{"id":25573,"date":"2024-01-22T17:05:44","date_gmt":"2024-01-22T09:05:44","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25573"},"modified":"2024-01-22T17:05:45","modified_gmt":"2024-01-22T09:05:45","slug":"%e6%8e%a2%e7%b4%a2%e5%9f%ba%e5%9b%a0%e5%85%b1%e8%a1%a8%e8%be%be%e7%bd%91%e7%bb%9c%ef%bc%9awgcna%e6%8f%ad%e7%a4%ba%e5%9f%ba%e5%9b%a0%e8%b0%83%e6%8e%a7%e7%bd%91%e7%bb%9c%e7%9a%84%e5%a5%a5%e7%a7%98","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25573","title":{"rendered":"\u63a2\u7d22\u57fa\u56e0\u5171\u8868\u8fbe\u7f51\u7edc\uff1aWGCNA\u63ed\u793a\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u7684\u5965\u79d8"},"content":{"rendered":"

\u5f15\u8a00\uff1a<\/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

WGCNA\u539f\u7406\uff1a<\/p>\n

WGCNA\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

\u5e94\u7528\u9886\u57df\uff1a<\/p>\n

WGCNA\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

\u5206\u6790\u6b65\u9aa4\uff1a<\/p>\n

\u4f7f\u7528WGCNA\u8fdb\u884c\u57fa\u56e0\u5171\u8868\u8fbe\u7f51\u7edc\u5206\u6790\u901a\u5e38\u5305\u62ec\u4ee5\u4e0b\u6b65\u9aa4\uff1a1\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\uff1b2\uff09\u6784\u5efa\u76f8\u5173\u7cfb\u6570\u77e9\u9635\uff0c\u8ba1\u7b97\u57fa\u56e0\u95f4\u7684\u76f8\u5173\u6027\uff1b3\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\uff1b4\uff09\u6a21\u5757\u53d1\u73b0\uff0c\u5c06\u9ad8\u5ea6\u76f8\u5173\u7684\u57fa\u56e0\u805a\u5408\u6210\u5171\u8868\u8fbe\u6a21\u5757\uff1b5\uff09\u6a21\u5757\u7684\u529f\u80fd\u6ce8\u91ca\u548c\u8c03\u63a7\u57fa\u56e0\u7684\u8bc6\u522b\uff1b6\uff09\u7f51\u7edc\u53ef\u89c6\u5316\u548c\u529f\u80fd\u5206\u6790\u3002<\/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\u679c\u7ed9\u5927\u5bb6\u9644\u5728\u6700\u540e\u3002<\/p>\n

\u3002Step1 \u6570\u636e\u8f93\u5165\u3001\u6e05\u6d17\u548c\u9884\u5904\u7406<\/p>\n

# 1.1 \u8f7d\u5165\u6570\u636e<\/p>\n

workingDir = “D:\/wanglab\/life\/ziyuan\/WGCNA\/”;<\/p>\n

setwd(workingDir);<\/p>\n

# Load the WGCNA package<\/p>\n

library(WGCNA);<\/p>\n

# The following setting is important, do not omit.<\/p>\n

options(stringsAsFactors = FALSE);<\/p>\n

#Read in the female liver data set<\/p>\n

femData = read.csv(“LiverFemale3600.csv”);<\/p>\n

# Take a quick look at what is in the data set:<\/p>\n

dim(femData);<\/p>\n

names(femData);<\/p>\n

#1.2\u521b\u5efa\u884c\u4e3a\u6837\u672c\uff0c\u5217\u4e3a\u57fa\u56e0\u7684\u8868\u8fbe\u77e9\u9635<\/p>\n

datExpr0 = as.data.frame(t(femData[, -c(1:8)]));<\/p>\n

names(datExpr0) = femData$substanceBXH;<\/p>\n

rownames(datExpr0) = names(femData)[-c(1:8)];<\/p>\n

### 1.3\u5224\u65ad\u6570\u636e\u8d28\u91cf–\u7f3a\u5931\u503c<\/p>\n

gsg = goodSamplesGenes(datExpr0, verbose = 3);<\/p>\n

gsg$allOK<\/p>\n

if (!gsg$allOK)<\/p>\n

{<\/p>\n

# Optionally, print the gene and sample names that were removed:<\/p>\n

if (sum(!gsg$goodGenes)>0)<\/p>\n

printFlush(paste(“Removing genes:”, paste(names(datExpr0)[!gsg$goodGenes], collapse = “, “)));<\/p>\n

if (sum(!gsg$goodSamples)>0)<\/p>\n

printFlush(paste(“Removing samples:”, paste(rownames(datExpr0)[!gsg$goodSamples], collapse = “, “)));<\/p>\n

