MakeKM <- function(data,average.risk,topHits,LT,numGroups,cuts,geneList){

  data$lvl4Groups <- rep(NA,nrow(data))

  qts <- as.numeric(quantile(average.risk,cuts))

  if(numGroups ==2){

    for(i in 1:nrow(data)){

      # for 2 level groups

      if(average.risk[i] < qts) {

        data$lvl4Groups[i] <- "Low"

      }

      if(average.risk[i] >= qts) {

        data$lvl4Groups[i] <- "High"

      }

    }

  }

  if(numGroups == 3){

    for(i in 1:nrow(data)){

      # for 3 level groups

      if(average.risk[i] < qts[1]) {

        data$lvl4Groups[i] <- "Low"

      }

      if(average.risk[i] >= qts[1] && average.risk[i] < qts[2]){

        data$lvl4Groups[i] <- "Intermediate"

      }

      if(average.risk[i] >= qts[2]) {

        data$lvl4Groups[i] <- "High"

      }

    }

  }

  if(numGroups == 4){

    #qts <- quantile(average.risk,probs = c(0.25,0.75,0.9))

    for(i in 1:nrow(data)){

      # for 4 level groups

      if(average.risk[i] < qts[1]) {

        data$lvl4Groups[i] <- "Low"

      }

      if(average.risk[i] >= qts[1] && average.risk[i] < qts[2]){

        data$lvl4Groups[i] <- "Low-intermediate"

      }

      if(average.risk[i] >= qts[2] && average.risk[i] < qts[3]){

        data$lvl4Groups[i] <- "High-intermediate"

      }

      if(average.risk[i] >= qts[3]) {

        data$lvl4Groups[i] <- "High"

      }

    }

  }

  count.dups <- function(DF){

    DT <- data.table(DF)

    DT[,.N, by = names(DT)]

  }

  ####

  if(length(geneList) == 0) {useGenes <- topHits[1:5]}

  else{

    geneList <- gsub(" ","",geneList)

    useGenes <- geneList}

  pie.data <- data[,match(useGenes,colnames(data))]

  Groups <- as.character(data[,match("lvl4Groups",colnames(data))])

  profile <- apply(pie.data,1,function(x){

    return(paste0(paste0(names(x),"=",as.numeric(x),collapse =",")))

  })

  make.pie.data <- as.data.frame(cbind(Groups,profile))

  ## Save top 2 scenarios for KM

  top.scenarios <- as.data.frame(matrix(nrow = 8,ncol = 5))

  colnames(top.scenarios) <- useGenes

  start <-1

  # high

  high.pie <- try(filter(make.pie.data,Groups == "High"))

  profiles.high <- cbind(count.dups(high.pie)[order(-count.dups(high.pie)$N),],

                         paste("Profile",1:nrow(count.dups(high.pie))))

  colnames(profiles.high) <- c("Groups","profile","N","Profile")

  scenarios <- lapply(1:nrow(profiles.high),function(x){

    geneList <- as.numeric(gsub(".*=","",unlist(strsplit(as.character(profiles.high$profile[x]),split=","))))

  })

  profiles.high$profile <- unlist(lapply(1:nrow(profiles.high),function(x){

    temp <- unlist(strsplit(as.character(profiles.high$profile[x]),split = ","))

    prof.temp <- temp[grep("=1",temp)]

    prof <- toString(gsub("=1","",prof.temp))

  }) )

  scenarios <- as.data.frame(do.call(rbind, scenarios))

  colnames(scenarios) <- useGenes

  #Prob3YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (3*MD))$surv)

  #Prob5YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (5*MD))$surv)

  #profiles.high <- profiles.high #cbind(profiles.high,Prob3YSurvival,Prob5YSurvival)

  profiles.high$profile[which(profiles.high$profile =="")] <- "No mutants"

  top.scenarios[start:(start+2),] <- scenarios[1:3,]

  rownames(top.scenarios)[start:(start+2)] <- c("High : Profile 1","High : Profile 2","High : Profile 3")

  start <- start+3

  ## Inter high

  if(numGroups==4){

    high.inter.pie <- try(filter(make.pie.data,Groups == "High-intermediate"))

    profiles.inter.high <- cbind(count.dups(high.inter.pie)[order(-count.dups(high.inter.pie)$N),],

