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data-recalc-dims=\"1\"><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<section style=\"display: contents;\" powered-by=\"xiumi.us\">\n<section style=\"display: contents;\">\n<section style=\"display: contents;\">\n<section style=\"height: 0px;overflow: hidden;\"> <\/span> <\/section>\n<\/section>\n<\/section>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<section style=\"letter-spacing: 0.578px;\">\n<section powered-by=\"xiumi.us\">\n<section style=\"letter-spacing: 0.578px;\">\n<section style=\"display: contents;\" powered-by=\"xiumi.us\">\n<section style=\"display: contents;\">\n<section style=\"display: contents;\">\n<section style=\"letter-spacing: 0.578px;\">\n <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"margin: 10px 0% 8px;justify-content: flex-start;display: flex;flex-flow: row nowrap;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;border-left: 3px solid rgb(219, 219, 219);border-bottom-left-radius: 0px;padding-left: 8px;align-self: flex-start;flex: 0 0 auto;\">\n<section style=\"color: rgba(0, 0, 0, 0.5);text-align: justify;font-size: 14px;\" powered-by=\"xiumi.us\">\n\u4eca\u5929\u5c0f\u679c\u7ed9\u5c0f\u4f19\u4f34\u5e26\u6765\u7684\u5206\u4eab\u662f\u5565\u5462\uff1f<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: justify;\" powered-by=\"xiumi.us\">\n <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section powered-by=\"xiumi.us\">\n\u4eca\u5929\u5c0f\u679c\u5c06\u590d\u73b0\u4e00\u7bc78+\u751f\u4fe1\u6587\u7ae0\u4e2d\u7684WGCNA\u5206\u6790\uff0c\u901a\u8fc7\u8be5\u63a8\u6587\u5c06\u638c\u63e1\u5982\u4f55\u5229\u7528\u81ea\u5df1\u7684\u6570\u636e\u8fdb\u884cWGCNA\u5206\u6790\uff0c\u6765\u8fdb\u884c\u4e0e\u8868\u578b\u6216\u8005\u75be\u75c5\u76f8\u5173\u5019\u9009\u57fa\u56e0\u7684\u6316\u6398\uff0c\u5c0f\u679c\u89c9\u5f97WGCNA\u662f\u4e00\u4e2a\u975e\u5e38\u4e0d\u9519\u7684\u5019\u9009\u57fa\u56e0\u6316\u6398\u7684\u65b9\u6cd5\uff0c\u503c\u5f97\u5c0f\u4f19\u4f34\u4eec\u8ba4\u771f\u5b66\u4e60\uff0c\u63a5\u4e0b\u6765\u8ddf\u7740\u5c0f\u679c\u5f00\u59cb\u4eca\u5929\u7684\u5206\u4eab\u5427\uff01<\/span><\/p>\n <\/span><\/em><\/strong><\/p>\n1.WGCNA\u4ecb\u7ecd<\/span><\/em><\/strong><\/p>\n\u5728\u5f00\u59cb\u5206\u6790\u4e4b\u524d\uff0c\u5c0f\u679c\u60f3\u4e3a\u5c0f\u4f19\u4f34\u7b80\u5355\u4ecb\u7ecd\u4e00\u4e0bWGCNA\uff0cWGCNA\u662f\u4e00\u79cd\u7528\u4e8e\u57fa\u56e0\u5171\u8868\u8fbe\u7684\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u5c06\u9ad8\u901a\u91cf\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u8f6c\u5316\u4e3a\u4e00\u4e2a\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u4ece\u800c\u53d1\u73b0\u5177\u6709\u76f8\u4f3c\u8868\u8fbe\u6a21\u5f0f\u7684\u57fa\u56e0\u6a21\u5757\uff0c\u4ee5\u53ca\u7814\u7a76\u8fd9\u4e9b\u6a21\u5757\u4e0e\u8868\u578b\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u6700\u7ec8\u6316\u6398\u4e0e\u8868\u578b\u76f8\u5173\u7684\u5019\u9009\u57fa\u56e0\u3002\u8fd9\u5c31\u662f\u5c0f\u679c\u5bf9WGCNA\u5206\u6790\u539f\u7406\u7684\u7b80\u5355\u4ecb\u7ecd\uff0c\u662f\u4e0d\u662f\u901a\u4fd7\u6613\u61c2\u5965\uff01\u5176\u5b9eWGCNA\u5206\u6790\u5f88\u7b80\u5355\uff0c\u5c0f\u4f19\u4f34\u4e0d\u8981\u56e0\u4e3a\u5206\u6790\u6b65\u9aa4\u591a\u800c\u4e0d\u6562\u5c1d\u8bd5\uff0c\u53ea\u9700\u8981\u8f93\u5165\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u548c\u5bf9\u5e94\u6837\u672c\u8868\u578b\u6570\u636e\u6587\u4ef6\uff0c\u5c31\u53ef\u4ee5\u5b8c\u6210\u5206\u6790\uff0c\u6709\u9700\u8981\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u8ddf\u7740\u5c0f\u679c\u5f00\u59cb\u4eca\u5929\u7684\u5b9e\u64cd\u3002<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n2.\u51c6\u5907\u9700\u8981\u7684R\u5305<\/span><\/em><\/strong><\/p>\n#\u5b89\u88c5\u9700\u8981\u7684R\u5305<\/span><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>BiocManager::install(\"WGCNA\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n <\/p>\n#\u52a0\u8f7d\u9700\u8981\u7684R\u5305<\/span><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>library(WGCNA)<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n <\/p>\n <\/span><\/p>\n3.WGCNA\u5206\u6790<\/span><\/em><\/strong><\/p>\n#\u8bfb\u53d6\u8868\u8fbe\u77e9\u9635\uff0c\u884c\u540d\u4e3a\u57fa\u56e0\uff0c\u5217\u540d\u4e3a\u6837\u672c\u4fe1\u606f<\/span><\/p>\nexpr<-read.table(“combined.expr.txt”,header=T,sep=”t”)<\/span><\/p>\n <\/span><\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.21660649819494585\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/2_od1ePDREop1dvaREUYN3EBM1uhjqzQ.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnyFb98riajcLfEgwSoLeiaQxRbgod1ePDREop1dvaREUYN3EBM1uhjqzQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u884c\u5217\u8f6c\u7f6e<\/span><\/code><code>mydata<-expr<\/span><\/code><code>expr2<-data.frame(t(mydata))<\/span><\/code><code>#\u786e\u5b9aexpr2\u5217\u540d<\/span><\/code><code>colnames(expr2)<-rownames(mydata)<\/span><\/code><code>#\u786e\u5b9aexpr2\u884c\u540d<\/span><\/code><code>rownames(expr2)<-colnames(mydata)<\/span><\/code><code>#\u57fa\u56e0\u8fc7\u6ee4<\/span><\/code><code>dataExpr1<-expr2<\/span><\/code><code>gsg=goodSamplesGenes(dataExpr1,verbose=3<\/span>);<\/span><\/code><code>gsg$allOK<\/span><\/span><\/code><code>if<\/span> (!gsg$allOK)<\/span><\/span>{<\/span><\/code><code> # Optionally, print the gene and<\/span> sample names that were removed:<\/span><\/code><code> if<\/span> (sum(!gsg$goodGenes)>0<\/span>)<\/span><\/code><code> printFlush(paste(\"Removing genes:\"<\/span>,<\/span><\/code><code> paste(names(dataExpr)[!gsg$goodGenes], collapse = \",\"<\/span>)));<\/span><\/code><code> if<\/span> (sum(!gsg$goodSamples)>0<\/span>)<\/span><\/code><code> printFlush(paste(\"Removing samples:\"<\/span>,<\/span><\/code><code> paste(rownames(dataExpr)[!gsg$goodSamples], collapse = \",\"<\/span>)));<\/span><\/code><code> # Remove the offending genes and<\/span> samples from the data:<\/span><\/code><code> dataExpr1 = dataExpr1[gsg$goodSamples, gsg$goodGenes]<\/span><\/code><code>}<\/span><\/code><code>#\u57fa\u56e0\u6570\u76ee<\/span><\/code><code>nGenes = ncol(dataExpr1)<\/span><\/code><code>#\u6837\u672c\u6570\u76ee<\/span><\/code><code>nSamples = nrow(dataExpr1)<\/span><\/code><code>#\u5bfc\u5165\u8868\u578b\u6570\u636e\uff0c\u7b2c\u4e00\u5217\u4e3a\u6837\u672c\u4fe1\u606f\uff0c\u5176\u4ed6\u5217\u4e3a\u8868\u578b\u6570\u636e\uff0c\u9700\u8981\u6ce8\u610f\u7684\u662f\u8868\u578b\u6570\u636e\u8981\u4e0e\u6837\u672c\u6570\u636e\u4e00\u4e00\u5bf9\u5e94<\/span><\/code><code>traitData=read.table(\"TraitData.txt\"<\/span>,header=T,sep=\"t\"<\/span>,row.names=1<\/span>)<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.26534296028880866\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/3_rFJJcicFvEKXzOssuwcuEicSFAXQ6Q.