\u6dfb\u52a0\u5230 gsPDX \u7684\u524d\u6cbf\u3002\u6700\u540e\u7ed8\u5236\u70ed\u56fe\u3002<\/p>\n
#\u8bfb\u53d6\u5b58\u50a8\u5728 \/results\/gmts\/ \u4e2d\u7684\u57fa\u56e0\u96c6<\/p>\n
PathwayBioM<-fgsea::gmtPathways(“\/data\/Final_BioMarker_List.gmt”)<\/p>\n
Resistors1<-fgsea::gmtPathways(“\/data\/Resistor_Genes1.gmt”)<\/p>\n
Resistors2<-fgsea::gmtPathways(“\/data\/Resistor_Genes2.gmt”)<\/p>\n
Sensitizers1<-fgsea::gmtPathways(“\/data\/Sensitizer_Genes1.gmt”)<\/p>\n
Sensitizers2<-fgsea::gmtPathways(“\/data\/Sensitizer_Genes2.gmt”)<\/p>\n
SCLC_rnk<-SCLC_rnk[order(SCLC_rnk, decreasing=T)]<\/p>\n
SCLC_rnk2<-SCLC_rnk2[order(SCLC_rnk2, decreasing=T)]<\/p>\n
YAP1_rnk<-YAP1_rnk[order(YAP1_rnk, decreasing=T)]<\/p>\n
YAP1_rnk2<-YAP1_rnk2[order(YAP1_rnk2, decreasing=T)]<\/p>\n
ASCL1_rnk<-ASCL1_rnk[order(ASCL1_rnk, decreasing=T)]<\/p>\n
ND1_rnk<-ND1_rnk[order(ND1_rnk, decreasing=T)]<\/p>\n
#\u5c06\u57fa\u56e0\u7ec4\u7ec4\u5408\u4e3a\u4e00\u4e2a\u5bf9\u8c61<\/p>\n
combGS<-c(PathwayBioM, Resistors1, Resistors2, Sensitizers1, Sensitizers2)<\/p>\n
names(combGS)<-c(names(PathwayBioM), paste(names(Resistors1),”res1″, sep=”.”), paste(names(Resistors2),”res2″, sep=”.”),<\/p>\n
paste(names(Sensitizers1), “sen1″,sep=”.”), paste(names(Sensitizers2),”sen2″, sep=”.”))<\/p>\n
#\u5220\u9664\u5c11\u4e8e\u4e24\u4e2a\u57fa\u56e0\u7684\u57fa\u56e0\u96c6\uff0c\u4ee5\u53ca\u6765\u81ea\u56de\u5f52\u5206\u6790\u7684\u91cd\u53e0\u7684\u57fa\u56e0\u96c6<\/p>\n
to_remove<-unique(c(which(lengths(combGS)<2), grep(“SCLC_”, names(combGS)),<\/p>\n
grep(“YAP1_”, names(combGS)), grep(“ASCL1_”, names(combGS))))<\/p>\n
combGS<-combGS[-to_remove]<\/p>\n
#\u4f7f\u7528\u7ec4\u5408\u7684\u57fa\u56e0\u96c6\u5bf9\u6240\u6709 SCLC \u7ec6\u80de\u7cfb\u8868\u8fbe\u6570\u636e\u8fd0\u884c ssgsea(\u5355\u6837\u672c\u57fa\u56e0\u96c6\u5bcc\u96c6\u5206\u6790)<\/p>\n
gsPDX<-GSVA::gsva(as.matrix(fixRows(CCLE, “counts”)),combGS , method=”ssgsea”)<\/p>\n
#\u68c0\u67e5\u836f\u7269\u53cd\u5e94\u548c gsPDX \u7ec6\u80de\u7cfb\u7684\u987a\u5e8f\u662f\u5426\u76f8\u540c<\/p>\n
all.equal(sapply(strsplit(colnames(gsPDX),”_”,T),'[[‘,1), rownames(x))<\/p>\n
#\u5c06 TAK243 \u54cd\u5e94\u6570\u636e\uff08EC50 \u548c AUC\uff09\u6dfb\u52a0\u5230 gsPDX \u7684\u524d\u6cbf<\/p>\n
gsPDX<-rbind(x$EC50, x$AUC, gsPDX)<\/p>\n
#z-score it<\/p>\n
tmp<-t(scale(t(gsPDX)))<\/p>\n
tmp[c(1:2),]<-scales::rescale(tmp[c(1:2),], to=c(min(tmp[3:nrow(tmp),]), max(tmp[3:nrow(tmp),])))<\/p>\n
#\u8bfb\u53d6\u70ed\u56fe\u7684\u6ce8\u91ca\u6570\u636e<\/p>\n
annodf<-read.csv(“\/data\/Annodf1.csv”, row.names=1)<\/p>\n
#\u6e05\u6670\u5316\u57fa\u56e0\u7ec4\u6765\u6e90<\/p>\n
annodf$Source<-ifelse(annodf$Source==”Pathway”,”Pathway_Analysis”,”Regression_Analysis”)<\/p>\n
#\u6ce8\u91ca\u6570\u636e\u6846<\/p>\n
colAnno<-data.frame(row.names=colnames(gsPDX),<\/p>\n
Subtype=typing$type[which(typing$Cell.line %in% colnames(gsPDX))])<\/p>\n
#\u8bbe\u7f6e\u70ed\u56fe\u4e0a\u6ce8\u91ca\u7684\u989c\u8272<\/p>\n
annoCols<-list(Subtype=colPal(“Dark2”)[1:6],<\/p>\n
Annotation=colPal(“Paired”)[1:length(unique(annodf$Annotation))],<\/p>\n
Status=colPal(“Dark2”)[5:6],<\/p>\n
Source=colPal(“Paired”)[1:2])<\/p>\n
names(annoCols$Annotation)<-unique(annodf$Annotation)<\/p>\n
names(annoCols$Subtype)<-c(“ASCL1″,”YAP1″,”NEUROD1″,”SCLC”,”ATOH1″,”POU2F3″)<\/p>\n
names(annoCols$Status)<-c(“Sensitizer”,”Resistor”)<\/p>\n
names(annoCols$Source)<-c(“Pathway_Analysis”,”Regression_Analysis”)<\/p>\n
#\u7ed8\u5236\u70ed\u56fe<\/p>\n
pdf(“\/results\/ssGSEA_Biomarker_Heatmap.pdf”)<\/p>\n
pheatmap::pheatmap(tmp, show_rownames = F, annotation_row = annodf, cluster_rows=F, treeheight_row=0, annotation_col=colAnno, annotation_colors = annoCols, main=”ssGSEA Enrichment of Biomarker Gene Sets”,na_col=”black”, gaps_row=2)<\/p>\n
dev.off()<\/p>\n
ssGSEA_Biomarker_Heatmap.pdf<\/p>\n
![](\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/02\/word-image-28006-11.png?resize=640%2C640\")
<\/p>\n
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