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15px;\">\n1\uff09\u6570\u636e\u9884\u5904\u7406\uff0c\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u6807\u51c6\u5316\u548c\u9009\u62e9\u611f\u5174\u8da3\u7684\u57fa\u56e0\u96c6<\/span><\/p>\n2\uff09\u6784\u5efa\u76f8\u5173\u7cfb\u6570\u77e9\u9635\uff0c\u8ba1\u7b97\u57fa\u56e0\u95f4\u7684\u76f8\u5173\u6027<\/span><\/p>\n3\uff09\u6784\u5efa\u57fa\u56e0\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u901a\u8fc7\u9009\u62e9\u5408\u9002\u7684\u76f8\u4f3c\u6027\u5ea6\u91cf\u548c\u8fde\u63a5\u9608\u503c<\/span><\/p>\n4\uff09\u6a21\u5757\u53d1\u73b0\uff0c\u5c06\u9ad8\u5ea6\u76f8\u5173\u7684\u57fa\u56e0\u805a\u5408\u6210\u5171\u8868\u8fbe\u6a21\u5757<\/span><\/p>\n5\uff09\u6a21\u5757\u7684\u529f\u80fd\u6ce8\u91ca\u548c\u8c03\u63a7\u57fa\u56e0\u7684\u8bc6\u522b<\/span><\/p>\n6\uff09\u7f51\u7edc\u53ef\u89c6\u5316\u548c\u529f\u80fd\u5206\u6790<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n <\/p>\n\u4e0b\u9762\u6211\u4eec\u4ee5\u809d\u764c\u6570\u636e\u4e3a\u4f8b\u8fdb\u884c\u5206\u6790\uff1a<\/p>\n\u6ce8\uff1a\u8fd9\u91cc\u7684”LiverFemale3600.csv”,\u201dClinicalTraits.csv\u201d\u662f\u81ea\u884c\u51c6\u5907\u7684\u672c\u5730\u6587\u4ef6\uff0c\u5c0f\u82b1\u7ed9\u5927\u5bb6\u9644\u5728\u6700\u540e\u3002<\/p>\n <\/p>\nStep1 \u6570\u636e\u8f93\u5165\u3001\u6e05\u6d17\u548c\u9884\u5904\u7406<\/span><\/strong><\/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<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=\"r\"><code># 1.1 \u8f7d\u5165\u6570\u636e<\/span><\/span><\/code><code>workingDir = \"D:\/wanglab\/life\/ziyuan\/WGCNA\/\"<\/span>;<\/span><\/code><code>setwd(workingDir);<\/span><\/code><code># Load the WGCNA package<\/span><\/span><\/code><code>library(WGCNA);<\/span><\/code><code># The following setting is important, do not omit.<\/span><\/span><\/code><code>options(stringsAsFactors = FALSE<\/span>);<\/span><\/code><code>#Read in the female liver data set<\/span><\/span><\/code><code>femData = read.csv(\"LiverFemale3600.csv\"<\/span>);<\/span><\/code><code># Take a quick look at what is in the data set:<\/span><\/span><\/code><code>dim<\/span>(femData);<\/span><\/code><code>names<\/span>(femData);<\/span><\/code><code>#1.2\u521b\u5efa\u884c\u4e3a\u6837\u672c\uff0c\u5217\u4e3a\u57fa\u56e0\u7684\u8868\u8fbe\u77e9\u9635<\/span><\/span><\/code><code>datExpr0 = as.data.frame(t(femData[, -c<\/span>(1<\/span>:8<\/span>)]));<\/span><\/code><code>names<\/span>(datExpr0) = femData$substanceBXH;<\/span><\/code><code>rownames(datExpr0) = names<\/span>(femData)[-c<\/span>(1<\/span>:8<\/span>)];<\/span><\/code><code>### 1.3\u5224\u65ad\u6570\u636e\u8d28\u91cf--\u7f3a\u5931\u503c<\/span><\/span><\/code><code>gsg = goodSamplesGenes(datExpr0, verbose = 3<\/span>);<\/span><\/code><code>gsg$allOK<\/span><\/code><code>if<\/span> (!gsg$allOK)<\/span><\/code><code>{<\/span><\/code><code># Optionally, print the gene and sample names that were removed:<\/span><\/span><\/code><code>if<\/span> (sum<\/span>(!gsg$goodGenes)>0<\/span>)<\/span><\/code><code>printFlush(paste(\"Removing genes:\"<\/span>, paste(names<\/span>(datExpr0)[!gsg$goodGenes], collapse = \", \"<\/span>)));<\/span><\/code><code>if<\/span> (sum<\/span>(!gsg$goodSamples)>0<\/span>)<\/span><\/code><code>printFlush(paste(\"Removing samples:\"<\/span>, paste(rownames(datExpr0)[!gsg$goodSamples], collapse = \", \"<\/span>)));<\/span><\/code><code># Remove the offending genes and samples from the data:<\/span><\/span><\/code><code>datExpr0 = datExpr0[gsg$goodSamples, gsg$goodGenes]<\/span><\/code><code>}<\/span><\/code><code>### 1.4\u7ed8\u5236\u6837\u54c1\u7684\u7cfb\u7edf\u805a\u7c7b\u6811<\/span><\/span><\/code><code>sampleTree = hclust(dist(datExpr0), method = \"average\"<\/span>);<\/span><\/code><code># Plot the sample tree: Open a graphic output window of size 12 by 9 inches<\/span><\/span><\/code><code># The user should change the dimensions if the window is too large or too small.<\/span><\/span><\/code><code>sizeGrWindow(12<\/span>,9<\/span>)<\/span><\/code><code>#pdf(file = \"Plots\/sampleClustering.pdf\", width = 12, height = 9);<\/span><\/span><\/code><code>par(cex = 0.6<\/span>);<\/span><\/code><code>par(mar = c<\/span>(0<\/span>,4<\/span>,2<\/span>,0<\/span>))<\/span><\/code><code>plot(sampleTree, main = \"Sample clustering to detect outliers\"<\/span>, sub=\"\"<\/span>, xlab=\"\"<\/span>, cex.lab = 1.5<\/span>,<\/span><\/code><code>cex.axis = 1.5<\/span>, cex.main = 2<\/span>)<\/span><\/code><code># Plot a line to show the cut<\/span><\/span><\/code><code>abline(h = 15<\/span>, col = \"red\"<\/span>);<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u7ed8\u5236\u6837\u672c\u6811\u805a\u7c7b\u6811<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.7481481481481481\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/1_2icG64aY8iagZDSGRfUrt5eaHmWz8A.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IEe4pr2TFFRxib8NJymzqsV8BJ24ia2icG64aY8iagZDSGRfUrt5eaHmWz8A\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\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<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>## 1.5\u82e5\u5b58\u5728\u663e\u8457\u79bb\u7fa4\u70b9\uff1b\u5254\u9664\u6389<\/span><\/span><\/code><code># Determine cluster under the line<\/span><\/span><\/code><code>clust = cutreeStatic(sampleTree, cutHeight = 15<\/span>, minSize = 10<\/span>)<\/span><\/code><code>table(clust)<\/span><\/code><code># clust 1 contains the samples we want to keep.<\/span><\/span><\/code><code>keepSamples = (clust==1<\/span>)<\/span><\/code><code>datExpr = datExpr0[keepSamples, ]<\/span><\/code><code>nGenes = ncol(datExpr)<\/span><\/code><code>nSamples = nrow(datExpr)<\/span><\/code><code>rownames(datExpr0)[!keepSamples]<\/span><\/code><code>### 1.6\u8bfb\u5165\u4e34\u5e8a\u8868\u578b\u6570\u636e<\/span><\/span><\/code><code>traitData = read.csv(\"ClinicalTraits.csv\"<\/span>);<\/span><\/code><code>dim<\/span>(traitData)<\/span><\/code><code>names<\/span>(traitData)<\/span><\/code><code># remove columns that hold information we do not need.<\/span><\/span><\/code><code>allTraits = traitData[, -c<\/span>(31<\/span>, 16<\/span>)];<\/span><\/code><code>allTraits = allTraits[, c<\/span>(2<\/span>, 11<\/span>:36<\/span>) ];<\/span><\/code><code>dim<\/span>(allTraits)<\/span><\/code><code>names<\/span>(allTraits)<\/span><\/code><code># Form a data frame analogous to expression data that will hold the clinical traits.