R/cytosee_shiny.R

In poipou/CytoSEE: automatic computation and evaluation of cytometry data

#' Opens \code{SC3} results in an interactive session in a web browser.
#'
#' Runs interactive \code{shiny} session of \code{SC3} based on precomputed clusterings.
#'
#' @param object an object of \code{SCESet} class
#'
#' @return Opens a browser window with an interactive \code{shiny} app and visualize
#' all precomputed clusterings.
#'
#' @name cytosee_go
#' @aliases cytosee_go
#'
#' @import shiny
#' @import ggplot2
#' @import visNetwork
#' @import vegan
#' @import pheatmap
#' @import recharts
#' @import hexbin
#' @importFrom  DT renderDataTable dataTableOutput datatable
#' @importFrom shinyjs useShinyjs showElement hideElement alert
#' @importFrom flowCore read.FCS logTransform arcsinhTransform exprs transformList transform colnames flowFrame parameters keyword
#' @importFrom shinydashboard dashboardPage sidebarMenu  dashboardHeader dashboardSidebar menuItem box tabBox tabItems tabItem updateTabItems
#' @export

cytosee_go <- function(object){



########################################################################################################
#
#                                       shiny UI part
#
########################################################################################################

ui = shinydashboard::dashboardPage(
  skin ="blue",
  shinydashboard::dashboardHeader(title = "CytoSEE"),

  ##### sider bar #####
  shinydashboard::dashboardSidebar(

    # use shinyjs
    useShinyjs(),
    disable = TRUE,

  tags$script(
    HTML(
      "Shiny.addCustomMessageHandler('unbind-DT', function(id) {
        Shiny.unbindAll($('#'+id).find('table').DataTable().table().node());
        })"
    )
  ),
 div(id = "Step3_sidebar",class="Step3",
     style="display:none",
     h4("Step 3 Result and Visualization"),
     hr(),
     shinydashboard::sidebarMenu(
       id = "result_display",
       shinydashboard::menuItem("ClusterLabel", icon=icon("tags",lib="font-awesome"), tabName = "ClusterLabel",selected = TRUE),
       shinydashboard::menuItem("ReportTable", icon=icon("sticky-note",lib="font-awesome"), tabName = "ReportTable"),
       shinydashboard::menuItem("Visualization", icon=icon("television",lib="font-awesome"),tabName ="Visualization")
     )
    )
 ),

##### UI Body #####
shinydashboard::dashboardBody(
 ##### UI Body step one #####
 div(
  class="Introduction",
  div(
   hr(),
   fluidRow(
     column(
       width = 12,
       h1("CytoSEE:",br(),span("Shiny based platform for single cells analyzing",style="font-family:'Georgia'; font-size:20px;"),
        br(),
        style =
        "font-family: 'Bell MT';
        font-size:50px;
        color: #000; text-align: center;
        background:#fff;
        padding: 30px;
        border-radius:5px")
     )
    )
   ),
   div(
    hr(),
    fluidRow(
    column(
      width = 4,
      box(
        title="Single-file analysis",
        status = "primary",
        width = 12,
        solidHeader=TRUE,
        collapsible=TRUE,
        div(class="InputFile",
            br(),
            h4("Note:"),
            h5("This module is an entrance for single file analysis and display."),
            hr(),
            textInput(inputId = "Project_name1",value = "CytoSEE",label = "Name the project here:"),
            br(),
            fileInput(inputId = "fcs",
                      label = "Only .fcs is permitted",
                      accept=c('.fcs')),
            br()
      )
     )
    ),
    column(
      width = 4,
      box(
        title="Rdata display",
        status = "primary",
        width = 12,
        solidHeader=TRUE,
        collapsible=TRUE,
        div(class="InputFile",
            br(),
            h4("Note:"),
            h5("This module is an entrance for dataset which is already analyzed by CytoSEE."),
            hr(),
            textInput(inputId = "Project_name2",value = "CytoSEE",label = "Name the project here:"),
            br(),
            fileInput(inputId = "Rdata",
                      label = "Only .Rdata is permitted",
                      accept=c('.Rdata')),
            br()
        )
      )
    ),
    column(
      width = 4,
      box(
        title="Multi-file analysis",
        status = "primary",
        width = 12,
        solidHeader=TRUE,

        collapsible=TRUE,
        div(class="InputFile",
            br(),
            h4("Note:"),
            h5("This module is an entrance for multi-file analysis."),
            hr(),
            textInput(inputId = "Project_name3",value = "CytoSEE",label = "Name the project here:"),
            br(),
            fileInput(inputId = "zip",
                      label = "Only .zip is permitted",
                      accept=c('.zip')),
            br()
        )
      )
     )
    ),
    h4("Contact:mchen@zju.edu.cn;yczhou@zju.edu.cn",br(),sprintf("CytoSEE VERSION: %s",package.version("cytosee")),
         style =
        "font-family: 'Bell MT';
         font-size:19px;
         color: #000; text-align: center;
         padding: 10px;
         margin-top:5%")
   )
  ),

 div(
     id="Step1_body",
     style="display:none",
     class="Step1",
     fluidRow(
      class="Preview",
      style="display:none",
      column(
        width=12,
        id="lgs",
        box(
         width = 12,
         title = "",
         fluidRow(
          column(
                  width=8,
                  h3("PreViewData"),
                  br(),
                  fluidRow(
                    column(
                      width = 6,
                      plotOutput("hex_plot",height = 450)
                    ),
                    column(
                      width = 6,
                      plotOutput("X_density",height = 220),
                      plotOutput("Y_density",height = 220)
                    )
                  ),
                  fluidRow(
                    column(
                      width=12,
                      class="Preview",
                      style="display:none",
                      uiOutput("XY_val"),
                      uiOutput("Plot_bin"),
                      uiOutput("data_transform")
                    )
                  )
          ),
          column(
                 width=4,
                 h3("Select Channel:"),
                 br(),
                 box(width = 12,
                 collapsible = FALSE,
                 solidHeader = TRUE,
                 title = "",
                 column(
                    width=12,
                    box(
                      width = 12,
                      DT::dataTableOutput("file")
                    ),
                    actionButton("all_choice","All",width = "25%",style="margin-bottom:5%;margin-left:17%"),
                    actionButton("Done","Done",width = "25%",style="margin-bottom:5%;margin-left:15%;background:#31353e;color:#eeeeee")
                 )
                )
               )
              )
             )
            )
           )
  ),

