DAVID富集分析
DAVID富集支持多种基因名类型，本次以genesymbol为例
DAVID：https://david.ncifcrf.gov/
进入之后跟着以下步骤操作即可
选择相应物种，以人和小鼠为例，载点击Submit
一般是进行GO（BP\CC\MF）、KEGG分析，注意选择
右击Download File -> 链接另存为，即可将富集分析结果保存至本地文档
```
R语言可视化
setwd("G:/富集分析") #设置工作路径
#此处以本次分析的上调基因为例，可以分别可视化GO和KEGG的结果，本次以GO_BP和KEGG为例
#上调富集分析绘图
DA_UP <- read.table("DAVID富集结果.txt",sep = "\t",header = T)
#select Pvalue<0.05 将P<0.05认为有统计学意义
DA_UP <- subset(DA_UP,DA_UP$PValue<0.05)
DA_UP <- subset(DA_UP,DA_UP$Count > 3)
DA_UP$PValue <- -(log10(DA_UP$PValue))
UP_BP <- subset(DA_UP,DA_UP$Category == "GOTERM_BP_DIRECT")
UP_KEGG <- subset(DA_UP,DA_UP$Category == "KEGG_PATHWAY")
UP_BP$Category <- sub("GOTERM_BP_DIRECT","GOBP_PATHWAY",UP_BP$Category)
#保留绘图所需的信息
library(tidyverse)
UP_KEGG <- UP_KEGG[,c("Category","Term","Count","PValue")]
#修改通路类别名称格式，使结果更好看
UP_KEGG <- separate(data = UP_KEGG, col = Term, into = c("ID", "Term"), sep = ":")
UP_KEGG <- UP_KEGG[,-2]
UP_BP <- UP_BP[,c("Category","Term","Count","PValue")]
UP_BP <- separate(data = UP_BP, col = Term, into = c("ID", "Term"), sep = "~")
UP_BP <- UP_BP[,-2]
UP <- rbind.data.frame(UP_KEGG,UP_BP)
DOWN <- rbind.data.frame(DOWN_KEGG,DOWN_BP)
write.table(UP,file = "UP_enrichment.txt",sep = "\t",quote = F,row.names = F)
write.table(DOWN,file = "DOWN_enrichment.txt",sep = "\t",quote = F,row.names = F)
##绘制柱状图
#使用reorder对柱状图排序
#想让y轴从大到小变化，只需在Change前面加个负号
##########################################################
##上下调差异基因富集图
########################################################
setwd("G:/科研/毕设/富集分析")
UP <- read.table("UP_enrichment.txt",header = T,sep = "\t")
DOWN <- read.table("DOWN_enrichment.txt",header = T,sep = "\t")
#因为富集结果过多，因此选择与研究内容相关需要的通路
UP_END <- UP[c(1,2,4,5,6,8,9,13,17,19,20,21,24,28,32,46,49,51,53,62),]
DOWN_END <- DOWN[c(1,4,6,7,10,13,14,17,20,21,31,33,34,36,40,44,45,53,59,71),]
library(ggplot2)
a <- UP_END
b <- "UP_END"
pngname <- paste(b,".png",sep = "")
pdfname <- paste(b,".pdf",sep = "")
p1 <- ggplot(data = a,
aes(x = reorder(Term, PValue),
y = PValue,
fill = Category)) +
geom_bar(stat = "identity",
width = 0.8,
position = position_dodge(width = 0.9)) +
geom_text(aes(label = Count),
vjust = 0.4, hjust = -0.2) +
facet_grid(Category~., scales = 'free_y') +
theme(panel.grid.major.x = element_line(colour = "white"),
panel.background = element_blank(),
axis.line.y = element_blank(),
axis.line.x = element_line(color = "black"),
axis.title.y = element_blank(),
strip.text.y = element_blank()) +
#scale_x_discrete(position = "top") +
ggtitle("UP_GENE")+ #添加标题
ylab("-log10PValue")+
#geom_text(aes(label = y))+
coord_flip()
p1
ggsave("UP_gene.png",p1, units="in", dpi=300, width=6, height=6, device="png")
```