R/sig_surv_plot.R
In IOBR/IOBR: Immune Oncology Biological Research
Defines functions
sig_surv_plot
Documented in
sig_surv_plot
#' Multiple survival KM-plot to a signature or gene
#'
#' @param input_pdata This parameter represents the input data for the survival analysis. It is expected to be a data frame.
#' @param signature This parameter specifies the column name in input_pdata that represents the target signature for survival analysis.
#' @param project This optional parameter represents the project name. The default value is "KM".
#' @param time This optional parameter represents the column name in input_pdata that contains the time variable for survival analysis. The default value is "time".
#' @param status This optional parameter represents the column name in input_pdata that contains the survival status variable. The default value is "status".
#' @param time_type This optional parameter specifies the time unit used in the analysis. The default value is "month".
#' @param break_month This optional parameter specifies the interval at which the time axis breaks should be made in the Kaplan-Meier plot. The default value is "auto".
#' @param palette This optional parameter allows the user to specify the palette type if cols is not provided. The default value is "jama".
#' @param save_path This optional parameter specifies the directory path for saving the plot. The default value is "KM-plot".
#' @param mini_sig This optional parameter represents the label for the mini signature in the legend. The default value is "score".
#' @param show_col logical variable, if TRUE, color will be print and show in the R studio
#' @param index This parameter represents the index for multiple plots generated in a single run. It is used as the prefix in the filename. The default value is 1.
#' @param cols This optional parameter allows the user to specify the color palette for the plot. If not specified, a default palette will be used.
#' @param fig.type This optional parameter specifies the file format for saving the plot. The default value is "png".
#' @param ID This optional parameter represents the column name in input_pdata that contains the unique identifier for each data point. The default value is "ID".
#'
#' @importFrom survminer surv_cutpoint
#' @import survival
#' @author Dongqiang Zeng
#' @return survival
#' @export
#'
#' @examples
#'
#'data("tcga_stad_pdata", package = "IOBR")
#'sig_surv_plot(input_pdata = tcga_stad_pdata, signature = "TMEscore_plus", time = "time", status = "OS_status")
#'
sig_surv_plot <- function(input_pdata,
signature,
project = "KM",
ID = "ID",
time = "time",
status = "status",
time_type = "month",
break_month = "auto",
cols = NULL,
palette = "jama",
show_col = TRUE,
mini_sig = "score",
fig.type = "png",
save_path = "KM-plot",
index = 1){
if(!signature%in%colnames(input_pdata)) stop("Target signature must be in the column name of input_pdata")
###############################
if (!file.exists(save_path)) dir.create(save_path)
abspath<-paste(getwd(),"/" ,save_path, "/",sep ="" )
###########################################
# if("time"%in%input_pdata & time!="time") stop(">>>- 'time' is already in column name of input_pdata... ")
# if("status"%in%input_pdata & time!="status") stop(">>>- 'status' is already in column name of input_pdata... ")
input_pdata<-as.data.frame(input_pdata[,colnames(input_pdata)%in%c(ID, time, status, signature)])
#filter data
colnames(input_pdata)[which(colnames(input_pdata)==time)]<-"time"
colnames(input_pdata)[which(colnames(input_pdata)==status)]<-"status"
colnames(input_pdata)[which(colnames(input_pdata)==ID)]<-"ID"
input_pdata$time<-as.numeric(input_pdata$time)
input_pdata$status<-as.numeric(as.character(input_pdata$status))
input_pdata<- input_pdata %>%
filter(!is.na(time)) %>%
filter(!is.na(status)) %>%
filter(!.$time=="NA") %>%
filter(!.$status=="NA") %>%
filter(.$time > 0)
###################################
input_pdata$time<-as.numeric(input_pdata$time)
input_pdata$status<-as.numeric(input_pdata$status)
###################################
input_pdata$ID<-as.character(input_pdata$ID)