 ##### flowAI UI #####
 div(id="Preprocess",class="Preprocess",style="display:none",
   box(
     width = 12,
     column(
       width=12,
       h4("flowAI"),
       br(),
       class="PreProcess",
       fluidPage(
         tabBox(
           id="AI_result",width = 8,
           tabPanel(
             "Flow Rate",
             fluidPage(
               hr(),
               textOutput("flowRateSummary"),
               hr(),
               plotOutput("flowRatePlot"),
               hr(),
               fluidRow(
                 column(4, offset = 1,uiOutput("timeSlider")),
                 column(4, offset = 2,uiOutput("rateSlider"))
               )
             )
           ),
           tabPanel(
             "Signal Acquisition",
             fluidPage(
               hr(),
               textOutput("flowSignalSummary"),
               hr(),
               uiOutput("signalBinSlider"),
               hr(),
               plotOutput("flowSignalPlot")
             )
           ),
           tabPanel(
             "Dynamic Range",
             fluidPage(
               hr(),
               fluidRow(
                 column(5,textOutput("flowMarginSummary")),
                 column(3, offset = 2,checkboxInput("checkbox", label = "Apply Margins Check", value = TRUE))
               ),
               hr(),
               plotOutput("flowMarginPlot")
             )
           )
         ) ,
        box(
           id="AI_setting",
           width = 4,
           title = "AI_setting",
           h4("Summary:"),
           textOutput("summaryText1"),
           textOutput("summaryText2"),
           hr(),
           h4("Parameters:"),
           numericInput("timeLenth", label = h5("Time step (sec)"), value = 0.1, step = 0.1),
           uiOutput("signalBinSize"),
           br(),
           actionButton(inputId = "AI_Skip",label = "Skip",width = "25%",style="margin-bottom:5%;margin-left:15%"),
           actionButton(inputId = "AI_Confirm",label = "Confirm",width = "25%",style="margin-bottom:5%;margin-left:15%;background:#31353e;color:#eeeeee")
         )
       )
     )
   )
 ),



 ##### UI body Step two  #####

 div(id="Step2_body",style="display:none",class="Step2",
        div(
          width=12,
          h3("Cell clustering "),
             column(
               width = 3,
               br(),
               mainPanel(
                   width = 10,
                   id = "Step2_sidebar",class="Step2",
                   style="display:none;background:#fff;border-radius:5px;padding:10px;box-shadow:6px 6px 3px #888888",
                   h3(icon("cog"),"Setting"),
                   br(),
                   radioButtons(inputId = "pre_reduce_method",
                                label = "Select dimension reduce method:",
                                choices=c("t-SNE","PCA","LargeVis"),
                                selected="PCA",
                                inline = TRUE),
                   radioButtons(inputId = "pre_mst_method",
                                label = "Select MST methods:",
                                choices = c("flowSOM"),
                                selected = "flowSOM",
                                inline = TRUE),
                   div(
                     div(
                       fileInput(inputId = "import_cluster",
                                 label = "Custom Clusters: '.csv' file is allowed.",
                                 accept = c(".csv"))
                     )
                   )
                 )
             ),
             column(
               width = 3,
               br(),
               box(
                width=12,
                solidHeader=TRUE,
                collapsible=TRUE,
                collapsed=FALSE,
                status="primary",
                title="flowSOM",
                h4("Introduction:",sytle="font-size:23px"),
                p("Self-Organizing Map;A two-level clustering(consensus clustering)."),
                h4("speed:",icon("battery-full")),
                br(),
                sliderInput(inputId = "flowSOM_K",label = "K value",min = 2,max = 150,step = 1,value = 10),
                actionButton(inputId = "flowSOM_run",label = "Calculate",style='margin-left:25%;width:50%')
                ),
               br(),
               box(
                 width=12,
                 solidHeader=TRUE,
                 collapsible=TRUE,
                 collapsed=FALSE,
                 status="primary",
                 title="DensityCut",
                 h4("Introduction",sytle="font-size:23px"),
                 p("K-nearest neighbour;random walk;hierarchical cluster tree"),
                 h4("speed:",icon("battery-half")),
                 br(),
                 sliderInput(inputId = "DensityCut_K",label = "The number of neighbours",min = 3,max = 50,step = 1,value = 10),
                 actionButton(inputId = "DensityCut_run",label = "Calculate",style='margin-left:25%;width:50%')
               )
              ),
             column(
              width = 3,
              br(),
              box(
                width=12,
                solidHeader=TRUE,
                collapsible=TRUE,
                collapsed=FALSE,
                status="primary",
                title="Consboost",
                h4("Introduction"),
                p("adaboost;consensus clustering;down-sampling"),
                h4("speed:",icon("battery-quarter")),
                br(),
                sliderInput(inputId = "Consboost_K",label = "K value",min = 2,max = 150,step = 1,value = 10),
                actionButton(inputId = "Consboost_run",label = "Calculate",style='margin-left:25%;width:50%')
              ),
              br(),
              box(
                width=12,
                solidHeader=TRUE,
                collapsible=TRUE,
                collapsed=FALSE,
                status="primary",
                title="Phenograph",
                h4("Introduction"),
                p("phenotypic similarities graph;Jaccard coefficient;Louvain method"),
                h4("speed:",icon("battery-quarter")),
                br(),
                sliderInput(inputId = "phenograph_K",label = "K value",min = 3,max = 50,step = 1,value = 30),
                actionButton(inputId = "phenograph_run",label = "Calculate",style='margin-left:25%;width:50%')
              )
            ),
             column(
              width = 3,
              br(),
              box(
                width=12,
                solidHeader=TRUE,
                collapsible=TRUE,
                collapsed=FALSE,
                status="primary",
                title="flowMeans",
                h4("Introduction"),
                p("k-means;find non-spherical clusters"),
                h4("speed:",icon("battery-three-quarters")),
                br(),
                column(
                  width = 6,
                  sliderInput(inputId = "flowMeans_K",label = "K value",min = 2,max = 150,step = 1,value = 10)
                ),
                column(
                  width = 6,
                  sliderInput(inputId = "flowMeans_maxK",label = "maxK value",min = 2,max = 150,step = 1,value = 20)
                ),
                actionButton(inputId = "flowMeans_run",label = "Calculate",style='margin-left:25%;width:50%')
              ),
              br(),
              box(
                width=12,
                solidHeader=TRUE,
                collapsible=TRUE,
                collapsed=FALSE,
                status="primary",
                title="SamSPECTRAL",
                h4("Introduction"),
                p("Data reduction;spectral clustering"),
                h4("speed:",icon("battery-quarter")),
                br(),
                column(
                    width = 6,
                    sliderInput(inputId = "SamSPECTRAL_sigma",
                                 label = "normal Sigma",
                                 min = 1,max = 300,step = 5,value = 200)
                  ),
                column(
                    width = 6,
                    sliderInput(inputId = "SamSPECTRAL_separation",
                                 label = "separation.factor",
                                 min = 0.3,max = 2,step = 0.03,value = 0.39)
                ),
                actionButton(inputId = "SamSPECTRAL_run",label = "Calculate",style='margin-left:25%;width:50%')
            )
          )
        )
      ),