<\/span><\/span><\/code><code>femaleSamples = rownames(datExpr);<\/span><\/code><code>traitRows = match(femaleSamples, allTraits$Mice);<\/span><\/code><code>datTraits = allTraits[traitRows, -1<\/span>];<\/span><\/code><code>rownames(datTraits) = allTraits[traitRows, 1<\/span>];<\/span><\/code><code>collectGarbage();<\/span><\/code><code>### 1.7\u518d\u6b21\u5bf9\u5220\u6389\u79bb\u7fa4\u503c\u7684\u6837\u672c\u8fdb\u884c\u805a\u7c7b<\/span><\/span><\/code><code># Re-cluster samples<\/span><\/span><\/code><code>sampleTree2 = hclust(dist(datExpr), method = \"average\"<\/span>)<\/span><\/code><code># Convert traits to a color representation: white means low, red means high, grey means missing entry<\/span><\/span><\/code><code>traitColors = numbers2colors(datTraits, signed = FALSE<\/span>);<\/span><\/code><code># Plot the sample dendrogram and the colors underneath.<\/span><\/span><\/code><code>plotDendroAndColors(sampleTree2, traitColors,<\/span><\/code><code>groupLabels = names<\/span>(datTraits),<\/span><\/code><code>main = \"Sample dendrogram and trait heatmap\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u7ed8\u5236\u6837\u672c\u6811\u805a\u7c7b\u6811\u548c\u8868\u578b\u70ed\u56fe<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.5194444444444445\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/2_VdK0pziaDFQngvVRSMgWMZGHK2ZKnw.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IEKvYtUMyy6jqPKBbk9ibJvibdkibhVdK0pziaDFQngvVRSMgWMZGHK2ZKnw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\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<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>### 1.8\u4fdd\u5b58\u8868\u578b\u548c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u4ee5\u4fbf\u540e\u7eed\u5206\u6790<\/span><\/span><\/code><code>save(datExpr, datTraits, file = \"FemaleLiver-01-dataInput.RData\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\nStep2\u7f51\u7edc\u642d\u5efa\u53ca\u6a21\u5757\u68c0\u6d4b<\/span><\/strong> <\/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<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>### 2.1 \u6311\u9009\u6700\u4f73\u9608\u503cpower<\/span><\/span><\/code><code># Load the data saved in the first part<\/span><\/span><\/code><code>lnames = load(file = \"FemaleLiver-01-dataInput.RData\"<\/span>);<\/span><\/code><code>#The variable lnames contains the names of loaded variables.<\/span><\/span><\/code><code>lnames<\/span><\/code><code># Choose a set of soft-thresholding powers<\/span><\/span><\/code><code>powers = c<\/span>(c<\/span>(1<\/span>:10<\/span>), seq(from = 12<\/span>, to=20<\/span>, by=2<\/span>))<\/span><\/code><code># Call the network topology analysis function<\/span><\/span><\/code><code>sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5<\/span>)<\/span><\/code><code># Plot the results:<\/span><\/span><\/code><code>sizeGrWindow(9<\/span>, 5<\/span>)<\/span><\/code><code>par(mfrow = c<\/span>(1<\/span>,2<\/span>));<\/span><\/code><code>cex1 = 0.9<\/span>;<\/span><\/code><code># Scale-free topology fit index as a function of the soft-thresholding power<\/span><\/span><\/code><code>plot(sft$fitIndices[,1<\/span>], -sign<\/span>(sft$fitIndices[,3<\/span>])*sft$fitIndices[,2<\/span>],<\/span><\/code><code>xlab=\"Soft Threshold (power)\"<\/span>,ylab=\"Scale Free Topology Model Fit,signed R^2\"<\/span>,type=\"n\"<\/span>,<\/span><\/code><code>main = paste(\"Scale independence\"<\/span>));<\/span><\/code><code>text(sft$fitIndices[,1<\/span>], -sign<\/span>(sft$fitIndices[,3<\/span>])*sft$fitIndices[,2<\/span>],<\/span><\/code><code>labels=powers,cex=cex1,col=\"red\"<\/span>);<\/span><\/code><code># this line 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(sft$fitIndices[,1<\/span>], sft$fitIndices[,5<\/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(sft$fitIndices[,1<\/span>], sft$fitIndices[,5<\/span>], labels=powers, cex=cex1,col=\"red\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u6311\u9009\u6700\u4f73\u9608\u503cpower<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.5514018691588785\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/3_iaK6V1ulR4z3zP0wicz4hsCXRxGhsw.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IEoG8CDDeNQkanh7AzG2duZeF74PiaK6V1ulR4z3zP0wicz4hsCXRxGhsw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"856\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\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<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>power = sft$powerEstimate<\/span><\/code><code>sft$powerEstimate<\/span><\/code><code># \u82e5\u65e0\u5411\u7f51\u7edc\u5728power\u5c0f\u4e8e15\u6216\u6709\u5411\u7f51\u7edcpower\u5c0f\u4e8e30\u5185\uff0c\u6ca1\u6709\u4e00\u4e2apower\u503c\u4f7f<\/span><\/span><\/code><code># \u65e0\u6807\u5ea6\u7f51\u7edc\u56fe\u8c31\u7ed3\u6784R^2\u8fbe\u52300.8\u4e14\u5e73\u5747\u8fde\u63a5\u5ea6\u5728100\u4ee5\u4e0b\uff0c\u53ef\u80fd\u662f\u7531\u4e8e<\/span><\/span><\/code><code># \u90e8\u5206\u6837\u54c1\u4e0e\u5176\u4ed6\u6837\u54c1\u5dee\u522b\u592a\u5927\u3002\u8fd9\u53ef\u80fd\u7531\u6279\u6b21\u6548\u5e94\u3001\u6837\u54c1\u5f02\u8d28\u6027\u6216\u5b9e\u9a8c\u6761\u4ef6\u5bf9<\/span><\/span><\/code><code># \u8868\u8fbe\u5f71\u54cd\u592a\u5927\u7b49\u9020\u6210\u3002\u53ef\u4ee5\u901a\u8fc7\u7ed8\u5236\u6837\u54c1\u805a\u7c7b\u67e5\u770b\u5206\u7ec4\u4fe1\u606f\u548c\u6709\u65e0\u5f02\u5e38\u6837\u54c1\u3002<\/span><\/span><\/code><code># \u5982\u679c\u8fd9\u786e\u5b9e\u662f\u7531\u6709\u610f\u4e49\u7684\u751f\u7269\u53d8\u5316\u5f15\u8d77\u7684\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u4e0b\u9762\u7684\u7ecf\u9a8cpower\u503c\u3002<\/span><\/span><\/code><code>if<\/span>(is.na<\/span>(power)){<\/span><\/code><code># \u5b98\u65b9\u63a8\u8350 \"signed\" \u6216 \"signed hybrid\"<\/span><\/span><\/code><code># \u4e3a\u4e0e\u539f\u6587\u6863\u4e00\u81f4\uff0c\u6545\u672a\u4fee\u6539<\/span><\/span><\/code><code>type = \"unsigned\"<\/span><\/span><\/code><code>nSamples=nrow(datExpr)<\/span><\/code><code>power = ifelse(nSamples<20<\/span>, ifelse(type == \"unsigned\"<\/span>, 9<\/span>, 18<\/span>),<\/span><\/code><code>ifelse(nSamples<30<\/span>, ifelse(type == \"unsigned\"<\/span>, 8<\/span>, 16<\/span>),<\/span><\/code><code>ifelse(nSamples<40<\/span>, ifelse(type == \"unsigned\"<\/span>, 7<\/span>, 14<\/span>),<\/span><\/code><code>ifelse(type == \"unsigned\"<\/span>, 6<\/span>, 12<\/span>))<\/span><\/code><code>)<\/span><\/code><code>)<\/span><\/code><code>}<\/span><\/code><code>### 2.2\u4e00\u6b65\u6cd5\u6784\u5efa\u52a0\u6743\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u8bc6\u522b\u57fa\u56e0\u6a21\u5757<\/span><\/span><\/code><code>net = blockwiseModules(datExpr, power = power,<\/span><\/code><code>TOMType = \"unsigned\"<\/span>, minModuleSize = 30<\/span>,<\/span><\/code><code>reassignThreshold = 0<\/span>, mergeCutHeight = 0.25<\/span>,<\/span><\/code><code>numericLabels = TRUE<\/span>, pamRespectsDendro = FALSE<\/span>,<\/span><\/code><code>saveTOMs = TRUE<\/span>,<\/span><\/code><code>saveTOMFileBase = \"femaleMouseTOM\"<\/span>,<\/span><\/code><code>verbose = 3<\/span>)<\/span><\/code><code>### 2.4\u6a21\u5757\u53ef\u89c6\u5316\uff0c\u5c42\u7ea7\u805a\u7c7b\u6811\u5c55\u793a\u5404\u4e2a\u6a21\u5757<\/span><\/span><\/code><code>sizeGrWindow(12<\/span>, 9<\/span>)<\/span><\/code><code># Convert labels to colors for plotting<\/span><\/span><\/code><code>mergedColors = labels2colors(net$colors)<\/span><\/code><code># Plot the dendrogram and the module colors underneath<\/span><\/span><\/code><code>plotDendroAndColors(net$dendrograms[[1<\/span>]], mergedColors[net$blockGenes[[1<\/span>]]],<\/span><\/code><code>\"Module colors\"<\/span>,<\/span><\/code><code>dendroLabels = FALSE<\/span>, hang = 0.03<\/span>,<\/span><\/code><code>addGuide = TRUE<\/span>, guideHang = 0.05<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u6a21\u5757\u5c42\u7ea7\u805a\u7c7b\u6811<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.7481481481481481\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/4_tBvMS6BJjFQREnRC88XXVUPJ0qShUg.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IEReicV3RQuXqcuva70D0qB8YVttBvMS6BJjFQREnRC88XXVUPJ0qShUg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\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<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>### 2.5\u4fdd\u5b58\u7ed3\u679c<\/span><\/span><\/code><code>moduleLabels = net$colors<\/span><\/code><code>moduleColors = labels2colors(net$colors)<\/span><\/code><code>MEs = net$MEs;<\/span><\/code><code>geneTree = net$dendrograms[[1<\/span>]];<\/span><\/code><code>save(MEs, moduleLabels, moduleColors, geneTree,<\/span><\/code><code>file = \"FemaleLiver-02-networkConstruction-auto.RData\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\nStep3 \u6a21\u5757\u4e0e\u5916\u90e8\u4e34\u5e8a\u7279\u5f81\u90fd\u76f8\u5173\u5173\u7cfb<\/span><\/strong><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>## 3.1\u6570\u636e\u51c6\u5907<\/span><\/span><\/code><code>load(file = \"FemaleLiver-01-dataInput.RData\"<\/span>);<\/span><\/code><code>load(file = \"FemaleLiver-02-networkConstruction-auto.RData\"<\/span>);<\/span><\/code><code># Define numbers of genes and samples<\/span><\/span><\/code><code>nGenes = ncol(datExpr);<\/span><\/code><code>nSamples = nrow(datExpr);<\/span><\/code><code># Recalculate MEs with color labels<\/span><\/span><\/code><code>MEs0 = moduleEigengenes(datExpr, moduleColors)$eigengenes<\/span><\/code><code>MEs = orderMEs(MEs0)<\/span><\/code><code>moduleTraitCor = cor(MEs, datTraits, use = \"p\"<\/span>);<\/span><\/code><code>moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples);<\/span><\/code><code>## 3.2\u6a21\u5757\u4e0e\u8868\u578b\u7684\u76f8\u5173\u6027\u70ed\u56fe<\/span><\/span><\/code><code>sizeGrWindow(10<\/span>,6<\/span>)<\/span><\/code><code># Will display correlations and their p-values<\/span><\/span><\/code><code>textMatrix = paste(signif<\/span>(moduleTraitCor, 2<\/span>), \"n(\"<\/span>,<\/span><\/code><code>signif<\/span>(moduleTraitPvalue, 1<\/span>), \")\"<\/span>, sep = \"\"<\/span>);<\/span><\/code><code>dim<\/span>(textMatrix) = dim<\/span>(moduleTraitCor)<\/span><\/code><code>par(mar = c<\/span>(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(Matrix = moduleTraitCor,<\/span><\/code><code>xLabels = names<\/span>(datTraits),<\/span><\/code><code>yLabels = names<\/span>(MEs),<\/span><\/code><code>ySymbols = names<\/span>(MEs),<\/span><\/code><code>colorLabels = FALSE<\/span>,<\/span><\/code><code>colors = greenWhiteRed(50<\/span>),<\/span><\/code><code>textMatrix = textMatrix,<\/span><\/code><code>setStdMargins = FALSE<\/span>,<\/span><\/code><code>cex.text = 0.5<\/span>,<\/span><\/code><code>zlim = c<\/span>(-1<\/span>,1<\/span>),<\/span><\/code><code>main = paste(\"Module-trait relationships\"<\/span>))<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u6a21\u5757\u4e0e\u8868\u578b\u7684\u76f8\u5173\u6027\u70ed\u56fe<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.5966386554621849\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/5_CcfvY2x0dvOibygfCZfNicx7K8kokg.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IExiczIUblDcfQkc4CDz0PYBcbQK7CcfvY2x0dvOibygfCZfNicx7K8kokg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"952\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\n<\/section>\n <\/p>\n<section style=\"justify-content: flex-start;display: flex;flex-flow: row;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;border-width: 1px;border-radius: 8px;border-style: dotted;border-color: rgb(0, 0, 0);overflow: hidden;align-self: flex-start;flex: 0 0 auto;\">\n<section style=\"margin: 15px 0%;\" powered-by=\"xiumi.us\">\n<section style=\"color: rgb(62, 62, 62);padding-right: 15px;padding-left: 15px;\">\n\u6ce8\uff1a<\/span><\/p>\n# \u8bf7\u786e\u4fdd\u5c06\u8def\u5f84”LiverFemale3600.csv”,\u201dClinicalTraits.csv\u201d\u66ff\u6362\u4e3a\u5b9e\u9645\u6570\u636e\u96c6\u6587\u4ef6\u7684\u8def\u5f84\uff0c\u5e76\u6839\u636e\u9700\u8981\u8c03\u6574\u6a21\u578b\u53c2\u6570\u548c\u5176\u4ed6\u914d\u7f6e\u3002<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 98%;vertical-align: top;align-self: flex-start;flex: 0 0 auto;height: auto;border-style: solid;border-width: 2px;border-color: rgb(160, 160, 160);padding: 27px;\">\n<section style=\"text-align: justify;line-height: 1.6;letter-spacing: 0px;padding-right: 6px;padding-left: 6px;\" powered-by=\"xiumi.us\">\n\u5173\u4e8eWGCNA\u5206\u6790\uff0c\u5c0f\u82b1\u5f3a\u70c8\u63a8\u8350\u5927\u5bb6\u4f7f\u7528\u4e91\u751f\u4fe1\u5e73\u53f0\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\u8fdb\u884cWGCNA\u5206\u6790\u4ee5\u53ca\u5176\u4ed6\u751f\u4fe1\u5206\u6790\u4efb\u52a1\u3002\u4e91\u751f\u4fe1\u5e73\u53f0\u662f\u4e00\u4e2a\u5f3a\u5927\u800c\u6709\u8da3\u7684\u5728\u7ebf\u5de5\u5177\uff0c\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u7b80\u5355\u3001\u5feb\u901f\u548c\u53ef\u89c6\u5316\u7684\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u4f53\u9a8c\u3002<\/span><\/p>\n <\/p>\n\u4f7f\u7528\u4e91\u751f\u4fe1\u5e73\u53f0\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u4e0a\u4f20\u548c\u5904\u7406\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u8fdb\u884cWGCNA\u5206\u6790\uff0c\u5e76\u83b7\u5f97\u8be6\u7ec6\u7684\u7ed3\u679c\u548c\u56fe\u8868\u5c55\u793a\u3002\u4f60\u53ef\u4ee5\u63a2\u7d22\u57fa\u56e0\u4e4b\u95f4\u7684\u5171\u8868\u8fbe\u7f51\u7edc\u3001\u53d1\u73b0\u5173\u952e\u6a21\u5757\u3001\u5206\u6790\u6a21\u5757\u4e0e\u4e34\u5e8a\u7279\u5f81\u7684\u76f8\u5173\u6027\u7b49\u3002\u540c\u65f6\uff0c\u4e91\u751f\u4fe1\u5e73\u53f0\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u5176\u4ed6\u751f\u4fe1\u5206\u6790\u5de5\u5177\u548c\u529f\u80fd\uff0c\u5982\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u3001\u529f\u80fd\u5bcc\u96c6\u5206\u6790\u3001\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u5206\u6790\u7b49\uff0c\u5e2e\u52a9\u4f60\u66f4\u5168\u9762\u5730\u7406\u89e3\u548c\u89e3\u91ca\u4f60\u7684\u6570\u636e\u3002<\/p>\n\u4f7f\u7528\u4e91\u751f\u4fe1\u5e73\u53f0\u8fdb\u884cWGCNA\u5206\u6790\u4e0d\u4ec5\u65b9\u4fbf\u5feb\u6377\uff0c\u8fd8\u80fd\u591f\u8282\u7701\u5927\u91cf\u7684\u8ba1\u7b97\u8d44\u6e90\u548c\u65f6\u95f4\u3002\u4f60\u53ef\u4ee5\u968f\u65f6\u968f\u5730\u8bbf\u95ee\u5e73\u53f0\uff0c\u65e0\u9700\u5b89\u88c5\u4efb\u4f55\u8f6f\u4ef6\uff0c\u76f4\u63a5\u5728\u6d4f\u89c8\u5668\u4e2d\u8fdb\u884c\u5206\u6790\u3002\u5e73\u53f0\u63d0\u4f9b\u53cb\u597d\u7684\u7528\u6237\u754c\u9762\u548c\u4ea4\u4e92\u5f0f\u64cd\u4f5c\uff0c\u4f7f\u5f97\u590d\u6742\u7684\u5206\u6790\u53d8\u5f97\u7b80\u5355\u6613\u61c2\u3002\u65e0\u8bba\u4f60\u662f\u751f\u7269\u5b66\u7814\u7a76\u8005\u3001\u751f\u7269\u4fe1\u606f\u5b66\u5bb6\u8fd8\u662f\u5b66\u751f\uff0c\u4e91\u751f\u4fe1\u5e73\u53f0\u90fd\u80fd\u6ee1\u8db3\u4f60\u7684\u5206\u6790\u9700\u6c42\uff0c\u5e76\u5e2e\u52a9\u4f60\u63a2\u7d22\u6570\u636e\u80cc\u540e\u7684\u5965\u79d8\u3002\u6240\u4ee5\uff0c\u8d76\u5feb\u6765\u4f53\u9a8c\u4e91\u751f\u4fe1\u5e73\u53f0\u5427\uff01\u8ba9\u751f\u4fe1\u5206\u6790\u53d8\u5f97\u66f4\u6709\u8da3\u3001\u66f4\u9ad8\u6548\uff01<\/p>\n<\/section>\n<section style=\"text-align: right;justify-content: flex-end;display: flex;flex-flow: row;transform: translate3d(40px, 0px, 0px);margin-bottom: -40px;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: auto;vertical-align: top;align-self: flex-start;flex: 0 0 auto;min-width: 5%;height: auto;\">\n<section style=\"transform: rotateZ(45deg);\" powered-by=\"xiumi.us\">\n<section style=\"text-align: center;\">\n<section style=\"display: inline-block;width: 26px;height: 26px;vertical-align: top;overflow: hidden;background-color: rgb(255, 255, 255);border-left: 1px solid rgb(160, 160, 160);border-bottom-left-radius: 0px;\">\n<section style=\"text-align: justify;\" powered-by=\"xiumi.us\">\n <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<a target=\"_blank\" href=\"https:\/\/mp.weixin.qq.com\/s?__biz=MzAwNjE0MDY3MQ==&mid=2650761303&idx=2&sn=6f73471d1e82c56c0b2160d0fcd32d53&scene=21#wechat_redirect\" textvalue=\"\" 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powered-by=\"xiumi.us\">\n\u5f15\u8a00\uff1a<\/strong><\/span><\/p>\n\u968f\u7740\u9ad8\u901a\u91cf\u6d4b\u5e8f\u6280\u672f\u7684\u5feb\u901f\u53d1\u5c55\uff0c\u6211\u4eec\u80fd\u591f\u83b7\u53d6\u5927\u91cf\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u8fd9\u4e3a\u6211\u4eec\u6df1\u5165\u7406\u89e3\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u63d0\u4f9b\u4e86\u5de8\u5927\u7684\u673a\u4f1a\u3002\u7136\u800c\uff0c\u5982\u4f55\u4ece\u8fd9\u4e9b\u6d77\u91cf\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u610f\u4e49\u7684\u4fe1\u606f\u4ecd\u7136\u662f\u4e00\u4e2a\u6311\u6218\u3002\u5728\u8fd9\u65b9\u9762\uff0cWGCNA\u6210\u4e3a\u4e86\u7814\u7a76\u4eba\u5458\u7684\u5f97\u529b\u5de5\u5177\uff0c\u5e2e\u52a9\u6211\u4eec\u63ed\u793a\u57fa\u56e0\u5171\u8868\u8fbe\u6a21\u5f0f\u3001\u53d1\u73b0\u5173\u952e\u8c03\u63a7\u57fa\u56e0\u4ee5\u53ca\u4e86\u89e3\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u7684\u529f\u80fd\u3002<\/p>\n <\/p>\nWGCNA\u539f\u7406\uff1a<\/strong><\/span><\/p>\nWGCNA\u57fa\u4e8e\u57fa\u56e0\u5171\u8868\u8fbe\u6a21\u5f0f\uff0c\u5373\u5177\u6709\u76f8\u4f3c\u8868\u8fbe\u6a21\u5f0f\u7684\u57fa\u56e0\u503e\u5411\u4e8e\u5728\u751f\u7269\u5b66\u529f\u80fd\u548c\u8c03\u63a7\u7f51\u7edc\u4e2d\u5177\u6709\u76f8\u4f3c\u7684\u89d2\u8272\u3002\u8be5\u65b9\u6cd5\u901a\u8fc7\u6784\u5efa\u57fa\u56e0\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u5c06\u9ad8\u5ea6\u76f8\u5173\u7684\u57fa\u56e0\u805a\u5408\u6210\u5171\u8868\u8fbe\u6a21\u5757\uff0c\u5e76\u901a\u8fc7\u6a21\u5757\u95f4\u548c\u6a21\u5757\u5185\u7684\u8fde\u63a5\u5f3a\u5ea6\u6765\u9274\u5b9a\u5173\u952e\u57fa\u56e0\u3002WGCNA\u8fd8\u4f7f\u7528\u7cfb\u7edf\u751f\u7269\u5b66\u548c\u7f51\u7edc\u7406\u8bba\u7684\u539f\u7406\u6765\u63ed\u793a\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u7684\u529f\u80fd\u548c\u673a\u5236\u3002<\/p>\n <\/p>\n\u5e94\u7528\u9886\u57df\uff1a<\/strong><\/span><\/p>\nWGCNA\u5e7f\u6cdb\u5e94\u7528\u4e8e\u751f\u7269\u5b66\u7814\u7a76\u7684\u591a\u4e2a\u9886\u57df\u3002\u5728\u764c\u75c7\u7814\u7a76\u4e2d\uff0c\u7814\u7a76\u4eba\u5458\u5229\u7528WGCNA\u5206\u6790\u80bf\u7624\u7ec4\u7ec7\u548c\u6b63\u5e38\u7ec4\u7ec7\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u53d1\u73b0\u4e86\u4e0e\u764c\u75c7\u76f8\u5173\u7684\u5171\u8868\u8fbe\u6a21\u5757\u548c\u5173\u952e\u8c03\u63a7\u57fa\u56e0\u3002\u5728\u690d\u7269\u7814\u7a76\u4e2d\uff0cWGCNA\u5e2e\u52a9\u63ed\u793a\u4e86\u690d\u7269\u53d1\u80b2\u548c\u6297\u9006\u6027\u7684\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u3002\u6b64\u5916\uff0cWGCNA\u5728\u795e\u7ecf\u79d1\u5b66\u3001\u514d\u75ab\u5b66\u3001\u4ee3\u8c22\u7ec4\u5b66\u7b49\u9886\u57df\u4e5f\u6709\u5e7f\u6cdb\u5e94\u7528\u3002<\/p>\n <\/p>\n\u5206\u6790\u6b65\u9aa4\uff1a<\/span><\/strong><\/p>\n\u4f7f\u7528WGCNA\u8fdb\u884c\u57fa\u56e0\u5171\u8868\u8fbe\u7f51\u7edc\u5206\u6790\u901a\u5e38\u5305\u62ec\u4ee5\u4e0b\u6b65\u9aa4\uff1a<\/p>\n<\/section>\n<section style=\"justify-content: flex-start;display: flex;flex-flow: row;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;border-width: 1px;border-radius: 8px;border-style: dotted;border-color: rgb(0, 0, 0);overflow: hidden;align-self: flex-start;flex: 0 0 auto;\">\n<section style=\"margin: 15px 0%;\" powered-by=\"xiumi.us\">\n<section style=\"color: rgb(62, 62, 62);padding-right: 15px;padding-left: 