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnnzCWND3KX3HduugEm4khb0CSgrrFJJcicFvEKXzOssuwcuEicSFAXQ6Q\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\" \/><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u63d0\u53d6\u8868\u8fbe\u77e9\u9635\u6837\u672c\u540d<\/span><\/code><code>fpkmSamples<-rownames(dataExpr1)<\/span><\/code><code>#\u63d0\u53d6\u8868\u578b\u6587\u4ef6\u6837\u672c\u540d<\/span><\/code><code>traitSamples<-rownames(traitData)<\/span><\/code><code>#\u8868\u8fbe\u77e9\u9635\u6837\u672c\u540d\u987a\u5e8f\u5339\u914d\u8868\u578b\u6587\u4ef6\u6837\u672c\u540d<\/span><\/code><code>traitRows<-match(fpkmSamples,traitSamples)<\/span><\/code><code>#\u83b7\u5f97\u5339\u914d\u597d\u6837\u672c\u987a\u5e8f\u7684\u8868\u578b\u6587\u4ef6<\/span><\/code><code>dataTraits<-traitData[traitRows,]<\/span><\/code><code>#\u7ed8\u5236\u6811+\u8868\u578b\u70ed\u56fe<\/span><\/code><code>sampleTree2<-hclust(dist(dataExpr1),method=\"average\"<\/span>)<\/span><\/code><code>traitColors<-numbers2colors(dataTraits,signed<\/span>=FALSE)<\/span><\/code><code>png(\"sample-subtype-cluster.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>plotDendroAndColors(sampleTree2,traitColors,groupLabels=names(dataTraits),main=\"Sample dendrogram and trait heatmap\"<\/span>,<\/span><\/code><code> cex.colorLabels=1.5<\/span>,cex.dendroLabels=1<\/span>,cex.rowText=2<\/span>)<\/span><\/code><code>dev.off()<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7504798464491362\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/4_c48VZCfOH2OGo17RWwRfQibuSRSEbw.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnS1DFzSibVRK8IwATI7XT022gI7ic48VZCfOH2OGo17RWwRfQibuSRSEbw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"521\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u8ba1\u7b97\u5408\u9002\u7684power\u503c\uff0c\u5e76\u7ed8\u5236Power\u503c\u66f2\u7ebf\u56fe<\/span><\/code><code>powers = c(c(1<\/span>:10<\/span>), seq(from = 12<\/span>, to=20<\/span>, by=2<\/span>))<\/span><\/code><code>sft = pickSoftThreshold(dataExpr1, powerVector = powers, verbose = 5<\/span>)<\/span><\/code><code>#\u8bbe\u7f6e\u7f51\u7edc\u6784\u5efa\u53c2\u6570\u9009\u62e9\u8303\u56f4\uff0c\u8ba1\u7b97\u65e0\u5c3a\u5ea6\u5206\u5e03\u62d3\u6251\u77e9\u9635<\/span><\/code><code>png(\"step2-beta-value.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>par(mfrow = c(1<\/span>,2<\/span>));<\/span><\/code><code>cex1 = 0.9<\/span>;<\/span><\/code><code> # Scale-free<\/span> topology fit index as a function of the soft-thresholding power<\/span><\/span><\/code><code>plot<\/span>(sft$fitIndices[,1<\/span>], -sign(sft$fitIndices[,3<\/span>])*sft$fitIndices[,2<\/span>],<\/span><\/span><\/code><code> xlab=\"Soft Threshold (power)\"<\/span>,ylab=\"Scale Free Topology Model Fit,signed R^2\"<\/span>,type=\"n\"<\/span>,<\/span><\/code><code> main = paste(\"Scale independence\"<\/span>))<\/span>;<\/span><\/code><code>text(sft$fitIndices[,1<\/span>], -sign(sft$fitIndices[,3<\/span>])*sft$fitIndices[,2<\/span>],<\/span><\/code><code> labels=powers,cex=cex1,col=\"red\"<\/span>);<\/span><\/code><code> # this line<\/span> corresponds to using an R^2 cut-off of h<\/span><\/span><\/code><code> abline(h=0.90<\/span>,col=\"red\"<\/span>)<\/span><\/code><code> # Mean connectivity as a function of the soft-thresholding power<\/span><\/span><\/code><code> plot<\/span>(sft$fitIndices[,1<\/span>], sft$fitIndices[,5<\/span>],<\/span><\/span><\/code><code> xlab=\"Soft Threshold (power)\"<\/span>,ylab=\"Mean Connectivity\"<\/span>, type=\"n\"<\/span>,<\/span><\/code><code> main = paste(\"Mean connectivity\"<\/span>))<\/span><\/code><code> text<\/span>(sft$fitIndices[,1<\/span>], sft$fitIndices[,5<\/span>], labels=powers, cex=cex1,col=\"red\"<\/span>)<\/span><\/span><\/code><code>dev.off<\/span>()<\/span><\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/5_ciahsmxfKlv6iccukFnSWUkWpWvmhg.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYn4NK6v1cdB0ht71PZZNDxhibHzGticiahsmxfKlv6iccukFnSWUkWpWvmhg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u4e00\u6b65\u6cd5\u6784\u5efa\u5171\u8868\u8fbe\u7f51\u7edc<\/span><\/code><code>##\u9700\u8981\u8c03\u7528WGCNA\u5305\u81ea\u5e26\u7684cor\u51fd\u6570\uff0c\u4e0d\u7136\u4f1a\u53d1\u751f\u62a5\u9519\u5965\uff01<\/span><\/code><code>cor<-WGCNA::cor<\/span><\/code><code>##\u5728\u8fdb\u884c\u5171\u8868\u8fbe\u7f51\u7edc\u6784\u5efa\u65f6\uff0cpower\u503c\u7684\u9009\u62e9\u975e\u5e38\u91cd\u8981\uff0c\u6700\u5f71\u54cd\u7ed3\u679c\u7684\u4e00\u4e2a\u53c2\u6570\uff0c\u9700\u8981\u7ecf\u8fc7\u591a\u6b21\u5c1d\u8bd5\uff0c\u624d\u80fd\u627e\u5230\u6700\u9002\u5408\u7684\u3002<\/span><\/code><code>net = blockwiseModules(dataExpr1, power = 7<\/span>, maxBlockSize = nGenes,<\/span><\/code><code> TOMType ='unsigned'<\/span>, minModuleSize = 30<\/span>,<\/span><\/code><code> reassignThreshold = 0<\/span>, mergeCutHeight = 0.25<\/span>,<\/span><\/code><code> numericLabels = TRUE, pamRespectsDendro = FALSE,<\/span><\/code><code> saveTOMs=TRUE, saveTOMFileBase = \"drought\"<\/span>,<\/span><\/code><code> verbose = 3<\/span>)<\/span><\/code><code>table(net$colors)<\/span><\/code><code>cor<-stats::cor<\/span><\/code><code>#\u7ed8\u5236\u57fa\u56e0\u805a\u7c7b\u6811\u548c\u6a21\u5757\u989c\u8272\u7ec4\u5408<\/span><\/code><code># Convert labels to colors for<\/span> plotting<\/span><\/code><code>moduleLabels = net$colors<\/span><\/code><code>moduleColors = labels2colors(moduleLabels)<\/span><\/code><code># Plot the dendrogram and<\/span> the module<\/span> colors underneath<\/span><\/code><code>png(\"step4-genes-modules.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>plotDendroAndColors(net$dendrograms[[1<\/span>]], moduleColors[net$blockGenes[[1<\/span>]]],<\/span><\/code><code> \"Module colors\"<\/span>,<\/span><\/code><code> dendroLabels = FALSE, hang = 0.03<\/span>,<\/span><\/code><code> addGuide = TRUE, guideHang = 0.05<\/span>)<\/span><\/code><code>dev.off()<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/6_ichc4Nh34qCtxiadGAqxia36t88EsQ.