GEO生信数据挖掘（九）WGCNA分析

第六节，我们使用结核病基因数据，做了一个数据预处理的实操案例。例子中结核类型，包括结核，潜隐进展，对照和潜隐，四个类别。第七节延续上个数据，进行了差异分析。 第八节对差异基因进行富集分析。本节进行WGCNA分析。

目录

加载数据，进行聚类

初次聚类观察

自己定义红线位置，进行切割划分

载入性状数据

增加形状信息后，再次聚类

网络构建

[选取soft-thresholding powers](#选取soft-thresholding powers)

基于tom的差异的基因聚类，绘制聚类树

根据聚类情况，设置颜色

计算eigengenes

模块的自动合并

[模块与临床形状的关系热图 （关键数据）](#模块与临床形状的关系热图 （关键数据）)

红色模块样本表达情况（相关性大）

产生了很多数据（各个模块的和临床性状）

后续挖掘核心基因时，需要用到Cytoscape，生成绘图所需要的数据

---

加载数据，进行聚类

library(WGCNA)
#读取目录名称，方便复制粘贴
dir()
#加载数据
load('DEG_TB_LTBI_step13.Rdata')


#这里行为样品名，列为基因名
################################样品聚类#################### 
datExpr = t(dataset_TB_LTBI_DEG)
#初次聚类
sampleTree = hclust(dist(datExpr), method = "average")
# Plot the sample tree: Open a graphic output window of size 20 by 15 inches
# The user should change the dimensions if the window is too large or too small.
sizeGrWindow(12,9)
#pdf(file='sampleCluestering.pdf',width = 12,height = 9)
par(cex=0.6)
par(mar=c(0,4,2,0))
plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 1.5,
     cex.axis = 1.5, cex.main = 2)

#结果图片自己导出PDF，文件名=1_sampleClustering.pdf



### Plot a line to show the cut
abline(h = 87, col = "red")##剪切高度不确定，故无红线

dev.off()

初次聚类观察

自己定义红线位置，进行切割划分

本例发现右侧有些样本孤立，适合被剔除，设置红线87切割。

左侧也被切成两块，需要做处理，保留。

### Determine cluster under the line
clust = cutreeStatic(sampleTree, cutHeight = 87, minSize = 10)
table(clust)
#clust
#0  1  2 
#5 57 40

### clust 1 contains the samples we want to keep.
keepSamples = (clust==1|clust==2)
datExpr0 = datExpr[keepSamples, ]
dim(datExpr0) #[1]   97 2813
#保存数据
save(datExpr0,file='datExpr0_cluster_filter.Rdata')

载入性状数据

匹配样本名称，性状数据与表达数据保证一致

#################### 载入性状数据###########################
#加载自己的性状数据
load('design_TB_LTBI.Rdata')
traitData=design
#Loading clinical trait data
#traitData = read.table("trait_D.txt",row.names=1,header=T,comment.char = "",check.names=F)########trait file name can be changed######性状数据文件名，根据实际修改，如果工作路径不是实际性状数据路径，需要添加正确的数据路径
dim(traitData)
#names(traitData)
# remove columns that hold information we do not need.
#allTraits = traitData
dim(traitData)
names(traitData)

# Form a data frame analogous to expression data that will hold the clinical traits.
fpkmSamples = rownames(datExpr0)
traitSamples =rownames(traitData)
#匹配样本名称，性状数据与表达数据保证一致
traitRows = match(fpkmSamples, traitSamples)
datTraits = traitData[traitRows,]
rownames(datTraits) 
collectGarbage()

增加形状信息后，再次聚类

# Re-cluster samples
sampleTree2 = hclust(dist(datExpr0), method = "average")
# Convert traits to a color representation: white means low, red means high, grey means missing entry
traitColors = numbers2colors(datTraits, signed = FALSE)
# Plot the sample dendrogram and the colors underneath.