15px;\">\n1\uff09\u6570\u636e\u9884\u5904\u7406\uff0c\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u6807\u51c6\u5316\u548c\u9009\u62e9\u611f\u5174\u8da3\u7684\u57fa\u56e0\u96c6<\/span><\/p>\n2\uff09\u6784\u5efa\u76f8\u5173\u7cfb\u6570\u77e9\u9635\uff0c\u8ba1\u7b97\u57fa\u56e0\u95f4\u7684\u76f8\u5173\u6027<\/span><\/p>\n3\uff09\u6784\u5efa\u57fa\u56e0\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u901a\u8fc7\u9009\u62e9\u5408\u9002\u7684\u76f8\u4f3c\u6027\u5ea6\u91cf\u548c\u8fde\u63a5\u9608\u503c<\/span><\/p>\n4\uff09\u6a21\u5757\u53d1\u73b0\uff0c\u5c06\u9ad8\u5ea6\u76f8\u5173\u7684\u57fa\u56e0\u805a\u5408\u6210\u5171\u8868\u8fbe\u6a21\u5757<\/span><\/p>\n5\uff09\u6a21\u5757\u7684\u529f\u80fd\u6ce8\u91ca\u548c\u8c03\u63a7\u57fa\u56e0\u7684\u8bc6\u522b<\/span><\/p>\n6\uff09\u7f51\u7edc\u53ef\u89c6\u5316\u548c\u529f\u80fd\u5206\u6790<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n <\/p>\n\u4e0b\u9762\u6211\u4eec\u4ee5\u809d\u764c\u6570\u636e\u4e3a\u4f8b\u8fdb\u884c\u5206\u6790\uff1a<\/p>\n\u6ce8\uff1a\u8fd9\u91cc\u7684”LiverFemale3600.csv”,\u201dClinicalTraits.csv\u201d\u662f\u81ea\u884c\u51c6\u5907\u7684\u672c\u5730\u6587\u4ef6\uff0c\u5c0f\u82b1\u7ed9\u5927\u5bb6\u9644\u5728\u6700\u540e\u3002<\/p>\n <\/p>\nStep1 \u6570\u636e\u8f93\u5165\u3001\u6e05\u6d17\u548c\u9884\u5904\u7406<\/span><\/strong><\/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<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=\"r\"><code># 1.1 \u8f7d\u5165\u6570\u636e<\/span><\/span><\/code><code>workingDir = \"D:\/wanglab\/life\/ziyuan\/WGCNA\/\"<\/span>;<\/span><\/code><code>setwd(workingDir);<\/span><\/code><code># Load the WGCNA package<\/span><\/span><\/code><code>library(WGCNA);<\/span><\/code><code># The following setting is important, do not omit.<\/span><\/span><\/code><code>options(stringsAsFactors = FALSE<\/span>);<\/span><\/code><code>#Read in the female liver data set<\/span><\/span><\/code><code>femData = read.csv(\"LiverFemale3600.csv\"<\/span>);<\/span><\/code><code># Take a quick look at what is in the data set:<\/span><\/span><\/code><code>dim<\/span>(femData);<\/span><\/code><code>names<\/span>(femData);<\/span><\/code><code>#1.2\u521b\u5efa\u884c\u4e3a\u6837\u672c\uff0c\u5217\u4e3a\u57fa\u56e0\u7684\u8868\u8fbe\u77e9\u9635<\/span><\/span><\/code><code>datExpr0 = as.data.frame(t(femData[, -c<\/span>(1<\/span>:8<\/span>)]));<\/span><\/code><code>names<\/span>(datExpr0) = femData$substanceBXH;<\/span><\/code><code>rownames(datExpr0) = names<\/span>(femData)[-c<\/span>(1<\/span>:8<\/span>)];<\/span><\/code><code>### 1.3\u5224\u65ad\u6570\u636e\u8d28\u91cf--\u7f3a\u5931\u503c<\/span><\/span><\/code><code>gsg = goodSamplesGenes(datExpr0, verbose = 3<\/span>);<\/span><\/code><code>gsg$allOK<\/span><\/code><code>if<\/span> (!gsg$allOK)<\/span><\/code><code>{<\/span><\/code><code># Optionally, print the gene and sample names that were removed:<\/span><\/span><\/code><code>if<\/span> (sum<\/span>(!gsg$goodGenes)>0<\/span>)<\/span><\/code><code>printFlush(paste(\"Removing genes:\"<\/span>, paste(names<\/span>(datExpr0)[!gsg$goodGenes], collapse = \", \"<\/span>)));<\/span><\/code><code>if<\/span> (sum<\/span>(!gsg$goodSamples)>0<\/span>)<\/span><\/code><code>printFlush(paste(\"Removing samples:\"<\/span>, paste(rownames(datExpr0)[!gsg$goodSamples], collapse = \", \"<\/span>)));<\/span><\/code><code># Remove the offending genes and samples from the data:<\/span><\/span><\/code><code>datExpr0 = datExpr0[gsg$goodSamples, gsg$goodGenes]<\/span><\/code><code>}<\/span><\/code><code>### 1.4\u7ed8\u5236\u6837\u54c1\u7684\u7cfb\u7edf\u805a\u7c7b\u6811<\/span><\/span><\/code><code>sampleTree = hclust(dist(datExpr0), method = \"average\"<\/span>);<\/span><\/code><code># Plot the sample tree: Open a graphic output window of size 12 by 9 inches<\/span><\/span><\/code><code># The user should change the dimensions if the window is too large or too small.<\/span><\/span><\/code><code>sizeGrWindow(12<\/span>,9<\/span>)<\/span><\/code><code>#pdf(file = \"Plots\/sampleClustering.pdf\", width = 12, height = 9);<\/span><\/span><\/code><code>par(cex = 0.6<\/span>);<\/span><\/code><code>par(mar = c<\/span>(0<\/span>,4<\/span>,2<\/span>,0<\/span>))<\/span><\/code><code>plot(sampleTree, main = \"Sample clustering to detect outliers\"<\/span>, sub=\"\"<\/span>, xlab=\"\"<\/span>, cex.lab = 1.5<\/span>,<\/span><\/code><code>cex.axis = 1.5<\/span>, cex.main = 2<\/span>)<\/span><\/code><code># Plot a line to show the cut<\/span><\/span><\/code><code>abline(h = 15<\/span>, col = \"red\"<\/span>);<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u7ed8\u5236\u6837\u672c\u6811\u805a\u7c7b\u6811<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.7481481481481481\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/1_2icG64aY8iagZDSGRfUrt5eaHmWz8A.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IEe4pr2TFFRxib8NJymzqsV8BJ24ia2icG64aY8iagZDSGRfUrt5eaHmWz8A\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\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<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>## 1.5\u82e5\u5b58\u5728\u663e\u8457\u79bb\u7fa4\u70b9\uff1b\u5254\u9664\u6389<\/span><\/span><\/code><code># Determine cluster under the line<\/span><\/span><\/code><code>clust = cutreeStatic(sampleTree, cutHeight = 15<\/span>, minSize = 10<\/span>)<\/span><\/code><code>table(clust)<\/span><\/code><code># clust 1 contains the samples we want to keep.<\/span><\/span><\/code><code>keepSamples = (clust==1<\/span>)<\/span><\/code><code>datExpr = datExpr0[keepSamples, ]<\/span><\/code><code>nGenes = ncol(datExpr)<\/span><\/code><code>nSamples = nrow(datExpr)<\/span><\/code><code>rownames(datExpr0)[!keepSamples]<\/span><\/code><code>### 1.6\u8bfb\u5165\u4e34\u5e8a\u8868\u578b\u6570\u636e<\/span><\/span><\/code><code>traitData = read.csv(\"ClinicalTraits.csv\"<\/span>);<\/span><\/code><code>dim<\/span>(traitData)<\/span><\/code><code>names<\/span>(traitData)<\/span><\/code><code># remove columns that hold information we do not need.<\/span><\/span><\/code><code>allTraits = traitData[, -c<\/span>(31<\/span>, 16<\/span>)];<\/span><\/code><code>allTraits = allTraits[, c<\/span>(2<\/span>, 11<\/span>:36<\/span>) ];<\/span><\/code><code>dim<\/span>(allTraits)<\/span><\/code><code>names<\/span>(allTraits)<\/span><\/code><code># Form a data frame analogous to expression data that will hold the clinical traits.<\/span><\/span><\/code><code>femaleSamples = rownames(datExpr);<\/span><\/code><code>traitRows = match(femaleSamples, allTraits$Mice);<\/span><\/code><code>datTraits = allTraits[traitRows, -1<\/span>];<\/span><\/code><code>rownames(datTraits) = allTraits[traitRows, 1<\/span>];<\/span><\/code><code>collectGarbage();<\/span><\/code><code>### 1.7\u518d\u6b21\u5bf9\u5220\u6389\u79bb\u7fa4\u503c\u7684\u6837\u672c\u8fdb\u884c\u805a\u7c7b<\/span><\/span><\/code><code># Re-cluster samples<\/span><\/span><\/code><code>sampleTree2 = hclust(dist(datExpr), method = \"average\"<\/span>)<\/span><\/code><code># Convert traits to a color representation: white means low, red means high, grey means missing entry<\/span><\/span><\/code><code>traitColors = numbers2colors(datTraits, signed = FALSE<\/span>);<\/span><\/code><code># Plot the sample dendrogram and the colors underneath.