# input_pdata$ProjectID<-project
##################################
#transform follow up time
if(time_type=="day") input_pdata$time<-input_pdata$time/30
###################################
#cut use quantile
#################################
# print(paste0(signature,"'s expression range: "))
# print( summary(cut(input_pdata[,signature],breaks = 3)))
q1<-quantile(input_pdata[,signature],probs = 1/3)
q2<-quantile(input_pdata[,signature],probs = 2/3)
input_pdata$group3<-input_pdata[,signature]
input_pdata$group3<-ifelse(input_pdata$group3<=q1,"Low",ifelse(input_pdata$group3>=q2,"High","Middle"))
input_pdata$group2<-input_pdata[,signature]
input_pdata$group2<-ifelse(input_pdata$group2 <= mean(input_pdata[,signature]),"Low","High")
####################################
message(paste(">>> Dataset's survival follow up time is range between",
paste(round(summary(input_pdata$time),2)[c(1,6)],collapse = " to "),"months"))
######################################
#calculate best cutoff
res.cut<-surv_cutpoint(input_pdata,time = "time",event = "status",variables = signature)
res.cut<-res.cut$cutpoint[[1]]
message(paste0(">>> The best cutoff for " ,signature, " is: ",round(res.cut,2)))
###################################
# data category
input_pdata<- best_cutoff(pdata = input_pdata, variable = signature, time ="time", status = "status", PrintResult = F)
# input_pdata<-as.data.frame(input_pdata)
#####################################
# signatureBinary <- as.character(paste(signature,"_binary",sep = ""))
colnames(input_pdata)[which(colnames(input_pdata)== paste0(signature,"_binary"))]<-"bestcutoff"
message(paste(">>> High ", signature," = ",summary(as.factor(input_pdata$bestcutoff)))[1],collapse = " ")
message(paste(">>> Low ",signature," = ",summary(as.factor(input_pdata$bestcutoff)))[2],collapse = " ")
save(input_pdata,file = paste0(abspath,index,"-0-",project,"-",signature,"-survival-analysis-input.RData"))
#######################################
# Sur <- Surv(time = input_pdata$time,event = input_pdata$status)
# 将high变量转化为1，才能准确计算出HR，否则是反的
#####################################
input_pdata$bestcutoff<-ifelse(input_pdata$bestcutoff=="High",1,0)
pvalue<-getHRandCIfromCoxph(coxph(Surv(time = input_pdata$time, event = input_pdata$status)~input_pdata$bestcutoff,data = input_pdata))
HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
cut_off<-paste("cutoff = ",round(res.cut,3),sep = "")
###########################################
sfit <- surv_fit(Surv(time = input_pdata$time, event = input_pdata$status) ~ input_pdata$bestcutoff,
data = input_pdata)
# input_pdata[,index]<-ifelse(input_pdata[,index]==1,"High","LOW")
############################################
#define break time
if(break_month == "auto"){
break_month<-break_month(input = input_pdata$time,block = 6)
}
max_month<- break_month*6
###########################################
if(is.null(cols)) cols<- palettes(category = "box",palette = palette, show_col = F)
###########################################
pp1<-ggsurvplot(sfit,
data = input_pdata,
censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0, max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
surv.median.line = "h", # draw horizontal line at median survival
legend.labs = c(paste0('Low ',mini_sig),paste0("High ",mini_sig)),
submain= paste0(signature,"-in-",project),
risk.table = T,tables.height = 0.20,
palette = cols,
pval.size = 8,
pval = paste(pval = ifelse(pvalue[,1] < 0.0001, "P < 0.0001",
paste("P = ",round(pvalue[,1],4), sep = "")),
HR, CI, cut_off,sep = "\n"))
res1 <- arrange_ggsurvplots(list(pp1), print = FALSE, ncol = 1, nrow = 1)
##############################
ggsave(res1,filename = paste0(index,"-1-KMplot-best-cutoff-",signature,"-",project,".", fig.type),
width =6 ,height =6.5,path = save_path)
################################################
input_pdata$bestcutoff<-ifelse(input_pdata$bestcutoff==1,"High","Low")
print(head(input_pdata))
################################################
# choose tri-sectional quantiles to conduct KM-plot
###########################################
# turn high to 1 to calculate HR
input_pdata<-input_pdata[!is.na(input_pdata$group3),]
