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basic visualization for expression matrixMarch 14, 2017安裝并加載必須的packages如果你還沒(méi)有安裝，就運(yùn)行下面的代碼安裝：
BiocInstaller::biocLite('CLL')install.packages('corrplot')install.packages('gpairs')install.packages('vioplot')如果你安裝好了，就直接加載它們即可
library(CLL)library(ggplot2)library(reshape2)library(gpairs)library(corrplot)加載內(nèi)置的測(cè)試數(shù)據(jù)：data(sCLLex)sCLLex=sCLLex[,1:8] ## 樣本太多，我就取前面8個(gè)
group_list=sCLLex$DiseaseexprSet=exprs(sCLLex)head(exprSet)## CLL11.CEL CLL12.CEL CLL13.CEL CLL14.CEL CLL15.CEL CLL16.CEL
## 1000_at 5.743132 6.219412 5.523328 5.340477 5.229904 4.920686
## 1001_at 2.285143 2.291229 2.287986 2.295313 2.662170 2.278040
## 1002_f_at 3.309294 3.318466 3.354423 3.327130 3.365113 3.568353
## 1003_s_at 1.085264 1.117288 1.084010 1.103217 1.074243 1.073097
## 1004_at 7.544884 7.671801 7.474025 7.152482 6.902932 7.368660
## 1005_at 5.083793 7.610593 7.631311 6.518594 5.059087 4.855161
## CLL17.CEL CLL18.CEL
## 1000_at 5.325348 4.826131
## 1001_at 2.350796 2.325163
## 1002_f_at 3.502440 3.394410
## 1003_s_at 1.091264 1.076470
## 1004_at 6.456285 6.824862
## 1005_at 5.176975 4.874563group_list## [1] progres. stable progres. progres. progres. progres. stable stable
## Levels: progres. stable接下來(lái)進(jìn)行一系列繪圖操作主要用到ggplot2這個(gè)包，需要把我們的寬矩陣用reshape2包變成長(zhǎng)矩陣
library(reshape2)exprSet_L=melt(exprSet)colnames(exprSet_L)=c('probe','sample','value')exprSet_L$group=rep(group_list,each=nrow(exprSet))head(exprSet_L)## probe sample value group
## 1 1000_at CLL11.CEL 5.743132 progres.
## 2 1001_at CLL11.CEL 2.285143 progres.
## 3 1002_f_at CLL11.CEL 3.309294 progres.
## 4 1003_s_at CLL11.CEL 1.085264 progres.
## 5 1004_at CLL11.CEL 7.544884 progres.
## 6 1005_at CLL11.CEL 5.083793 progres.boxplotp=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()print(p)
vioplot#library(vioplot)#?vioplot#vioplot(exprSet)#do.call(vioplot,c(unname(exprSet),col='red',drawRect=FALSE,names=list(names(exprSet))))p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_violin()print(p)
histogramp=ggplot(exprSet_L,aes(value,fill=group))+geom_histogram(bins = 200)+facet_wrap(~sample, nrow = 4)print(p)
densityp=ggplot(exprSet_L,aes(value,col=group))+geom_density()+facet_wrap(~sample, nrow = 4)print(p)
p=ggplot(exprSet_L,aes(value,col=group))+geom_density() print(p)
gpairslibrary(gpairs)gpairs(exprSet
#,upper.pars = list(scatter = 'stats')
#,lower.pars = list(scatter = 'corrgram')
)
clusterout.dist=dist(t(exprSet),method='euclidean')out.hclust=hclust(out.dist,method='complete')plot(out.hclust)
PCApc <- prcomp(t(exprSet),scale=TRUE)pcx=data.frame(pc$x)pcr=cbind(samples=rownames(pcx),group_list, pcx) p=ggplot(pcr, aes(PC1, PC2))+geom_point(size=5, aes(color=group_list)) +
geom_text(aes(label=samples),hjust=-0.1, vjust=-0.3)print(p)
heatmapchoose_gene=names(sort(apply(exprSet, 1, mad),decreasing = T)[1:50])choose_matrix=exprSet[choose_gene,]choose_matrix=scale(choose_matrix)heatmap(choose_matrix)
library(gplots)##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowessheatmap.2(choose_matrix)
library(pheatmap)pheatmap(choose_matrix)
DEG && volcano plotlibrary(limma)##
## Attaching package: 'limma'## The following object is masked from 'package:BiocGenerics':
##
## plotMAdesign=model.matrix(~factor(group_list))fit=lmFit(exprSet,design)fit=eBayes(fit)DEG=topTable(fit,coef=2,n=Inf)with(DEG, plot(logFC, -log10(P.Value), pch=20, main="Volcano plot"))
logFC_cutoff <- with(DEG,mean(abs( logFC)) + 2*sd(abs( logFC)) )DEG$change = as.factor(ifelse(DEG$P.Value < 0.05 & abs(DEG$logFC) > logFC_cutoff, ifelse(DEG$logFC > logFC_cutoff ,'UP','DOWN'),'NOT')
)this_tile <- paste0('Cutoff for logFC is ',round(logFC_cutoff,3), '\nThe number of up gene is ',nrow(DEG[DEG$change =='UP',]) , '\nThe number of down gene is ',nrow(DEG[DEG$change =='DOWN',]))g = ggplot(data=DEG, aes(x=logFC, y=-log10(P.Value), color=change)) +
geom_point(alpha=0.4, size=1.75) +
theme_set(theme_set(theme_bw(base_size=20)))+
xlab("log2 fold change") + ylab("-log10 p-value") +
ggtitle( this_tile ) + theme(plot.title = element_text(size=15,hjust = 0.5))+
scale_colour_manual(values = c('blue','black','red')) ## corresponding to the levels(res$change)print(g)
ggplot畫圖是可以切換主題的其實(shí)繪圖有非常多的細(xì)節(jié)可以調(diào)整，還是略微有點(diǎn)繁瑣的！
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()print(p)
p=p+stat_summary(fun.y="mean",geom="point",shape=23,size=3,fill="red")p=p+theme_set(theme_set(theme_bw(base_size=20)))p=p+theme(text=element_text(face='bold'),axis.text.x=element_text(angle=30,hjust=1),axis.title=element_blank())print(p)
可以很明顯看到，換了主題之后的圖美觀一些。
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