WGCNA分析

WGCNA分析 | 全流程分析代码 | 代码一

2023-02-18  本文已影响0人  小杜的生信筆記

注意：今天的教程比较长，请规划好你的时间。本文是付费内容，在本文文末有本教程的全部的代码和示例数据。

输出结果

6 7 10

分析代码

关于WGCNA分析，如果你的数据量较大，建议使用服务期直接分析，本地分析可能导致R崩掉。

设置文件位置

setwd("~/00_WGCNA/20230217_WGCNA/WGCNA_01")

加载分析所需的安装包

install.packages("WGCNA")
#BiocManager::install('WGCNA')
library(WGCNA)
options(stringsAsFactors = FALSE)

注意，如果你想打开多线程分析，可以使用一下代码

enableWGCNAThreads()
取决于你的电脑线程数量

一、导入基因表达量数据

## 读取txt文件格式数据
WGCNA.fpkm = read.table("ExpData_WGCNA.txt",header=T,
                        comment.char = "",
                        check.names=F)
###############
# 读取csv文件格式
WGCNA.fpkm = read.csv("ExpData_WGCNA.csv", header = T, check.names = F)
输入数据格式

数据处理

dim(WGCNA.fpkm)
names(WGCNA.fpkm)
datExpr0 = as.data.frame(t(WGCNA.fpkm[,-1]))
names(datExpr0) = WGCNA.fpkm$sample;##########如果第一行不是ID命名，就写成fpkm[,1]
rownames(datExpr0) = names(WGCNA.fpkm[,-1])

过滤数据

gsg = goodSamplesGenes(datExpr0, verbose = 3)
gsg$allOK
if (!gsg$allOK)
{
  if (sum(!gsg$goodGenes)>0)
    printFlush(paste("Removing genes:", paste(names(datExpr0)[!gsg$goodGenes], collapse = ", ")))
  if (sum(!gsg$goodSamples)>0)
    printFlush(paste("Removing samples:", paste(rownames(datExpr0)[!gsg$goodSamples], collapse = ", ")))
  # Remove the offending genes and samples from the data:
  datExpr0 = datExpr0[gsg$goodSamples, gsg$goodGenes]
}

过滤低于设定的值的基因

##filter
meanFPKM=0.5  ###--过滤标准，可以修改
n=nrow(datExpr0)
datExpr0[n+1,]=apply(datExpr0[c(1:nrow(datExpr0)),],2,mean)
datExpr0=datExpr0[1:n,datExpr0[n+1,] > meanFPKM]
# for meanFpkm in row n+1 and it must be above what you set--select meanFpkm>opt$meanFpkm(by rp)
filtered_fpkm=t(datExpr0)
filtered_fpkm=data.frame(rownames(filtered_fpkm),filtered_fpkm)
names(filtered_fpkm)[1]="sample"
head(filtered_fpkm)
write.table(filtered_fpkm, file="mRNA.filter.txt",
            row.names=F, col.names=T,quote=FALSE,sep="\t")

Sample cluster

sampleTree = hclust(dist(datExpr0), method = "average")
pdf(file = "1.sampleClustering.pdf", width = 15, height = 8)
par(cex = 0.6)
par(mar = c(0,6,6,0))
plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 2,
     cex.axis = 1.5, cex.main = 2)
### Plot a line to show the cut
#abline(h = 180, col = "red")##剪切高度不确定，故无红线
dev.off()

不过滤数据

如果你的数据不进行过滤直接进行一下操作,此步与前面的操作相同，任选异种即可。

## 不过滤
## Determine cluster under the line
clust = cutreeStatic(sampleTree, cutHeight = 50000, minSize = 10)
table(clust)
# clust 1 contains the samples we want to keep.
keepSamples = (clust!=0)
datExpr0 = datExpr0[keepSamples, ]
write.table(datExpr0, file="mRNA.symbol.uniq.filter.sample.txt",
            row.names=T, col.names=T,quote=FALSE,sep="\t")

###
#############Sample cluster###########
sampleTree = hclust(dist(datExpr0), method = "average")
pdf(file = "1.sampleClustering.filter.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)
### Plot a line to show the cut
#abline(h = 50000, col = "red")##剪切高度不确定，故无红线
dev.off()

二、导入性状数据

traitData = read.table("TraitData.txt",row.names=1,header=T,comment.char = "",check.names=F)
allTraits = traitData
dim(allTraits)
names(allTraits)

## 形成一个类似于表达数据的数据框架
fpkmSamples = rownames(datExpr0)
traitSamples =rownames(allTraits)
traitRows = match(fpkmSamples, traitSamples)
datTraits = allTraits[traitRows,]
rownames(datTraits)
collectGarbage()

再次样本聚类

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)

输出样本聚类图

pdf(file="2.Sample_dendrogram_and_trait_heatmap.pdf",width=20,height=12)
plotDendroAndColors(sampleTree2, traitColors,
                    groupLabels = names(datTraits),
                    main = "Sample dendrogram and trait heatmap",cex.colorLabels = 1.5, cex.dendroLabels = 1, cex.rowText = 2)
dev.off()

三、WGCNA分析（后面都是重点）

筛选软阈值

enableWGCNAThreads()
# 设置soft-thresholding powers的数量
powers = c(1:30)
sft = pickSoftThreshold(datExpr0, powerVector = powers, verbose = 5)

此步骤是比较耗费时间的，静静等待即可。


绘制soft Threshold plot

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=0.8,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()

选择softpower

选择softpower是一个玄学的过程，可以直接使用软件自己认为是最好的softpower值，但是不一定你要获得最好结果；其次，我们自己选择自己认为比较好的softpower值，但是，需要自己不断的筛选。因此，从这里开始WGCNA的分析结果就开始受到不同的影响。

## 选择软件认为是最好的softpower值
#softPower =sft$powerEstimate
---
# 自己设定softpower值
softPower = 9

继续分析

adjacency = adjacency(datExpr0, power = softPower)

将邻接转化为拓扑重叠

这一步建议去服务器上跑，后面的步骤就在服务器上跑吧，数据量太大；如果你的数据量较小，本地也就可以

TOM = TOMsimilarity(adjacency);
dissTOM = 1-TOM

geneTree = hclust(as.dist(dissTOM), method = "average");

绘制聚类树(树状图)

pdf(file="4_Gene clustering on TOM-based dissimilarity.pdf",width=24,height=18)
plot(geneTree, xlab="", sub="", main = "Gene clustering on TOM-based dissimilarity",
     labels = FALSE, hang = 0.04)
dev.off()

加入模块

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()

合并模块

做出的WGCNA分析中，具有较多的模块，但是在我们后续的分析中，是使用不到这么多的模块，以及模块越多对我们的分析越困难，那么就必须合并模块信息。具体操作如下。

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 = "")

######剪切高度可修改
MEDissThres = 0.4  
# Plot the cut line into the dendrogram
abline(h=MEDissThres, col = "red")
dev.off()

合并及绘图 
 = mergeCloseModules(datExpr0, dynamicColors, cutHeight = MEDissThres, verbose = 3)
# The merged module colors
mergedColors = merge$colors
# Eigengenes of the new merged modules:
mergedMEs = merge$newMEs
table(mergedColors)

#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

性状数据与基因模块进行分析

nGenes = ncol(datExpr0)
nSamples = nrow(datExpr0)
moduleTraitCor = cor(MEs, datTraits, use = "p")
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)

绘制模块性状相关性图

查看教程详细代码文本及代码链接：https://mp.weixin.qq.com/s/M0LAlE-61f2ZfpMiWN-iQg

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