 ##### UI body Step three #####
 div(
  id="Step3_body",
  style="display:none",
  class="Step3",
  tabItems(
    tabItem(
      tabName="ClusterLabel",
      div(
        id="change_marker_name",
        h3("Cluster Label Generation",style="color:#111111"),
        style="background:#ffffff;padding:10px;border-radius:5px;",
        fluidRow(
          column(
           width=4,
           box(
            width=12,
            h4("Note:"),
            p("It is an Cell type identification method for labeling the clusters divided by our clustering methods."),
            p("This approach labeling the clusters based on the relative abundence of cell surface markers e.g.CD38, CCR7."),
            p("Users can change the marker name in the following textbox:"),
            sliderInput(inputId = "CL_MaxHitsPht",label = "MaxHitsPht",min = 1,max = 15,step = 1,value = 5),
            uiOutput("alias")
           )
         ),
         column(
           width=8,
           box(
             width=12,
             height=720,
             id="MarkerLine",
             eChartOutput("Marker_line",height=680)
           ),
           actionButton("skip1","Skip",width="25%",style="margin-top:1%;margin-left:17%"),
           actionButton("label_button",label = "Label",width="25%",style="margin-top:1%;margin-left:15%;background:#31353e;color:#eeeeee")
         )
        )
      ),
      div(
        id="celltype_tree_div",
        h3("Clusters Table",style="color:#111111"),
        style="display:none;background:#ffffff;padding:10px;border-radius:5px;",
        fluidRow(
          column(
            width = 5,
            box(
              width=12,
              DT::dataTableOutput(outputId = "Clusters_table")
              )
          ),
          column(
            width = 7,
            box(
              width = 12,
              height = 720,
              visNetwork::visNetworkOutput("CellType_tree",height = 680,width = "100%")
            ),
            actionButton("Skip",label="Skip",width="25%",style="margin-top:1%;margin-left:17%"),
            actionButton("Next",label = "Next",width="25%",style="margin-top:1%;margin-left:15%;background:#31353e;color:#eeeeee")
          )
        )
      )
    ),
    tabItem(
     tabName ="ReportTable",
     div(style="background:#ffffff;padding:10px;border-radius:5px;",
      h4("Project Report:"),
      DT::dataTableOutput(outputId = "Project_report",width = "98%"),
      br(),
      h4("Clusters Report:"),
      fluidRow(
        column(
          width = 6,
          DT::dataTableOutput(outputId = "Cluster_report",width = "95%")
        ),
        column(
          width = 6,
          uiOutput(outputId = "confirm_marker_choose"),
          plotOutput(outputId = "cell_type_confirm_plot",height = 330,width = "95%")
        )
      ),
      br(),
      actionButton("previous",label="Previous",width="15%",style="margin-bottom:1%;margin-left:30%"),
      actionButton("confirm",label="Confirm",width="15%",style="margin-bottom:1%;margin-left:10%;background:#31353e;color:#eeeeee")
     )
    ),

    ######### Visualization ########
    tabItem(
     tabName ="Visualization",
     tabBox(
       width=12,
       side = "right",
       id = "DP",
       title = "Visual Display",
       height = "680px",
       selected = "Scatter Plot",

       ### scatter Plot ###
       tabPanel(
         title="Scatter Plot",
         fluidRow(
           column(
             width=9,
             plotOutput(outputId = "scatter_plot",height = 670,width = "100%")
           ),
           column(
             width=3,
             box(
               width = 12,
               title = div(icon("cog"),"\t","Setting"),
               radioButtons(
                 inputId="red_choice",
                 label = "Dimensionality reduction methods:",
                 choices = c("t_SNE","LargeVis","PCA"),selected = "PCA",
                 inline = TRUE),
               br(),
               radioButtons(
                 inputId="Cluster_Marker",
                 label = "Choose a way to display:",
                 choices = c("Clusters","Markers"),selected = "Clusters",
                 inline = TRUE),
               br(),
               selectizeInput(
                 inputId = "scatter_colors",
                 label = "Color style:",
                 choices = c("set1","set2"),
                 selected = "set1"),
               br(),
               uiOutput("Marker_or_Cluster"),
               br()
             )
           )
         )
       ),

      ### MST ###
       tabPanel(
         id="MST",
         title = "MST",
         fluidRow(
           column(
             width=9,
             column(
               width = 6,
               plotOutput("MST_pie",height = 670)
             ),
             column(
               width = 5,
               plotOutput("MST_marker",height = 670)
             )
           ),
           column(
             width=3,
             box(
               width = 12,
               title = div(icon("cog"),"\t","Setting"),
               uiOutput("MST_panel",height=100),
               br()
             )
           )
         )
       ),

       ### Marker heatmap ###
       tabPanel(
         id="Marker_heatmap",
         title="HeatMap",
         fluidRow(
          column(
            width = 9,
            plotOutput("Marker_heatmap",width = "100%",height = 670)
          ),
          column(
            width = 3,
            box(
              width = 12,
              title = div(icon("cog"),"\t","Setting"),
              br()
            )
          )
         )
       ),

       ### DMT ###
       tabPanel(
         id="Marker_pop_vis",
         title="Population Marker",
         fluidRow(
          column(
            width = 6,
            eChartOutput("cell_type_marker_line",width = "100%",height = 670)
          ),
          column(
            width = 6,
            visNetwork::visNetworkOutput("DMT",width = "100%",height = 670)
          )
         )
       )
     ),
     absolutePanel(
       width = "230px",
       draggable = TRUE,
       bottom = 30,
       right = 30,
       div(
        style="width:100%;background:#eeeeee;border-radius:5px;",
        h4(icon("cog"),"\t","Setting Panel",style="background:#121212;color:#efefef;padding:5px"),
        div(
          style="width:100%;background:#eeeeee;padding:15px;text-align: center",
          radioButtons(inputId = "IDexchangeLabel",label = "ID transform into Cell Label:",choices = c("ID","Cell_Label"),inline = TRUE),
          downloadButton(outputId = "DownloadRdata",
                         label = "Download Rdata",
                         style="width:33%,margin-left:33%;background:#333333;color:#eeeeee;border-radius:5px")
        )
      )
     )
    )
   )
  )
 ) #dashbody end
)  #UI end