# input_pdata$group3<-ifelse(input_pdata$group3=="High",3,ifelse(input_pdata$group3=="Middle",2,1))
# Sur <- Surv(input_pdata$time,input_pdata$status)
# pvalue<-getHRandCIfromCoxph(coxph(Surv(input_pdata$time,input_pdata$status)~input_pdata$group3,data = input_pdata))
# HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
# CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
##########################################
sfit <- surv_fit(Surv(input_pdata$time,input_pdata$status) ~ input_pdata$group3,data=input_pdata)
# hack strata for better survival curve
names(sfit$strata) <- gsub("group3=", "", names(sfit$strata))
##########################################
# ###########################################
pp2<-ggsurvplot(sfit,data = input_pdata,censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
# legend.labs = c(paste0('Low ',mini_sig),paste0("Middle ",mini_sig),paste0("High ",mini_sig)),
submain= paste0(signature,"-in-",project),
surv.median.line = "h", # draw horizontal line at median survival
risk.table = T,tables.height = 0.25,
palette = cols,
pval.size = 8)
fitd <- survdiff(Surv(time,status)~ group3,
data= input_pdata,
na.action = na.exclude)
p.val <- 1 - pchisq(fitd$chisq, length(fitd$n) - 1)
# add nominal pvalue for log-rank test
p.lab <- paste0("Overall P",
ifelse(p.val < 0.001, " < 0.001",
paste0(" = ",round(p.val, 3))))
pp2$plot <- pp2$plot + annotate("text",
x = 0, y = 0.55,
hjust = 0,
fontface = 3,
label = p.lab)
# calculate pair-wise survival comparison
ps <- pairwise_survdiff(Surv(time,status)~ group3,
data= input_pdata,
p.adjust.method = "none")
# add pair-wise comparison table
# options(stringsAsFactors = FALSE)
addTab <- as.data.frame(as.matrix(ifelse(round(ps$p.value, 3) < 0.001, "<0.001",
round(ps$p.value, 3))))
addTab[is.na(addTab)] <- "-"
# options(stringsAsFactors = TRUE)
df <- tibble(x = 0, y = 0, tb = list(addTab))
pp2$plot <- pp2$plot + ggpp::geom_table(data = df, aes(x = x, y = y, label = tb), table.rownames = TRUE)
res2 <- arrange_ggsurvplots(list(pp2), print = FALSE, ncol = 1, nrow = 1)
##############################
ggsave(res2,filename = paste0(index,"-2-KMplot-3group-",signature,"-",project,".", fig.type),
width =6 ,height =6.5,path = save_path)
################################################
# input_pdata$group3<-ifelse(input_pdata$group3==3,"High",ifelse(input_pdata$group3==2,"Middle","Low"))
################################################
# choose mean value-category data to conduct KM-plot
# input_pdata = input_pdata
# turn high to 1 to calculate HR
input_pdata<-input_pdata[!is.na(input_pdata$group2),]
input_pdata$group2<-ifelse(input_pdata$group2=="High",1,0)
# ###########################################
# # turn high to 1 to calculate HR
pvalue<-getHRandCIfromCoxph(coxph(Surv(input_pdata$time,input_pdata$status)~input_pdata[,"group2"],data = input_pdata))
HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
sfit <- surv_fit(Surv(input_pdata$time,input_pdata$status) ~ input_pdata$group2,data=input_pdata)
pp3<-ggsurvplot(sfit,data = input_pdata,censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
legend.labs = c(paste0('Low ',mini_sig),paste0("High ",mini_sig)),
submain= paste0(signature,"-in-",project),
surv.median.line = "h", # draw horizontal line at median survival
risk.table = T,
tables.height = 0.20,
palette = cols,
pval.size = 8,
pval = paste(pval = ifelse(pvalue[,1] < 0.0001, "P < 0.0001",
paste("P = ",round(pvalue[,1],4), sep = "")),
HR, CI,sep = "\n"))
res3 <- arrange_ggsurvplots(list(pp3), print = FALSE, ncol = 1, nrow = 1)
##############################
ggsave(res3,filename = paste0(index,"-3-KMplot-2group-",signature,"-",project,".", fig.type),
width =6 ,height =6.5,path = save_path)
#############################################
input_pdata$group2<-ifelse(input_pdata$group2==1,"High","Low")
plots <- list()
plots[[1]] <- pp1
plots[[2]] <- pp3
plots[[3]] <- pp2
print(">>>>>>>>>")
plots <- arrange_ggsurvplots(plots, print = FALSE, ncol = 3, nrow = 1)
res <- list("data" = input_pdata, "plots" = plots)
return(res)
}
IOBR/IOBR documentation built on May 5, 2024, 2:34 p.m.
