WGCNA是一个R包，对一个完全不会R的人来说，确实费了一番功夫，不过也将我对R的学习提前提上日程。
分析步骤：
1.WGCNA安装
source("http://bioconductor.org/biocLite.R")
biocLite(c("AnnotationDbi", "impute", "GO.db", "preprocessCore"))
install.packages("WGCNA")
2.输入数据准备
原始数据https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48213，共56个样本；对数据的合并可参考http://www.biotrainee.com:8080/thread-603-1-1.html，输入文件格式如下：
setwd('WGCNA/')
fpkm=read.table('test.txt',sep = '\t',stringsAsFactors = F)
head(fpkm)
dim(fpkm) ##共56个细胞系，36953个基因的表达矩阵
names(fpkm)##细胞系名称
3.数据预处理
library(reshape2)
library('WGCNA')
RNAseq_voom <- fpkm
WGCNA_matrix = t(RNAseq_voom[order(apply(RNAseq_voom,1,mad), decreasing = T)[1:3000],]) ##top 3000 mad genes，t参数将矩阵变为符合WGCNA要求的形式：行名为gene，列名为样品
datExpr <- WGCNA_matrix
save(datExpr, file = "AS-green-FPKM-01-dataInput.RData")
4.确定最佳beta值
powers = c(c(1:10), seq(from = 12, to=20, by=2))
# Call the network topology analysis function
sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)
# Plot the results:
##sizeGrWindow(9, 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")
最佳的beta值就是sft$powerEstimate,此例为6
5.一步法构建共表达矩阵
net = blockwiseModules(datExpr, power = 6, maxBlockSize = 6000,
TOMType = "unsigned", minModuleSize = 30,
reassignThreshold = 0, mergeCutHeight = 0.25,
numericLabels = TRUE, pamRespectsDendro = FALSE,
saveTOMs = TRUE,
saveTOMFileBase = "AS-green-FPKM-TOM",
verbose = 3)
table(net$colors)
#绘图结果展示
mergedColors = labels2colors(net$colors)
# Plot the dendrogram and the module colors underneath
plotDendroAndColors(net$dendrograms[[1]], mergedColors[net$blockGenes[[1]]],
"Module colors",
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)
#结果保存
moduleLabels = net$colors
moduleColors = labels2colors(net$colors)
table(moduleColors)
MEs = net$MEs;
geneTree = net$dendrograms[[1]];
save(MEs, moduleLabels, moduleColors, geneTree,
file = "AS-green-FPKM-02-networkConstruction-auto.RData")
#对样本做个系统聚类树
#明确基因数和样本数
nGenes = ncol(datExpr)
nSamples = nrow(datExpr)
datExpr_tree<-hclust(dist(datExpr), method = "average")
par(mar = c(0,5,2,0))
plot(datExpr_tree, main = "Sample clustering", sub="", xlab="", cex.lab = 2,
cex.axis = 1, cex.main = 1,cex.lab=1)
6.导出网络到Cytoscape
# Recalculate topological overlap if needed
TOM = TOMsimilarityFromExpr(datExpr, power = 6);
# Read in the annotation file
# annot = read.csv(file = "GeneAnnotation.csv");
# Select modules需要修改，选择需要导出的模块颜色
modules = c("turquoise", "blue");
# Select module probes选择模块探测
probes = colnames(datExpr)
inModule = is.finite(match(moduleColors, modules));
modProbes = probes[inModule];
#modGenes = annot$gene_symbol[match(modProbes, annot$substanceBXH)];
# 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("AS-green-FPKM-One-step-CytoscapeInput-edges-", paste(modules, collapse="-"), ".txt", sep=""),
nodeFile = paste("AS-green-FPKM-One-step-CytoscapeInput-nodes-", paste(modules, collapse="-"), ".txt", sep=""),
weighted = TRUE,
threshold = 0.02,
nodeNames = modProbes,
#altNodeNames = modGenes,
nodeAttr = moduleColors[inModule]);
7.分析网络可视化
用heatmap可视化权重网络，heatmap每一行或列对应一个基因，颜色越深表示有较高的邻近
options(stringsAsFactors = FALSE);
lnames = load(file = "AS-green-FPKM-01-dataInput.RData");
lnames
lnames = load(file = "AS-green-FPKM-02-networkConstruction-auto.RData");
lnames
nGenes = ncol(datExpr)
nSamples = nrow(datExpr)