########################################################################################################
#
#                                       shiny servers part
#
########################################################################################################

server = function(input,output,session){
 options(shiny.maxRequestSize=250*1024^2)
 options(rgl.useNULL = TRUE)

 ##### step one #####


 ##### Pie Plot #####
 output$Statisic_pie<-renderEChart({
   Cell_parents = CL_lib[which(CL_lib[,4]=="native cell"),2]
   cell_cont=function(parent){
     if(length(which(CL_lib[,4]==parent))>0){
       children=CL_lib[which(CL_lib[,4]==parent),2]
       count=length(children)
       for(i in 1:length(children)){
         count=count+cell_cont(as.character(children[i]))
       }
       return(count)
     }
     else{
       return(0)
     }
   }
   Count=c()
   cell_type=c()
   for(i in 1:length(Cell_parents)){
     Count=c(cell_cont(as.character(Cell_parents[i]))+1,Count)
     cell_type=c(as.character(Cell_parents[i]),cell_type)
   }
   Cell=data.frame(cell_type,Count)
   Cell=Cell[which(Cell$Count>10),]
   chart = echartr(Cell,cell_type,y=Count,type = "pie") %>%
     setTitle("Cell Ontology main cell types") %>%
     setLegend(show = TRUE,pos = 9) %>%
     setToolbox(language = "en") %>%
     setTheme('roma')
   chart
 })



 # vector for saving choosen markers #
 dy_markers=reactiveValues(data=NULL)

 # a vector for saving file #
 file=reactiveValues(data=NULL)

 ##### file loading setting #
 observeEvent(input$fcs,{
   # saving result as cytoSet
   inFile <- input$fcs
   file$data<-read.FCS(inFile$datapath)
   cyto<<-initiflow(fcs.data = exprs(file$data),projectname = input$Project_name1)
   cyto@autoLabel<<-FALSE
   cyto@preprocess<<-"None"

   showElement("Step1_body",animType ="slide",anim = TRUE,time = 0.5)
   showElement("Step1_bar",animType ="slide",anim = TRUE,time = 0.5)
   hideElement(selector = ".Introduction")
   hideElement(selector = ".InputFile")
   showElement(selector = ".Preview")
 })

 observeEvent(input$Rdata,{
   inFile <- input$Rdata
   load(inFile$datapath)
   if(is.null(cyto@ClusterID)){
      alert("Invaild input file !")
      return()
   }
   else{
     hideElement(selector = ".Introduction")
     hideElement(selector = ".InputFile")
     showElement(selector=".Step3")
     }
 })





 ##### plot bin set
 output$Plot_bin<-renderUI({
   if(is.null(file$data)){
     return()
   }
   fluidRow(
     column(
      width=6,
      sliderInput(inputId = "bin_val",
                  label = "bin value",
                  min=0,
                  max=512,
                  value = 256,
                  step=16)
     ),
     column(
       width=6,
       sliderInput(inputId = "scale_val",
                   label = "scale value",
                   min=10,
                   max=500,
                   value = 30,
                   step=10)
     )
   )
 })

 #####data Preview hex
 output$hex_plot<-renderPlot({
   data<- file$data
   if(is.null(input$transform)){
     return()
   }

   if (input$transform=="log10"){
     ### check data ###
     # make sure the transform methods can be used
     X=data@exprs
     for(i in 1:length(X[1,])){
       minimal=min(X[,i])
       if(minimal<=0 && tolower(colnames(X)[i])!="time"){
         X[,i]=X[,i]+abs(minimal)+0.1
       }
     }
     data@exprs=X
     tran <- logTransform(transformationId="log10-transformation", logbase=10, r=1, d=1)
     channeltouse=colnames(data)[which(tolower(colnames(data))!="time")]
     tranlist_log <- transformList(channeltouse, tran)
     data<-transform(data, tranlist_log)
     cyto@transform_method<<-"log10"
   }

   else if (input$transform=="arcsinh"){
     tran <- arcsinhTransform("arcsinh-transformation",a = 1,b=1,c=0)
     channeltouse=colnames(data)[which(tolower(colnames(data))!="time")]
     tranlist_arcsinh <- transformList(channeltouse, tran)
     data<-transform(data, tranlist_arcsinh)
     cyto@transform_method<<-"arcsinh"
   }

   else{
     cyto@transform_method<<-"None"
   }
   data_trans$data<-data
   scale=input$scale_val
   cyto@fcs.data<<-as.data.frame(exprs(data))
   cyto@event.use<<-c(1:length(cyto@fcs.data))
   data<-cyto@fcs.data
   ggplot(data,
          aes(x=data[input$plotx],
              y=data[input$ploty]))+
     geom_hex(bins=input$bin_val)+
     theme_bw()+
     theme(panel.grid.major = element_blank(),
           panel.grid.minor = element_blank())+
     xlab(input$plotx)+
     ylab(input$ploty)+
     theme(panel.border  = element_blank())+
     theme(axis.line = element_line(colour = "black"))+
     scale_fill_gradientn(colours = c("black","darkred","red","orange","yellow","white"),
                          limits=c(0,scale),
                          name="Count of cells\n")
 })

 ###### Data Preview density plot  ######
 output$X_density<-renderPlot({
   if(is.null(data_trans$data)){
     return()
   }
   data=as.data.frame(exprs(data_trans$data))
   X=data[input$plotx]
   diff=abs(range(X)[2]-range(X)[1])
   bin=diff/200
   ggplot(NULL,aes(x=X))+
     geom_histogram(binwidth =bin,fill="#c7b3e5",colour="#c7b3e5")+
     ggtitle("Marker Density Map\n")+
     theme_bw()+
     theme(panel.grid.major = element_blank(),
           panel.grid.minor = element_blank())+
     theme(panel.border  = element_blank())+
     theme(axis.line = element_line(colour = "black"))+xlab(input$plotx)
 })

 output$Y_density<-renderPlot({
   if(is.null(data_trans$data)){
     return()
   }
   data=as.data.frame(exprs(data_trans$data))
   Y=data[input$ploty]
   diff=abs(range(Y)[2]-range(Y)[1])
   bin=diff/200
   ggplot(NULL,aes(x=Y))+
     geom_histogram(binwidth =bin,fill="#daf9ca",colour="#daf9ca")+
     ggtitle("Marker Density Map\n")+
     theme_bw()+
     theme(panel.grid.major = element_blank(),
           panel.grid.minor = element_blank())+
     theme(panel.border  = element_blank())+
     theme(axis.line = element_line(colour = "black"))+xlab(input$ploty)
 })



