#if (!requireNamespace("BiocManager", quietly = TRUE))
#    install.packages("BiocManager")
#BiocManager::install("limma")

#install.packages("reshape2")
#install.packages("ggpubr")
#install.packages("corrplot")


#引用包
library(limma)
library(reshape2)
library(ggpubr)
library(corrplot)

expFile="GSE19151.normalize.txt"      #表达数据文件
geneFile="Genes.txt"           #基因列表文件
setwd("F:\\血栓\\2.五个候选基因差异分析")     #设置工作目录

#读取表达数据文件
rt=read.table(expFile, header=T, sep="\t", check.names=F)
rt=as.matrix(rt)
rownames(rt)=rt[,1]
exp=rt[,2:ncol(rt)]
dimnames=list(rownames(exp), colnames(exp))
data=matrix(as.numeric(as.matrix(exp)), nrow=nrow(exp), dimnames=dimnames)
data=avereps(data)

#读取基因列表文件, 提取特征基因的表达量
geneRT=read.table(geneFile, header=F, sep="\t", check.names=F)
sameGene=intersect(as.vector(geneRT[,1]), row.names(data))
data=t(data[sameGene,])

#获取样品的分组信息(对照组和实验组)
Type=gsub("(.*)\\_(.*?)", "\\2", row.names(data))
treatData=data[Type=="Treat",]
rt=cbind(as.data.frame(data), Type)

#将数据转换为箱线图的输入文件
data=melt(rt, id.vars=c("Type"))
colnames(data)=c("Type", "Gene", "Expression")

#绘制箱线图
p=ggboxplot(data, x="Gene", y="Expression", color = "Type",
	     xlab="", ylab="Gene expression", legend.title="Type",
	     palette = c("blue", "red"), add="jitter", width=0.8)
p=p+rotate_x_text(45)
p1=p+stat_compare_means(aes(group=Type),
	      method="wilcox.test",
	      symnum.args=list(cutpoints = c(0, 0.001, 0.01, 0.05, 1), symbols = c("***", "**", "*", " ")),
	      label = "p.signif")

#输出图形
pdf(file="boxplot.pdf", width=6, height=4.5)
print(p1)
dev.off()


#绘制相关性图形
M=cor(treatData)
res1=cor.mtest(treatData, conf.level = .95)
pdf(file="corrplot.pdf", width=5, height=5)
corrplot(M,
         type = "upper",        #图形展示在右上方
         method = "circle",    #图形以圆圈的形式展示
         col=colorRampPalette(c('blue', 'white', 'red'),alpha = TRUE)(100), tl.pos="lt",   #图形的颜色
         p.mat=res1$p, insig="label_sig", sig.level = c(.001, .01, .05), pch.cex=2)   #加上显著性标记
corrplot(M, type="lower", add=TRUE, method="number",col=colorRampPalette(c('blue', 'white', 'red'), alpha = TRUE)(100), tl.pos = "n", cl.pos="n", diag=FALSE, number.cex=1.5) 
dev.off()