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Day7-basic visualization for expression matrix-…

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R数据分析和画图

参考原文:生信技能树论坛basic visualization for expression matrix
结合Jimmy大神的B站视频(P24)生信人应该这样学R语言

安装并加载必须的packages

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)

加载内置的测试数据:

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

接下来进行一系列绘图操作

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.

boxplot

p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)


image.png

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)


image.png

boxplot

p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)


image.png

histogram

p=ggplot(exprSet_L,aes(value,fill=group))+geom_histogram(bins = 200)+facet_wrap(~sample, nrow = 4)
print(p)


image.png

boxplot

p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)


image.png

density

p=ggplot(exprSet_L,aes(value,col=group))+geom_density()+facet_wrap(~sample, nrow = 4)
print(p)

[图片上传中…(image-1167c7-1560694447736-9)]

p=ggplot(exprSet_L,aes(value,col=group))+geom_density() 
print(p)

[图片上传中…(image-48616c-1560694447736-8)]

gpairs

library(gpairs)
gpairs(exprSet
       #,upper.pars = list(scatter = 'stats') 
       #,lower.pars = list(scatter = 'corrgram')
       )


image.png

cluster

out.dist=dist(t(exprSet),method='euclidean')
out.hclust=hclust(out.dist,method='complete')
plot(out.hclust)


image.png

PCA

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)


image.png

heatmap

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)


image.png

library(gplots)
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
heatmap.2(choose_matrix)

[图片上传中…(image-376031-1560694447735-3)]

library(pheatmap)
pheatmap(choose_matrix)

[图片上传中…(image-9b4742-1560694447735-2)]

DEG && volcano plot

library(limma)
## 
## Attaching package: 'limma'
## The following object is masked from 'package:BiocGenerics':
## 
##     plotMA
design=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"))      


image.png

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)


image.png

ggplot画图是可以切换主题的

其实绘图有非常多的细节可以调整,还是略微有点繁琐的!

p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)


image.png

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)


image.png

可以很明显看到,换了主题之后的图美观一些。

boxplot <object classid=”clsid:D27CDB6E-AE6D-11cf-96B8-444553540000″ id=”speach_flash” align=”absmiddle” codebase=”http://download.macromedia.com/pub/shockwave/cabs/flash/swflash.cab#version=6,0,29,0″ width=”15″ height=”15″ style=”box-sizing: border-box; width: 15px; height: 15px;”><embed src=”http://dict.youdao.com/test.nobound.swf?audio=http://dict.youdao.com/speach?audio=boxplot” quality=”high” pluginspage=”http://www.macromedia.com/go/getflashplayer” type=”application/x-shockwave-flash” width=”15″ height=”15″ swliveconnect=”true” id=”tranSondSwf” name=”tranSondSwf” allowscriptaccess=”always” style=”box-sizing: border-box;”></object>详细X

基本翻译

n. 箱形图;盒形图

网络释义

Boxplot outlier: 箱线图极端值

Boxplot Graph: 箱线图

Boxplot t: 盒式图

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