#sizeGrWindow(20,20)
##pdf(file="2_Sample dendrogram and trait heatmap.pdf",width=12,height=12)
plotDendroAndColors(sampleTree2, traitColors,
                    groupLabels = names(datTraits),
                    main = "Sample dendrogram and trait heatmap")

dev.off()

下方红色，大致分成了两类，效果不错。

网络构建

#############################network constr########################################

# Allow multi-threading within WGCNA. At present this call is necessary.
# Any error here may be ignored but you may want to update WGCNA if you see one.
# Caution: skip this line if you run RStudio or other third-party R environments.
# See note above.
enableWGCNAThreads()


# Choose a set of soft-thresholding powers
powers = c(1:15)

# Call the network topology analysis function
sft = pickSoftThreshold(datExpr0, powerVector = powers, verbose = 5)

# Plot the results:
sizeGrWindow(15, 9)
#pdf(file="3_Scale independence.pdf",width=9,height=5)
#pdf(file="Rplot03.pdf",width=9,height=5)
par(mfrow = c(1,2))
cex1 = 0.9
# Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
     xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
     main = paste("Scale independence"));
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
     labels=powers,cex=cex1,col="red");
# this line corresponds to using an R^2 cut-off of h
abline(h=0.90,col="red")
# Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[,1], sft$fitIndices[,5],
     xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
     main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
dev.off()

选取soft-thresholding powers

测试阈值，注意观察，突破红线的附近时取值，下方代码时候的是自适应的方法选取 soft-thresholding powers

######chose the softPower
#datExpr0= datExpr0[,-1]
softPower =sft$powerEstimate
adjacency = adjacency(datExpr0, power = softPower)

##### Turn adjacency into topological overlap
TOM = TOMsimilarity(adjacency);
dissTOM = 1-TOM

# Call the hierarchical clustering function
geneTree = hclust(as.dist(dissTOM), method = "average");
# Plot the resulting clustering tree (dendrogram)

#sizeGrWindow(12,9)
pdf(file="4_Gene clustering on TOM-based dissimilarity.pdf",width=12,height=9)
plot(geneTree, xlab="", sub="", main = "Gene clustering on TOM-based dissimilarity",
     labels = FALSE, hang = 0.04)
dev.off()

基于tom的差异的基因聚类，绘制聚类树

根据聚类情况，设置颜色

# We like large modules, so we set the minimum module size relatively high:
minModuleSize = 30
# Module identification using dynamic tree cut:
dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM,
                            deepSplit = 2, pamRespectsDendro = FALSE,
                            minClusterSize = minModuleSize);
table(dynamicMods)



# Convert numeric lables into colors
dynamicColors = labels2colors(dynamicMods)
table(dynamicColors)
# Plot the dendrogram and colors underneath
#sizeGrWindow(8,6)
pdf(file="5_Dynamic Tree Cut.pdf",width=8,height=6)
plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut",
                    dendroLabels = FALSE, hang = 0.03,
                    addGuide = TRUE, guideHang = 0.05,
                    main = "Gene dendrogram and module colors")
dev.off()

计算eigengenes

# Calculate eigengenes
MEList = moduleEigengenes(datExpr0, colors = dynamicColors)
MEs = MEList$eigengenes
# Calculate dissimilarity of module eigengenes
MEDiss = 1-cor(MEs);
# Cluster module eigengenes
METree = hclust(as.dist(MEDiss), method = "average")
# Plot the result
#sizeGrWindow(7, 6)
pdf(file="6_Clustering of module eigengenes.pdf",width=7,height=6)
plot(METree, main = "Clustering of module eigengenes",
     xlab = "", sub = "")
MEDissThres = 0.25######剪切高度可修改
# Plot the cut line into the dendrogram
abline(h=MEDissThres, col = "red")
dev.off()

模块的自动合并

# Call an automatic merging function
merge = mergeCloseModules(datExpr0, dynamicColors, cutHeight = MEDissThres, verbose = 3)
# The merged module colors
mergedColors = merge$colors
# Eigengenes of the new merged modules:
mergedMEs = merge$newMEs

#sizeGrWindow(12, 9)
pdf(file="7_merged dynamic.pdf", width = 9, height = 6)
plotDendroAndColors(geneTree, cbind(dynamicColors, mergedColors),
                    c("Dynamic Tree Cut", "Merged dynamic"),
                    dendroLabels = FALSE, hang = 0.03,
                    addGuide = TRUE, guideHang = 0.05)
dev.off()


# Rename to moduleColors
moduleColors = mergedColors
# Construct numerical labels corresponding to the colors
colorOrder = c("grey", standardColors(50))
moduleLabels = match(moduleColors, colorOrder)-1
MEs = mergedMEs

# Save module colors and labels for use in subsequent parts
save(MEs, TOM, dissTOM,  moduleLabels, moduleColors, geneTree, sft, file = "networkConstruction-stepByStep.RData")