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnibjgIb7yTp9nPvc3PSrOopZuKMkuichc4Nh34qCtxiadGAqxia36t88EsQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u8ba1\u7b97\u6a21\u5757\u4e0e\u6027\u72b6\u95f4\u7684\u76f8\u5173\u6027\u53ca\u7ed8\u5236\u76f8\u5173\u6027\u70ed\u56fe<\/span><\/code><code>nGenes = ncol(dataExpr1)<\/span><\/code><code>nSamples = nrow(dataExpr1)<\/span><\/code><code>design=read.table(\"TraitData.txt\"<\/span>, sep = 't'<\/span>, header = T, row.names = 1<\/span>)<\/span><\/code><code># Recalculate MEs with color labels<\/span><\/code><code>MEs0 = moduleEigengenes(dataExpr1, moduleColors)$eigengenes<\/span><\/code><code>##\u4e0d\u540c\u989c\u8272\u7684\u6a21\u5757\u7684ME\u503c\u77e9 (\u6837\u672cvs\u6a21\u5757)<\/span><\/code><code>MEs = orderMEs(MEs0);<\/span><\/code><code>moduleTraitCor = cor(MEs, design , use = \"p\"<\/span>);<\/span><\/code><code>moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)<\/span><\/code><code># Will display correlations and<\/span> their p-values<\/span><\/code><code>textMatrix = paste(signif(moduleTraitCor, 2<\/span>), \"n(\"<\/span>,<\/span><\/code><code> signif(moduleTraitPvalue, 1<\/span>), \")\"<\/span>, sep = \"\"<\/span>);<\/span><\/code><code>dim(textMatrix) = dim(moduleTraitCor)<\/span><\/code><code>png(\"step5-Module-trait-relationships.png\"<\/span>,width = 800<\/span>,height = 1200<\/span>,res = 120<\/span>)<\/span><\/code><code>par(mar = c(6<\/span>, 8.5<\/span>, 3<\/span>, 3<\/span>));<\/span><\/code><code># Display the correlation values within a heatmap plot<\/span><\/span><\/code><code>labeledHeatmap<\/span>(Matrix = moduleTraitCor,<\/span><\/span><\/code><code> xLabels = colnames(design),<\/span><\/code><code> yLabels = names(MEs),<\/span><\/code><code> ySymbols = names(MEs),<\/span><\/code><code> colorLabels = FALSE,<\/span><\/code><code> colors = greenWhiteRed(50<\/span>),<\/span><\/code><code> textMatrix = textMatrix,<\/span><\/code><code> setStdMargins = FALSE,<\/span><\/code><code> cex.text = 0.5<\/span>,<\/span><\/code><code> zlim = c(-1<\/span>,1<\/span>),<\/span><\/code><code> main = paste(\"Module-trait relationships\"<\/span>))<\/span><\/code><code>dev.off<\/span>()<\/span><\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"1.5\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/7_TVg99kibYShJR8Ik72patTGyX5zL3Q.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnqDTia7ygRk9ECttmNy0Rbibr5MPTVg99kibYShJR8Ik72patTGyX5zL3Q\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"488\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u8ba1\u7b97MM\u503c\u548cGS\u503c\u5e76\u7ed8\u56fe<\/span><\/code><code>##\u5207\u5272\uff0c\u4ece\u7b2c\u4e09\u4e2a\u5b57\u7b26\u5f00\u59cb\u4fdd\u5b58<\/span><\/code><code>modNames = substring(names(MEs), 3<\/span>)<\/span><\/code><code>geneModuleMembership = as.data.frame(cor(dataExpr1, MEs, use = \"p\"<\/span>));<\/span><\/code><code>## \u7b97\u51fa\u6bcf\u4e2a\u6a21\u5757\u8ddf\u57fa\u56e0\u7684\u76ae\u5c14\u68ee\u76f8\u5173\u7cfb\u6570\u77e9<\/span><\/code><code>## MEs\u662f\u6bcf\u4e2a\u6a21\u5757\u5728\u6bcf\u4e2a\u6837\u672c\u91cc\u9762\u76bf<\/span><\/code><code>## dataExpr1\u662f\u6bcf\u4e2a\u57fa\u56e0\u5728\u6bcf\u4e2a\u6837\u672c\u7684\u8868\u8fbe\u91cf<\/span><\/code><code>MMPvalue = as.data.frame(<\/span><\/code><code> corPvalueStudent(as.matrix(geneModuleMembership), nSamples) ##\u8ba1\u7b97MM\u503c\u5bf9\u5e94\u7684P\u503c<\/span><\/code><code> );<\/span><\/code><code>names(geneModuleMembership) = paste(\"MM\"<\/span>, modNames, sep=\"\"<\/span>); ##\u7ed9MM\u5bf9\u8c61\u7edf\u4e00\u8d4b\u540d<\/span><\/code><code>names(MMPvalue) = paste(\"p.MM\"<\/span>, modNames, sep=\"\"<\/span>); ##\u7ed9MMPvalue\u5bf9\u8c61\u7edf\u4e00\u8d4b\u540d<\/span><\/code><code>##\u8ba1\u7b97\u57fa\u56e0\u4e0e\u6bcf\u4e2a\u6027\u72b6\u7684\u663e\u8457\u6027\uff08\u76f8\u5173\u6027\uff09\u53capvalue\u503c<\/span><\/code><code>geneTraitSignificance = as.data.frame(cor(dataExpr1, design, use = \"p\"<\/span>));<\/span><\/code><code>GSPvalue = as.data.frame(<\/span><\/code><code> corPvalueStudent(as.matrix(geneTraitSignificance), nSamples) ##\u8ba1\u7b97GS\u503c\u5bf9\u5e94\u7684pvalue\u503c<\/span><\/code><code> );<\/span><\/code><code>names(geneTraitSignificance) = paste(\"GS.\"<\/span>, colnames(design), sep=\"\"<\/span>);<\/span><\/code><code>names(GSPvalue) = paste(\"p.GS.\"<\/span>, colnames(design), sep=\"\"<\/span>);<\/span><\/code><code>module<\/span> = \"brown\"<\/span><\/span><\/code><code>column = match(module<\/span>, modNames); ##\u5728\u6240\u6709\u6a21\u5757\u4e2d\u5339\u914d\u9009\u62e9\u7684\u6a21\u5757\uff0c\u8fd4\u56de\u6240\u5728\u7684\u4f4d\u7f6e<\/span><\/code><code>moduleGenes = moduleColors==module<\/span>; ##==\u5339\u914d\uff0c\u5339\u914d\u4e0a\u7684\u8fd4\u56deTrue\uff0c\u5426\u5219\u8fd4\u56defalse<\/span><\/span><\/code><code>png(\"step6-Module_membership-gene_significance.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>par(mfrow = c(1<\/span>,1<\/span>)); ##\u8bbe\u5b9a\u8f93\u51fa\u56fe\u7247\u884c\u5217\u6570\u91cf\uff0c\uff081<\/span>\uff3f1<\/span>\uff09\u4ee3\u8868\u884c\u4e00\u4e2a\uff0c\u5217\u4e00\u4e3f<\/span><\/code><code>verboseScatterplot(abs<\/span>(geneModuleMembership[moduleGenes, column]), ## \u7ed8\u5236MM\u548cGS\u6563\u70b9\u56ff<\/span><\/code><code> abs<\/span>(geneTraitSignificance[moduleGenes, 1<\/span>]),<\/span><\/code><code> xlab = paste(\"Module Membership in\"<\/span>, module<\/span>, \"module\"<\/span>),<\/span><\/code><code> ylab = \"Gene significance for Basal\"<\/span>,<\/span><\/code><code> main = paste(\"Module membership vs. gene significancen\"<\/span>),<\/span><\/code><code> cex.main = 1.2<\/span>, cex.lab = 1.2<\/span>, cex.axis = 1.2<\/span>, col = module<\/span>)<\/span><\/code><code>dev.off()<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/8_qUNj7fVVExR62r5ibjvbIicpkqlVyQ.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnW9N5cIYGHBLoKUkBMGHmhAaDk6qUNj7fVVExR62r5ibjvbIicpkqlVyQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u7ed8\u5236TOM\u70ed\u56fe+\u6a21\u5757\u6027\u72b6\u7ec4\u5408\u56fe<\/span><\/code><code>geneTree = net$dendrograms[[1<\/span>]]; ##\u63d0\u53d6\u57fa\u56e0\u805a\u7c7b<\/span><\/code><code>dissTOM = 1<\/span>-TOMsimilarityFromExpr(dataExpr1, power = 7<\/span>); ##\u8ba1\u7b97TOM\u8ddd\u79bb<\/span><\/code><code>plotTOM = dissTOM^7<\/span>; ##\u901a\u8fc77<\/span>\u6b21\u65b9\u5904\u7406\u8fdb\u884c\u6807\u51c6\u5316\uff0c\u964d\u4f4e\u57fa\u56e0\u95f4\u7684\u8bef\u5dee<\/span><\/code><code>diag(plotTOM) = NA; ##\u66ff\u6362\u659c\u5bf9\u89d2\u77e9\u9635\u4e2d\u7684\u5185\u5bb9\u4e3aNA<\/span><\/span><\/code><code>TOMplot<\/span>(plotTOM, geneTree, moduleColors, main = \"Network heatmap plot, all genes\"<\/span>)<\/span><\/span><\/code><code>nSelect <\/span>= 400<\/span> ##\u6311\u9009\u9700\u8981\u63d0\u53d6\u7684\u57fa\u56e0<\/span><\/code><code>set<\/span>.seed(10<\/span>); ##\u8bbe\u7f6e\u968f\u673a\u79cd\u5b50\u6570\uff0c\u4fdd\u8bc1\u9009\u53d6\u7684\u968f\u673a\u6027<\/span><\/code><code>select = sample(nGenes, size = nSelect); ##\u4eff5000<\/span>\u4e2a\u57fa\u56e0\u4e2d\u968f\u673a\u53ff400<\/span>\u4e3f<\/span><\/code><code>selectTOM = dissTOM[select, select]; ##\u63d0\u53d6\u6311\u51fa\u6765\u7684\u57fa\u56e0\u7684TOM\u8ddd\u79bb<\/span><\/code><code># There\u2019s no simple way of restricting a clustering tree to a subset of genes, so we must re-cluster.