                                 paste("Profile",1:nrow(count.dups(high.inter.pie))))

    colnames(profiles.inter.high) <- c("Groups","profile","N","Profile")

    scenarios <- lapply(1:nrow(profiles.inter.high),function(x){

      geneList <- as.numeric(gsub(".*=","",unlist(strsplit(as.character(profiles.inter.high$profile[x]),split=","))))

    })

    profiles.inter.high$profile <- unlist(lapply(1:nrow(profiles.inter.high),function(x){

      temp <- unlist(strsplit(as.character(profiles.inter.high$profile[x]),split = ","))

      prof.temp <- temp[grep("=1",temp)]

      prof <- toString(gsub("=1","",prof.temp))

    }) )

    scenarios <- as.data.frame(do.call(rbind, scenarios))

    colnames(scenarios) <- useGenes

    #Prob3YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (3*MD))$surv)

    #Prob5YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (5*MD))$surv)

    #profiles.inter.high <- profiles.inter.high #cbind(profiles.inter.high,Prob3YSurvival,Prob5YSurvival)

    profiles.inter.high$profile[which(profiles.inter.high$profile =="")] <- "No mutants"

    top.scenarios[start:(start+2),] <- scenarios[1:3,]

    rownames(top.scenarios)[start:(start+2)] <- c("High-Intermediate : Profile 1","High-Intermediate : Profile 2","High-Intermediate : Profile 3")

    start <- start+3}

  ## INTERMEDIATE

  if(numGroups==3){

    inter.pie <- try(filter(make.pie.data,Groups == "Intermediate"))

    profiles.inter <- cbind(count.dups(inter.pie)[order(-count.dups(inter.pie)$N),],

                            paste("Profile",1:nrow(count.dups(inter.pie))))

    colnames(profiles.inter) <- c("Groups","profile","N","Profile")

    scenarios <- lapply(1:nrow(profiles.inter),function(x){

      geneList <- as.numeric(gsub(".*=","",unlist(strsplit(as.character(profiles.inter$profile[x]),split=","))))

    })

    profiles.inter$profile <- unlist(lapply(1:nrow(profiles.inter),function(x){

      temp <- unlist(strsplit(as.character(profiles.inter$profile[x]),split = ","))

      prof.temp <- temp[grep("=1",temp)]

      prof <- toString(gsub("=1","",prof.temp))

    }) )

    scenarios <- as.data.frame(do.call(rbind, scenarios))

    colnames(scenarios) <- useGenes

    #Prob3YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (3*MD))$surv)

    #Prob5YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (5*MD))$surv)

    #profiles.inter <- profiles.inter #cbind(profiles.inter,Prob3YSurvival,Prob5YSurvival)

    profiles.inter$profile[which(profiles.inter$profile =="")] <- "No mutants"

    top.scenarios[start:(start+2),] <- scenarios[1:3,]

    rownames(top.scenarios)[start:(start+2)] <- c("Intermediate : Profile 1","Intermediate : Profile 2","Intermediate : Profile 3")

    start <- start+3}

  ## LOW INTER

  if(numGroups==4){

    low.inter.pie <- try(filter(make.pie.data,Groups == "Low-intermediate"))

    profiles.inter.low <- cbind(count.dups(low.inter.pie)[order(-count.dups(low.inter.pie)$N),],

                                paste("Profile",1:nrow(count.dups(low.inter.pie))))

    colnames(profiles.inter.low) <- c("Groups","profile","N","Profile")

    scenarios <- lapply(1:nrow(profiles.inter.low),function(x){

      geneList <- as.numeric(gsub(".*=","",unlist(strsplit(as.character(profiles.inter.low$profile[x]),split=","))))

    })

    profiles.inter.low$profile <- unlist(lapply(1:nrow(profiles.inter.low),function(x){

      temp <- unlist(strsplit(as.character(profiles.inter.low$profile[x]),split = ","))

      prof.temp <- temp[grep("=1",temp)]

      prof <- toString(gsub("=1","",prof.temp))