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Defines functions sig_surv_plot
Documented in sig_surv_plot
#' Multiple survival KM-plot to a signature or gene
#'
#' @param input_pdata This parameter represents the input data for the survival analysis. It is expected to be a data frame.
#' @param signature This parameter specifies the column name in input_pdata that represents the target signature for survival analysis.
#' @param project This optional parameter represents the project name. The default value is "KM".
#' @param time This optional parameter represents the column name in input_pdata that contains the time variable for survival analysis. The default value is "time".
#' @param status This optional parameter represents the column name in input_pdata that contains the survival status variable. The default value is "status".
#' @param time_type This optional parameter specifies the time unit used in the analysis. The default value is "month".
#' @param break_month This optional parameter specifies the interval at which the time axis breaks should be made in the Kaplan-Meier plot. The default value is "auto".
#' @param palette This optional parameter allows the user to specify the palette type if cols is not provided. The default value is "jama".
#' @param save_path This optional parameter specifies the directory path for saving the plot. The default value is "KM-plot".
#' @param mini_sig This optional parameter represents the label for the mini signature in the legend. The default value is "score".
#' @param show_col logical variable, if TRUE, color will be print and show in the R studio
#' @param index This parameter represents the index for multiple plots generated in a single run. It is used as the prefix in the filename. The default value is 1.
#' @param cols This optional parameter allows the user to specify the color palette for the plot. If not specified, a default palette will be used.
#' @param fig.type This optional parameter specifies the file format for saving the plot. The default value is "png".
#' @param ID This optional parameter represents the column name in input_pdata that contains the unique identifier for each data point. The default value is "ID".
#'
#' @importFrom survminer surv_cutpoint
#' @import survival
#' @author Dongqiang Zeng
#' @return survival
#' @export
#'
#' @examples
#'
#'data("tcga_stad_pdata", package = "IOBR")
#'sig_surv_plot(input_pdata = tcga_stad_pdata, signature = "TMEscore_plus", time = "time", status = "OS_status")
#'
sig_surv_plot <- function(input_pdata,
signature,
project = "KM",
ID = "ID",
time = "time",
status = "status",
time_type = "month",
break_month = "auto",
cols = NULL,
palette = "jama",
show_col = TRUE,
mini_sig = "score",
fig.type = "png",
save_path = "KM-plot",
index = 1){
if(!signature%in%colnames(input_pdata)) stop("Target signature must be in the column name of input_pdata")
###############################
if (!file.exists(save_path)) dir.create(save_path)
abspath<-paste(getwd(),"/" ,save_path, "/",sep ="" )
###########################################
# if("time"%in%input_pdata & time!="time") stop(">>>- 'time' is already in column name of input_pdata... ")
# if("status"%in%input_pdata & time!="status") stop(">>>- 'status' is already in column name of input_pdata... ")
input_pdata<-as.data.frame(input_pdata[,colnames(input_pdata)%in%c(ID, time, status, signature)])
#filter data
colnames(input_pdata)[which(colnames(input_pdata)==time)]<-"time"
colnames(input_pdata)[which(colnames(input_pdata)==status)]<-"status"
colnames(input_pdata)[which(colnames(input_pdata)==ID)]<-"ID"
input_pdata$time<-as.numeric(input_pdata$time)
input_pdata$status<-as.numeric(as.character(input_pdata$status))
input_pdata<- input_pdata %>%
filter(!is.na(time)) %>%
filter(!is.na(status)) %>%
filter(!.$time=="NA") %>%
filter(!.$status=="NA") %>%
filter(.$time > 0)
###################################
input_pdata$time<-as.numeric(input_pdata$time)
input_pdata$status<-as.numeric(input_pdata$status)
###################################
input_pdata$ID<-as.character(input_pdata$ID)
# input_pdata$ProjectID<-project
##################################
#transform follow up time