 ###### file description #
 output$file <- DT::renderDataTable({
   inFile <- file$data
   if (is.null(inFile))
     return(NULL)
   fcs=file$data
   # define the base view table for files
   table<-data.frame(as.data.frame(fcs@parameters@data$name),
                     as.data.frame(fcs@parameters@data$desc),
                     row.names = NULL)

   colnames(table)<-c("name","desc")

   DT::datatable(
     table,
     class = "hover",
     style = "bootstrap",
     options = list(dom="rpit",paging=FALSE,scrollY=450),
     selection = list(mode="multiple",
                      selected=r_all_choice$data,
                      target="row")
   )
 })

 # choose the display markers
 output$XY_val <- renderUI({
   fcs<-file$data
   if (is.null(fcs)){
     return()
   }
   tagList(
     fluidRow(
       column(
         width = 6,
         selectInput("plotx", "plot-x",colnames(fcs),selected = colnames(fcs)[1])
         ),
       column(
         width = 6,
         selectInput("ploty", "plot-y",colnames(fcs),selected = colnames(fcs)[2]))
   )
  )
 })

 # data_transform
 data_trans<-reactiveValues(data=NULL)
 output$data_transform <- renderUI({
   if(is.null(file$data)){
     return()
   }
   tagList(
     radioButtons(inputId = "transform",label = "data transform",
                  choices = c("log10","arcsinh","None"),
                  selected = "None",inline = TRUE)
   )
 })

 # function of "all" button #
 r_all_choice=reactiveValues(data=NULL)
 observeEvent(input$all_choice,{
   r_all_choice$data=NULL
   r_all_choice$data=c(1:length(file$data@parameters@data$name))
 })


 # function of "Done" button #
 observeEvent(input$Done,{
   inFile<-input$fcs
   if (is.null(inFile)){
     return("A .fcs is needed")
   }
   fcs=read.FCS(inFile$datapath)
   channel_id = input$file_rows_selected
   dy_markers$data=colnames(exprs(fcs))[as.integer(channel_id)]
   if(length(dy_markers$data)==0){
     alert("No Channel was chosen, at least one Channel is required!")
     return()
   }
   hideElement(selector = ".InputFile")
   showElement(selector = ".Preview")
   hideElement(selector = ".Step1",animType ="fade",anim = TRUE,time = 0.3)
   hideElement("Step1_bar")
   showElement(selector = ".Preprocess",animType ="slide",anim = TRUE,time = 0.3)

   # showElement(selector = ".Step2",animType ="slide",anim = TRUE,time = 1)
   # showElement("Step2_bar")
   cyto@channel.use<<-channel_id
 })

 ######## flowAI Skip #########
 observeEvent(input$AI_Skip,{
   data=data_trans$data
   if(is.null(data)){
     return()
   }
   hideElement(selector = ".Preprocess",animType ="fade",anim = TRUE,time = 0.3)
   showElement(selector = ".Step2",animType ="slide",anim = TRUE,time = 1)
   showElement("Step2_bar")
 })



 ####### flowAI Servers #######

  # flowAI Confirm #
 observeEvent(input$AI_Confirm,{
   data=data_trans$data
   if(is.null(data)){
     return()
   }
   hideElement(selector = ".Preprocess",animType ="fade",anim = TRUE,time = 0.3)
   showElement(selector = ".Step2",animType ="slide",anim = TRUE,time = 1)
   showElement("Step2_bar")
   cyto@event.use<<-cellCheck()[[3]]$goodCellIDs
   cyto@preprocess<<-"flowAI"
 })


  ## load flowset data
  set <- reactive({
    isolate({fcsFiles <- input$fcs
    if (is.null(fcsFiles))
      return(NULL)
    set <- data_trans$data
    set@description$FILENAME <- fcsFiles$name})
    return(set)
  })

  ## time channel name
  timeChannel <- reactive({
    if(is.null(set()))
      return(NULL)
    x <- set()
    time <- findTimeChannel(x)
    return(time)
  })

  ## time step
  timeStep <- reactive({
    if(is.null(set()))
      return(NULL)
    word <- which(grepl("TIMESTEP", names(set()@description),
                        ignore.case = TRUE))
    timestep <- as.numeric(set()@description[[word[1]]])
    if( !length(timestep) ){
      warning("The timestep keyword was not found in the FCS file and it was set to 0.01. Graphs labels indicating time might not be correct", call. =FALSE)
      timestep <- 0.01
    }
    return(timestep)
  })


  TimeChCheck <- reactive({
    if (!is.null(timeChannel())) {
      if (length(unique(exprs(set())[, timeChannel()])) == 1){
        TimeChCheck <- "single_value"
      }else{
        TimeChCheck <- NULL
      }
    }else{
      TimeChCheck <- "NoTime"
    }
    return(TimeChCheck)
  })


  ## order fcs expression according acquisition time
  ordFCS <- reactive({
    if(is.null(set()))
      return(NULL)
    if(is.null(TimeChCheck())){
      ordFCS <- ord_fcs_time(set(), timeChannel())
    }else{
      ordFCS <- set()
    }
    return(ordFCS)
  })


  ## signal bin size UI
  output$signalBinSize <- renderUI({
    if(is.null(set())){
      optSize <- NULL
      maxSize <- Inf
    }else{
      maxSize <- nrow(ordFCS())
      optSize <- min(max(1, floor(maxSize/100)), 500)
    }
    numericInput("signalBinSize", label = h5("Number of events per bin:"),
                 value = optSize, min = 1, max = maxSize)
  })


  ## cell quality check
  cellCheck <- reactive({
    if(is.null(ordFCS()))
      return(NULL)
    if(is.null(TimeChCheck())){
      flowRateData <- flow_rate_bin(ordFCS(), second_fraction = input$timeLenth,
                                    timeCh = timeChannel(), timestep = timeStep())
    }else{
      flowRateData <- list()
    }
    flowSignalData <- flow_signal_bin(ordFCS(), channels = NULL,
                                      binSize = input$signalBinSize, timeCh = timeChannel(),
                                      timestep = timeStep(), TimeChCheck = TimeChCheck() )

    flowMarginData <- flow_margin_check(ordFCS())

    res <- list(flowRateData, flowSignalData, flowMarginData)
    return(res)
  })


