如果你还没有安装,就运行下面的代码安装:
BiocInstaller::biocLite('CLL')
install.packages('corrplot')
install.packages('gpairs')
install.packages('vioplot')
如果你安装好了,就直接加载它们即可
library(CLL)
library(ggplot2)
library(reshape2)
library(gpairs)
library(corrplot)
data(sCLLex)
sCLLex=sCLLex[,1:8] ## 样本太多,我就取前面8个
group_list=sCLLex$Disease
exprSet=exprs(sCLLex)
head(exprSet)
## CLL11.CEL CLL12.CEL CLL13.CEL CLL14.CEL CLL15.CEL CLL16.CEL
## 1000_at 5.743132 6.219412 5.523328 5.340477 5.229904 4.920686
## 1001_at 2.285143 2.291229 2.287986 2.295313 2.662170 2.278040
## 1002_f_at 3.309294 3.318466 3.354423 3.327130 3.365113 3.568353
## 1003_s_at 1.085264 1.117288 1.084010 1.103217 1.074243 1.073097
## 1004_at 7.544884 7.671801 7.474025 7.152482 6.902932 7.368660
## 1005_at 5.083793 7.610593 7.631311 6.518594 5.059087 4.855161
## CLL17.CEL CLL18.CEL
## 1000_at 5.325348 4.826131
## 1001_at 2.350796 2.325163
## 1002_f_at 3.502440 3.394410
## 1003_s_at 1.091264 1.076470
## 1004_at 6.456285 6.824862
## 1005_at 5.176975 4.874563
group_list
## [1] progres. stable progres. progres. progres. progres. stable stable
## Levels: progres. stable
主要用到ggplot2这个包,需要把我们的宽矩阵用reshape2包变成长矩阵
library(reshape2)
exprSet_L=melt(exprSet)
colnames(exprSet_L)=c('probe','sample','value')
exprSet_L$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.
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)
#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)
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)
p=ggplot(exprSet_L,aes(value,fill=group))+geom_histogram(bins = 200)+facet_wrap(~sample, nrow = 4)
print(p)
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)
p=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)
library(gpairs)
gpairs(exprSet
#,upper.pars = list(scatter = 'stats')
#,lower.pars = list(scatter = 'corrgram')
)
out.dist=dist(t(exprSet),method='euclidean')
out.hclust=hclust(out.dist,method='complete')
plot(out.hclust)
pc <- 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)
choose_gene=names(sort(apply(exprSet, 1, mad),decreasing = T)[1:50])
choose_matrix=exprSet[choose_gene,]
choose_matrix=scale(choose_matrix)
heatmap(choose_matrix)