# Remove the offending genes and samples from the data:<\/p>\n

datExpr0 = datExpr0[gsg$goodSamples, gsg$goodGenes]<\/p>\n

}<\/p>\n

### 1.4\u7ed8\u5236\u6837\u54c1\u7684\u7cfb\u7edf\u805a\u7c7b\u6811<\/p>\n

sampleTree = hclust(dist(datExpr0), method = “average”);<\/p>\n

# Plot the sample tree: Open a graphic output window of size 12 by 9 inches<\/p>\n

# The user should change the dimensions if the window is too large or too small.<\/p>\n

sizeGrWindow(12,9)<\/p>\n

#pdf(file = “Plots\/sampleClustering.pdf”, width = 12, height = 9);<\/p>\n

par(cex = 0.6);<\/p>\n

par(mar = c(0,4,2,0))<\/p>\n

plot(sampleTree, main = “Sample clustering to detect outliers”, sub=””, xlab=””, cex.lab = 1.5,<\/p>\n

cex.axis = 1.5, cex.main = 2)<\/p>\n

# Plot a line to show the cut<\/p>\n

abline(h = 15, col = “red”);<\/p>\n

\u7ed8\u5236\u6837\u672c\u6811\u805a\u7c7b\u6811<\/strong><\/p>\n

<\/p>\n

## 1.5\u82e5\u5b58\u5728\u663e\u8457\u79bb\u7fa4\u70b9\uff1b\u5254\u9664\u6389<\/p>\n

# Determine cluster under the line<\/p>\n

clust = cutreeStatic(sampleTree, cutHeight = 15, minSize = 10)<\/p>\n

table(clust)<\/p>\n

# clust 1 contains the samples we want to keep.<\/p>\n

keepSamples = (clust==1)<\/p>\n

datExpr = datExpr0[keepSamples, ]<\/p>\n

nGenes = ncol(datExpr)<\/p>\n

nSamples = nrow(datExpr)<\/p>\n

rownames(datExpr0)[!keepSamples]<\/p>\n

### 1.6\u8bfb\u5165\u4e34\u5e8a\u8868\u578b\u6570\u636e<\/p>\n

traitData = read.csv(“ClinicalTraits.csv”);<\/p>\n

dim(traitData)<\/p>\n

names(traitData)<\/p>\n

# remove columns that hold information we do not need.<\/p>\n

allTraits = traitData[, -c(31, 16)];<\/p>\n

allTraits = allTraits[, c(2, 11:36) ];<\/p>\n

dim(allTraits)<\/p>\n

names(allTraits)<\/p>\n

# Form a data frame analogous to expression data that will hold the clinical traits.<\/p>\n

femaleSamples = rownames(datExpr);<\/p>\n

traitRows = match(femaleSamples, allTraits$Mice);<\/p>\n

datTraits = allTraits[traitRows, -1];<\/p>\n

rownames(datTraits) = allTraits[traitRows, 1];<\/p>\n

collectGarbage();<\/p>\n

### 1.7\u518d\u6b21\u5bf9\u5220\u6389\u79bb\u7fa4\u503c\u7684<\/a>\u6837\u672c\u8fdb\u884c\u805a\u7c7b<\/p>\n

# Re-cluster samples<\/p>\n

sampleTree2 = hclust(dist(datExpr), method = “average”)<\/p>\n

# Convert traits to a color representation: white means low, red means high, grey means missing entry<\/p>\n

traitColors = numbers2colors(datTraits, signed = FALSE);<\/p>\n

# Plot the sample dendrogram and the colors underneath.<\/p>\n

plotDendroAndColors(sampleTree2, traitColors,<\/p>\n

groupLabels = names(datTraits),<\/p>\n

main = “Sample dendrogram and trait heatmap”)<\/p>\n

\u7ed8\u5236\u6837\u672c\u6811\u805a\u7c7b\u6811\u548c\u8868\u578b\u70ed\u56fe<\/strong><\/p>\n