<\/span><\/code><code>selectTree = hclust(as.dist(selectTOM), method = \"average\"<\/span>) ##\u5bf9\u6311\u51fa\u6765\u5bf9\u57fa\u56e0TOM\u8ddd\u79bb\u91cd\u65b0\u805a\u7c7b<\/span><\/code><code>selectColors = moduleColors[select];<\/span><\/code><code># Open a graphical window<\/span><\/span><\/code><code>sizeGrWindow<\/span>(9<\/span>,9<\/span>)<\/span><\/span><\/code><code># Taking the dissimilarity to a power, say 10, makes the plot more informative by effectively changing<\/span><\/code><code># the color palette; setting the diagonal to NA also improves the clarity of the plot<\/span><\/span><\/code><code>plotDiss <\/span>= selectTOM^7<\/span>; ##\u5bf9\u6311\u9009\u51fa\u5bf9\u57fa\u56e0TOM\u8ddd\u79bb7<\/span>\u6b21\u65b9\u5904\u7406<\/span><\/code><code>diag(plotDiss) = NA; ##\u66ff\u6362\u659c\u5bf9\u89d2\u77e9\u9635\u4e2d\u7684\u5185\u5bb9\u4e3aNA<\/span><\/span><\/code><code>png<\/span>(\"step7-Network-heatmap.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/span><\/code><code>TOMplot<\/span>(plotDiss, selectTree, selectColors, ##\u7ed8\u5236TOM\u56fe<\/span><\/span><\/code><code> main = \"Network heatmap plot, selected genes\"<\/span>)<\/span><\/code><code>dev.off<\/span>()<\/span><\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/9_6B9ib0iceMgxxic5gHgpGJUh52iaOA.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnzqgnicO3saVUxz3lTy4zMa1rtM18k6B9ib0iceMgxxic5gHgpGJUh52iaOA\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code># \u6a21\u5757\u6027\u72b6\u7ec4\u5408\u56fe<\/span><\/code><code>RA = as.data.frame(design[,2<\/span>]); ##\u63d0\u53d6\u7279\u5b9a\u6027\u72b6<\/span><\/code><code>names(RA) = \"RA\"<\/span><\/span><\/code><code># Add the weight to existing module<\/span> eigengenes<\/span><\/code><code>MET = orderMEs(cbind(MEs, RA))<\/span><\/code><code># Plot the relationships among the eigengenes and<\/span> the trait<\/span><\/code><code>sizeGrWindow(5<\/span>,7.5<\/span>);<\/span><\/code><code>par(cex = 0.9<\/span>)<\/span><\/code><code>png(\"step7-Eigengene-dendrogram.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>plotEigengeneNetworks(MET, \"\"<\/span>, ##\u7ed8\u5236\u6a21\u5757\u805a\u7c7b\u56fe\u548c\u70ed\u56fe<\/span><\/code><code> marDendro =c(0<\/span>,5<\/span>,1<\/span>,5<\/span>), ##\u6811\u7c7b\u56fe\u533a\u95f4\u5927\u5c0f\u8c01\u5bbf<\/span><\/code><code> marHeatmap = c(5<\/span>,6<\/span>,1<\/span>,2<\/span>), cex.lab = 0.8<\/span>, ##\u6811\u7c7b\u56fe\u533a\u95f4\u5927\u5c0f\u8c01\u5bbf<\/span><\/code><code> xLabelsAngle = 90<\/span>) ##\u8f74\u9c9c\u827f90<\/span>\u5ebf<\/span><\/code><code>dev.off()<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/10_8Cbne6zvhZN3ia37AHDAmL42EIlXjw.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnTwTW8icJ2EGL7dlUOicTUzSqxHU8Cbne6zvhZN3ia37AHDAmL42EIlXjw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u5bfc\u51fa\u5355\u4e2a\u6a21\u5757\u7684\u7f51\u7edc\u7ed3\u679c<\/span><\/code><code>TOM = TOMsimilarityFromExpr(dataExpr1, power = 7<\/span>); ##\u9700\u8981\u8f83\u957f\u65f6\u95f4<\/span><\/code><code># \u9009\u62e9brown\u6a21\u5757<\/span><\/code><code>module<\/span> = \"brown\"<\/span>;<\/span><\/code><code># Select module<\/span> probes<\/span><\/code><code>probes = colnames(dataExpr1)<\/span><\/code><code>inModule = (moduleColors==module<\/span>);<\/span><\/code><code>## \u63d0\u53d6\u6307\u5b9a\u6a21\u5757\u7684\u57fa\u56e0\u540d<\/span><\/code><code>modProbes = probes[inModule];<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.36823104693140796\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/11_XQTKedyHIaWvFNkaZnQazibia6THYg.png?w=640\" onerror=\"this.src='\u56fe\u7247\/\u751f\u4fe1\u679c_2023-07-17_\u9ad8\u5206\u751f\u4fe1SCI-WGCNA\u5206\u6790\u590d\u73b0\/12_XQTKedyHIaWvFNkaZnQazibia6THYg.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnjtFpat6CPXne5ICibI4icAnfGf9oXQTKedyHIaWvFNkaZnQazibia6THYg\/640?wx_fmt=png''\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\" \/><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>modTOM = TOM[inModule, inModule];<\/span><\/code><code>dimnames(modTOM) = list<\/span>(modProbes, modProbes)<\/span><\/code><code>## \u6a21\u5757\u5bf9\u5e94\u7684\u57fa\u56e0\u5173\u7cfb\u77e9<\/span><\/code><code>cyt = exportNetworkToCytoscape( ##\u5bfc\u51fa\u5355\u4e2a\u6a21\u5757\u7684Cytoscape\u8f93\u5165\u6587\u4ef6<\/span><\/code><code> modTOM,<\/span><\/code><code> edgeFile = paste(\"CytoscapeInput-edges-\"<\/span>, paste(module<\/span>, collapse=\"-\"<\/span>), \".txt\"<\/span>, sep=\"\"<\/span>), ##\u8f93\u51fa\u8fb9\u6587\u4ef6\u540d<\/span><\/code><code> nodeFile = paste(\"CytoscapeInput-nodes-\"<\/span>, paste(module<\/span>, collapse=\"-\"<\/span>), \".txt\"<\/span>, sep=\"\"<\/span>), ##\u8f93\u51fa\u70b9\u6587\u4ef6\u540d<\/span><\/code><code> weighted = TRUE,<\/span><\/code><code> threshold = 0.02<\/span>,<\/span><\/code><code> nodeNames = modProbes,<\/span><\/code><code> nodeAttr = moduleColors[inModule]<\/span><\/code><code> )<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\nCytoscapeInput-edges-brown.txt\uff0c\u8fb9\u6587\u4ef6<\/span><\/p>\n <\/span><\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.36823104693140796\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/11_XQTKedyHIaWvFNkaZnQazibia6THYg.png?w=640\" onerror=\"this.src='\u56fe\u7247\/\u751f\u4fe1\u679c_2023-07-17_\u9ad8\u5206\u751f\u4fe1SCI-WGCNA\u5206\u6790\u590d\u73b0\/12_XQTKedyHIaWvFNkaZnQazibia6THYg.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnjtFpat6CPXne5ICibI4icAnfGf9oXQTKedyHIaWvFNkaZnQazibia6THYg\/640?wx_fmt=png''\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/span><\/p>\nCytoscapeInput-nodes-brown.txt,\u70b9\u6587\u4ef6<\/span><\/p>\n <\/span><\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.34115523465703973\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/13_JMcJnBuJ83wcgibuMZRpyqp8XG9nwA.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnx3j4Gm3H0djeG0ib4nIsVE7hPdJMcJnBuJ83wcgibuMZRpyqp8XG9nwA\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" 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data-recalc-dims=\"1\"><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<section style=\"display: contents;\" powered-by=\"xiumi.us\">\n<section style=\"display: contents;\">\n<section style=\"display: contents;\">\n<section style=\"height: 0px;overflow: hidden;\"> <\/span> <\/section>\n<\/section>\n<\/section>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<section style=\"letter-spacing: 0.578px;\">\n<section powered-by=\"xiumi.us\">\n<section style=\"letter-spacing: 0.578px;\">\n<section style=\"display: contents;\" powered-by=\"xiumi.us\">\n<section style=\"display: contents;\">\n<section style=\"display: contents;\">\n<section style=\"letter-spacing: 0.578px;\">\n <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"margin: 10px 0% 8px;justify-content: flex-start;display: flex;flex-flow: row nowrap;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;border-left: 3px solid rgb(219, 219, 219);border-bottom-left-radius: 0px;padding-left: 8px;align-self: flex-start;flex: 0 0 