Supplementary plot (barplot of 22 immune cell abundance by CIBERSORT in GSE43974 GSE90861 and GSE126805).R

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#install.packages('e1071')

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

#出图前数据处理
setwd("")
rt=read.table("",header=T,sep="\t",check.names=F)  

library(reshape2)
library(ggpubr)
# 2.2 融合数据
TME_New = melt(rt,id.vars = c("id","Group"))#id.vars指定不合并的列，其余列将会合并为一列

## Using group, sample as id variables

colnames(TME_New)=c("Sample","Group","Celltype","Composition")  #设置行名
head(TME_New)



# 3.3 出图

mytheme <- theme(plot.title = element_text(size = 12,color="black",hjust = 0.5),
                   axis.title = element_text(size = 12,color ="black"), 
                   axis.text = element_text(size= 12,color = "black"),
                   panel.grid.minor.y = element_blank(),
                   panel.grid.minor.x = element_blank(),
                   axis.text.x = element_text(angle = 45, hjust = 1 ),
                   panel.grid=element_blank(),
                   legend.position = "top",
                   legend.text = element_text(size= 12),
                   legend.title= element_text(size= 12))

box_TME <- ggplot(TME_New, aes(x = Celltype, y = Composition))+ 
  labs(y="Cell composition",x= NULL,title = "TME Cell composition")+  
  geom_boxplot(aes(fill = Group),position=position_dodge(0.5),width=0.5,outlier.alpha = 0)+ 
  scale_fill_manual(values = c("#1CB4B8", "#EB7369"))+
  theme_classic() + mytheme + 
  stat_compare_means(aes(group =  Group),
                     label = "p.signif",
                     method = "wilcox.test",
                     hide.ns = T)

box_TME