  ## flow rate time slider UI and check sliders. if they are null, a default value is returned for the QC
  sliders <- reactive({
    flowRateData <- cellCheck()[[1]]
    flowSignalData <- cellCheck()[[2]]
    return(c(
      min(flowRateData$frequencies[,3]) - 0.1,
      max(flowRateData$frequencies[,3]) + 0.1,
      min(flowRateData$frequencies[,4]) - 10,
      max(flowRateData$frequencies[,4]) + 10,
      0,
      nrow(flowSignalData$exprsBin) + 1)
    )
  })

  output$timeSlider <- renderUI({
    if(is.null(set()) || is.null(cellCheck()) || !is.null(TimeChCheck()))
      return(NULL)
    sliderInput("timeSlider", strong("Time cut:"),
                min = sliders()[1], max = sliders()[2],
                value = c(sliders()[1], sliders()[2]), step = 0.1)
  })
  timeSlider <- reactive({
    if(is.null(input$timeSlider)){
      return(c(sliders()[1], sliders()[2]))
    }else{
      return(c(input$timeSlider[1],  input$timeSlider[2]))
    }

  })

  output$rateSlider <- renderUI({
    if(is.null(set()) || is.null(cellCheck()) || !is.null(TimeChCheck()))
      return(NULL)
    sliderInput("rateSlider", strong("Flow rate cut:"),
                min = sliders()[3], max = sliders()[4],
                value = c(sliders()[3], sliders()[4]), step = 0.1)
  })
  rateSlider <- reactive({
    if(is.null(input$rateSlider)){
      flowRateData <- cellCheck()[[1]]
      return(c(sliders()[3], sliders()[4]))
    }else{
      return(c(input$rateSlider[1],  input$rateSlider[2]))
    }

  })

  output$signalBinSlider <- renderUI({
    if(is.null(set()) || is.null(cellCheck()))
      return(NULL)
    sliderInput("signalBinSlider", strong("Signal acquisition cut:"), width = "90%",
                min = sliders()[5], max = sliders()[6],
                value = c(sliders()[5], sliders()[6]), step = 1)
  })
  signalSlider <- reactive({
    if(is.null(input$signalBinSlider)){
      return(c(sliders()[5], sliders()[6]))
    }else{
      return(c(input$signalBinSlider[1],  input$signalBinSlider[2]))
    }
  })


  ## plot
  output$flowRatePlot <- renderPlot({
    if(is.null(ordFCS()) || is.null(cellCheck()) || !is.null(TimeChCheck()))
      return(NULL)
    flowRateData <- cellCheck()[[1]]
    frp <- flow_rate_plot(flowRateData, input$rateSlider[1], input$rateSlider[2],
                          input$timeSlider[1], input$timeSlider[2])
    print(frp)
  })

  output$flowSignalPlot <- renderPlot({
    if(is.null(set()) || is.null(cellCheck()))
      return(NULL)
    flowSignalData <- cellCheck()[[2]]
    fsp <- flow_signal_plot(flowSignalData, input$signalBinSlider[1], input$signalBinSlider[2])
    print(fsp)
  })

  output$flowMarginPlot <- renderPlot({
    if(is.null(set()) || is.null(cellCheck()))
      return(NULL)
    flowMarginData <- cellCheck()[[3]]
    fmp <- flow_margin_plot(flowMarginData, input$signalBinSize)
    print(fmp)
  })



  ## check results
  checkRes <- reactive({
    if(is.null(set()) || is.null(cellCheck()))
      return(NULL)

    ordFCS <- ordFCS()
    totalCellNum <- nrow(ordFCS)
    origin_cellIDs <- 1:totalCellNum
    if(is.null(TimeChCheck())){
      FlowRateQC <- flow_rate_check(cellCheck()[[1]], rateSlider()[1], rateSlider()[2],
                                    timeSlider()[1], timeSlider()[2])
    }else{
      FlowRateQC <- origin_cellIDs
    }
    FlowSignalQC <- flow_signal_check(cellCheck()[[2]], signalSlider()[1], signalSlider()[2])

    if(input$checkbox[1] == TRUE){
      FlowMarginQC <- cellCheck()[[3]]$goodCellIDs
    }else{
      FlowMarginQC <- origin_cellIDs
    }

    goodCellIDs <- intersect(FlowRateQC, intersect(FlowSignalQC, FlowMarginQC))
    badCellIDs <- setdiff(origin_cellIDs, goodCellIDs)

    flowRatePerc <- 1 - length(FlowRateQC)/length(origin_cellIDs)
    flowSignalPerc <- 1 - length(FlowSignalQC)/length(origin_cellIDs)
    flowMarginPerc <- 1 - length(FlowMarginQC)/length(origin_cellIDs)
    totalBadPerc <- length(badCellIDs)/length(origin_cellIDs)
    params <- parameters(ordFCS)
    keyval <- keyword(ordFCS)
    sub_exprs <- exprs(ordFCS)

    good_sub_exprs <- sub_exprs[goodCellIDs, ]
    goodfcs <- flowFrame(exprs = good_sub_exprs, parameters = params, description = keyval)

    bad_sub_exprs <- sub_exprs[badCellIDs, ]
    badfcs <- flowFrame(exprs = bad_sub_exprs, parameters = params,description = keyval)

    tempQCvector <- cellCheck()[[2]]
    QCvector <- tempQCvector$cellBinID[,"binID"]
    QCvector[badCellIDs] <- runif(length(badCellIDs), min=10000, max=20000)
    QCfcs <- addQC(QCvector, sub_exprs, params, keyval)

    return(list(totalCellNum, totalBadPerc, goodfcs, badfcs,
                flowRatePerc, flowSignalPerc, flowMarginPerc, QCfcs))
  })

