#RAW code

install.packages("ggplot2")

library(ggplot2)

setwd("C:\\xxx\\xxx") 


rt=read.table("input.txt",sep="\t",header=T,check.names=F)  

options(max.print=999999)

as.data.frame(rt)



p <- ggplot(data=rt, mapping=aes(x=group, y=CELSR3)) 



p <- ggplot(rt, aes(group,CELSR3,fill=group))
p+geom_boxplot()+scale_fill_manual(values=c( "#029386", "#653700"))+geom_jitter(shape=16, position=position_jitter(0.2))+labs(title="XXX.", x=element_blank(), y="XXX")+theme(plot.title = element_text(family="Times",color = "black", size = 18,face = "bold"),axis.title.x = element_text(family="Times",color="black", size = 14,face = "bold"),axis.title.y = element_text(family="Times",color="#000000", size = 16, face = "bold"))+ theme(legend.position="top")+ labs(fill="type")+theme(plot.title = element_text(hjust = 0.5)) + theme(axis.text.x = element_text(size = 15, family = "Times", color = "black", face = "bold", vjust = 0.5, hjust = 0.5))



============================================================

# The ggplot2 package in the statistical software R (version 3.5.2) was used for graph generation. 

install.packages('survminer')
library(ggplot2)
library('survminer')
library(survival)


setwd("C:\\xxx\\xxx) 

rt=read.table('input.txt',sep='\t',header=T)


head(rt)


fit<- survfit(Surv(os, censor) ~ CELSR3, data = rt)

print(fit)

ggsurvplot(fit)

ggsurvplot(fit,palette = c('red','#0504aa'),risk.table =TRUE,pval =TRUE,conf.int =TRUE,xlab ='Time in months', ggtheme =theme_light(), ncensor.plot = TRUE)

===============================

# The pROC package in the statistical software R (version 3.5.2) was used for graph generation. 


library(ggplot2)

install.packages('pROC') 

library(pROC)



setwd("C:\\xxx\\xxx) 

rt=read.table("input.txt",sep="\t",header=T,check.names=F)

options(max.print=999999)

as.data.frame(rt)

roc(rt$outcome, rt$CELSR3, plot=TRUE, print.thres=TRUE, print.auc=TRUE)

rocobj1 <- roc(rt$outcome, rt$CELSR3)  

auc(rocobj1) 

plot(rocobj1)

plot(rocobj1, print.auc=TRUE, auc.polygon=TRUE, grid=c(0.1, 0.2), grid.col=c("green", "red"), max.auc.polygon=TRUE,auc.polygon.col="skyblue", print.thres=TRUE)

=============

#source("http://bioconductor.org/biocLite.R")
#biocLite("affy")

library(affy)
setwd("C:/XXX/XXX")
affydata=ReadAffy()
normalData=rma(affydata)
write.table(file="all.exp.xls",exprs(normalData),sep="\t",quote=FALSE)

========================
#source("https://bioconductor.org/biocLite.R")
#biocLite("sva")
#biocLite("limma")

library(sva)
library(limma)
setwd("C:/XXX/XXX")
rt=read.table("merge.txt",sep="\t",header=T,check.names=F)
rt=as.matrix(rt)
rownames(rt)=rt[,1]
exp=rt[,2:ncol(rt)]
dimnames=list(rownames(exp),colnames(exp))
data=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
batchType=c(rep(1,36),rep(2,91))
modType=c(rep("normal",18),rep("tumor",18),rep("normal",10),rep("tumor",81))
mod = model.matrix(~as.factor(modType))
outTab=ComBat(data, batchType, mod, par.prior=TRUE)
outTab=rbind(geneNames=colnames(outTab),outTab)
write.table(outTab,file="normalize.txt",sep="\t",quote=F,col.names=F)

============================
library(plyr)
library(ggplot2)
library(grid)
library(gridExtra)

setwd("C:/XXX/XXX")        

files=grep(".xls",dir(),value=T)                                         
data = lapply(files,read.delim)                                          
names(data) = files

dataSet = ldply(data, data.frame)
dataSet$pathway = gsub(".xls","",dataSet$.id)                            
gseaCol=c("#58CDD9","#7A142C","#5D90BA","#431A3D","#91612D","#6E568C","#E0367A","#D8D155","#64495D","#7CC767","#223D6C","#D20A13","#FFD121","#088247","#11AA4D")
pGsea=ggplot(dataSet,aes(x=RANK.IN.GENE.LIST,y=RUNNING.ES,colour=pathway,group=pathway))+
  geom_line(size = 1.5) + scale_color_manual(values = gseaCol[1:nrow(dataSet)]) +   
  labs(x = "", y = "Enrichment Score", title = "") + scale_x_continuous(expand = c(0, 0)) + 
  scale_y_continuous(expand = c(0, 0),limits = c(min(dataSet$RUNNING.ES - 0.02), max(dataSet$RUNNING.ES + 0.02))) +   
  theme_bw() + theme(panel.grid = element_blank()) + theme(panel.border = element_blank()) + theme(axis.line = element_line(colour = "black")) + theme(axis.line.x = element_blank(),axis.ticks.x = element_blank(),axis.text.x = element_blank()) + 
  geom_hline(yintercept = 0) +   theme(legend.position = c(0,0),legend.justification = c(0,0)) + #legend×¢Ê͵Äλֵ
  guides(colour = guide_legend(title = NULL)) + theme(legend.background = element_blank()) + theme(legend.key = element_blank())+theme(legend.key.size=unit(0.5,'cm'))
pGene=ggplot(dataSet,aes(RANK.IN.GENE.LIST,pathway,colour=pathway))+geom_tile()+
  scale_color_manual(values = gseaCol[1:nrow(dataSet)]) + 
  labs(x = "high expression<----------->low expression", y = "", title = "") + 
  scale_x_discrete(expand = c(0, 0)) + scale_y_discrete(expand = c(0, 0)) +  
  theme_bw() + theme(panel.grid = element_blank()) + theme(panel.border = element_blank()) + theme(axis.line = element_line(colour = "black"))+
  theme(axis.line.y = element_blank(),axis.ticks.y = element_blank(),axis.text.y = element_blank())+ guides(color=FALSE)

gGsea = ggplot_gtable(ggplot_build(pGsea))
gGene = ggplot_gtable(ggplot_build(pGene))
maxWidth = grid::unit.pmax(gGsea$widths, gGene$widths)
gGsea$widths = as.list(maxWidth)
gGene$widths = as.list(maxWidth)
dev.off()


pdf('multipleGSEA.pdf',      
     width=7,                
     height=5.5)             
par(mar=c(5,5,2,5))
grid.arrange(arrangeGrob(gGsea,gGene,nrow=2,heights=c(.8,.3)))
dev.off()