auto;\">\n<section style=\"color: rgba(0, 0, 0, 0.5);text-align: justify;font-size: 14px;\" powered-by=\"xiumi.us\">\n\u4eca\u5929\u5c0f\u679c\u7ed9\u5c0f\u4f19\u4f34\u5e26\u6765\u7684\u5206\u4eab\u662f\u5565\u5462\uff1f<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: justify;\" powered-by=\"xiumi.us\">\n <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section powered-by=\"xiumi.us\">\n\u4eca\u5929\u5c0f\u679c\u5c06\u590d\u73b0\u4e00\u7bc78+\u751f\u4fe1\u6587\u7ae0\u4e2d\u7684WGCNA\u5206\u6790\uff0c\u901a\u8fc7\u8be5\u63a8\u6587\u5c06\u638c\u63e1\u5982\u4f55\u5229\u7528\u81ea\u5df1\u7684\u6570\u636e\u8fdb\u884cWGCNA\u5206\u6790\uff0c\u6765\u8fdb\u884c\u4e0e\u8868\u578b\u6216\u8005\u75be\u75c5\u76f8\u5173\u5019\u9009\u57fa\u56e0\u7684\u6316\u6398\uff0c\u5c0f\u679c\u89c9\u5f97WGCNA\u662f\u4e00\u4e2a\u975e\u5e38\u4e0d\u9519\u7684\u5019\u9009\u57fa\u56e0\u6316\u6398\u7684\u65b9\u6cd5\uff0c\u503c\u5f97\u5c0f\u4f19\u4f34\u4eec\u8ba4\u771f\u5b66\u4e60\uff0c\u63a5\u4e0b\u6765\u8ddf\u7740\u5c0f\u679c\u5f00\u59cb\u4eca\u5929\u7684\u5206\u4eab\u5427\uff01<\/span><\/p>\n <\/span><\/em><\/strong><\/p>\n1.WGCNA\u4ecb\u7ecd<\/span><\/em><\/strong><\/p>\n\u5728\u5f00\u59cb\u5206\u6790\u4e4b\u524d\uff0c\u5c0f\u679c\u60f3\u4e3a\u5c0f\u4f19\u4f34\u7b80\u5355\u4ecb\u7ecd\u4e00\u4e0bWGCNA\uff0cWGCNA\u662f\u4e00\u79cd\u7528\u4e8e\u57fa\u56e0\u5171\u8868\u8fbe\u7684\u65b9\u6cd5\uff0c\u5b83\u53ef\u4ee5\u5c06\u9ad8\u901a\u91cf\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u8f6c\u5316\u4e3a\u4e00\u4e2a\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u4ece\u800c\u53d1\u73b0\u5177\u6709\u76f8\u4f3c\u8868\u8fbe\u6a21\u5f0f\u7684\u57fa\u56e0\u6a21\u5757\uff0c\u4ee5\u53ca\u7814\u7a76\u8fd9\u4e9b\u6a21\u5757\u4e0e\u8868\u578b\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u6700\u7ec8\u6316\u6398\u4e0e\u8868\u578b\u76f8\u5173\u7684\u5019\u9009\u57fa\u56e0\u3002\u8fd9\u5c31\u662f\u5c0f\u679c\u5bf9WGCNA\u5206\u6790\u539f\u7406\u7684\u7b80\u5355\u4ecb\u7ecd\uff0c\u662f\u4e0d\u662f\u901a\u4fd7\u6613\u61c2\u5965\uff01\u5176\u5b9eWGCNA\u5206\u6790\u5f88\u7b80\u5355\uff0c\u5c0f\u4f19\u4f34\u4e0d\u8981\u56e0\u4e3a\u5206\u6790\u6b65\u9aa4\u591a\u800c\u4e0d\u6562\u5c1d\u8bd5\uff0c\u53ea\u9700\u8981\u8f93\u5165\u57fa\u56e0\u8868\u8fbe\u77e9\u9635\u548c\u5bf9\u5e94\u6837\u672c\u8868\u578b\u6570\u636e\u6587\u4ef6\uff0c\u5c31\u53ef\u4ee5\u5b8c\u6210\u5206\u6790\uff0c\u6709\u9700\u8981\u7684\u5c0f\u4f19\u4f34\u53ef\u4ee5\u8ddf\u7740\u5c0f\u679c\u5f00\u59cb\u4eca\u5929\u7684\u5b9e\u64cd\u3002<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n2.\u51c6\u5907\u9700\u8981\u7684R\u5305<\/span><\/em><\/strong><\/p>\n#\u5b89\u88c5\u9700\u8981\u7684R\u5305<\/span><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>BiocManager::install(\"WGCNA\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n <\/p>\n#\u52a0\u8f7d\u9700\u8981\u7684R\u5305<\/span><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>library(WGCNA)<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n <\/p>\n <\/span><\/p>\n3.WGCNA\u5206\u6790<\/span><\/em><\/strong><\/p>\n#\u8bfb\u53d6\u8868\u8fbe\u77e9\u9635\uff0c\u884c\u540d\u4e3a\u57fa\u56e0\uff0c\u5217\u540d\u4e3a\u6837\u672c\u4fe1\u606f<\/span><\/p>\nexpr<-read.table(“combined.expr.txt”,header=T,sep=”t”)<\/span><\/p>\n <\/span><\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.21660649819494585\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/2_od1ePDREop1dvaREUYN3EBM1uhjqzQ.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnyFb98riajcLfEgwSoLeiaQxRbgod1ePDREop1dvaREUYN3EBM1uhjqzQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u884c\u5217\u8f6c\u7f6e<\/span><\/code><code>mydata<-expr<\/span><\/code><code>expr2<-data.frame(t(mydata))<\/span><\/code><code>#\u786e\u5b9aexpr2\u5217\u540d<\/span><\/code><code>colnames(expr2)<-rownames(mydata)<\/span><\/code><code>#\u786e\u5b9aexpr2\u884c\u540d<\/span><\/code><code>rownames(expr2)<-colnames(mydata)<\/span><\/code><code>#\u57fa\u56e0\u8fc7\u6ee4<\/span><\/code><code>dataExpr1<-expr2<\/span><\/code><code>gsg=goodSamplesGenes(dataExpr1,verbose=3<\/span>);<\/span><\/code><code>gsg$allOK<\/span><\/span><\/code><code>if<\/span> (!gsg$allOK)<\/span><\/span>{<\/span><\/code><code> # Optionally, print the gene and<\/span> sample names that were removed:<\/span><\/code><code> if<\/span> (sum(!gsg$goodGenes)>0<\/span>)<\/span><\/code><code> printFlush(paste(\"Removing genes:\"<\/span>,<\/span><\/code><code> paste(names(dataExpr)[!gsg$goodGenes], collapse = \",\"<\/span>)));<\/span><\/code><code> if<\/span> (sum(!gsg$goodSamples)>0<\/span>)<\/span><\/code><code> printFlush(paste(\"Removing samples:\"<\/span>,<\/span><\/code><code> paste(rownames(dataExpr)[!gsg$goodSamples], collapse = \",\"<\/span>)));<\/span><\/code><code> # Remove the offending genes and<\/span> samples from the data:<\/span><\/code><code> dataExpr1 = dataExpr1[gsg$goodSamples, gsg$goodGenes]<\/span><\/code><code>}<\/span><\/code><code>#\u57fa\u56e0\u6570\u76ee<\/span><\/code><code>nGenes = ncol(dataExpr1)<\/span><\/code><code>#\u6837\u672c\u6570\u76ee<\/span><\/code><code>nSamples = nrow(dataExpr1)<\/span><\/code><code>#\u5bfc\u5165\u8868\u578b\u6570\u636e\uff0c\u7b2c\u4e00\u5217\u4e3a\u6837\u672c\u4fe1\u606f\uff0c\u5176\u4ed6\u5217\u4e3a\u8868\u578b\u6570\u636e\uff0c\u9700\u8981\u6ce8\u610f\u7684\u662f\u8868\u578b\u6570\u636e\u8981\u4e0e\u6837\u672c\u6570\u636e\u4e00\u4e00\u5bf9\u5e94<\/span><\/code><code>traitData=read.table(\"TraitData.txt\"<\/span>,header=T,sep=\"t\"<\/span>,row.names=1<\/span>)<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.26534296028880866\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/3_rFJJcicFvEKXzOssuwcuEicSFAXQ6Q.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnnzCWND3KX3HduugEm4khb0CSgrrFJJcicFvEKXzOssuwcuEicSFAXQ6Q\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\" \/><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u63d0\u53d6\u8868\u8fbe\u77e9\u9635\u6837\u672c\u540d<\/span><\/code><code>fpkmSamples<-rownames(dataExpr1)<\/span><\/code><code>#\u63d0\u53d6\u8868\u578b\u6587\u4ef6\u6837\u672c\u540d<\/span><\/code><code>traitSamples<-rownames(traitData)<\/span><\/code><code>#\u8868\u8fbe\u77e9\u9635\u6837\u672c\u540d\u987a\u5e8f\u5339\u914d\u8868\u578b\u6587\u4ef6\u6837\u672c\u540d<\/span><\/code><code>traitRows<-match(fpkmSamples,traitSamples)<\/span><\/code><code>#\u83b7\u5f97\u5339\u914d\u597d\u6837\u672c\u987a\u5e8f\u7684\u8868\u578b\u6587\u4ef6<\/span><\/code><code>dataTraits<-traitData[traitRows,]<\/span><\/code><code>#\u7ed8\u5236\u6811+\u8868\u578b\u70ed\u56fe<\/span><\/code><code>sampleTree2<-hclust(dist(dataExpr1),method=\"average\"<\/span>)<\/span><\/code><code>traitColors<-numbers2colors(dataTraits,signed<\/span>=FALSE)<\/span><\/code><code>png(\"sample-subtype-cluster.