复现《nature communications》图表（二）：一劳永逸，R语言一键画表达量箱线图并添加显著性

library(RColorBrewer)library(ggpubr)library(ggplot2)library(cowplot)

setwd("D:/生物信息学")Exp <- read.csv("Exp.csv",header=T,row.names=1)#读入源文件gene <- c("CD28","CD3D","CD8A","LCK",          "GATA3","EOMES","IL23A","CXCL8",          "IL1R2","IL1R1","MMP8","MMP9")#这里我们只选择这几个基因做数据gene <- as.vector(gene)Exp <- log2(Exp+1) #因为是FPKM数据，标准化一下Exp_plot <- Exp[,gene]#提取需要作图得基因表达信息

#加载样本信息info <- read.csv("info.csv",header=T)Exp_plot<- Exp_plot[info$Sample,]Exp_plot$sam=info$TypeExp_plot$sam <- factor(Exp_plot$sam,levels=c("Asymptomatic","Mild","Severe","Critical"))

col <-c("#5CB85C","#337AB7","#F0AD4E","#D9534F")

plist2<-list()#创建一个空列表，用来存储循环的产出for (i in 1:length(gene)){  bar_tmp<-Exp_plot[,c(gene[i],"sam")]#循环提取每个基因表达信息  colnames(bar_tmp)<-c("Expression","sam")#统一命名  my_comparisons1 <- list(c("Asymptomatic", "Mild")) #设置比较组  my_comparisons2 <- list(c("Asymptomatic", "Severe"))#设置比较组  my_comparisons3 <- list(c("Asymptomatic", "Critical"))#设置比较组  my_comparisons4 <- list(c("Mild", "Severe"))#设置比较组  my_comparisons5 <- list(c("Mild", "Critical"))#设置比较组  my_comparisons6 <- list(c("Severe", "Critical"))#设置比较组  pb1<-ggboxplot(bar_tmp,#ggboxplot画箱线图                 x="sam",#x轴为组别                 y="Expression",#y轴为表达量                 color="sam",#用样本分组填充                 fill=NULL,                 add = "jitter",#添加散点                 bxp.errorbar.width = 0.6,                 width = 0.4,                 size=0.01,                 font.label = list(size=30),                  palette = col)+theme(panel.background =element_blank())  pb1<-pb1+theme(axis.line=element_line(colour="black"))+theme(axis.title.x = element_blank())#坐标轴修饰  pb1<-pb1+theme(axis.title.y = element_blank())+theme(axis.text.x = element_text(size = 15,angle = 45,vjust = 1,hjust = 1))#横坐标文字设置  pb1<-pb1+theme(axis.text.y = element_text(size = 15))+ggtitle(gene[i])+theme(plot.title = element_text(hjust = 0.5,size=15,face="bold"))#标题设置  pb1<-pb1+theme(legend.position = "NA")#（因为有组图，横坐标分组了，所以不需要设置legend）  pb1<-pb1+stat_compare_means(method="t.test",hide.ns = F,                              comparisons =c(my_comparisons1,my_comparisons2,my_comparisons3,my_comparisons4,my_comparisons5,my_comparisons6),                              label="p.signif")#显著性检验用t检验，添加不同比较组。详情可以查看stat_compare_means函数帮助信息  plist2[[i]]<-pb1 #将画好的图储存于plist2列表，并不断赋值循环直到结束}

plot_grid(plist2[[1]],plist2[[2]],plist2[[3]],                plist2[[4]],plist2[[5]],plist2[[6]],                plist2[[7]],plist2[[8]],plist2[[9]],                plist2[[10]],plist2[[11]],plist2[[12]],ncol=4)#ncol=4表示图片排为几列