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10.Differential analysis boxplots.R
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10.Differential analysis boxplots.R
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library(ggpubr)
library(limma)
library(ggsci)
expfile="GSE104954.txt"
hub="LASSO.txt"
sample="sample.txt"
Ccol="blue"
Pcol="orange"
afmethod="t.test"
rt=read.table(expfile,sep="\t",header=T,check.names=F)
rt=as.matrix(rt)
rownames(rt)=rt[,1]
exp=rt[,2:ncol(rt)]
dimnames=list(rownames(exp),colnames(exp))
rt=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
rt=avereps(rt)
rt=t(rt)
rt2=read.table(sample,sep="\t",header=F,check.names=F,row.names = 1)
ssample=as.character(intersect(rownames(rt2),rownames(rt)))
rt=rt[ssample,]
rt2=rt2[ssample,,drop=F]
rt1=cbind(rt2,rt)
colnames(rt1)[1]="Type"
rt1$ID=rownames(rt1)
genes=read.table(hub,sep="\t",header=F,check.names=F)[,1]
rt1=rt1[,c("ID","Type",genes)]
af=colnames(rt1)
df=c(3:ncol(rt1))
for (i in df) {
outFile=paste0(af[i],"_","boxplotdiff.pdf")
rt=as.data.frame(rt1[,c(1,2,i)])
x=colnames(rt)[2]
y=colnames(rt)[3]
colnames(rt)=c("id","Type","Expression")
group=levels(factor(rt$Type))
rt$Type=factor(rt$Type, levels=group)
rt$Expression=as.numeric(rt$Expression)
comp=combn(group,2)
my_comparisons=list()
for(i in 1:ncol(comp)){my_comparisons[[i]]<-comp[,i]}
boxplot=ggboxplot(rt, x="Type", y="Expression", fill="Type",
xlab=x,
ylab=y,
legend.title=x,
palette = c(Ccol,Pcol),
#add = "jitter"
)+
stat_compare_means(comparisons = my_comparisons,method=afmethod)
pdf(file=outFile,width=4,height=4)
print(boxplot)
dev.off()
}