library(shiny)

library(shinydashboard)

library(plotly)

library(reshape2)

library(survminer)

library(survival)

library(data.table)

library(dplyr)

library(scales)

load("LungDemoData.Rdata")

load("Results.Rdata")

load("Means.Rdata")

ui <- dashboardPage(

  dashboardHeader(title = "Prognostic models in metastatic lung adenocarcinoma (BETA)",titleWidth = 400),

  dashboardSidebar(width = 200,

                   sidebarMenu(

                     menuItem("Dashboard", tabName = "fixed", icon = icon("bar-chart")),

                     menuItem("Risk Group Stratification", tabName = "strat", icon = icon("scissors")),

                     menuItem("Gene View", tabName = "gene", icon = icon("gears")),

                     menuItem("Patient View", tabName = "patient", icon = icon("user-circle-o")),

                     menuItem("Generate Risk Score", tabName = "risk", icon = icon("gears"))

                   )

  ),

  dashboardBody(

    tabItems(

      # First tab content

      tabItem(tabName = "fixed",

              sidebarLayout(

                sidebarPanel(

                  width=12,

                  h2("Prognostic Performance")

                  # selectInput("StudyName", "Choose a Study :", 

                  #             choices = c("Lung"

                  #             )),

                  # checkboxInput("AddCNV", "Include copy number data", FALSE),

                  # checkboxInput("OnlyCNV", "Consider only copy number data", FALSE),

                  # 

                  # selectInput("Method", "Choose a Method :", 

                  #             choices = c("Penalized regression"="LASSO")),

                  # submitButton("Submit")

                ),

                mainPanel(width=12,

                          #htmlOutput("VariableHeader"),

                          htmlOutput("RiskHeader"),

                          plotOutput("RiskHistogram"),

                          tableOutput("RiskSummary"),

                          htmlOutput("CIHeader"),

                          tableOutput("CI"),

                          htmlOutput("RefitHeader"),

                          tableOutput("RefitRisk"),

                          htmlOutput("CommentPval"),

                          tableOutput("ClinRefit")#,

                          #htmlOutput("EffectHeader"),

                          #htmlOutput("FreqHeader")#,

                          #plotOutput("influencePlot"),

                )

              )

      ),

      # First tab content

      tabItem(tabName = "strat",

              #

              #   sidebarPanel( width = 12,

              #     checkboxInput("ShowVolvano", "Show Volcano Plot", TRUE),

              #                 checkboxInput("ShowPies", "Show Pie Charts", TRUE),

              #                 submitButton("Submit")

              # ),

              sidebarLayout(

                sidebarPanel(width = 12,

                             h2("Risk Group Stratification")

                ),

                mainPanel(width = 12,

                          htmlOutput("predRiskText"),

                          htmlOutput("KMText"),

                          plotOutput("KM"),

                          tableOutput("SurvSum"),

                          htmlOutput("MutGroupText"),

                          htmlOutput("MutBPText"),

                          plotOutput("Mut")

                )

              )

      ),

      tabItem(tabName = "gene",

              h2("Exploratory interactive gene plots"),

              #   sidebarPanel( width = 12,

              #     checkboxInput("ShowVolvano", "Show Volcano Plot", TRUE),

              #                 checkboxInput("ShowPies", "Show Pie Charts", TRUE),

              #                 submitButton("Submit")

              # ),

              sidebarLayout(

                sidebarPanel(width = 12,

                             textInput("GeneListRisk", 

                                       "Find gene(s) : ",

                                       value = ""),

                             submitButton("Submit")),

                mainPanel(

                  htmlOutput("VolcanoHeader"),width = 12,

                  plotlyOutput("effectPlot"),

                  htmlOutput("profiletext"),

                  plotlyOutput("ProfilePie")#, width="800px", height="400px")

                )

              )

      ),

      tabItem(tabName = "patient",

              h2("Predicting survival for an incoming patient"),

              sidebarLayout(

                sidebarPanel(width = 12,

                             textInput("GeneList", 

                                       "Names of the genes you wish to use to create predictive risk : example STK11,KEAP1,KRAS",

                                       value = ""),

                             ##### FOR LUNG ONLY #####

                             checkboxGroupInput(inputId = "Demographics", label = "Choose demographic variables :",

                                                choices = c("Male" = "Sex",

                                                            "Smoker" = "Smoker",

                                                            "Age > 65" = "Age"),

                                                inline = TRUE),

                             submitButton("Submit")

                ),

                mainPanel(width = 12,

                          htmlOutput("predtext"),

                          plotlyOutput("IndSurvKM"),

                          tableOutput("IndPredTable")

                )

              )

      ),

      tabItem(tabName = "risk",

              h2("Generate risk score in a new cohort"),

              #   sidebarPanel( width = 12,

              #     checkboxInput("ShowVolvano", "Show Volcano Plot", TRUE),

              #                 checkboxInput("ShowPies", "Show Pie Charts", TRUE),

              #                 submitButton("Submit")

              # ),

              sidebarLayout(

                sidebarPanel(width = 12,

                             fileInput(inputId = "File1", label = "Choose your dataset (.csv file)",

                                       accept = c(".csv")