模块与临床形状的关系热图 （关键数据）

#############################relate modules to external clinical triats######################################
# Define numbers of genes and samples
nGenes = ncol(datExpr0)
nSamples = nrow(datExpr0)

moduleTraitCor = cor(MEs, datTraits, use = "p")
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)

#sizeGrWindow(10,6)
pdf(file="8_Module-trait relationships.pdf",width=10,height=6)
# Will display correlations and their p-values
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
                   signif(moduleTraitPvalue, 1), ")", sep = "")

dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(6, 8.5, 3, 3))

# Display the correlation values within a heatmap plot #修改性状类型 data.frame
labeledHeatmap(Matrix = moduleTraitCor,
               xLabels = names(data.frame(datTraits)),
               yLabels = names(MEs),
               ySymbols = names(MEs),
               colorLabels = FALSE,
               colors = greenWhiteRed(50),
               textMatrix = textMatrix,
               setStdMargins = FALSE,
               cex.text = 0.5,
               zlim = c(-1,1),
               main = paste("Module-trait relationships"))
dev.off()

挑选相关性最高的，具有统计学意义的（p<0.05）,red模块最佳！

红色模块样本表达情况（相关性大）

产生了很多数据（各个模块的和临床性状）

######## Define variable weight containing all column of datTraits

###MM and GS


# names (colors) of the modules
modNames = substring(names(MEs), 3)

geneModuleMembership = as.data.frame(cor(datExpr0, MEs, use = "p"))
MMPvalue = as.data.frame(corPvalueStudent(as.matrix(geneModuleMembership), nSamples))

names(geneModuleMembership) = paste("MM", modNames, sep="")
names(MMPvalue) = paste("p.MM", modNames, sep="")

#names of those trait
traitNames=names(data.frame(datTraits))
class(datTraits)

geneTraitSignificance = as.data.frame(cor(datExpr0, datTraits, use = "p"))
GSPvalue = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance), nSamples))

names(geneTraitSignificance) = paste("GS.", traitNames, sep="")
names(GSPvalue) = paste("p.GS.", traitNames, sep="")


####plot MM vs GS for each trait vs each module


##########example:royalblue and CK
module="red"
column = match(module, modNames)
moduleGenes = moduleColors==module

trait="TB"
traitColumn=match(trait,traitNames)

sizeGrWindow(7, 7)

#par(mfrow = c(1,1))
verboseScatterplot(abs(geneModuleMembership[moduleGenes, column]),
abs(geneTraitSignificance[moduleGenes, traitColumn]),
xlab = paste("Module Membership in", module, "module"),
ylab = paste("Gene significance for ",trait),
main = paste("Module membership vs. gene significance\n"),
cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, col = module)
######

for (trait in traitNames){
  traitColumn=match(trait,traitNames)
  
  for (module in modNames){
    column = match(module, modNames)
    moduleGenes = moduleColors==module
    
    if (nrow(geneModuleMembership[moduleGenes,]) > 1){####进行这部分计算必须每个模块内基因数量大于2，由于前面设置了最小数量是30，这里可以不做这个判断，但是grey有可能会出现1个gene,它会导致代码运行的时候中断，故设置这一步
      
      #sizeGrWindow(7, 7)
      pdf(file=paste("9_", trait, "_", module,"_Module membership vs gene significance.pdf",sep=""),width=7,height=7)
      par(mfrow = c(1,1))
      verboseScatterplot(abs(geneModuleMembership[moduleGenes, column]),
                         abs(geneTraitSignificance[moduleGenes, traitColumn]),
                         xlab = paste("Module Membership in", module, "module"),
                         ylab = paste("Gene significance for ",trait),
                         main = paste("Module membership vs. gene significance\n"),
                         cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, col = module)
      dev.off()
    }
  }
}

#####
names(datExpr0)
probes = names(datExpr0)


#################export GS and MM############### 

geneInfo0 = data.frame(probes= probes,
                       moduleColor = moduleColors)

for (Tra in 1:ncol(geneTraitSignificance))
{
  oldNames = names(geneInfo0)
  geneInfo0 = data.frame(geneInfo0, geneTraitSignificance[,Tra],
                         GSPvalue[, Tra])
  names(geneInfo0) = c(oldNames,names(geneTraitSignificance)[Tra],
                       names(GSPvalue)[Tra])
}

for (mod in 1:ncol(geneModuleMembership))
{
  oldNames = names(geneInfo0)
  geneInfo0 = data.frame(geneInfo0, geneModuleMembership[,mod],
                         MMPvalue[, mod])
  names(geneInfo0) = c(oldNames,names(geneModuleMembership)[mod],
                       names(MMPvalue)[mod])
}
geneOrder =order(geneInfo0$moduleColor)
geneInfo = geneInfo0[geneOrder, ]

write.table(geneInfo, file = "10_GS_and_MM.xls",sep="\t",row.names=F)

