library(dplyr)
library(ggplot2)
library(ggrepel)
BP <- read.table(file ="CC.txt",header = TRUE, sep = "\t")
View(BP)
objects(BP)
BP <- arrange(BP,desc(BP[,2]))
BP$Biological.Processes.Term <- factor(BP$Biological.Processes.Term,levels = rev(BP$Biological.Processes.Term))
p <- ggplot(BP,aes(x=Fold_Enrichment,y=Biological.Processes.Term,
colour=p.value,size=Count))+geom_point()+
scale_size(range=c(2, 8))+
scale_colour_gradient(low = "blue",high = "red")+
theme_bw()+
ylab("Biological.Processes.Term")+
xlab("Fold Enrichment")+
labs(color=expression(-log[10](PValue)))+
theme(legend.title=element_text(size=14), legend.text = element_text(size=14))+
theme(axis.title.y = element_text(margin = margin(r = 50)),axis.title.x = element_text(margin = margin(t = 20)))+
theme(axis.text.x = element_text(face ="bold",color="black",angle=0,vjust=1))
p
CC <- read.table(file ="CC.txt",header = TRUE, sep = "\t")
CC <- arrange(CC,desc(CC[,2]))
CC$Cellular.Components.Term <- factor(CC$Cellular.Components.Term,levels = rev(CC$Cellular.Components.Term))
p1 <- ggplot(CC,aes(x=Fold_Enrichment,y=Cellular.Components.Term,
colour=p.value,size=Count))+geom_point()+
scale_size(range=c(2, 8))+
scale_colour_gradient(low = "blue",high = "red")+
theme_bw()+
ylab("Cellular.Components.Term")+
xlab("Fold Enrichment")+
labs(color=expression(-log[10](PValue)))+
theme(legend.title=element_text(size=14), legend.text = element_text(size=14))+
theme(axis.title.y = element_text(margin = margin(r = 50)),axis.title.x = element_text(margin = margin(t = 20)))+
theme(axis.text.x = element_text(face ="bold",color="black",angle=0,vjust=1))
MF <- read.table(file ="MF.txt",header = TRUE, sep = "\t")
MF <- arrange(MF,desc(MF[,2]))
MF$Molecular.Function.Term <- factor(MF$Molecular.Function.Term,levels = rev(MF$Molecular.Function.Term))
p2 <- ggplot(MF,aes(x=Fold_Enrichment,y=Molecular.Function.Term,
colour=p.value,size=Count))+geom_point()+
scale_size(range=c(2, 8))+
scale_colour_gradient(low = "blue",high = "red")+
theme_bw()+
ylab("Molecular.Function.Term")+
xlab("Fold Enrichment")+
labs(color=expression(-log[10](PValue)))+
theme(legend.title=element_text(size=14), legend.text = element_text(size=14))+
theme(axis.title.y = element_text(margin = margin(r = 50)),axis.title.x = element_text(margin = margin(t = 20)))+
theme(axis.text.x = element_text(face ="bold",color="black",angle=0,vjust=1))
KEGG <- read.table(file ="KEGG.txt",header = TRUE, sep = "\t")
View(KEGG)
objects(KEGG)
KEGG <- arrange(KEGG,desc(KEGG[,2]))
KEGG$KEGG.Pathway.Term <- factor(KEGG$KEGG.Pathway.Term,levels = rev(KEGG$KEGG.Pathway.Term))
p3 <- ggplot(KEGG,aes(x=Fold_Enrichment,y=KEGG.Pathway.Term,
colour=p.value,size=Count))+geom_point()+
scale_size(range=c(2, 8))+
scale_colour_gradient(low = "blue",high = "red")+
theme_bw()+
ylab("KEGG.Pathway.Term")+
xlab("Fold Enrichment")+
labs(color=expression(-log[10](PValue)))+
theme(legend.title=element_text(size=14), legend.text = element_text(size=14))+
theme(axis.title.y = element_text(margin = margin(r = 50)),axis.title.x = element_text(margin = margin(t = 20)))+
theme(axis.text.x = element_text(face ="bold",color="black",angle=0,vjust=1))
allgokegg <- read.table(file ="allgokegg.txt",header = TRUE, sep = "\t")
View(allgokegg)
objects(allgokegg)
allgokegg <- arrange(allgokegg,desc(allgokegg[,2]))
allgokegg$all.Term <- factor(allgokegg$all.Term,levels = rev(allgokegg$all.Term))
p4 <- ggplot(allgokegg,aes(x=Fold_Enrichment,y=all.Term,
colour=p.value,size=Count))+geom_point()+
scale_size(range=c(2, 8))+
scale_colour_gradient(low = "blue",high = "red")+
theme_bw()+
ylab("all.Term")+
xlab("Fold Enrichment")+
labs(color=expression(-log[10](PValue)))+
theme(legend.title=element_text(size=14), legend.text = element_text(size=14))+
theme(axis.title.y = element_text(margin = margin(r = 50)),axis.title.x = element_text(margin = margin(t = 20)))+
theme(axis.text.x = element_text(face ="bold",color="black",angle=0,vjust=1))
go 和KEGG
最后編輯于 :
?著作權(quán)歸作者所有,轉(zhuǎn)載或內(nèi)容合作請聯(lián)系作者
【社區(qū)內(nèi)容提示】社區(qū)部分內(nèi)容疑似由AI輔助生成,瀏覽時請結(jié)合常識與多方信息審慎甄別。
平臺聲明:文章內(nèi)容(如有圖片或視頻亦包括在內(nèi))由作者上傳并發(fā)布,文章內(nèi)容僅代表作者本人觀點,簡書系信息發(fā)布平臺,僅提供信息存儲服務(wù)。
【社區(qū)內(nèi)容提示】社區(qū)部分內(nèi)容疑似由AI輔助生成,瀏覽時請結(jié)合常識與多方信息審慎甄別。
平臺聲明:文章內(nèi)容(如有圖片或視頻亦包括在內(nèi))由作者上傳并發(fā)布,文章內(nèi)容僅代表作者本人觀點,簡書系信息發(fā)布平臺,僅提供信息存儲服務(wù)。