                             )#,

                             # textInput("OutName", 

                             #           "Name or the output risk file :",

                             #           value = "")#,

                             #submitButton("Submit")

                ),

                mainPanel(

                  width = 12,

                  plotOutput("RiskHistogram.new"),

                  downloadLink("downloadData", "Download")

                )

              )

      )

    )

  )

)

# Define server logic required to draw a histogram

server <- function(input, output) {

  # load functions of interest

  source("./Scripts/GetResultsVarSelect.R")

  source("./Scripts/MakeKM.R")

  source("./Scripts/GetKMStuff.R")

  # source("./Scripts/PlotTree.R")

  source("./Scripts/PredictIncoming.R")

  # load("FirstRun.Rdata")

  # load("RiskGroupsResults.Rdata")

  output$VariableHeader <- renderText({ paste("<h3> <u> <font color=\"black\"><b>","Prognostic performance", "</b></font> </u> </h3>") })

  output$RiskHeader <- renderText({paste("<h4> <u> <font color=\"black\"><b>","Histogram of predicted risk score", "</b></font> </u> </h4>")})

  output$CIHeader <- renderText({paste("<h4> <u> <font color=\"black\"><b>","Concordance index in predicting overall survival", "</b></font> </u> </h4>")})

  output$RefitHeader <- renderText({paste("<h4> <u> <font color=\"black\"><b>","Cox regression estimates and significance P-values", "</b></font> </u> </h4>")})

  output$EffectHeader <- renderText({ paste("<h3> <u> <font color=\"black\"><b>","Individual gene effect size and relative importance", "</b></font> </u> </h3>") })

  output$FreqHeader <- renderText({paste("<h4> <u> <font color=\"black\"><b>","Gene selection frequency", "</b></font> </u> </h4>")})

  output$VolcanoHeader <- renderText({paste("<h4> <u> <font color=\"black\"><b>","Interactive Volcano plot", "</b></font> </u> </h4>")})

  output$CommentPval <- renderText({paste("<font color=\"black\">","*Note that the pvalue is 0 here because it goes beyond the precision of the machine.", "</font>")})

  output$RiskHistogram <- renderPlot({FirstRun$RiskHistogram})

  output$RiskSummary <- renderTable({FirstRun$RiskScoreSummary}, rownames = TRUE)

  output$CI <- renderTable({FirstRun$ciSummary}, rownames = TRUE)

  output$RefitRisk <- renderTable({FirstRun$RiskRefit}, rownames = TRUE)

  output$ClinRefit <- renderTable({FirstRun$ClinRefitTable}, rownames = TRUE)

  output$influencePlot <- renderPlot({FirstRun$inflPlot})

  output$predRiskText <- renderText({ paste("<h3> <u> <font color=\"black\"><b>","Risk group stratification", "</b></font> </u> </h3>") })

  GetResultsReactive <- reactive({getResults(studyType = "Lung",

                                             method="LASSO",

                                             geneList = unlist(strsplit(input$GeneListRisk, split ="," )))})

  output$effectPlot <- renderPlotly({GetResultsReactive()$selectInflPlot})

  #output$effectPlot <- renderPlotly({FirstRun$selectInflPlot})

  KMStuffReactive <- reactive({

    KMStuff(FirstRun$data.out,FirstRun$average.risk,

            FirstRun$topHits,4,

            c(0.25,0.75,0.9),

            geneList=unlist(strsplit(input$GeneListRisk, split ="," )))

  })

  output$KMText <- renderText({ paste("<h4> <u> <font color=\"black\"><b>","Kaplan-Meier plot of overall survival", "</b></font> </u> </h4>") })

  output$KM <- renderPlot(FirstRun$KM_Plot)

  output$SurvSum <- renderTable(FirstRun$SurvSum,rownames = TRUE)

  output$MutGroupText <- renderText({ paste("<h3> <u> <font color=\"black\"><b>","Mutation profiles by risk groups", "</b></font> </u> </h3>") })

  output$MutBPText <- renderText({ paste("<h4> <u> <font color=\"black\"><b>","Barplot of mutation frequency", "</b></font> </u> </h4>") })

  output$Mut <- renderPlot({

    print(FirstRun$mut_Plot)})

  ## Tab 2

  output$profiletext <- renderText({ paste("<h4> <u> <font color=\"black\"><b>","Piechart of most representative mutation profiles : ",

                                           KMStuffReactive()$GenesUsed, "</b></font> </u> </h4>") })

  output$ProfilePie <- renderPlotly({

    KMStuffReactive()$PieChart

  })

  makePredictionsReactive <- reactive({

    predictIncomingPatient(mutGenes = unlist(strsplit(input$GeneList, split =",")),

                           clinical=c(input$Demographics),

                           ClinRefit=FirstRun$ClinRefit,

                           time.type=FirstRun$time.type,

                           MD=FirstRun$MD,

                           LassoFits=FirstRun$LassoFits,

                           RiskScore=FirstRun$average.risk,

                           means.train=means.train)