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>plotDendroAndColors(sampleTree2,traitColors,groupLabels=names(dataTraits),main=\"Sample dendrogram and trait heatmap\"<\/span>,<\/span><\/code><code> cex.colorLabels=1.5<\/span>,cex.dendroLabels=1<\/span>,cex.rowText=2<\/span>)<\/span><\/code><code>dev.off()<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7504798464491362\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/4_c48VZCfOH2OGo17RWwRfQibuSRSEbw.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnS1DFzSibVRK8IwATI7XT022gI7ic48VZCfOH2OGo17RWwRfQibuSRSEbw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"521\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u8ba1\u7b97\u5408\u9002\u7684power\u503c\uff0c\u5e76\u7ed8\u5236Power\u503c\u66f2\u7ebf\u56fe<\/span><\/code><code>powers = c(c(1<\/span>:10<\/span>), seq(from = 12<\/span>, to=20<\/span>, by=2<\/span>))<\/span><\/code><code>sft = pickSoftThreshold(dataExpr1, powerVector = powers, verbose = 5<\/span>)<\/span><\/code><code>#\u8bbe\u7f6e\u7f51\u7edc\u6784\u5efa\u53c2\u6570\u9009\u62e9\u8303\u56f4\uff0c\u8ba1\u7b97\u65e0\u5c3a\u5ea6\u5206\u5e03\u62d3\u6251\u77e9\u9635<\/span><\/code><code>png(\"step2-beta-value.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>par(mfrow = c(1<\/span>,2<\/span>));<\/span><\/code><code>cex1 = 0.9<\/span>;<\/span><\/code><code> # Scale-free<\/span> topology fit index as a function of the soft-thresholding power<\/span><\/span><\/code><code>plot<\/span>(sft$fitIndices[,1<\/span>], -sign(sft$fitIndices[,3<\/span>])*sft$fitIndices[,2<\/span>],<\/span><\/span><\/code><code> xlab=\"Soft Threshold (power)\"<\/span>,ylab=\"Scale Free Topology Model Fit,signed R^2\"<\/span>,type=\"n\"<\/span>,<\/span><\/code><code> main = paste(\"Scale independence\"<\/span>))<\/span>;<\/span><\/code><code>text(sft$fitIndices[,1<\/span>], -sign(sft$fitIndices[,3<\/span>])*sft$fitIndices[,2<\/span>],<\/span><\/code><code> labels=powers,cex=cex1,col=\"red\"<\/span>);<\/span><\/code><code> # this line<\/span> corresponds to using an R^2 cut-off of h<\/span><\/span><\/code><code> abline(h=0.90<\/span>,col=\"red\"<\/span>)<\/span><\/code><code> # Mean connectivity as a function of the soft-thresholding power<\/span><\/span><\/code><code> plot<\/span>(sft$fitIndices[,1<\/span>], sft$fitIndices[,5<\/span>],<\/span><\/span><\/code><code> xlab=\"Soft Threshold (power)\"<\/span>,ylab=\"Mean Connectivity\"<\/span>, type=\"n\"<\/span>,<\/span><\/code><code> main = paste(\"Mean connectivity\"<\/span>))<\/span><\/code><code> text<\/span>(sft$fitIndices[,1<\/span>], sft$fitIndices[,5<\/span>], labels=powers, cex=cex1,col=\"red\"<\/span>)<\/span><\/span><\/code><code>dev.off<\/span>()<\/span><\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/5_ciahsmxfKlv6iccukFnSWUkWpWvmhg.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYn4NK6v1cdB0ht71PZZNDxhibHzGticiahsmxfKlv6iccukFnSWUkWpWvmhg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u4e00\u6b65\u6cd5\u6784\u5efa\u5171\u8868\u8fbe\u7f51\u7edc<\/span><\/code><code>##\u9700\u8981\u8c03\u7528WGCNA\u5305\u81ea\u5e26\u7684cor\u51fd\u6570\uff0c\u4e0d\u7136\u4f1a\u53d1\u751f\u62a5\u9519\u5965\uff01<\/span><\/code><code>cor<-WGCNA::cor<\/span><\/code><code>##\u5728\u8fdb\u884c\u5171\u8868\u8fbe\u7f51\u7edc\u6784\u5efa\u65f6\uff0cpower\u503c\u7684\u9009\u62e9\u975e\u5e38\u91cd\u8981\uff0c\u6700\u5f71\u54cd\u7ed3\u679c\u7684\u4e00\u4e2a\u53c2\u6570\uff0c\u9700\u8981\u7ecf\u8fc7\u591a\u6b21\u5c1d\u8bd5\uff0c\u624d\u80fd\u627e\u5230\u6700\u9002\u5408\u7684\u3002<\/span><\/code><code>net = blockwiseModules(dataExpr1, power = 7<\/span>, maxBlockSize = nGenes,<\/span><\/code><code> TOMType ='unsigned'<\/span>, minModuleSize = 30<\/span>,<\/span><\/code><code> reassignThreshold = 0<\/span>, mergeCutHeight = 0.25<\/span>,<\/span><\/code><code> numericLabels = TRUE, pamRespectsDendro = FALSE,<\/span><\/code><code> saveTOMs=TRUE, saveTOMFileBase = \"drought\"<\/span>,<\/span><\/code><code> verbose = 3<\/span>)<\/span><\/code><code>table(net$colors)<\/span><\/code><code>cor<-stats::cor<\/span><\/code><code>#\u7ed8\u5236\u57fa\u56e0\u805a\u7c7b\u6811\u548c\u6a21\u5757\u989c\u8272\u7ec4\u5408<\/span><\/code><code># Convert labels to colors for<\/span> plotting<\/span><\/code><code>moduleLabels = net$colors<\/span><\/code><code>moduleColors = labels2colors(moduleLabels)<\/span><\/code><code># Plot the dendrogram and<\/span> the module<\/span> colors underneath<\/span><\/code><code>png(\"step4-genes-modules.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>plotDendroAndColors(net$dendrograms[[1<\/span>]], moduleColors[net$blockGenes[[1<\/span>]]],<\/span><\/code><code> \"Module colors\"<\/span>,<\/span><\/code><code> dendroLabels = FALSE, hang = 0.03<\/span>,<\/span><\/code><code> addGuide = TRUE, guideHang = 0.05<\/span>)<\/span><\/code><code>dev.off()<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/6_ichc4Nh34qCtxiadGAqxia36t88EsQ.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnibjgIb7yTp9nPvc3PSrOopZuKMkuichc4Nh34qCtxiadGAqxia36t88EsQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u8ba1\u7b97\u6a21\u5757\u4e0e\u6027\u72b6\u95f4\u7684\u76f8\u5173\u6027\u53ca\u7ed8\u5236\u76f8\u5173\u6027\u70ed\u56fe<\/span><\/code><code>nGenes = ncol(dataExpr1)<\/span><\/code><code>nSamples = nrow(dataExpr1)<\/span><\/code><code>design=read.table(\"TraitData.txt\"<\/span>, sep = 't'<\/span>, header = T, row.names = 1<\/span>)<\/span><\/code><code># Recalculate MEs with color labels<\/span><\/code><code>MEs0 = moduleEigengenes(dataExpr1, moduleColors)$eigengenes<\/span><\/code><code>##\u4e0d\u540c\u989c\u8272\u7684\u6a21\u5757\u7684ME\u503c\u77e9 (\u6837\u672cvs\u6a21\u5757)<\/span><\/code><code>MEs = orderMEs(MEs0);<\/span><\/code><code>moduleTraitCor = cor(MEs, design , use = \"p\"<\/span>);<\/span><\/code><code>moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)<\/span><\/code><code># Will display correlations and<\/span> their p-values<\/span><\/code><code>textMatrix = paste(signif(moduleTraitCor, 2<\/span>), \"n(\"<\/span>,<\/span><\/code><code> signif(moduleTraitPvalue, 1<\/span>), \")\"<\/span>, sep = \"\"<\/span>);<\/span><\/code><code>dim(textMatrix) = dim(moduleTraitCor)<\/span><\/code><code>png(\"step5-Module-trait-relationships.png\"<\/span>,width = 800<\/span>,height = 1200<\/span>,res = 120<\/span>)<\/span><\/code><code>par(mar = c(6<\/span>, 8.5<\/span>, 3<\/span>, 3<\/span>));<\/span><\/code><code># Display the correlation values within a heatmap plot<\/span><\/span><\/code><code>labeledHeatmap<\/span>(Matrix = moduleTraitCor,<\/span><\/span><\/code><code> xLabels = colnames(design),<\/span><\/code><code> yLabels = names(MEs),<\/span><\/code><code> ySymbols = names(MEs),<\/span><\/code><code> colorLabels = FALSE,<\/span><\/code><code> colors = greenWhiteRed(50<\/span>),<\/span><\/code><code> textMatrix = textMatrix,<\/span><\/code><code> setStdMargins = FALSE,<\/span><\/code><code> cex.text = 0.5<\/span>,<\/span><\/code><code> zlim = c(-1<\/span>,1<\/span>),<\/span><\/code><code> main = paste(\"Module-trait relationships\"<\/span>))<\/span><\/code><code>dev.off<\/span>()<\/span><\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"1.5\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/7_TVg99kibYShJR8Ik72patTGyX5zL3Q.