<\/span><\/span><\/code><code>plotDendroAndColors(sampleTree2, traitColors,<\/span><\/code><code>groupLabels = names<\/span>(datTraits),<\/span><\/code><code>main = \"Sample dendrogram and trait heatmap\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u7ed8\u5236\u6837\u672c\u6811\u805a\u7c7b\u6811\u548c\u8868\u578b\u70ed\u56fe<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.5194444444444445\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/2_VdK0pziaDFQngvVRSMgWMZGHK2ZKnw.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IEKvYtUMyy6jqPKBbk9ibJvibdkibhVdK0pziaDFQngvVRSMgWMZGHK2ZKnw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\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<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>### 1.8\u4fdd\u5b58\u8868\u578b\u548c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u4ee5\u4fbf\u540e\u7eed\u5206\u6790<\/span><\/span><\/code><code>save(datExpr, datTraits, file = \"FemaleLiver-01-dataInput.RData\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\nStep2\u7f51\u7edc\u642d\u5efa\u53ca\u6a21\u5757\u68c0\u6d4b<\/span><\/strong> <\/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<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>### 2.1 \u6311\u9009\u6700\u4f73\u9608\u503cpower<\/span><\/span><\/code><code># Load the data saved in the first part<\/span><\/span><\/code><code>lnames = load(file = \"FemaleLiver-01-dataInput.RData\"<\/span>);<\/span><\/code><code>#The variable lnames contains the names of loaded variables.<\/span><\/span><\/code><code>lnames<\/span><\/code><code># Choose a set of soft-thresholding powers<\/span><\/span><\/code><code>powers = c<\/span>(c<\/span>(1<\/span>:10<\/span>), seq(from = 12<\/span>, to=20<\/span>, by=2<\/span>))<\/span><\/code><code># Call the network topology analysis function<\/span><\/span><\/code><code>sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5<\/span>)<\/span><\/code><code># Plot the results:<\/span><\/span><\/code><code>sizeGrWindow(9<\/span>, 5<\/span>)<\/span><\/code><code>par(mfrow = c<\/span>(1<\/span>,2<\/span>));<\/span><\/code><code>cex1 = 0.9<\/span>;<\/span><\/code><code># Scale-free topology fit index as a function of the soft-thresholding power<\/span><\/span><\/code><code>plot(sft$fitIndices[,1<\/span>], -sign<\/span>(sft$fitIndices[,3<\/span>])*sft$fitIndices[,2<\/span>],<\/span><\/code><code>xlab=\"Soft Threshold (power)\"<\/span>,ylab=\"Scale Free Topology Model Fit,signed R^2\"<\/span>,type=\"n\"<\/span>,<\/span><\/code><code>main = paste(\"Scale independence\"<\/span>));<\/span><\/code><code>text(sft$fitIndices[,1<\/span>], -sign<\/span>(sft$fitIndices[,3<\/span>])*sft$fitIndices[,2<\/span>],<\/span><\/code><code>labels=powers,cex=cex1,col=\"red\"<\/span>);<\/span><\/code><code># this line 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(sft$fitIndices[,1<\/span>], sft$fitIndices[,5<\/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(sft$fitIndices[,1<\/span>], sft$fitIndices[,5<\/span>], labels=powers, cex=cex1,col=\"red\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u6311\u9009\u6700\u4f73\u9608\u503cpower<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.5514018691588785\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/3_iaK6V1ulR4z3zP0wicz4hsCXRxGhsw.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IEoG8CDDeNQkanh7AzG2duZeF74PiaK6V1ulR4z3zP0wicz4hsCXRxGhsw\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"856\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\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<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>power = sft$powerEstimate<\/span><\/code><code>sft$powerEstimate<\/span><\/code><code># \u82e5\u65e0\u5411\u7f51\u7edc\u5728power\u5c0f\u4e8e15\u6216\u6709\u5411\u7f51\u7edcpower\u5c0f\u4e8e30\u5185\uff0c\u6ca1\u6709\u4e00\u4e2apower\u503c\u4f7f<\/span><\/span><\/code><code># \u65e0\u6807\u5ea6\u7f51\u7edc\u56fe\u8c31\u7ed3\u6784R^2\u8fbe\u52300.8\u4e14\u5e73\u5747\u8fde\u63a5\u5ea6\u5728100\u4ee5\u4e0b\uff0c\u53ef\u80fd\u662f\u7531\u4e8e<\/span><\/span><\/code><code># \u90e8\u5206\u6837\u54c1\u4e0e\u5176\u4ed6\u6837\u54c1\u5dee\u522b\u592a\u5927\u3002\u8fd9\u53ef\u80fd\u7531\u6279\u6b21\u6548\u5e94\u3001\u6837\u54c1\u5f02\u8d28\u6027\u6216\u5b9e\u9a8c\u6761\u4ef6\u5bf9<\/span><\/span><\/code><code># \u8868\u8fbe\u5f71\u54cd\u592a\u5927\u7b49\u9020\u6210\u3002\u53ef\u4ee5\u901a\u8fc7\u7ed8\u5236\u6837\u54c1\u805a\u7c7b\u67e5\u770b\u5206\u7ec4\u4fe1\u606f\u548c\u6709\u65e0\u5f02\u5e38\u6837\u54c1\u3002<\/span><\/span><\/code><code># \u5982\u679c\u8fd9\u786e\u5b9e\u662f\u7531\u6709\u610f\u4e49\u7684\u751f\u7269\u53d8\u5316\u5f15\u8d77\u7684\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u4e0b\u9762\u7684\u7ecf\u9a8cpower\u503c\u3002<\/span><\/span><\/code><code>if<\/span>(is.na<\/span>(power)){<\/span><\/code><code># \u5b98\u65b9\u63a8\u8350 \"signed\" \u6216 \"signed hybrid\"<\/span><\/span><\/code><code># \u4e3a\u4e0e\u539f\u6587\u6863\u4e00\u81f4\uff0c\u6545\u672a\u4fee\u6539<\/span><\/span><\/code><code>type = \"unsigned\"<\/span><\/span><\/code><code>nSamples=nrow(datExpr)<\/span><\/code><code>power = ifelse(nSamples<20<\/span>, ifelse(type == \"unsigned\"<\/span>, 9<\/span>, 18<\/span>),<\/span><\/code><code>ifelse(nSamples<30<\/span>, ifelse(type == \"unsigned\"<\/span>, 8<\/span>, 16<\/span>),<\/span><\/code><code>ifelse(nSamples<40<\/span>, ifelse(type == \"unsigned\"<\/span>, 7<\/span>, 14<\/span>),<\/span><\/code><code>ifelse(type == \"unsigned\"<\/span>, 6<\/span>, 12<\/span>))<\/span><\/code><code>)<\/span><\/code><code>)<\/span><\/code><code>}<\/span><\/code><code>### 2.2\u4e00\u6b65\u6cd5\u6784\u5efa\u52a0\u6743\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u8bc6\u522b\u57fa\u56e0\u6a21\u5757<\/span><\/span><\/code><code>net = blockwiseModules(datExpr, power = power,<\/span><\/code><code>TOMType = \"unsigned\"<\/span>, minModuleSize = 30<\/span>,<\/span><\/code><code>reassignThreshold = 0<\/span>, mergeCutHeight = 0.25<\/span>,<\/span><\/code><code>numericLabels = TRUE<\/span>, pamRespectsDendro = FALSE<\/span>,<\/span><\/code><code>saveTOMs = TRUE<\/span>,<\/span><\/code><code>saveTOMFileBase = \"femaleMouseTOM\"<\/span>,<\/span><\/code><code>verbose = 3<\/span>)<\/span><\/code><code>### 2.4\u6a21\u5757\u53ef\u89c6\u5316\uff0c\u5c42\u7ea7\u805a\u7c7b\u6811\u5c55\u793a\u5404\u4e2a\u6a21\u5757<\/span><\/span><\/code><code>sizeGrWindow(12<\/span>, 9<\/span>)<\/span><\/code><code># Convert labels to colors for plotting<\/span><\/span><\/code><code>mergedColors = labels2colors(net$colors)<\/span><\/code><code># Plot the dendrogram and the module colors underneath<\/span><\/span><\/code><code>plotDendroAndColors(net$dendrograms[[1<\/span>]], mergedColors[net$blockGenes[[1<\/span>]]],<\/span><\/code><code>\"Module colors\"<\/span>,<\/span><\/code><code>dendroLabels = FALSE<\/span>, hang = 0.03<\/span>,<\/span><\/code><code>addGuide = TRUE<\/span>, guideHang = 0.05<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u6a21\u5757\u5c42\u7ea7\u805a\u7c7b\u6811<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.7481481481481481\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/4_tBvMS6BJjFQREnRC88XXVUPJ0qShUg.