  })

  output$IndSurvKM <- renderPlotly({makePredictionsReactive()$IndSurvKM})

  output$IndPredTable <- renderTable({makePredictionsReactive()$IndPredTable},rownames = TRUE)

  #output$IndSurvKM <- renderPlotly({FirstRun$IndSurvKM})

  # output$IndPredTable <- renderTable({FirstRun$IndPredTable},rownames = TRUE) 

  ### GENERATING THE RISK SCORE FOR NEW DATA ###

  ### GENERATING THE RISK SCORE FOR NEW DATA ###

  dset <- reactive({

    inFile <- input$File1

    if(is.null(inFile)) return(NULL)

    LassoFits <- as.matrix(Results$LassoFits)

    ori.risk <- Results$ori.risk

    file.rename(inFile$datapath, paste0(inFile$datapath, ".csv"))

    in.data <- read.csv(paste0(inFile$datapath, ".csv"), header = T,row.names = 1)

    features <- colnames(LassoFits)

    features[match(c("alk","ros1","ret"),features)] <- paste0(features[match(c("alk","ros1","ret"),features)],".fusion")

    colnames(LassoFits)[match(c("alk","ros1","ret"),colnames(LassoFits))] <- paste0(colnames(LassoFits)[match(c("alk","ros1","ret"),colnames(LassoFits))],".fusion")

    # in.data <- as.data.frame(matrix(rbinom(1100,1,prob=0.5),ncol =11))

    # colnames(in.data) <- c("KEAP1","STK11","TP53","EGFR","KRAS","SMARCA4","alk","ros1","BRCA1","AXIN1","noNameTest")

    if(!all(is.na(match(colnames(in.data),features)))){

      matched.genes <- c(na.omit(match(colnames(in.data),features)))

      new.dat <- in.data[,which(!is.na(match(colnames(in.data),features)))]

      ## ADD ALL MISSING GENES TO BE ALL zero ##

      missing <- features[which(is.na(match(features,colnames(new.dat))))]

      to.add <- as.data.frame(matrix(0L,nrow=nrow(new.dat),ncol=length(missing)))

      colnames(to.add) <- missing

      rownames(to.add) <- rownames(new.dat)

      new.dat <- as.data.frame(cbind(new.dat,to.add))

      new.dat <- new.dat[,match(features,colnames(new.dat))]

      #############################################

      all.pred <- lapply(1:nrow(LassoFits),function(x){

        ### Subset to the coefs of that cv ###

        coefs <- LassoFits[x,LassoFits[x,] != 0]

        new.temp <- select(new.dat,names(coefs))

        if(!all(is.na(match(c("alk","ros1","ret"),names(means.train[[x]]))))){

          names(means.train[[x]])[na.omit(match(c("alk","ros1","ret"),names(means.train[[x]])))] <- 

            paste0(names(means.train[[x]])[na.omit(match(c("alk","ros1","ret"),names(means.train[[x]])))],".fusion")

        }

        ## substract mean mutation rate of TRAINING SET !!!###

        new.x <- new.temp - rep(means.train[[x]][match(names(coefs),names(means.train[[x]]))], each = nrow(new.temp))

        cal.risk.test <- drop(as.matrix(new.x) %*% coefs)

        return(cal.risk.test)

      })

      all.pred <- do.call("cbind",all.pred)

      Risk <- apply(all.pred,1,mean)

      names(Risk) <- rownames(new.dat)

      # Risk.all <- as.matrix(coefs) %*% as.matrix(t(new.dat))

      # Risk <- apply(Risk.all,2,mean)

      #in.data$Risk <- Risk

      ##########################################

      ori.risk.range <- range(ori.risk)

      in.data$OncoCastRiskScore <- rescale(Risk, to = c(0, 10), from = ori.risk.range) #WithOriginal

      #in.data$rescaledRisk <- rescale(in.data$Risk, to = c(0, 10), from = range(in.data$Risk, na.rm = TRUE, finite = TRUE))

      RiskHistogram.new <- ggplot(in.data, aes(x = OncoCastRiskScore, y = ..density..)) +

        geom_histogram(show.legend = FALSE, aes(fill=..x..),

                       breaks=seq(min(in.data$OncoCastRiskScore,na.rm = T), max(in.data$OncoCastRiskScore,na.rm = T), by=20/nrow(in.data))) +

        geom_density(show.legend = FALSE) +

        theme_minimal() +

        labs(x = "Average risk score", y = "Density") +

        scale_fill_gradient(high = "red", low = "green")

      return(list("RiskHistogram.new"=RiskHistogram.new,"out.data"=in.data))

    }

    else{

      stop("No gene in your dataset overlapped with the IMPACT platform. Please rename genes or check your dataset.")

    }

  })

  output$RiskHistogram.new <- renderPlot(dset()$RiskHistogram.new)

  #data <- renderTable(dset()$out.data)

  output$downloadData <- downloadHandler(

    filename = function() {

      paste("NewRiskData.csv", sep="")

    },

    content = function(file) {

      write.csv(dset()$out.data, file)

    }

  )

}

shinyApp(ui, server)