WGCNA分析(五)网络可视化
2023-07-20 本文已影响0人
Bioinfor生信云
基因共表达网络可视化
计算 dissTOM, dissTOM = 1 - TOM
dissTOM = 1-TOMsimilarityFromExpr(Texp0, power = x$powerEstimate);
取 10次方,仅为展示更显著
plotTOM = dissTOM^10
绘图
diag(plotTOM) = NA;
sizeGrWindow(9,9)
geneTree = net$dendrograms[[1]]
TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")
![](https://img.haomeiwen.com/i27313279/f2159f2640db023c.png)
模块特征向量网络可视化
# 重新计算MEs
MEs = moduleEigengenes(Texp0, moduleColors)$eigengenes
和性状之间的关系图
sizeGrWindow(5,7.5)
par(cex = 0.9)
plotEigengeneNetworks(MEs, "", marDendro = c(0,4,1,2), marHeatmap = c(3,4,1,2), cex.lab = 0.8, xLabelsAngle
= 90)
![](https://img.haomeiwen.com/i27313279/a9334ca18d01b587.png)
绘制树状图
sizeGrWindow(6,6)
par(cex = 1.0)
plotEigengeneNetworks(MEs, "Eigengene dendrogram", marDendro = c(0,4,2,0),
plotHeatmaps = FALSE)
![](https://img.haomeiwen.com/i27313279/2de85e8d2f47d79e.png)
绘制热图矩阵
par(cex = 1.0)
plotEigengeneNetworks(MEs, "Eigengene adjacency heatmap", marHeatmap = c(3,4,2,2),
plotDendrograms = FALSE, xLabelsAngle = 90)
![](https://img.haomeiwen.com/i27313279/20d8935a999d0ad9.png)