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IEReicV3RQuXqcuva70D0qB8YVttBvMS6BJjFQREnRC88XXVUPJ0qShUg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"1080\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\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<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>### 2.5\u4fdd\u5b58\u7ed3\u679c<\/span><\/span><\/code><code>moduleLabels = net$colors<\/span><\/code><code>moduleColors = labels2colors(net$colors)<\/span><\/code><code>MEs = net$MEs;<\/span><\/code><code>geneTree = net$dendrograms[[1<\/span>]];<\/span><\/code><code>save(MEs, moduleLabels, moduleColors, geneTree,<\/span><\/code><code>file = \"FemaleLiver-02-networkConstruction-auto.RData\"<\/span>)<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\nStep3 \u6a21\u5757\u4e0e\u5916\u90e8\u4e34\u5e8a\u7279\u5f81\u90fd\u76f8\u5173\u5173\u7cfb<\/span><\/strong><\/p>\n<\/section>\n<section class=\"code-snippet__fix code-snippet__js\">\n<ul class=\"code-snippet__line-index code-snippet__js\">\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<li><\/li>\n<\/ul>\n<pre class=\"code-snippet__js\" data-lang=\"r\"><code>## 3.1\u6570\u636e\u51c6\u5907<\/span><\/span><\/code><code>load(file = \"FemaleLiver-01-dataInput.RData\"<\/span>);<\/span><\/code><code>load(file = \"FemaleLiver-02-networkConstruction-auto.RData\"<\/span>);<\/span><\/code><code># Define numbers of genes and samples<\/span><\/span><\/code><code>nGenes = ncol(datExpr);<\/span><\/code><code>nSamples = nrow(datExpr);<\/span><\/code><code># Recalculate MEs with color labels<\/span><\/span><\/code><code>MEs0 = moduleEigengenes(datExpr, moduleColors)$eigengenes<\/span><\/code><code>MEs = orderMEs(MEs0)<\/span><\/code><code>moduleTraitCor = cor(MEs, datTraits, use = \"p\"<\/span>);<\/span><\/code><code>moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples);<\/span><\/code><code>## 3.2\u6a21\u5757\u4e0e\u8868\u578b\u7684\u76f8\u5173\u6027\u70ed\u56fe<\/span><\/span><\/code><code>sizeGrWindow(10<\/span>,6<\/span>)<\/span><\/code><code># Will display correlations and their p-values<\/span><\/span><\/code><code>textMatrix = paste(signif<\/span>(moduleTraitCor, 2<\/span>), \"n(\"<\/span>,<\/span><\/code><code>signif<\/span>(moduleTraitPvalue, 1<\/span>), \")\"<\/span>, sep = \"\"<\/span>);<\/span><\/code><code>dim<\/span>(textMatrix) = dim<\/span>(moduleTraitCor)<\/span><\/code><code>par(mar = c<\/span>(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(Matrix = moduleTraitCor,<\/span><\/code><code>xLabels = names<\/span>(datTraits),<\/span><\/code><code>yLabels = names<\/span>(MEs),<\/span><\/code><code>ySymbols = names<\/span>(MEs),<\/span><\/code><code>colorLabels = FALSE<\/span>,<\/span><\/code><code>colors = greenWhiteRed(50<\/span>),<\/span><\/code><code>textMatrix = textMatrix,<\/span><\/code><code>setStdMargins = FALSE<\/span>,<\/span><\/code><code>cex.text = 0.5<\/span>,<\/span><\/code><code>zlim = c<\/span>(-1<\/span>,1<\/span>),<\/span><\/code><code>main = paste(\"Module-trait relationships\"<\/span>))<\/span><\/code><\/pre>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;transform: translate3d(-15px, 0px, 0px);\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;vertical-align: middle;width: auto;min-width: 5%;flex: 0 0 auto;height: auto;align-self: center;\"><svg viewbox=\"0 0 1 1\" style=\"float:left;line-height:0;width:0;vertical-align:top;\"><\/svg><\/section>\n<section style=\"display: inline-block;vertical-align: middle;width: auto;align-self: center;flex: 0 0 auto;min-width: 5%;height: auto;background-color: rgb(129, 167, 211);padding: 13px;\">\n<section style=\"text-align: justify;color: rgb(19, 53, 102);font-size: 16px;padding-right: 12px;padding-left: 12px;\" powered-by=\"xiumi.us\">\n\u6a21\u5757\u4e0e\u8868\u578b\u7684\u76f8\u5173\u6027\u70ed\u56fe<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<section style=\"text-align: center;margin-top: 10px;margin-bottom: 10px;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.5966386554621849\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/5_CcfvY2x0dvOibygfCZfNicx7K8kokg.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IExiczIUblDcfQkc4CDz0PYBcbQK7CcfvY2x0dvOibygfCZfNicx7K8kokg\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"952\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\n<\/section>\n <\/p>\n<section style=\"justify-content: flex-start;display: flex;flex-flow: row;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 100%;vertical-align: top;border-width: 1px;border-radius: 8px;border-style: dotted;border-color: rgb(0, 0, 0);overflow: hidden;align-self: flex-start;flex: 0 0 auto;\">\n<section style=\"margin: 15px 0%;\" powered-by=\"xiumi.us\">\n<section style=\"color: rgb(62, 62, 62);padding-right: 15px;padding-left: 15px;\">\n\u6ce8\uff1a<\/span><\/p>\n# \u8bf7\u786e\u4fdd\u5c06\u8def\u5f84”LiverFemale3600.csv”,\u201dClinicalTraits.csv\u201d\u66ff\u6362\u4e3a\u5b9e\u9645\u6570\u636e\u96c6\u6587\u4ef6\u7684\u8def\u5f84\uff0c\u5e76\u6839\u636e\u9700\u8981\u8c03\u6574\u6a21\u578b\u53c2\u6570\u548c\u5176\u4ed6\u914d\u7f6e\u3002<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n <\/p>\n<section style=\"text-align: left;justify-content: flex-start;display: flex;flex-flow: row;margin-top: 10px;margin-bottom: 10px;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 98%;vertical-align: top;align-self: flex-start;flex: 0 0 auto;height: auto;border-style: solid;border-width: 2px;border-color: rgb(160, 160, 160);padding: 27px;\">\n<section style=\"text-align: justify;line-height: 1.6;letter-spacing: 0px;padding-right: 