<\/p>\n

### 1.8\u4fdd\u5b58\u8868\u578b\u548c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\uff0c\u4ee5\u4fbf\u540e\u7eed\u5206\u6790<\/p>\n

save(datExpr, datTraits, file = “FemaleLiver-01-dataInput.RData”)<\/p>\n

<\/a> Step2\u7f51\u7edc\u642d\u5efa\u53ca\u6a21\u5757\u68c0\u6d4b<\/p>\n

<\/a> ### 2.1 \u6311\u9009\u6700\u4f73\u9608\u503cpower<\/p>\n

# Load the data saved in the first part<\/p>\n

lnames = load(file = “FemaleLiver-01-dataInput.RData”);<\/p>\n

#The variable lnames contains the names of loaded variables.<\/p>\n

lnames<\/p>\n

# Choose a set of soft-thresholding powers<\/p>\n

powers = c(c(1:10), seq(from = 12, to=20, by=2))<\/p>\n

# Call the network topology analysis function<\/p>\n

sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)<\/p>\n

# Plot the results:<\/p>\n

sizeGrWindow(9, 5)<\/p>\n

par(mfrow = c(1,2));<\/p>\n

cex1 = 0.9;<\/p>\n

# Scale-free topology fit index as a function of the soft-thresholding power<\/p>\n

plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],<\/p>\n

xlab=”Soft Threshold (power)”,ylab=”Scale Free Topology Model Fit,signed R^2″,type=”n”,<\/p>\n

main = paste(“Scale independence”));<\/p>\n

text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],<\/p>\n

labels=powers,cex=cex1,col=”red”);<\/p>\n

# this line corresponds to using an R^2 cut-off of h<\/p>\n

abline(h=0.90,col=”red”)<\/p>\n

# Mean connectivity as a function of the soft-thresholding power<\/p>\n

plot(sft$fitIndices[,1], sft$fitIndices[,5],<\/p>\n

xlab=”Soft Threshold (power)”,ylab=”Mean Connectivity”, type=”n”,<\/p>\n

main = paste(“Mean connectivity”))<\/p>\n

text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col=”red”)<\/p>\n

\u6311\u9009\u6700\u4f73\u9608\u503cpower<\/strong><\/p>\n

<\/p>\n

power = sft$powerEstimate<\/p>\n

sft$powerEstimate<\/p>\n

# \u82e5\u65e0\u5411\u7f51\u7edc\u5728power\u5c0f\u4e8e15\u6216\u6709\u5411\u7f51\u7edcpower\u5c0f\u4e8e30\u5185\uff0c\u6ca1\u6709\u4e00\u4e2apower\u503c\u4f7f<\/p>\n

# \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<\/p>\n

# \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<\/p>\n

# \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<\/p>\n

# \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<\/p>\n

if(is.na(power)){<\/p>\n

# \u5b98\u65b9\u63a8\u8350 “signed” \u6216 “signed hybrid”<\/p>\n

# \u4e3a\u4e0e\u539f\u6587\u6863\u4e00\u81f4\uff0c\u6545\u672a\u4fee\u6539<\/p>\n

type = “unsigned”<\/p>\n

nSamples=nrow(datExpr)<\/p>\n

power = ifelse(nSamples<20, ifelse(type == “unsigned”, 9, 18),<\/p>\n

ifelse(nSamples<30, ifelse(type == “unsigned”, 8, 16),<\/p>\n

ifelse(nSamples<40, ifelse(type == “unsigned”, 7, 14),<\/p>\n

ifelse(type == “unsigned”, 6, 12))<\/p>\n

)<\/p>\n

)<\/p>\n

}<\/p>\n

### 2.2\u4e00\u6b65\u6cd5\u6784\u5efa\u52a0\u6743\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u8bc6\u522b\u57fa\u56e0\u6a21\u5757<\/p>\n