    }) )

    scenarios <- as.data.frame(do.call(rbind, scenarios))

    colnames(scenarios) <- useGenes

    #Prob3YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (3*MD))$surv)

    #Prob5YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (5*MD))$surv)

    profiles.inter.low <- profiles.inter.low #cbind(profiles.inter.low,Prob3YSurvival,Prob5YSurvival)

    profiles.inter.low$profile[which(profiles.inter.low$profile =="")] <- "No mutants"

    top.scenarios[start:(start+2),] <- scenarios[1:3,]

    rownames(top.scenarios)[start:(start+2)] <- c("Low-Intermediate : Profile 1","Low-Intermediate : Profile 2","Low-Intermediate : Profile 3")

    start <- start+3

    }

  ## LOW

  low.pie <- try(filter(make.pie.data,Groups == "Low"))

  profiles.low <- cbind(count.dups(low.pie)[order(-count.dups(low.pie)$N),],

                        paste("Profile",1:nrow(count.dups(low.pie))))

  colnames(profiles.low) <- c("Groups","profile","N","Profile")

  scenarios <- lapply(1:nrow(profiles.low),function(x){

    geneList <- as.numeric(gsub(".*=","",unlist(strsplit(as.character(profiles.low$profile[x]),split=","))))

  })

  profiles.low$profile <- unlist(lapply(1:nrow(profiles.low),function(x){

    temp <- unlist(strsplit(as.character(profiles.low$profile[x]),split = ","))

    prof.temp <- temp[grep("=1",temp)]

    prof <- toString(gsub("=1","",prof.temp))

  }) )

  scenarios <- as.data.frame(do.call(rbind, scenarios))

  colnames(scenarios) <- useGenes

  #Prob3YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (3*MD))$surv)

  #Prob5YSurvival <- as.numeric(summary(survfit(fit.topHits, newdata = scenarios, se.fit = F, conf.int = F), times = (5*MD))$surv)

  #profiles.low <- cbind(profiles.low,Prob3YSurvival,Prob5YSurvival)

  profiles.low$profile[which(profiles.low$profile =="")] <- "No mutants"

  top.scenarios[start:(start+2),] <- scenarios[1:3,]

  rownames(top.scenarios)[start:(start+2)] <- c("Low : Profile 1","Low : Profile 2","Low : Profile 3")

  start <- start+3

  if(numGroups == 2){

    pie.chart <- plot_ly() %>%

      add_pie(data = profiles.low, labels = ~profile, values = ~N,

              name = "Low Group", domain = list(x = c(0, 0.4), y = c(0.3, 0.8)),

              hoverinfo="hovertext",

              hovertext = ~paste('',Profile

                                 #'</br> 3 year survival',round(Prob3YSurvival,digits = 2),

                                 #'</br> 5 year survival',round(Prob5YSurvival,digits = 2)

                                 ),

              type="pie") %>%

      add_pie(data = profiles.high, labels = ~profile, values = ~N,

              name = "High Group", domain = list(x = c(0.45, 0.95), y = c(0.3, 0.8)),hoverinfo="hovertext",

              hovertext = ~paste('',Profile

                                 #'</br> 3 year survival',round(Prob3YSurvival,digits = 2),

                                 #'</br> 5 year survival',round(Prob5YSurvival,digits = 2)

                                 ),

              type="pie") %>%

      layout(title = "Genetic Profile Distribution", showlegend = F,

             xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),

             yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

  }

  if(numGroups == 3){

    pie.chart <- plot_ly() %>%

      add_pie(data = profiles.low, labels = ~profile, values = ~N,

              name = "Low Group", domain = list(x = c(0, 0.4), y = c(0.5, 1)),hoverinfo="hovertext",

              hovertext = ~paste('',Profile

                                 #'</br> 3 year survival',round(Prob3YSurvival,digits = 2),

                                 #'</br> 5 year survival',round(Prob5YSurvival,digits = 2)

