GEO生信数据挖掘（九）WGCNA分析

人工智能学术前沿（真)2023-10-17 4:01

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

复制代码 
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。

复制代码 
### 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

复制代码 
#################### 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

复制代码 
# 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

复制代码 
#############################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

复制代码 
######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

复制代码 
# 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

复制代码 
# 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

复制代码 
# 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）

复制代码 
#############################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）

复制代码 
######## 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

复制代码 
###########################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？

相关推荐
cwn_38 分钟前
回归（多项式回归）
人工智能·机器学习·数据挖掘·回归
优乐美香芋味好喝1 小时前
2025年7月11日学习笔记&一周归纳——模式识别与机器学习
笔记·学习·机器学习
众链网络2 小时前
AI进化论08：机器学习的崛起——数据和算法的“二人转”，AI“闷声发大财”
人工智能·算法·机器学习
生命是有光的2 小时前
【机器学习】机器学习基础
人工智能·机器学习
平和男人杨争争2 小时前
机器学习13——支持向量机下
人工智能·机器学习·支持向量机
胖达不服输2 小时前
「日拱一码」025 机器学习——评价指标
人工智能·python·机器学习·评价指标
默辨2 小时前
机器学习（ML）、深度学习（DL）、强化学习（RL）关系和区别
人工智能·深度学习·机器学习
音程2 小时前
什么是Jaccard 相似度（Jaccard Similarity）
深度学习·数据挖掘
zhb20002 小时前
预测概率的先验校准
机器学习
乙真仙人2 小时前
AI Agents时代，数据分析将彻底被颠覆
人工智能·数据挖掘·数据分析
热门推荐
01KGG转MP3工具|非KGM文件|解密音频02集群聊天服务器---MySQL数据库的建立03Coze扣子平台完整体验和实践（附国内和国际版对比）04绿色建筑新态势：楼宇自控助力能效提升，推动成本优化新路径05使用Ruby接入实时行情API教程06扣子（coze）实战|我用扣子搭建了一个自动分析小红薯笔记内容的AI应用|详细步骤拆解07【无标题】08基于odoo17的设计模式详解---单例模式09DeepSeek各版本说明与优缺点分析10全球最强模型Grok4，国内已可免费使用！（附教程）