if(time_type=="day") input_pdata$time<-input_pdata$time/30
###################################
#cut use quantile
#################################
# print(paste0(signature,"'s expression range: "))
# print( summary(cut(input_pdata[,signature],breaks = 3)))
q1<-quantile(input_pdata[,signature],probs = 1/3)
q2<-quantile(input_pdata[,signature],probs = 2/3)
input_pdata$group3<-input_pdata[,signature]
input_pdata$group3<-ifelse(input_pdata$group3<=q1,"Low",ifelse(input_pdata$group3>=q2,"High","Middle"))
input_pdata$group2<-input_pdata[,signature]
input_pdata$group2<-ifelse(input_pdata$group2 <= mean(input_pdata[,signature]),"Low","High")
####################################
message(paste(">>> Dataset's survival follow up time is range between",
paste(round(summary(input_pdata$time),2)[c(1,6)],collapse = " to "),"months"))
######################################
#calculate best cutoff
res.cut<-surv_cutpoint(input_pdata,time = "time",event = "status",variables = signature)
res.cut<-res.cut$cutpoint[[1]]
message(paste0(">>> The best cutoff for " ,signature, " is: ",round(res.cut,2)))
###################################
# data category
input_pdata<- best_cutoff(pdata = input_pdata, variable = signature, time ="time", status = "status", PrintResult = F)
# input_pdata<-as.data.frame(input_pdata)
#####################################
# signatureBinary <- as.character(paste(signature,"_binary",sep = ""))
colnames(input_pdata)[which(colnames(input_pdata)== paste0(signature,"_binary"))]<-"bestcutoff"
message(paste(">>> High ", signature," = ",summary(as.factor(input_pdata$bestcutoff)))[1],collapse = " ")
message(paste(">>> Low ",signature," = ",summary(as.factor(input_pdata$bestcutoff)))[2],collapse = " ")
save(input_pdata,file = paste0(abspath,index,"-0-",project,"-",signature,"-survival-analysis-input.RData"))
#######################################
# Sur <- Surv(time = input_pdata$time,event = input_pdata$status)
# 将high变量转化为1，才能准确计算出HR，否则是反的
#####################################
input_pdata$bestcutoff<-ifelse(input_pdata$bestcutoff=="High",1,0)
pvalue<-getHRandCIfromCoxph(coxph(Surv(time = input_pdata$time, event = input_pdata$status)~input_pdata$bestcutoff,data = input_pdata))
HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
cut_off<-paste("cutoff = ",round(res.cut,3),sep = "")
###########################################
sfit <- surv_fit(Surv(time = input_pdata$time, event = input_pdata$status) ~ input_pdata$bestcutoff,
data = input_pdata)
# input_pdata[,index]<-ifelse(input_pdata[,index]==1,"High","LOW")
############################################
#define break time
if(break_month == "auto"){
break_month<-break_month(input = input_pdata$time,block = 6)
}
max_month<- break_month*6
###########################################
if(is.null(cols)) cols<- palettes(category = "box",palette = palette, show_col = F)
###########################################
pp1<-ggsurvplot(sfit,
data = input_pdata,
censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0, max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
surv.median.line = "h", # draw horizontal line at median survival
legend.labs = c(paste0('Low ',mini_sig),paste0("High ",mini_sig)),
submain= paste0(signature,"-in-",project),
risk.table = T,tables.height = 0.20,
palette = cols,
pval.size = 8,
pval = paste(pval = ifelse(pvalue[,1] < 0.0001, "P < 0.0001",
paste("P = ",round(pvalue[,1],4), sep = "")),
HR, CI, cut_off,sep = "\n"))
res1 <- arrange_ggsurvplots(list(pp1), print = FALSE, ncol = 1, nrow = 1)
##############################
ggsave(res1,filename = paste0(index,"-1-KMplot-best-cutoff-",signature,"-",project,".", fig.type),
width =6 ,height =6.5,path = save_path)
################################################
input_pdata$bestcutoff<-ifelse(input_pdata$bestcutoff==1,"High","Low")
print(head(input_pdata))
################################################
# choose tri-sectional quantiles to conduct KM-plot
###########################################
# turn high to 1 to calculate HR
input_pdata<-input_pdata[!is.na(input_pdata$group3),]