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnqDTia7ygRk9ECttmNy0Rbibr5MPTVg99kibYShJR8Ik72patTGyX5zL3Q\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"488\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u8ba1\u7b97MM\u503c\u548cGS\u503c\u5e76\u7ed8\u56fe<\/span><\/code><code>##\u5207\u5272\uff0c\u4ece\u7b2c\u4e09\u4e2a\u5b57\u7b26\u5f00\u59cb\u4fdd\u5b58<\/span><\/code><code>modNames = substring(names(MEs), 3<\/span>)<\/span><\/code><code>geneModuleMembership = as.data.frame(cor(dataExpr1, MEs, use = \"p\"<\/span>));<\/span><\/code><code>## \u7b97\u51fa\u6bcf\u4e2a\u6a21\u5757\u8ddf\u57fa\u56e0\u7684\u76ae\u5c14\u68ee\u76f8\u5173\u7cfb\u6570\u77e9<\/span><\/code><code>## MEs\u662f\u6bcf\u4e2a\u6a21\u5757\u5728\u6bcf\u4e2a\u6837\u672c\u91cc\u9762\u76bf<\/span><\/code><code>## dataExpr1\u662f\u6bcf\u4e2a\u57fa\u56e0\u5728\u6bcf\u4e2a\u6837\u672c\u7684\u8868\u8fbe\u91cf<\/span><\/code><code>MMPvalue = as.data.frame(<\/span><\/code><code> corPvalueStudent(as.matrix(geneModuleMembership), nSamples) ##\u8ba1\u7b97MM\u503c\u5bf9\u5e94\u7684P\u503c<\/span><\/code><code> );<\/span><\/code><code>names(geneModuleMembership) = paste(\"MM\"<\/span>, modNames, sep=\"\"<\/span>); ##\u7ed9MM\u5bf9\u8c61\u7edf\u4e00\u8d4b\u540d<\/span><\/code><code>names(MMPvalue) = paste(\"p.MM\"<\/span>, modNames, sep=\"\"<\/span>); ##\u7ed9MMPvalue\u5bf9\u8c61\u7edf\u4e00\u8d4b\u540d<\/span><\/code><code>##\u8ba1\u7b97\u57fa\u56e0\u4e0e\u6bcf\u4e2a\u6027\u72b6\u7684\u663e\u8457\u6027\uff08\u76f8\u5173\u6027\uff09\u53capvalue\u503c<\/span><\/code><code>geneTraitSignificance = as.data.frame(cor(dataExpr1, design, use = \"p\"<\/span>));<\/span><\/code><code>GSPvalue = as.data.frame(<\/span><\/code><code> corPvalueStudent(as.matrix(geneTraitSignificance), nSamples) ##\u8ba1\u7b97GS\u503c\u5bf9\u5e94\u7684pvalue\u503c<\/span><\/code><code> );<\/span><\/code><code>names(geneTraitSignificance) = paste(\"GS.\"<\/span>, colnames(design), sep=\"\"<\/span>);<\/span><\/code><code>names(GSPvalue) = paste(\"p.GS.\"<\/span>, colnames(design), sep=\"\"<\/span>);<\/span><\/code><code>module<\/span> = \"brown\"<\/span><\/span><\/code><code>column = match(module<\/span>, modNames); ##\u5728\u6240\u6709\u6a21\u5757\u4e2d\u5339\u914d\u9009\u62e9\u7684\u6a21\u5757\uff0c\u8fd4\u56de\u6240\u5728\u7684\u4f4d\u7f6e<\/span><\/code><code>moduleGenes = moduleColors==module<\/span>; ##==\u5339\u914d\uff0c\u5339\u914d\u4e0a\u7684\u8fd4\u56deTrue\uff0c\u5426\u5219\u8fd4\u56defalse<\/span><\/span><\/code><code>png(\"step6-Module_membership-gene_significance.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>par(mfrow = c(1<\/span>,1<\/span>)); ##\u8bbe\u5b9a\u8f93\u51fa\u56fe\u7247\u884c\u5217\u6570\u91cf\uff0c\uff081<\/span>\uff3f1<\/span>\uff09\u4ee3\u8868\u884c\u4e00\u4e2a\uff0c\u5217\u4e00\u4e3f<\/span><\/code><code>verboseScatterplot(abs<\/span>(geneModuleMembership[moduleGenes, column]), ## \u7ed8\u5236MM\u548cGS\u6563\u70b9\u56ff<\/span><\/code><code> abs<\/span>(geneTraitSignificance[moduleGenes, 1<\/span>]),<\/span><\/code><code> xlab = paste(\"Module Membership in\"<\/span>, module<\/span>, \"module\"<\/span>),<\/span><\/code><code> ylab = \"Gene significance for Basal\"<\/span>,<\/span><\/code><code> main = paste(\"Module membership vs. gene significancen\"<\/span>),<\/span><\/code><code> cex.main = 1.2<\/span>, cex.lab = 1.2<\/span>, cex.axis = 1.2<\/span>, col = module<\/span>)<\/span><\/code><code>dev.off()<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/8_qUNj7fVVExR62r5ibjvbIicpkqlVyQ.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnW9N5cIYGHBLoKUkBMGHmhAaDk6qUNj7fVVExR62r5ibjvbIicpkqlVyQ\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u7ed8\u5236TOM\u70ed\u56fe+\u6a21\u5757\u6027\u72b6\u7ec4\u5408\u56fe<\/span><\/code><code>geneTree = net$dendrograms[[1<\/span>]]; ##\u63d0\u53d6\u57fa\u56e0\u805a\u7c7b<\/span><\/code><code>dissTOM = 1<\/span>-TOMsimilarityFromExpr(dataExpr1, power = 7<\/span>); ##\u8ba1\u7b97TOM\u8ddd\u79bb<\/span><\/code><code>plotTOM = dissTOM^7<\/span>; ##\u901a\u8fc77<\/span>\u6b21\u65b9\u5904\u7406\u8fdb\u884c\u6807\u51c6\u5316\uff0c\u964d\u4f4e\u57fa\u56e0\u95f4\u7684\u8bef\u5dee<\/span><\/code><code>diag(plotTOM) = NA; ##\u66ff\u6362\u659c\u5bf9\u89d2\u77e9\u9635\u4e2d\u7684\u5185\u5bb9\u4e3aNA<\/span><\/span><\/code><code>TOMplot<\/span>(plotTOM, geneTree, moduleColors, main = \"Network heatmap plot, all genes\"<\/span>)<\/span><\/span><\/code><code>nSelect <\/span>= 400<\/span> ##\u6311\u9009\u9700\u8981\u63d0\u53d6\u7684\u57fa\u56e0<\/span><\/code><code>set<\/span>.seed(10<\/span>); ##\u8bbe\u7f6e\u968f\u673a\u79cd\u5b50\u6570\uff0c\u4fdd\u8bc1\u9009\u53d6\u7684\u968f\u673a\u6027<\/span><\/code><code>select = sample(nGenes, size = nSelect); ##\u4eff5000<\/span>\u4e2a\u57fa\u56e0\u4e2d\u968f\u673a\u53ff400<\/span>\u4e3f<\/span><\/code><code>selectTOM = dissTOM[select, select]; ##\u63d0\u53d6\u6311\u51fa\u6765\u7684\u57fa\u56e0\u7684TOM\u8ddd\u79bb<\/span><\/code><code># There\u2019s no simple way of restricting a clustering tree to a subset of genes, so we must re-cluster.<\/span><\/code><code>selectTree = hclust(as.dist(selectTOM), method = \"average\"<\/span>) ##\u5bf9\u6311\u51fa\u6765\u5bf9\u57fa\u56e0TOM\u8ddd\u79bb\u91cd\u65b0\u805a\u7c7b<\/span><\/code><code>selectColors = moduleColors[select];<\/span><\/code><code># Open a graphical window<\/span><\/span><\/code><code>sizeGrWindow<\/span>(9<\/span>,9<\/span>)<\/span><\/span><\/code><code># Taking the dissimilarity to a power, say 10, makes the plot more informative by effectively changing<\/span><\/code><code># the color palette; setting the diagonal to NA also improves the clarity of the plot<\/span><\/span><\/code><code>plotDiss <\/span>= selectTOM^7<\/span>; ##\u5bf9\u6311\u9009\u51fa\u5bf9\u57fa\u56e0TOM\u8ddd\u79bb7<\/span>\u6b21\u65b9\u5904\u7406<\/span><\/code><code>diag(plotDiss) = NA; ##\u66ff\u6362\u659c\u5bf9\u89d2\u77e9\u9635\u4e2d\u7684\u5185\u5bb9\u4e3aNA<\/span><\/span><\/code><code>png<\/span>(\"step7-Network-heatmap.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/span><\/code><code>TOMplot<\/span>(plotDiss, selectTree, selectColors, ##\u7ed8\u5236TOM\u56fe<\/span><\/span><\/code><code> main = \"Network heatmap plot, selected genes\"<\/span>)<\/span><\/code><code>dev.off<\/span>()<\/span><\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/9_6B9ib0iceMgxxic5gHgpGJUh52iaOA.