#可视化全部基因网络
# Calculate topological overlap anew: this could be done more efficiently by saving the TOM
# calculated during module detection, but let us do it again here.
dissTOM = 1-TOMsimilarityFromExpr(datExpr, power = 6);
# 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)
TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")
#随便选取1000个基因来可视化
nSelect = 1000
# 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;
TOMplot(plotDiss, selectTree, selectColors, main = "Network heatmap plot, selected genes")
8.多步法构建网络
#2.选择合适的阀值，同上
#3. 网络构建：(1) Co-expression similarity and adjacency
softPower = 6;
adjacency = adjacency(datExpr, power = softPower);
#(2) 邻近矩阵到拓扑矩阵的转换，Turn adjacency into topological overlap
TOM = TOMsimilarity(adjacency);
dissTOM = 1-TOM
# (3) 聚类拓扑矩阵
#Call the hierarchical clustering function
geneTree = hclust(as.dist(dissTOM), method = "average");
# Plot the resulting clustering tree (dendrogram)
#sizeGrWindow(12,9)
plot(geneTree, xlab="", sub="", main = "Gene clustering on TOM-based dissimilarity",
labels = FALSE, hang = 0.04);
#(4) 聚类分支的休整dynamicTreeCut
# 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)
#4. 绘画结果展示
# Convert numeric lables into colors
dynamicColors = labels2colors(dynamicMods)
table(dynamicColors)
# Plot the dendrogram and colors underneath
#sizeGrWindow(8,6)
plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut",
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05,
main = "Gene dendrogram and module colors")
#5. 聚类结果相似模块的融合，Merging of modules whose expression profiles are very similar
#在聚类树中每一leaf是一个短线，代表一个基因，
#不同分之间靠的越近表示有高的共表达基因，将共表达极其相似的modules进行融合
# Calculate eigengenes
MEList = moduleEigengenes(datExpr, 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)
plot(METree, main = "Clustering of module eigengenes",
xlab = "", sub = "")
#选择有75%相关性的进行融合
MEDissThres = 0.25
# Plot the cut line into the dendrogram
abline(h=MEDissThres, col = "red")
# Call an automatic merging function
merge = mergeCloseModules(datExpr, dynamicColors, cutHeight = MEDissThres, verbose = 3)
# The merged module colors
mergedColors = merge$colors;
# Eigengenes of the new merged modules:
mergedMEs = merge$newMEs;
#绘制融合前(Dynamic Tree Cut)和融合后(Merged dynamic)的聚类图
#sizeGrWindow(12, 9)
#pdf(file = "Plots/geneDendro-3.pdf", wi = 9, he = 6)
plotDendroAndColors(geneTree, cbind(dynamicColors, mergedColors),
c("Dynamic Tree Cut", "Merged dynamic"),
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)
#dev.off()
# 只是绘制融合后聚类图
plotDendroAndColors(geneTree,mergedColors,"Merged dynamic",
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)
#5.结果保存
# 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, moduleLabels, moduleColors, geneTree, file = "AS-green-FPKM-02-networkConstruction-stepByStep.RData")
#6. 导出网络到Cytoscape
# Recalculate topological overlap if needed
TOM = TOMsimilarityFromExpr(datExpr, power = 6);
# Read in the annotation file
# annot = read.csv(file = "GeneAnnotation.csv");
# Select modules需要修改
modules = c("brown", "red");
# Select module probes
probes = colnames(datExpr)
inModule = is.finite(match(moduleColors, modules));
modProbes = probes[inModule];
#modGenes = annot$gene_symbol[match(modProbes, annot$substanceBXH)];
# 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("AS-green-FPKM-Step-by-step-CytoscapeInput-edges-", paste(modules, collapse="-"), ".txt", sep=""),
nodeFile = paste("AS-green-FPKM-Step-by-step-CytoscapeInput-nodes-", paste(modules, collapse="-"), ".txt", sep=""),
weighted = TRUE,
threshold = 0.02,
nodeNames = modProbes,
#altNodeNames = modGenes,
nodeAttr = moduleColors[inModule]);
#=====================================================================================
# 分析网络可视化，用heatmap可视化权重网络，heatmap每一行或列对应一个基因，颜色越深表示有较高的邻近
#=====================================================================================
options(stringsAsFactors = FALSE);
lnames = load(file = "AS-green-FPKM-01-dataInput.RData");
lnames
lnames = load(file = "AS-green-FPKM-02-networkConstruction-stepByStep.RData");
lnames
nGenes = ncol(datExpr)
nSamples = nrow(datExpr)
#1. 可视化全部基因网络
# Calculate topological overlap anew: this could be done more efficiently by saving the TOM
# calculated during module detection, but let us do it again here.
dissTOM = 1-TOMsimilarityFromExpr(datExpr, power = 6);
# 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)
TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")
#随便选取1000个基因来可视化
nSelect = 1000
# 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;
TOMplot(plotDiss, selectTree, selectColors, main = "Network heatmap plot, selected genes")
根据基因间表达量进行模块聚类（前边得到）和所得到的各模块间的相关性
MEs = moduleEigengenes(datExpr, moduleColors)$eigengenes
MET = orderMEs(MEs)
sizeGrWindow(7, 6)
plotEigengeneNetworks(MET, "Eigengene adjacency heatmap", marHeatmap = c(3,4,2,2), plotDendrograms = FALSE, xLabelsAngle = 90)
脚本来自：
http://tiramisutes.github.io/2016/09/14/WGCNA.html
https://bioconductor.org/packages/devel/bioc/vignettes/CVE/inst/doc/WGCNA_from_TCGA_RNAseq.html
http://www.bio-info-trainee.com/2535.html