6px;padding-left: 6px;\" powered-by=\"xiumi.us\">\n\u5173\u4e8eWGCNA\u5206\u6790\uff0c\u5c0f\u82b1\u5f3a\u70c8\u63a8\u8350\u5927\u5bb6\u4f7f\u7528\u4e91\u751f\u4fe1\u5e73\u53f0\uff08http:\/\/www.biocloudservice.com\/home.html\uff09\u8fdb\u884cWGCNA\u5206\u6790\u4ee5\u53ca\u5176\u4ed6\u751f\u4fe1\u5206\u6790\u4efb\u52a1\u3002\u4e91\u751f\u4fe1\u5e73\u53f0\u662f\u4e00\u4e2a\u5f3a\u5927\u800c\u6709\u8da3\u7684\u5728\u7ebf\u5de5\u5177\uff0c\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u7b80\u5355\u3001\u5feb\u901f\u548c\u53ef\u89c6\u5316\u7684\u751f\u7269\u4fe1\u606f\u5b66\u5206\u6790\u4f53\u9a8c\u3002<\/span><\/p>\n <\/p>\n\u4f7f\u7528\u4e91\u751f\u4fe1\u5e73\u53f0\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u4e0a\u4f20\u548c\u5904\u7406\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u8fdb\u884cWGCNA\u5206\u6790\uff0c\u5e76\u83b7\u5f97\u8be6\u7ec6\u7684\u7ed3\u679c\u548c\u56fe\u8868\u5c55\u793a\u3002\u4f60\u53ef\u4ee5\u63a2\u7d22\u57fa\u56e0\u4e4b\u95f4\u7684\u5171\u8868\u8fbe\u7f51\u7edc\u3001\u53d1\u73b0\u5173\u952e\u6a21\u5757\u3001\u5206\u6790\u6a21\u5757\u4e0e\u4e34\u5e8a\u7279\u5f81\u7684\u76f8\u5173\u6027\u7b49\u3002\u540c\u65f6\uff0c\u4e91\u751f\u4fe1\u5e73\u53f0\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u5176\u4ed6\u751f\u4fe1\u5206\u6790\u5de5\u5177\u548c\u529f\u80fd\uff0c\u5982\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u3001\u529f\u80fd\u5bcc\u96c6\u5206\u6790\u3001\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u5206\u6790\u7b49\uff0c\u5e2e\u52a9\u4f60\u66f4\u5168\u9762\u5730\u7406\u89e3\u548c\u89e3\u91ca\u4f60\u7684\u6570\u636e\u3002<\/p>\n\u4f7f\u7528\u4e91\u751f\u4fe1\u5e73\u53f0\u8fdb\u884cWGCNA\u5206\u6790\u4e0d\u4ec5\u65b9\u4fbf\u5feb\u6377\uff0c\u8fd8\u80fd\u591f\u8282\u7701\u5927\u91cf\u7684\u8ba1\u7b97\u8d44\u6e90\u548c\u65f6\u95f4\u3002\u4f60\u53ef\u4ee5\u968f\u65f6\u968f\u5730\u8bbf\u95ee\u5e73\u53f0\uff0c\u65e0\u9700\u5b89\u88c5\u4efb\u4f55\u8f6f\u4ef6\uff0c\u76f4\u63a5\u5728\u6d4f\u89c8\u5668\u4e2d\u8fdb\u884c\u5206\u6790\u3002\u5e73\u53f0\u63d0\u4f9b\u53cb\u597d\u7684\u7528\u6237\u754c\u9762\u548c\u4ea4\u4e92\u5f0f\u64cd\u4f5c\uff0c\u4f7f\u5f97\u590d\u6742\u7684\u5206\u6790\u53d8\u5f97\u7b80\u5355\u6613\u61c2\u3002\u65e0\u8bba\u4f60\u662f\u751f\u7269\u5b66\u7814\u7a76\u8005\u3001\u751f\u7269\u4fe1\u606f\u5b66\u5bb6\u8fd8\u662f\u5b66\u751f\uff0c\u4e91\u751f\u4fe1\u5e73\u53f0\u90fd\u80fd\u6ee1\u8db3\u4f60\u7684\u5206\u6790\u9700\u6c42\uff0c\u5e76\u5e2e\u52a9\u4f60\u63a2\u7d22\u6570\u636e\u80cc\u540e\u7684\u5965\u79d8\u3002\u6240\u4ee5\uff0c\u8d76\u5feb\u6765\u4f53\u9a8c\u4e91\u751f\u4fe1\u5e73\u53f0\u5427\uff01\u8ba9\u751f\u4fe1\u5206\u6790\u53d8\u5f97\u66f4\u6709\u8da3\u3001\u66f4\u9ad8\u6548\uff01<\/p>\n<\/section>\n<section style=\"text-align: right;justify-content: flex-end;display: flex;flex-flow: row;transform: translate3d(40px, 0px, 0px);margin-bottom: -40px;\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: auto;vertical-align: top;align-self: flex-start;flex: 0 0 auto;min-width: 5%;height: auto;\">\n<section style=\"transform: rotateZ(45deg);\" powered-by=\"xiumi.us\">\n<section style=\"text-align: center;\">\n<section style=\"display: inline-block;width: 26px;height: 26px;vertical-align: top;overflow: hidden;background-color: rgb(255, 255, 255);border-left: 1px solid rgb(160, 160, 160);border-bottom-left-radius: 0px;\">\n<section style=\"text-align: justify;\" powered-by=\"xiumi.us\">\n <\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<a target=\"_blank\" href=\"https:\/\/mp.weixin.qq.com\/s?__biz=MzAwNjE0MDY3MQ==&mid=2650761303&idx=2&sn=6f73471d1e82c56c0b2160d0fcd32d53&scene=21#wechat_redirect\" textvalue=\"\" linktype=\"image\" imgurl=\"https:\/\/mmbiz.qpic.cn\/mmbiz_jpg\/KDvR2NrGicSYCoxhZfqr5L0fxy3C9eSFdt72DV7zqsmrNaiaaoOhic3sHzUpBN4r4MaBCk9p9v16yZwvMvS5nM7sQ\/0?wx_fmt=jpeg\" imgdata=\"[object Object]\" tab=\"innerlink\" data-linktype=\"1\" rel=\"noopener\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.35083798882681566\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/6_pBN4r4MaBCk9p9v16yZwvMvS5nM7sQ-9.jpg\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_jpg\/KDvR2NrGicSYCoxhZfqr5L0fxy3C9eSFdt72DV7zqsmrNaiaaoOhic3sHzUpBN4r4MaBCk9p9v16yZwvMvS5nM7sQ\/640?wx_fmt=jpeg'\" data-type=\"jpeg\" data-w=\"895\" style=\"\" \/><\/span><\/a> <\/p>\n<img decoding=\"async\" class=\"rich_pages wxw-img\" data-galleryid=\"\" data-ratio=\"0.3488888888888889\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/7_uKaqczIP6EMQhkT9sW7c1bKv6z9AAg-9.jpg\" 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onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_gif\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IE93jxiajNI6hr88gNpkl3OibkBN74uUnBerT1OMPNup57R9DnmqWDVXlQ\/640?wx_fmt=gif'\" data-type=\"gif\" data-w=\"500\" height=\"15\" style=\"width: 18px;height: 15px;\" width=\"18\" \/> \u70b9\u4e00\u4e0b\u9605\u8bfb\u539f\u6587\u4e86\u89e3\u66f4\u591a\u8d44\u8baf<\/strong><\/p>\n<\/section>\n<\/section>\n<section style=\"\" powered-by=\"xiumi.us\">\n<section style=\"display: inline-block;width: 50px;height: 40px;vertical-align: top;overflow: hidden;\">\n<section style=\"text-align: center;margin-right: 0%;margin-left: 0%;line-height: 0;\" powered-by=\"xiumi.us\">\n<section style=\"vertical-align: middle;display: inline-block;line-height: 0;\"><img decoding=\"async\" class=\"rich_pages wxw-img\" data-ratio=\"0.7709090909090909\" data-s=\"300,640\" src=\"http:\/\/www.biocloudservice.com\/wordpress\/wp-content\/uploads\/2024\/06\/9_yT1L14erCtfqSaIXuW0IqibNQ5icLA.png\" onerror=\"this.src='https:\/\/mmbiz.qpic.cn\/mmbiz_png\/KDvR2NrGicSZSZOHwibZ8WYC7HEnhkm6IEIOXFYoaJUO7IrdTLuvrDDggx8OyT1L14erCtfqSaIXuW0IqibNQ5icLA\/640?wx_fmt=png'\" data-type=\"png\" data-w=\"275\" style=\"vertical-align: middle;width: 100%;\" \/><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<mp-style-type data-value=\"3\"><\/mp-style-type><\/p>\n<div class=\"msg_source_url\"><a href=\"http:\/\/www.biocloudservice.com\/home.html\" target=\"_blank\" rel=\"noopener\">\u9605\u8bfb\u539f\u6587<\/a><\/div>\n<\/content><\/p>\n<div class=\"msg_source_url\"><a href=\"http:\/\/www.biocloudservice.com\/home.html\" target=\"_blank\" rel=\"noopener\">\u9605\u8bfb\u539f\u6587<\/a><\/div>\n<\/div>\n<\/body> \n<\/html><\/p>\n","protected":false},"excerpt":{"rendered":"\u63a2\u7d22\u57fa\u56e0\u5171\u8868\u8fbe\u7f51\u7edc\uff1aWGCNA\u63ed\u793a\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u7684\u5965\u79d8 \u5c0f\u82b1 \u751f\u4fe1\u679c &nbsp 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