# input_pdata$group3<-ifelse(input_pdata$group3=="High",3,ifelse(input_pdata$group3=="Middle",2,1))
# Sur <- Surv(input_pdata$time,input_pdata$status)
# pvalue<-getHRandCIfromCoxph(coxph(Surv(input_pdata$time,input_pdata$status)~input_pdata$group3,data = input_pdata))
# HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
# CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
##########################################
sfit <- surv_fit(Surv(input_pdata$time,input_pdata$status) ~ input_pdata$group3,data=input_pdata)
# hack strata for better survival curve
names(sfit$strata) <- gsub("group3=", "", names(sfit$strata))
##########################################
# ###########################################
pp2<-ggsurvplot(sfit,data = input_pdata,censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
# legend.labs = c(paste0('Low ',mini_sig),paste0("Middle ",mini_sig),paste0("High ",mini_sig)),
submain= paste0(signature,"-in-",project),
surv.median.line = "h", # draw horizontal line at median survival
risk.table = T,tables.height = 0.25,
palette = cols,
pval.size = 8)
fitd <- survdiff(Surv(time,status)~ group3,
data= input_pdata,
na.action = na.exclude)
p.val <- 1 - pchisq(fitd$chisq, length(fitd$n) - 1)
# add nominal pvalue for log-rank test
p.lab <- paste0("Overall P",
ifelse(p.val < 0.001, " < 0.001",
paste0(" = ",round(p.val, 3))))
pp2$plot <- pp2$plot + annotate("text",
x = 0, y = 0.55,
hjust = 0,
fontface = 3,
label = p.lab)
# calculate pair-wise survival comparison
ps <- pairwise_survdiff(Surv(time,status)~ group3,
data= input_pdata,
p.adjust.method = "none")
# add pair-wise comparison table
# options(stringsAsFactors = FALSE)
addTab <- as.data.frame(as.matrix(ifelse(round(ps$p.value, 3) < 0.001, "<0.001",
round(ps$p.value, 3))))
addTab[is.na(addTab)] <- "-"
# options(stringsAsFactors = TRUE)
df <- tibble(x = 0, y = 0, tb = list(addTab))
pp2$plot <- pp2$plot + ggpp::geom_table(data = df, aes(x = x, y = y, label = tb), table.rownames = TRUE)
res2 <- arrange_ggsurvplots(list(pp2), print = FALSE, ncol = 1, nrow = 1)
##############################
ggsave(res2,filename = paste0(index,"-2-KMplot-3group-",signature,"-",project,".", fig.type),
width =6 ,height =6.5,path = save_path)
################################################
# input_pdata$group3<-ifelse(input_pdata$group3==3,"High",ifelse(input_pdata$group3==2,"Middle","Low"))
################################################
# choose mean value-category data to conduct KM-plot
# input_pdata = input_pdata
# turn high to 1 to calculate HR
input_pdata<-input_pdata[!is.na(input_pdata$group2),]
input_pdata$group2<-ifelse(input_pdata$group2=="High",1,0)
# ###########################################
# # turn high to 1 to calculate HR
pvalue<-getHRandCIfromCoxph(coxph(Surv(input_pdata$time,input_pdata$status)~input_pdata[,"group2"],data = input_pdata))
HR <- paste("Hazard Ratio = ", round(pvalue[,2],2), sep = "")
CI <- paste("95% CI: ", paste(round(pvalue[,3],2), round(pvalue[,4],2), sep = " - "), sep = "")
sfit <- surv_fit(Surv(input_pdata$time,input_pdata$status) ~ input_pdata$group2,data=input_pdata)
pp3<-ggsurvplot(sfit,data = input_pdata,censor = TRUE,
ncensor.plot = F,conf.int = F,
xlim = c(0,max_month),
break.time.by = break_month,
xlab = "Months after diagnosis",
legend.labs = c(paste0('Low ',mini_sig),paste0("High ",mini_sig)),
submain= paste0(signature,"-in-",project),
surv.median.line = "h", # draw horizontal line at median survival
risk.table = T,
tables.height = 0.20,
palette = cols,
pval.size = 8,
pval = paste(pval = ifelse(pvalue[,1] < 0.0001, "P < 0.0001",
paste("P = ",round(pvalue[,1],4), sep = "")),
HR, CI,sep = "\n"))
res3 <- arrange_ggsurvplots(list(pp3), print = FALSE, ncol = 1, nrow = 1)
##############################
ggsave(res3,filename = paste0(index,"-3-KMplot-2group-",signature,"-",project,".", fig.type),
width =6 ,height =6.5,path = save_path)
#############################################
input_pdata$group2<-ifelse(input_pdata$group2==1,"High","Low")
plots <- list()
plots[[1]] <- pp1
plots[[2]] <- pp3
plots[[3]] <- pp2
print(">>>>>>>>>")
plots <- arrange_ggsurvplots(plots, print = FALSE, ncol = 3, nrow = 1)
res <- list("data" = input_pdata, "plots" = plots)
return(res)
}
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