R/kmplot.R

In Wei-V-Zhou/KMPPc: KMPPc: K-M Plot for Pan-cancer

#' kmplot
#'
#' kmplot for TCGA
#'
#' By default, for this function you can draw the kmplot.
#'
#' @param data The data should be the clean dat2 from dataprocess.
#' @param plotType This argument decides which style ("simple", "specified", "typical".
#' @return There is a image for kmplot.
#' @export
#' @import survival survminer ggplot2 stringr
#' @author Wei Zhou <247328181@@qq.com>
#' @examples
#' \dontrun{
#' ## Plot the kmplot automatically
#' kmplot()
#'
#' ## Plot kmfit using "specified" style
#' kmplot(plotType = "specified")
#' }

kmplot <- function(data = NULL, plotType = "typical") {

  ## 0. prepare environment and load libraries
  # rm(list = ls())
  # gc()
  # set.seed(12345)
  # graphics.off()
  # options(stringsAsFactors = FALSE)
  # load packages
  # pkgs <- c("ggplot2", "stringr", "ggpubr", "survival", "survminer", "export")
  # installpkgs <- function(pkgs){
  #   new.pkgs <- pkgs[!(pkgs %in% installed.packages()[ , "Package"])]
  #   if (length(new.pkgs))
  #    BiocManager::install(new.pkgs, ask = F, update = F)
  #   sapply(pkgs, require, character.only = T)
  # }
  # installpkgs(pkgs)
  # lapply(pkgs, library, character.only = T)

  ## 3. K-M plot analysis
  if (is.null(data)) {
    load("../data/kmplotdata.Rdata")
    dat2 <<- dat2
  } else {
    print("Please guarantee your file is dat2!")
    cat("***Notation: Or you will load the kmplotdata.Rdata on your own!\n")
  }

  # survival plot
  # choose the right status number
  factor <- names(table(dat2[ , 2]))
  for (i in 1:length(factor)) {
    cat(c(i, ": ", factor[i], "\n"))
  }
  if(interactive()){
    repeat{
      ANSWER <- readline("Please input the STATUS number (Progress or Dead): ")
      num <- as.numeric(ANSWER)
      if(is.na(num) || num <= 0 || num > length(factor)){
        print("Please type right number format!")
      } else if (num > 0 && num <= length(factor)) {
        break;
      }
    }
    STATUS <- factor[num]
  }
  # survival fit
  my.surv <<- Surv(dat2[ , 1], dat2[ , 2] == STATUS)
  kmfit <<- survfit(my.surv~group, data = dat2)
  if (plotType == "simple") {
    plot(kmfit, col = c("red", "blue"))
  } else if (plotType == "specified") {
    img <- ggsurvplot(kmfit, palette = c("#E7B800","#2E9FDF"),
                      conf.int = TRUE, pval = TRUE, xlab = "Time / Month",
                      ggtheme = theme_light(), risk.table = TRUE, ncensor.plot = TRUE)
    print(img)
  } else if (plotType == "typical") {
    label <- data.frame(kmfit$strata)
    labs <- cbind(rownames(label), label)
    colnames(labs) <- c("gene_group", "n")
    gene_high <- paste0(labs[1, 1] , "\n (n = ", labs[1, 2], ")")
    gene_low  <- paste0(labs[2, 1] , "\n (n = ", labs[2, 2], ")")
    names(kmfit$strata) <- c(gene_high, gene_low)
    g <- theme_survminer(font.main = c(16, "bold", "darkblue"),
                         font.x = c(14, "bold", "black"),
                         font.y = c(14, "bold", "black"),
                         font.tickslab = c(14, "bold", "black")) +
      theme(axis.line.x = element_line(size = 1),
            axis.line.y = element_line(size = 1),
            axis.ticks.x = element_line(size = 1),
            axis.ticks.y = element_line(size = 1),
            plot.title = element_text(hjust = 0.5),
            legend.title = element_blank(),
            legend.direction = "horizontal",
            legend.text = element_text(size = 12, face = "bold"))
    p <- ggsurvplot(kmfit, palette = c("red","blue"),
                    conf.int = F, pval = T, pval.method = T, surv.scale = "percent",
                    xlab ="Months" , ylab = "% Disease-free",
                    ggtheme = g, risk.table = F, ncensor.plot = F)

    print(p)
  }

}