WGCNA（7）重新绘制GS—MM散点图

GS-MM散点图用于筛选hub gene，但是之前的图实在是有点丑，放在文章里好像有点拉胯，所以准备重新绘制。前期过程在专题（3）中已经介绍过了，这里只有后期优化。
WGCNA（3）：基因模块与性状关联识别重要基因 - 简书 (jianshu.com)
首先看一下，原图长这样，如果是颜色浅一点的module就完全看不清楚点。

Fig.0

1.更改点的形状

通过pch()进行调整

> verboseScatterplot(abs(geneModuleMembership[moduleGenes, 
  column]),abs(geneTraitSignificance[moduleGenes, 1]), xlab = 
  paste("Module Membership in", module, "module"), ylab = "Gene 
  significance for TL", main = paste("Module membership 
  vs. gene significance"), pch = 20)

Fig.1

2.加入筛选hub gene的标准线

• abline()：h水平线，v垂直线，lwd设置线粗细

> verboseScatterplot(abs(geneModuleMembership[moduleGenes, 
column]),abs(geneTraitSignificance[moduleGenes, 1]), xlab = 
 paste("Module Membership in", module, "module"), ylab = "Gene 
   significance for TL", main = paste("Module membership 
   vs. gene significance"), pch = 20,col="grey")
> abline(h=0.2,v=0.8,col="red",lwd=1.5)

Fig.2

3.区分点的颜色

这个图出来，我还是觉得不是很清楚，点实在是太多了，是不是可以筛选出的hub gene用鲜艳的颜色，其余基因用灰色。

3.1 提取感兴趣的module内所有的基因名称

> module = "yellow"
> column = match(module, modNames)
> moduleGenes = moduleColors==module
> table(moduleGenes)
moduleGenes
FALSE  TRUE 
38899  2726 
> yellow_module<-as.data.frame(dimnames(data.frame(datExpr))[[2]][moduleGenes]) 
> names(yellow_module)="genename"

3.2 筛选hub gene

> hub<- abs(geneModuleMembership$MMyellow)>0.8 & abs(geneTraitSignificance)>0.2
> table(hub)
hub
FALSE  TRUE 
41563    62 
> hubgene_yellow <- dimnames(data.frame(datExpr))[[2]][hub]

3.3 构建散点图矩阵

> MM<-abs(geneModuleMembership[moduleGenes,column])
> GS<-abs(geneTraitSignificance[moduleGenes, 1])
> c<-as.data.frame(cbind(MM,GS))
> rownames(c)=yellow_module$genename
> head(c)
                                   MM         GS
evm.model.scaffold_735.38   0.3925084 0.44454629
evm.model.scaffold_439.8    0.4592395 0.39223841
evm.model.scaffold_9970.56  0.3424278 0.28887226
evm.model.scaffold_1431.175 0.4297850 0.23670725
evm.model.scaffold_248.130  0.5169041 0.33374737
evm.model.scaffold_1265.20  0.3013020 0.02636731

3.4 对基因进行分组

# hub基因和module内全部基因进行匹配，匹配成功返回1，没有匹配到的返回0
> match<- yellow_module$genename %in% hub_gene$yellow
# 将匹配信息添加到散点图矩阵最后一列
> c$group<-match
> head(c)
                                   MM         GS group
evm.model.scaffold_735.38   0.3925084 0.44454629 FALSE
evm.model.scaffold_439.8    0.4592395 0.39223841 FALSE
evm.model.scaffold_9970.56  0.3424278 0.28887226 FALSE
evm.model.scaffold_1431.175 0.4297850 0.23670725 FALSE
evm.model.scaffold_248.130  0.5169041 0.33374737 FALSE
evm.model.scaffold_1265.20  0.3013020 0.02636731 FALSE

3.5 利用ggplot2绘图

> library(ggplot2)
> pdf("MM vs. GS_yellow_TL.pdf",width = 7,height = 7)
> ggplot(data=c, aes(x=MM, y=GS, color=group))+geom_point(size=1.5)+scale_colour_manual(values=c("grey60", "#DE6757"))+ theme_bw()+
+     theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank())+  labs(x="Module Membership in yellow module", y="Gene significance for TL",title = "Module membership vs. gene significance ")+
+     theme(axis.title.x =element_text(size=14), axis.title.y=element_text(size=14),axis.text = element_text(size = 12),axis.text.x = element_text(colour = "black"),axis.text.y = element_text(colour = "black"),plot.title = element_text(hjust = 0.5,size = 16,face = "bold"),plot.margin = unit(rep(2,4),'lines')) +theme(legend.position = 'none')+geom_hline(aes(yintercept=0.2),colour="#5B9BD5",lwd=1,linetype=5)+geom_vline(aes(xintercept=0.8),colour="#5B9BD5",lwd=1,linetype=5)
> dev.off()

Fig.3