  ## summary text
  output$summaryText1 <- renderText({
    if(is.null(checkRes()))
      return(NULL)
    paste0("Total number of events: ", checkRes()[[1]])
  })

  output$summaryText2 <- renderText({
    if(is.null(checkRes()))
      return(NULL)
    paste0("Percentage of low-Q events: ", round(checkRes()[[2]]*100,2), "%")
  })

  output$flowRateSummary <- renderText({
    if(is.null(checkRes()))
      return(NULL)
    if(is.null(TimeChCheck())){
      paste0("Percentage of low-Q events in flow rate check: ", round(checkRes()[[5]]*100,2), "%")
    }else if(!is.null(TimeChCheck()) && TimeChCheck() == "NoTime"){
      "It is not possible to recreate the flow rate because the time channel is missing."
    }else if(!is.null(TimeChCheck()) && TimeChCheck() == "single_value"){
      "It is not possible to recreate the flow rate because the time channel contains a single value."
    }
  })

  output$flowSignalSummary <- renderText({
    if(is.null(checkRes()))
      return(NULL)
    paste0("Percentage of low-Q events in signal acquisition check: ", round(checkRes()[[6]]*100,2), "%")
  })

  output$flowMarginSummary <- renderText({
    if(is.null(checkRes()))
      return(NULL)
    paste0("Percentage of low-Q events in dynamic range check: ", round(checkRes()[[7]]*100,2), "%")
  })

  file_base <- reactive({
    file_ext <- description(ordFCS())$FILENAME
    file_base <- sub("^([^.]*).*", "\\1", file_ext)
    return(file_base)
  })

  ## download processed FCS files
  output$downloadGoodFCS <- downloadHandler(
    filename = function(){
      paste0(file_base(), "_HighQ.fcs")
    },

    content = function(file){
      data <- checkRes()[[3]]
      if(is.null(data)){
        return(NULL)
      }
      write.FCS(data, file)
      #tar(tarfile = file, files = tempdir)
    }
  )

  output$downloadBadFCS <- downloadHandler(
    filename = function(){
      paste0(file_base(), "_LowQ.fcs")
    },

    content = function(file){
      data <- checkRes()[[4]]
      if(is.null(data)){
        return(NULL)
      }
      write.FCS(data, file)
      #tar(tarfile = file, files = tempdir)
    }
  )

  ## download processed FCS files
  output$downloadQCFCS <- downloadHandler(
    filename = function(){
      paste(file_base(), "_QC.fcs", sep='')
    },

    content = function(file){
      data <- checkRes()[[8]]
      if(is.null(data)){
        return(NULL)
      }
      write.FCS(data, file)

    })

  ##### Step 2 Cell clustering #####

  ##### User submit clusters #####
  observeEvent(input$import_cluster,{
    inFile <- input$import_cluster
    withProgress(message = 'Calculation in progress',
                 detail = 'This may take a while...',
                 value = 0, {
                   file=flowFrame(exprs = as.matrix(cyto@fcs.data[cyto@event.use,]))
                   incProgress(1/10,message="Import ClusterID ...")
                   ClusterID = read.csv(file=inFile$datapath,header = TRUE)
                   ClusterID = as.data.frame(ClusterID)
                   data=cbind(ClusterID,cyto@fcs.data[cyto@event.use,][,cyto@channel.use])
                   cyto@label<<-ClusterID
                   Cluster_id <- unique(cyto@label)
                   cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = Cluster_id)
                   cyto@ClusterID<<-ClusterID
                   cyto@clust_method<<-"External"
                   incProgress(1/10,message="Build DMT...")
                   cyto@dmt<<-DMT(data)
                   incProgress(1/10,message="Build MST...")
                   if(input$pre_mst_method=="flowSOM"){
                     file=flowFrame(exprs = as.matrix(cyto@fcs.data[cyto@event.use,]))
                     fsom=BuildSOM(ReadInput(file),colsToUse = cyto@channel.use)
                     MST=BuildMST(fsom)
                   }
                   cyto@mst<<-MST
                   incProgress(6/10,message="Reduce the Dimension...")
                   cyto@dim.red<<-cytosee_reduce_dim(cyto@fcs.data[cyto@event.use,][,cyto@channel.use],method = input$pre_reduce_method)
                   incProgress(1/10,message="Complete!")
                 })

    hideElement(selector = ".Step2",animType ="fade",anim = TRUE,time = 0.3)
    showElement(selector = ".Step3",animType ="slide",anim = TRUE,time = 1)
  })


  ##### flowSOM #####
  observeEvent(input$flowSOM_run,{
    withProgress(message = 'Calculation in progress',
                 detail = 'This may take a while...',
                 value = 0, {
                  file=flowFrame(exprs = as.matrix(cyto@fcs.data[cyto@event.use,]))
                  incProgress(1/10,message="Run flowSOM...")
                  ClusterID=cytosee_flowSOM(File = file,K = input$flowSOM_K,colsToUse = cyto@channel.use)
                  ClusterID=as.data.frame(ClusterID)
                  data=cbind(ClusterID,cyto@fcs.data[cyto@event.use,][,cyto@channel.use])
                  cyto@label<<-ClusterID
                  Cluster_id <- unique(cyto@label)
                  cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = Cluster_id)
                  cyto@ClusterID<<-ClusterID
                  cyto@clust_method<<-"flowSOM"
                  incProgress(1/10,message="Build DMT...")
                  cyto@dmt<<-DMT(data)
                  incProgress(1/10,message="Build MST...")
                  if(input$pre_mst_method=="flowSOM"){
                    file=flowFrame(exprs = as.matrix(cyto@fcs.data[cyto@event.use,]))
                    fsom=BuildSOM(ReadInput(file),colsToUse = cyto@channel.use)
                    MST=BuildMST(fsom)
                  }
                  cyto@mst<<-MST
                  incProgress(6/10,message="Reduce the Dimension...")
                  cyto@dim.red<<-cytosee_reduce_dim(cyto@fcs.data[cyto@event.use,][,cyto@channel.use],method = input$pre_reduce_method)
                  incProgress(1/10,message="Complete!")
                 })
    hideElement(selector = ".Step2",animType ="fade",anim = TRUE,time = 0.3)
    showElement(selector = ".Step3",animType ="slide",anim = TRUE,time = 1)
  })