net = blockwiseModules(datExpr, power = power,<\/p>\n

TOMType = “unsigned”, minModuleSize = 30,<\/p>\n

reassignThreshold = 0, mergeCutHeight = 0.25,<\/p>\n

numericLabels = TRUE, pamRespectsDendro = FALSE,<\/p>\n

saveTOMs = TRUE,<\/p>\n

saveTOMFileBase = “femaleMouseTOM”,<\/p>\n

verbose = 3)<\/p>\n

### 2.4\u6a21\u5757\u53ef\u89c6\u5316\uff0c\u5c42\u7ea7\u805a\u7c7b\u6811\u5c55\u793a\u5404\u4e2a\u6a21\u5757<\/p>\n

sizeGrWindow(12, 9)<\/p>\n

# Convert labels to colors for plotting<\/p>\n

mergedColors = labels2colors(net$colors)<\/p>\n

# Plot the dendrogram and the module colors underneath<\/p>\n

plotDendroAndColors(net$dendrograms[[1]], mergedColors[net$blockGenes[[1]]],<\/p>\n

“Module colors”,<\/p>\n

dendroLabels = FALSE, hang = 0.03,<\/p>\n

addGuide = TRUE, guideHang = 0.05)<\/p>\n

\u6a21\u5757\u5c42\u7ea7\u805a\u7c7b\u6811<\/strong> <\/p>\n

### 2.5\u4fdd\u5b58\u7ed3\u679c<\/p>\n

moduleLabels = net$colors<\/p>\n

moduleColors = labels2colors(net$colors)<\/p>\n

MEs = net$MEs;<\/p>\n

geneTree = net$dendrograms[[1]];<\/p>\n

save(MEs, moduleLabels, moduleColors, geneTree,<\/p>\n

file = “FemaleLiver-02-networkConstruction-auto.RData”)<\/p>\n

Step3 <\/a>\u6a21\u5757\u4e0e\u5916\u90e8\u4e34\u5e8a\u7279\u5f81\u90fd\u76f8\u5173\u5173\u7cfb<\/p>\n

## 3.1\u6570\u636e\u51c6\u5907<\/p>\n

load(file = “FemaleLiver-01-dataInput.RData”);<\/p>\n

load(file = “FemaleLiver-02-networkConstruction-auto.RData”);<\/p>\n

# Define numbers of genes and samples<\/p>\n

nGenes = ncol(datExpr);<\/p>\n

nSamples = nrow(datExpr);<\/p>\n

# Recalculate MEs with color labels<\/p>\n

MEs0 = moduleEigengenes(datExpr, moduleColors)$eigengenes<\/p>\n

MEs = orderMEs(MEs0)<\/p>\n

moduleTraitCor = cor(MEs, datTraits, use = “p”);<\/p>\n

moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples);<\/p>\n

## 3.2\u6a21\u5757\u4e0e\u8868\u578b\u7684\u76f8\u5173\u6027\u70ed\u56fe<\/p>\n

sizeGrWindow(10,6)<\/p>\n

# Will display correlations and their p-values<\/p>\n

textMatrix = paste(signif(moduleTraitCor, 2), “\\n(“,<\/p>\n

signif(moduleTraitPvalue, 1), “)”, sep = “”);<\/p>\n

dim(textMatrix) = dim(moduleTraitCor)<\/p>\n

par(mar = c(6, 8.5, 3, 3));<\/p>\n

# Display the correlation values within a heatmap plot<\/p>\n

labeledHeatmap(Matrix = moduleTraitCor,<\/p>\n

xLabels = names(datTraits),<\/p>\n

yLabels = names(MEs),<\/p>\n

ySymbols = names(MEs),<\/p>\n

colorLabels = FALSE,<\/p>\n

colors = greenWhiteRed(50),<\/p>\n

textMatrix = textMatrix,<\/p>\n

setStdMargins = FALSE,<\/p>\n

cex.text = 0.5,<\/p>\n

zlim = c(-1,1),<\/p>\n

main = paste(“Module-trait relationships”))<\/p>\n

\u6a21\u5757\u4e0e\u8868\u578b\u7684\u76f8\u5173\u6027\u70ed\u56fe<\/strong><\/p>\n

<\/p>\n

\u6ce8\uff1a<\/p>\n

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