                                 ),

              type="pie") %>%

      add_pie(data = profiles.inter, labels = ~profile, values = ~N,

              name = "Intermediate Group", domain = list(x = c(0.45, 0.95), y = c(0.5, 1)),hoverinfo="hovertext",

              hovertext = ~paste('',Profile

                                 #'</br> 3 year survival',round(Prob3YSurvival,digits = 2),

                                 #'</br> 5 year survival',round(Prob5YSurvival,digits = 2)

                                 ),

              type="pie") %>%

      add_pie(data = profiles.high, labels = ~profile, values = ~N,

              name = "High Group", domain = list(x = c(0.2, 0.6), y = c(0, 0.5)),hoverinfo="hovertext",

              hovertext = ~paste('',Profile

                                 #'</br> 3 year survival',round(Prob3YSurvival,digits = 2),

                                 #'</br> 5 year survival',round(Prob5YSurvival,digits = 2)

                                 ),

              type="pie") %>%

      layout(title = "Genetic Profile Distribution", showlegend = F,

             xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),

             yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

  }

  if(numGroups == 4){

    pie.chart <- plot_ly() %>%

      add_pie(data = profiles.low, labels = ~profile, values = ~N,

              name = "Lo", domain = list(x = c(0, 0.22), y = c(0, 0.9)),hoverinfo="hovertext",

              hovertext = ~paste('',Profile

                                 #'</br> Mutant genes',profile

                                 #'</br> 3 year survival',round(Prob3YSurvival,digits = 2),

                                 #'</br> 5 year survival',round(Prob5YSurvival,digits = 2)

                                 ),

              type="pie") %>%

      add_pie(data = profiles.inter.low, labels = ~profile, values = ~N,

              name = "ILo", domain = list(x = c(0.26, 0.48), y = c(0, 0.9)),hoverinfo="hovertext",

              hovertext = ~paste('',Profile

                                 #'</br> Mutant genes',profile

                                 #'</br> 3 year survival',round(Prob3YSurvival,digits = 2),

                                 #'</br> 5 year survival',round(Prob5YSurvival,digits = 2)

                                 ),

              type="pie") %>%

      add_pie(data = profiles.inter.high, labels = ~profile, values = ~N,

              name = "IHi", domain = list(x = c(0.52, 0.74), y = c(0, 0.9)),hoverinfo="hovertext",

              hovertext = ~paste('',Profile

                                 #'</br> Mutant genes',profile

                                 #'</br> 3 year survival',round(Prob3YSurvival,digits = 2),

                                 #'</br> 5 year survival',round(Prob5YSurvival,digits = 2)

                                 ),

              type="pie") %>%

      add_pie(data = profiles.high, labels = ~profile, values = ~N,

              name = "Hi", domain = list(x = c(0.78, 1), y = c(0, 0.9)),hoverinfo="hovertext",

              hovertext = ~paste('',Profile

                                 #'</br> Mutant genes',profile

                                 #'</br> 3 year survival',round(Prob3YSurvival,digits = 2),

                                 #'</br> 5 year survival',round(Prob5YSurvival,digits = 2)

                                 ),

              type="pie") %>%

      add_annotations(x= 0.05, y= 1, xref = "paper", yref = "paper", text = "<b>Low risk</b>", showarrow = F) %>%

      add_annotations(x= 0.3, y= 1, xref = "paper", yref = "paper", text = "<b>Intermediate low</b>", showarrow = F) %>%

      add_annotations(x= 0.61, y= 1, xref = "paper", yref = "paper", text = "<b>Intermediate high</b>", showarrow = F) %>%

      add_annotations(x= 0.9, y= 1, xref = "paper", yref = "paper", text = "<b>High risk</b>", showarrow = F) %>%

      layout(title = "Genetic Profile Distribution", showlegend = F,

             xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),

             yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

  }

  return(list("PieChart"=pie.chart,"GenesUsed"=paste0(useGenes,collapse = ",")))

}

# RiskGroupsResults <- MakeKM(data,average.risk,topHits,LT,numGroups,cuts,geneList = NULL)

# 

# # Make2KM(data = data.gen,average.risk = averaged.risk.gen.boot.bag,

# #         file = "GenOnly_Bagging",type.data = "GenOnly")

# save(RiskGroupsResults,file="RiskGroupsResults.Rdata")