####################################################Visualizing the gene network#######################################################


nGenes = ncol(datExpr0)
nSamples = nrow(datExpr0)


# Transform dissTOM with a power to make moderately strong connections more visible in the heatmap
plotTOM = dissTOM^7
# Set diagonal to NA for a nicer plot
diag(plotTOM) = NA



# Call the plot function

sizeGrWindow(9,9)  #这个耗电脑内存
pdf(file="12_Network heatmap plot_all gene.pdf",width=9, height=9)
TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")
dev.off()


nSelect = 400
# For reproducibility, we set the random seed
set.seed(10)
select = sample(nGenes, size = nSelect)
selectTOM = dissTOM[select, select]
# There's no simple way of restricting a clustering tree to a subset of genes, so we must re-cluster.
selectTree = hclust(as.dist(selectTOM), method = "average")
selectColors = moduleColors[select]

# Open a graphical window
#sizeGrWindow(9,9)
# Taking the dissimilarity to a power, say 10, makes the plot more informative by effectively changing
# the color palette; setting the diagonal to NA also improves the clarity of the plot
plotDiss = selectTOM^7
diag(plotDiss) = NA

pdf(file="13_Network heatmap plot_selected genes.pdf",width=9, height=9)
TOMplot(plotDiss, selectTree, selectColors, main = "Network heatmap plot, selected genes")
dev.off()



####################################################Visualizing the gene network of eigengenes####################################################


#sizeGrWindow(5,7.5)
pdf(file="14_Eigengene dendrogram and Eigengene adjacency heatmap.pdf", width=5, height=7.5)
par(cex = 0.9)
plotEigengeneNetworks(MEs, "", marDendro = c(0,4,1,2), marHeatmap = c(3,4,1,2), cex.lab = 0.8, xLabelsAngle= 90)
dev.off()

#or devide into two parts
# Plot the dendrogram
#sizeGrWindow(6,6);
pdf(file="15_Eigengene dendrogram_2.pdf",width=6, height=6)
par(cex = 1.0)
plotEigengeneNetworks(MEs, "Eigengene dendrogram", marDendro = c(0,4,2,0), plotHeatmaps = FALSE)
dev.off()

pdf(file="15_Eigengene adjacency heatmap_2.pdf",width=6, height=6)
# Plot the heatmap matrix (note: this plot will overwrite the dendrogram plot)
par(cex = 1.0)
plotEigengeneNetworks(MEs, "Eigengene adjacency heatmap", marHeatmap = c(3,4,2,2), plotDendrograms = FALSE, xLabelsAngle = 90)
dev.off()

后续挖掘核心基因时，需要用到Cytoscape，生成绘图所需要的数据

###########################Exporting to Cytoscape all one by one ##########################




# Select each module
'''
Error in exportNetworkToCytoscape(modTOM, edgeFile = paste("CytoscapeInput-edges-",  : 
  Cannot determine node names: nodeNames is NULL and adjMat has no dimnames.
  datExpr0 格式需要dataframe
'''
modules =module
for (mod in 1:nrow(table(moduleColors)))
{
  
  modules = names(table(moduleColors))[mod]
  # Select module probes
  probes = names(data.frame(datExpr0))  # 
  inModule = (moduleColors == modules)
  modProbes = probes[inModule]
  modGenes = modProbes
  # Select the corresponding Topological Overlap
  modTOM = TOM[inModule, inModule]
  
  dimnames(modTOM) = list(modProbes, modProbes)
  # Export the network into edge and node list files Cytoscape can read
  cyt = exportNetworkToCytoscape(modTOM,
                                 edgeFile = paste("CytoscapeInput-edges-", modules , ".txt", sep=""),
                                 nodeFile = paste("CytoscapeInput-nodes-", modules, ".txt", sep=""),
                                 weighted = TRUE,
                                 threshold = 0.02,
                                 nodeNames = modProbes,
                                 altNodeNames = modGenes,
                                 nodeAttr = moduleColors[inModule])
}

关系网络的构建完毕，绘图找核心基因，Cytoscape 到底怎么玩？