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnzqgnicO3saVUxz3lTy4zMa1rtM18k6B9ib0iceMgxxic5gHgpGJUh52iaOA\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code># \u6a21\u5757\u6027\u72b6\u7ec4\u5408\u56fe<\/span><\/code><code>RA = as.data.frame(design[,2<\/span>]); ##\u63d0\u53d6\u7279\u5b9a\u6027\u72b6<\/span><\/code><code>names(RA) = \"RA\"<\/span><\/span><\/code><code># Add the weight to existing module<\/span> eigengenes<\/span><\/code><code>MET = orderMEs(cbind(MEs, RA))<\/span><\/code><code># Plot the relationships among the eigengenes and<\/span> the trait<\/span><\/code><code>sizeGrWindow(5<\/span>,7.5<\/span>);<\/span><\/code><code>par(cex = 0.9<\/span>)<\/span><\/code><code>png(\"step7-Eigengene-dendrogram.png\"<\/span>,width = 800<\/span>,height = 600<\/span>)<\/span><\/code><code>plotEigengeneNetworks(MET, \"\"<\/span>, ##\u7ed8\u5236\u6a21\u5757\u805a\u7c7b\u56fe\u548c\u70ed\u56fe<\/span><\/code><code> marDendro =c(0<\/span>,5<\/span>,1<\/span>,5<\/span>), ##\u6811\u7c7b\u56fe\u533a\u95f4\u5927\u5c0f\u8c01\u5bbf<\/span><\/code><code> marHeatmap = c(5<\/span>,6<\/span>,1<\/span>,2<\/span>), cex.lab = 0.8<\/span>, ##\u6811\u7c7b\u56fe\u533a\u95f4\u5927\u5c0f\u8c01\u5bbf<\/span><\/code><code> xLabelsAngle = 90<\/span>) ##\u8f74\u9c9c\u827f90<\/span>\u5ebf<\/span><\/code><code>dev.off()<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.7509025270758123\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/10_8Cbne6zvhZN3ia37AHDAmL42EIlXjw.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnTwTW8icJ2EGL7dlUOicTUzSqxHU8Cbne6zvhZN3ia37AHDAmL42EIlXjw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>#\u5bfc\u51fa\u5355\u4e2a\u6a21\u5757\u7684\u7f51\u7edc\u7ed3\u679c<\/span><\/code><code>TOM = TOMsimilarityFromExpr(dataExpr1, power = 7<\/span>); ##\u9700\u8981\u8f83\u957f\u65f6\u95f4<\/span><\/code><code># \u9009\u62e9brown\u6a21\u5757<\/span><\/code><code>module<\/span> = \"brown\"<\/span>;<\/span><\/code><code># Select module<\/span> probes<\/span><\/code><code>probes = colnames(dataExpr1)<\/span><\/code><code>inModule = (moduleColors==module<\/span>);<\/span><\/code><code>## \u63d0\u53d6\u6307\u5b9a\u6a21\u5757\u7684\u57fa\u56e0\u540d<\/span><\/code><code>modProbes = probes[inModule];<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.36823104693140796\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/11_XQTKedyHIaWvFNkaZnQazibia6THYg.png?w=640\" onerror=\"this.src='\u56fe\u7247\/\u751f\u4fe1\u679c_2023-07-17_\u9ad8\u5206\u751f\u4fe1SCI-WGCNA\u5206\u6790\u590d\u73b0\/12_XQTKedyHIaWvFNkaZnQazibia6THYg.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnjtFpat6CPXne5ICibI4icAnfGf9oXQTKedyHIaWvFNkaZnQazibia6THYg\/640?wx_fmt=png''\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\" \/><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"c\"><code>modTOM = TOM[inModule, inModule];<\/span><\/code><code>dimnames(modTOM) = list<\/span>(modProbes, modProbes)<\/span><\/code><code>## \u6a21\u5757\u5bf9\u5e94\u7684\u57fa\u56e0\u5173\u7cfb\u77e9<\/span><\/code><code>cyt = exportNetworkToCytoscape( ##\u5bfc\u51fa\u5355\u4e2a\u6a21\u5757\u7684Cytoscape\u8f93\u5165\u6587\u4ef6<\/span><\/code><code> modTOM,<\/span><\/code><code> edgeFile = paste(\"CytoscapeInput-edges-\"<\/span>, paste(module<\/span>, collapse=\"-\"<\/span>), \".txt\"<\/span>, sep=\"\"<\/span>), ##\u8f93\u51fa\u8fb9\u6587\u4ef6\u540d<\/span><\/code><code> nodeFile = paste(\"CytoscapeInput-nodes-\"<\/span>, paste(module<\/span>, collapse=\"-\"<\/span>), \".txt\"<\/span>, sep=\"\"<\/span>), ##\u8f93\u51fa\u70b9\u6587\u4ef6\u540d<\/span><\/code><code> weighted = TRUE,<\/span><\/code><code> threshold = 0.02<\/span>,<\/span><\/code><code> nodeNames = modProbes,<\/span><\/code><code> nodeAttr = moduleColors[inModule]<\/span><\/code><code> )<\/span><\/code><\/pre>\n<\/section>\n<section powered-by=\"xiumi.us\">\nCytoscapeInput-edges-brown.txt\uff0c\u8fb9\u6587\u4ef6<\/span><\/p>\n <\/span><\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.36823104693140796\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/11_XQTKedyHIaWvFNkaZnQazibia6THYg.png?w=640\" onerror=\"this.src='\u56fe\u7247\/\u751f\u4fe1\u679c_2023-07-17_\u9ad8\u5206\u751f\u4fe1SCI-WGCNA\u5206\u6790\u590d\u73b0\/12_XQTKedyHIaWvFNkaZnQazibia6THYg.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnjtFpat6CPXne5ICibI4icAnfGf9oXQTKedyHIaWvFNkaZnQazibia6THYg\/640?wx_fmt=png''\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/span><\/p>\nCytoscapeInput-nodes-brown.txt,\u70b9\u6587\u4ef6<\/span><\/p>\n <\/span><\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.34115523465703973\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/13_JMcJnBuJ83wcgibuMZRpyqp8XG9nwA.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAyL6u7wJswOp8vtpzGp6vYnx3j4Gm3H0djeG0ib4nIsVE7hPdJMcJnBuJ83wcgibuMZRpyqp8XG9nwA\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"554\" style=\"\" data-recalc-dims=\"1\"><\/p>\n<\/span><\/p>\n\u6700\u7ec8\u5c0f\u679c\u6210\u529f\u5b8c\u6210\u4e86WGCNA\u5206\u6790\uff0c\u57fa\u672c\u8fbe\u5230\u4e86\u590d\u73b0\uff0c\u4eca\u5929\u5c0f\u679c\u7684\u5206\u4eab\u5c31\u5230\u8fd9\u91cc\u5566\uff01\u5982\u679c\u5c0f\u4f19\u4f34\u6709\u5176\u4ed6\u6570\u636e\u5206\u6790\u9700\u6c42\uff0c\u53ef\u4ee5\u5c1d\u8bd5\u672c\u516c\u53f8\u65b0\u5f00\u53d1\u7684\u751f\u4fe1\u5206\u6790\u5c0f\u5de5\u5177\u4e91\u5e73\u53f0\uff0c\u96f6\u4ee3\u7801\u5b8c\u6210\u5206\u6790\uff0c\u975e\u5e38\u65b9\u4fbf\u5965\uff0c\u4e91\u5e73\u53f0\u7f51\u5740\u4e3a\uff1ahttp:\/\/www.biocloudservice.com\/home.html,\u5305\u62ec\u4e86WGCNA\u5206\u6790(http:\/\/www.biocloudservice.com\/336\/336.php),GEO\u6570\u636e\u4e0b\u8f7d(http:\/\/www.biocloudservice.com\/371\/371.php)\u7b49\u5c0f\u5de5\u5177\uff0c\u6b22\u8fce\u5927\u5bb6\u548c\u5c0f\u679c\u4e00\u8d77\u8ba8\u8bba\u5b66\u4e60\u54c8\uff01\uff01\uff01\uff01<\/span><\/strong><\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-cropselx1=\"0\" data-cropselx2=\"578\" data-cropsely1=\"0\" data-cropsely2=\"322\" data-galleryid=\"\" data-ratio=\"0.7193195625759417\" data-s=\"300,640\" src=\"https:\/\/i0.wp.com\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/14_oRxs73HBHUoxCtAqBPQnq1AXricRvg.png?w=640\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/sz_mmbiz_png\/zcicibWZessAxenwVZiaibuOTpj6MMDUj2LoPdS6tic40bbGQV7QyM6QOQY8EFoRxs73HBHUoxCtAqBPQnq1AXricRvg\/640?wx_fmt=png'\" data-type=\"jpeg\" data-w=\"823\" style=\"width: 578px;height: 416px;\" data-recalc-dims=\"1\"><\/p>\n<\/span><\/strong><\/p>\n<section style=\"margin-bottom: 0px;letter-spacing: 0.578px;white-space: normal;caret-color: rgba(0, 0, 0, 0);font-size: 16px;\">\n<section>\n<section style=\"margin-top: 0.5em;\">\n<section style=\"padding: 0.5em;border-width: 1px;border-style: solid;box-shadow: rgb(226, 226, 226) 0px 16px 1px -13px;border-color: rgb(0, 150, 136);\">\n<section style=\"text-align: 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