富集分析制作气泡图
###自定义作气泡图
x<-read.csv(file.choose(),stringsAsFactors = F)
#筛选p<0.05
x<-x[x$PValue<0.05,]
x_go=x[,1:5]
xbp=x_go[grep("BP",x_go$Category),]
xcc=x_go[grep("CC",x_go$Category),]
xmf=x_go[grep("MF",x_go$Category),]
xkegg=x_go[grep("KEGG",x_go$Category),]
xbp$Term=gsub(".*\\~","",xbp$Term)#Biological Process
xcc$Term=gsub(".*\\~","",xcc$Term)#Cell Component
xmf$Term=gsub(".*\\~","",xmf$Term)#Molecular Function
xkegg$Term=gsub(".*\\:","",xkegg$Term)#KEGG pathway
#加载ggplot2
library(ggplot2)
make_GO_bubble<-function(go_data,term_name){
#选择top10的数据（count）
GO_DATA=go_data[order(go_data$Count,decreasing = T),]
GO_DATA=head(GO_DATA,10)
# 四维数据的展示
p = ggplot(GO_DATA,aes(X.,Term))
bubble=p+ geom_point(aes(size=Count,color=-log10(PValue)))
# 自定义渐变颜色
bubble =bubble+ scale_colour_gradient(low="green",high="red")
# 改变图片的样式（主题）
pr=bubble + theme_test(base_size = 16,base_rect_size = 1)
pr=pr+labs(x="Rich factor",y=term_name,title="Enrichment of DEGs")
return(pr) }
#BP
make_GO_bubble(xbp,term_name="Biological Process")#HEIGHT 550
make_GO_bubble(xcc,term_name = "Cell Component")
make_GO_bubble(xmf,term_name = "Molecular Function")
make_GO_bubble(xkegg,term_name = "KEGG pathway")
