R數據分析和畫圖
參考原文：生信技能樹論壇basic visualization for expression matrix
結合Jimmy大神的B站視頻(P24)生信人應該這樣學R語言
安裝并加載必須的packages
如果你還沒有安裝，就運行下面的代碼安裝：
# rm(list = ls())
options()$repos
options()$BioC_mirror
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")
options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
options()$repos
options()$BioC_mirror
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if(! require("CLL")) BiocManager::install("CLL")
if(! require("corrplot")) BiocManager::install("corrplot")
if(! require("gpairs")) BiocManager::install("gpairs")
if(! require("vioplot")) BiocManager::install("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)
加載內置的測試數據：
data(sCLLex)
sCLLex=sCLLex[,1:8] ## 樣本太多，我就取前面8個
group_list=sCLLex$Disease
exprSet=exprs(sCLLex)
head(exprSet)
## CLL11.CEL CLL12.CEL CLL13.CEL CLL14.CEL CLL15.CEL CLL16.CEL
## 1000_at 5.743132 6.219412 5.523328 5.340477 5.229904 4.920686
## 1001_at 2.285143 2.291229 2.287986 2.295313 2.662170 2.278040
## 1002_f_at 3.309294 3.318466 3.354423 3.327130 3.365113 3.568353
## 1003_s_at 1.085264 1.117288 1.084010 1.103217 1.074243 1.073097
## 1004_at 7.544884 7.671801 7.474025 7.152482 6.902932 7.368660
## 1005_at 5.083793 7.610593 7.631311 6.518594 5.059087 4.855161
## CLL17.CEL CLL18.CEL
## 1000_at 5.325348 4.826131
## 1001_at 2.350796 2.325163
## 1002_f_at 3.502440 3.394410
## 1003_s_at 1.091264 1.076470
## 1004_at 6.456285 6.824862
## 1005_at 5.176975 4.874563
group_list
## [1] progres. stable progres. progres. progres. progres. stable stable
## Levels: progres. stable
接下來進行一系列繪圖操作
主要用到ggplot2這個包，需要把我們的寬矩陣用reshape2包變成長矩陣
library(reshape2)
exprSet_L=melt(exprSet)
colnames(exprSet_L)=c('probe','sample','value')
exprSet_L$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.
boxplot
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)
boxplot
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)
vioplot
boxplot
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)
boxplot
histogram
p=ggplot(exprSet_L,aes(value,fill=group))+geom_histogram(bins = 200)+facet_wrap(~sample, nrow = 4)
print(p)
histogram
boxplot
p=ggplot(exprSet_L,aes(x=sample,y=value,fill=group))+geom_boxplot()
print(p)
boxplot
density
p=ggplot(exprSet_L,aes(value,col=group))+geom_density()+facet_wrap(~sample, nrow = 4)
print(p)
density.png
p=ggplot(exprSet_L,aes(value,col=group))+geom_density()
print(p)
Rplot.png
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$x)
pcr=cbind(samples=rownames(pcx),group_list, pcx)
p=ggplot(pcr, aes(PC1, PC2))+geom_point(size=5, aes(color=group_list)) +
geom_text(aes(label=samples),hjust=-0.1, vjust=-0.3)
print(p)
image.png
heatmap
choose_gene=names(sort(apply(exprSet, 1, mad),decreasing = T)[1:50])
choose_matrix=exprSet[choose_gene,]
choose_matrix=scale(choose_matrix)
heatmap(choose_matrix)
image.png
library(gplots)
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
heatmap.2(choose_matrix)
#Error in plot.new() : figure margins too large
Rplot02.png
library(pheatmap)
pheatmap(choose_matrix)
[圖片上傳中...(image-9b4742-1560694447735-2)]
DEG && volcano plot
library(limma)
##
## Attaching package: 'limma'
## The following object is masked from 'package:BiocGenerics':
##
## plotMA
design=model.matrix(~factor(group_list))
fit=lmFit(exprSet,design)
fit=eBayes(fit)
DEG=topTable(fit,coef=2,n=Inf)
with(DEG, plot(logFC, -log10(P.Value), pch=20, main="Volcano plot"))
image.png
logFC_cutoff <- with(DEG,mean(abs( logFC)) + 2*sd(abs( logFC)) )
DEG$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畫圖是可以切換主題的
其實繪圖有非常多的細節可以調整，還是略微有點繁瑣的！
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
可以很明顯看到，換了主題之后的圖美觀一些。
