绘出后的图如下：
所需的数据文件格式如下：
####下面是有关代码
library(ggplot2)
library(reshape2)
inputFile="input.txt"
outFile="boxplot.pdf"
#读取输入文件
rt=read.table(inputFile, header=T,sep="\t",check.names=F,row.names=1)
x=colnames(rt)[1]
colnames(rt)[1]="Type"
#差异分析
geneSig=c("")
for(gene in colnames(rt)[2:ncol(rt)]){
rt1=rt[,c(gene,"Type")]
colnames(rt1)=c("expression","Type")
p=1
if(length(levels(factor(rt1$Type)))>2){
test=kruskal.test(expression ~ Type, data = rt1)
p=test$p.value
}else{
test=wilcox.test(expression ~ Type, data = rt1)
p=test$p.value
}
Sig=ifelse(p<0.001,"***",ifelse(p<0.01,"**",ifelse(p<0.05,"*","")))
geneSig=c(geneSig,Sig)
}
colnames(rt)=paste0(colnames(rt),geneSig)
#把数据转换成ggplot2输入文件
data=melt(rt,id.vars=c("Type"))
colnames(data)=c("Type","Gene","Expression")
#绘制
p1=ggplot(data,aes(x=Type,y=Expression,fill=Type))+
guides(fill=guide_legend(title=x))+
labs(x = x, y = "Gene expression")+
geom_boxplot()+ facet_wrap(~Gene,nrow =1)+ theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#输出
pdf(file=outFile, width=9, height=5)
print(p1)
dev.off()
