在前面的帖子中介绍了数据的导入和清洗，网络构建的两种方法，模块与性状的关联，这篇文章将介绍如果进行模块可视化。
WGCNA（1）：R包安装及数据导入清洗 - 简书 (jianshu.com)
WGCNA（2a）：一步法完成网络构建和模块检测 - 简书 (jianshu.com)
WGCNA（2b）：分步法完成网络构建和模块检测 - 简书 (jianshu.com)
WGCNA（3）：基因模块与性状关联识别重要基因 - 简书 (jianshu.com)
1. 准备工作
导入前期数据，这里我选择了分步法构建网络的结果，大家可以根据自己的数据选择使用哪种方法构建网络。
# 设置工作目录
> setwd("D:/RNA-seq/WGCNA/mad0.3/cor 0.25")
# 载入WGCNA包
> library('WGCNA')
# 允许R语言以最大线程运行
> options(stringsAsFactors = FALSE)
> allowWGCNAThreads()
Allowing multi-threading with up to 4 threads.
# 载入第一步中的表达量和表型值
> lnames = load(file = "WGCNA0.3-dataInput.RData")
> lnames
# 载入第二步的网络数据
> lnames = load(file = "networkConstruction-stepByStep.RData")
> lnames
> nGenes = ncol(datExpr)
> nSamples = nrow(datExpr)
2. 利用R进行网络可视化
2.1 可视化TOM矩阵
以下代码只适用于一步法或分步法构建的网络，如果是 block-wise方法，则需要各个部分分开绘图。
# 模块检测时的计算，重新算一次
> dissTOM = 1-TOMsimilarityFromExpr(datExpr, power = 6)
# 转换dissTOM，方便在热图中显示
> plotTOM = dissTOM^7
# 将对角线设置为NA
> diag(plotTOM) = NA
# 绘图
> pdf("Network heatmap plot.pdf", width=9, height=9)
> TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot_all genes")
> dev.off()
Figure 1: Visualizing the gene network using a heatmap plot. The heatmap depicts the Topological Overlap Matrix
(TOM) among all genes in the analysis. Light color represents low overlap and progressively darker red color
represents higher overlap. Blocks of darker colors along the diagonal are the modules. The gene dendrogram and
module assignment are also shown along the left side and the top.
部分可视化TOM矩阵
上面这张图画起来非常非常慢，又占时间，又占内存，而且意义也不是很大，感觉是在凑图，4w个基因，服务器上要画半个多小时，所以，如果实在想要这张图凑数据，其实选部分基因就可以了（Fig.2）。
> 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
> pdf("Network heatmap plot_selected genes.pdf", width = 9, height = 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")
> dev.off()
这个热图的配色可以改，详情见：https://cloud.tencent.com/developer/article/1628251?from=article.detail.1516749
Fig.2 Network heatmap plot_selected genes.
2.2 eigengenes可视化
在eigengenes中加入表型
# 提取性状weight
> weight = as.data.frame(datTraits$weight_g)
> names(weight) = "weight"
# 在eigengenes模块中加入性状
> MET = orderMEs(cbind(MEs, weight))
# 绘制eigengenes和性状之间的关系图
> pdf("Visualization of the eigengene network representing the relationships among the modules and the clinical trait weight.pdf",width = 7, height=6)
#一页多图，一行2列
> par(mfrow = c(1,2))
#字号
> cex1 = 0.9
> plotEigengeneNetworks(MET, "Eigengene dendrogram", marDendro = c(0,4,2,0), plotHeatmaps = FALSE)
> plotEigengeneNetworks(MET, "Eigengene adjacency heatmap", marHeatmap = c(3,4,2,2), plotDendrograms = FALSE, xLabelsAngle = 90)
> dev.off()
图中可以看出weight性状和blue、brown还有red模块聚类在一起。
Figure 3: Visualization of the eigengene network representing the relationships among the modules and the clinical
trait weight. Panel (a) shows a hierarchical clustering dendrogram of the eigengenes in which the dissimilarity of
eigengenes EI , EJ is given by 1 − cor(EI , EJ ). The heatmap in panel (b) shows the eigengene adjacency AIJ =
(1 + cor(EI , EJ ))/2.
