学习WGCNA总结

分析构建的网络寻找以下有用信息

这类处于调控网络中心的基因称为核心基因（hub gene），这类基因通常是转录因子等关键的调控因子，是值得我们优先深入分析和挖掘的对象。

在网络中，被调控线连接的基因，其表达模式是相似的。那么它们潜在有相似的功能。所以，在这个网络中，如果线条一端的基因功能是已知的，那么就可以预测线条另一端的功能未知的基因也有相似的功能。
输入数据为RNA-seq不同处理或组织所有样本的FPKM值组成的矩阵，切记含有 0 的要去掉；
setwd("F:/WGCNA")
library(WGCNA)
options(stringsAsFactors = FALSE)
enableWGCNAThreads()
#############################################################################
####################### 一、 数据读入，处理和保存 ##############################
#############################################################################
fpkm<- read.csv("trans_counts.counts.matrix.TMM_normalized.FPKM.nozero.csv")
#~~~~~~~~~~~~~~~~~~
head(fpkm)
   GeneID PN_1_TPM PN_1_07_TPM PN_1_08_TPM PN_2_TPM PN_2_09_TPM PN_2_10_TPM
1 MSTRG.1 0.000000    1.456143    1.093308 0.204315    0.000000    0.000000
2 MSTRG.2 1.516181    0.849313    2.010783 1.567867    2.045446    2.246402
3 MSTRG.3 1.305084    1.207246    0.889166 1.470162    0.340003    0.421222
4 MSTRG.4 2.744250    2.791988    2.500786 2.719017    1.954149    2.468110
5 MSTRG.5 1.946825    1.470012    1.263171 0.205806    1.644505    1.638583
6 MSTRG.6 1.325277    0.793530    1.932236 1.210156    1.834274    2.153466
#~~~~~~~~~~~~~~~~~~
dim(fpkm)
names(fpkm)
datExpr0=as.data.frame(t(fpkm[,-c(1)]));
names(datExpr0)=fpkm$trans;
rownames(datExpr0)=names(fpkm)[-c(1)];
#data<-log10(date[,-1]+0.01)
# *************************************************************
# 检测输入基因是否含有缺失值,并处理 ******************************
# *************************************************************
gsg = goodSamplesGenes(datExpr0, verbose = 3);
gsg$allOK
#~~~~~~~~~~~~~~~~~~
# 如果上一步返回TRUE则跳过此步，如果返回FALSE则执行如下if语句去掉存在较多缺失值的基因所在行
if (!gsg$allOK)
{
  # Optionally, print the gene and sample names that were removed:
  if (sum(!gsg$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$goodGenes]
}
# 再次检测
dim(datExpr0)
gsg = goodSamplesGenes(datExpr0, verbose = 3);
gsg$allOK
#~~~~~~~~~~~~~~~~~~
# ***************************************************************
# 聚类检测输入样本是否含有异常值（obvious outliers）,并处理 ********
# ***************************************************************
sampleTree = hclust(dist(datExpr0), method = "average")
#sizeGrWindow(12,9)
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)
abline(h = 80000, col = "red");
clust = cutreeStatic(sampleTree, cutHeight = 80000, minSize = 10)
table(clust)
keepSamples = (clust==1)
datExpr = datExpr0[keepSamples, ]
nGenes = ncol(datExpr)
nSamples = nrow(datExpr)
save(datExpr, file = "AS-green-FPKM-01-dataInput.RData")
#############################################################################
############################ 二、 选择合适的阀值 ##############################
#############################################################################
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")

#############################################################################
############################ 三、 网络构建和可视化 #############################
#############################################################################
#=====================================================================================
#=====================================================================================
# 网络构建有两种方法，One-step和Step-by-step；
# 第一种：一步法进行网络构建
#=====================================================================================
#=====================================================================================

################################################################################
### 3.1 一步法网络构建：One-step network construction and module detection ######
#3.1.1. 网络构建 ###############################################################
###############################################################################
dim(datExpr)
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$colors)
###############################################################################
#3.1.2. 绘画结果展示 ###########################################################
###############################################################################
# open a graphics window
#sizeGrWindow(12, 9)
# Convert labels to colors for plotting
mergedColors = labels2colors(net$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)
###############################################################################
#3.1.3. 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")
###############################################################################
#3.1.4. 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，根据table(moduleColors)的结果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 = names(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]);
#################################################################################################
#3.1.5. 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)
#====================================
#3.1.5.1. 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")
#====================================
#3.1.5.2. 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")
#=====================================================================================
#=====================================================================================
#  第二种：一步步的进行网络构建
#=====================================================================================
#=====================================================================================

###############################################################################
### 3.2 Step-by-step network construction and module detection ################
###############################################################################
#2.选择合适的阀值，同上
###############################################################################
#3.2.1. 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)
###############################################################################
#3.2.2. 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")
###############################################################################
#3.2.3. 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$newMEs;
#绘制融合前(Dynamic Tree Cut)和融合后(Merged dynamic)的聚类图
#sizeGrWindow(12, 9)
#pdf(file = "Plots/geneDendro-3.pdf", wi = 9, he = 6)
plotDendroAndColors(geneTree, cbind(dynamicColors, mergedColors),
                    c("Dynamic Tree Cut", "Merged dynamic"),
                    dendroLabels = FALSE, hang = 0.03,
                    addGuide = TRUE, guideHang = 0.05)
#dev.off()
# 只是绘制融合后聚类图
plotDendroAndColors(geneTree,mergedColors,"Merged dynamic",
                    dendroLabels = FALSE, hang = 0.03,
                    addGuide = TRUE, guideHang = 0.05)
###############################################################################
#3.2.4. 结果保存 ###############################################################
###############################################################################
# Rename to moduleColors
moduleColors = mergedColors
# Construct numerical labels corresponding to the colors
colorOrder = c("grey", standardColors(50));
moduleLabels = match(moduleColors, colorOrder)-1;
MEs = mergedMEs;
# Save module colors and labels for use in subsequent parts
save(MEs, moduleLabels, moduleColors, geneTree, file = "AS-green-FPKM-02-networkConstruction-stepByStep.RData")
###############################################################################
#3.2.5. 导出网络到Cytoscape ####################################################
###############################################################################
# Recalculate topological overlap if needed
TOM = TOMsimilarityFromExpr(datExpr, power = 6);
# Read in the annotation file
# annot = read.csv(file = "GeneAnnotation.csv");
# Select modules需要修改
modules = c("brown", "red");
# Select module probes
probes = names(datExpr)
inModule = is.finite(match(moduleColors, modules));
modProbes = probes[inModule];
#modGenes = annot$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]);
#################################################################################################
#3.2.6. 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)
#====================================
#3.2.6.1. 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")
#====================================
#3.2.6.2. 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")
#此处画的是根据基因间表达量进行聚类所得到的各模块间的相关性图
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)
分析构建的网络寻找以下有用信息

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