  ##### DensityCut #####
  observeEvent(input$DensityCut_run,{
    withProgress(message = 'Calculation in progress',
                 detail = 'This may take a while...',
                 value = 0, {
                   file= cyto@fcs.data[cyto@event.use,][,cyto@channel.use]
                   incProgress(1/10,message="Run DesnityCut")
                   ClusterID=cytosee_densitycut(file,NumC = input$DensityCut_K)
                   ClusterID=as.data.frame(ClusterID)
                   data=cbind(ClusterID,cyto@fcs.data[cyto@event.use,][,cyto@channel.use])
                   cyto@label<<-ClusterID
                   Cluster_id <- unique(cyto@label)
                   cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = Cluster_id)
                   cyto@ClusterID<<-ClusterID
                   cyto@clust_method<<-"DensityCut"
                   incProgress(1/10,message="Build DMT...")
                   cyto@dmt<<-DMT(data)
                   incProgress(1/10,message="Build MST...")
                   if(input$pre_mst_method=="flowSOM"){
                     file=flowFrame(exprs = as.matrix(cyto@fcs.data[cyto@event.use,]))
                     fsom=BuildSOM(ReadInput(file),colsToUse = cyto@channel.use)
                     MST=BuildMST(fsom)
                   }
                   cyto@mst<<-MST
                   incProgress(6/10,message="Reduce the Dimension...")
                   cyto@dim.red<<-cytosee_reduce_dim(cyto@fcs.data[cyto@event.use,][,cyto@channel.use],method = input$pre_reduce_method)
                   incProgress(1/10,message="Complete!")
                 })
    hideElement(selector = ".Step2",animType ="fade",anim = TRUE,time = 0.3)
    showElement(selector = ".Step3",animType ="slide",anim = TRUE,time = 1)
  })


  ##### Consboost #####
  observeEvent(input$Consboost_run,{
    withProgress(message = 'Calculation in progress',
                 detail = 'This may take a while...',
                 value = 0, {
                   file= cyto@fcs.data[cyto@event.use,][,cyto@channel.use]
                   incProgress(1/10,message="Run Consboost")
                   ClusterID=cytosee_consboost(file,K=input$Consboost_K)
                   ClusterID=as.data.frame(ClusterID)
                   data=cbind(ClusterID,cyto@fcs.data[cyto@event.use,][,cyto@channel.use])
                   cyto@label<<-ClusterID
                   Cluster_id <- unique(cyto@label)
                   cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = Cluster_id)
                   cyto@ClusterID<<-ClusterID
                   cyto@clust_method<<-"Consboost"
                   incProgress(1/10,message="Build DMT...")
                   cyto@dmt<<-DMT(data)
                   incProgress(1/10,message="Build MST...")
                   if(input$pre_mst_method=="flowSOM"){
                     file=flowFrame(exprs = as.matrix(cyto@fcs.data[cyto@event.use,]))
                     fsom=BuildSOM(ReadInput(file),colsToUse = cyto@channel.use)
                     MST=BuildMST(fsom)
                   }
                   cyto@mst<<-MST
                   incProgress(6/10,message="Reduce the Dimension...")
                   cyto@dim.red<<-cytosee_reduce_dim(cyto@fcs.data[cyto@event.use,][,cyto@channel.use],method = input$pre_reduce_method)
                   incProgress(1/10,message="Complete!")
                 })
    hideElement(selector = ".Step2",animType ="fade",anim = TRUE,time = 0.3)
    showElement(selector = ".Step3",animType ="slide",anim = TRUE,time = 1)
  })


  ##### FlowMeans #####
  observeEvent(input$flowMeans_run,{
    withProgress(message = 'Calculation in progress',
                 detail = 'This may take a while...',
                 value = 0, {
                   file= cyto@fcs.data[cyto@event.use,][,cyto@channel.use]
                   incProgress(1/10,message="Run FlowMeans")
                   ClusterID=cytosee_flowMeans(file,MaxN=input$flowMeans_maxK,NumC=input$flowMeans_K)
                   ClusterID=as.data.frame(ClusterID)
                   data=cbind(ClusterID,cyto@fcs.data[cyto@event.use,][,cyto@channel.use])
                   cyto@label<<-ClusterID
                   Cluster_id <- unique(cyto@label)
                   cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = Cluster_id)
                   cyto@ClusterID<<-ClusterID
                   cyto@clust_method<<-"FlowMeans"
                   incProgress(1/10,message="Build DMT...")
                   cyto@dmt<<-DMT(data)
                   incProgress(1/10,message="Build MST...")
                   if(input$pre_mst_method=="flowSOM"){
                     file=flowFrame(exprs = as.matrix(cyto@fcs.data[cyto@event.use,]))
                     fsom=BuildSOM(ReadInput(file),colsToUse = cyto@channel.use)
                     MST=BuildMST(fsom)
                   }
                   cyto@mst<<-MST
                   incProgress(6/10,message="Reduce the Dimension...")
                   cyto@dim.red<<-cytosee_reduce_dim(cyto@fcs.data[cyto@event.use,][,cyto@channel.use],method = input$pre_reduce_method)
                   incProgress(1/10,message="Complete!")
                 })
    hideElement(selector = ".Step2",animType ="fade",anim = TRUE,time = 0.3)
    showElement(selector = ".Step3",animType ="slide",anim = TRUE,time = 1)
  })


  ##### Phenograph #####
  observeEvent(input$phenograph_run,{
    withProgress(message = 'Calculation in progress',
                 detail = 'This may take a while...',
                 value = 0, {
                   file= cyto@fcs.data[cyto@event.use,][,cyto@channel.use]
                   incProgress(1/10,message="Run Phenograph")
                   ClusterID=cytosee_phenograph(file,K=input$phenograph_K)
                   ClusterID=as.data.frame(ClusterID)
                   data=cbind(ClusterID,cyto@fcs.data[cyto@event.use,][,cyto@channel.use])
                   cyto@label<<-ClusterID
                   Cluster_id <- unique(cyto@label)
                   cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = Cluster_id)
                   cyto@ClusterID<<-ClusterID
                   cyto@clust_method<<-"Phenograph"
                   incProgress(1/10,message="Build DMT...")
                   cyto@dmt<<-DMT(data)
                   incProgress(1/10,message="Build MST...")
                   if(input$pre_mst_method=="flowSOM"){
                     file=flowFrame(exprs = as.matrix(cyto@fcs.data[cyto@event.use,]))
                     fsom=BuildSOM(ReadInput(file),colsToUse = cyto@channel.use)
                     MST=BuildMST(fsom)
                   }
                   cyto@mst<<-MST
                   incProgress(6/10,message="Reduce the Dimension...")
                   cyto@dim.red<<-cytosee_reduce_dim(cyto@fcs.data[cyto@event.use,][,cyto@channel.use],method = input$pre_reduce_method)
                   incProgress(1/10,message="Complete!")
                 })
    hideElement(selector = ".Step2",animType ="fade",anim = TRUE,time = 0.3)
    showElement(selector = ".Step3",animType ="slide",anim = TRUE,time = 1)
  })


  ##### SamSPECTRAL #####
  observeEvent(input$SamSPECTRAL_run,{
    withProgress(message = 'Calculation in progress',
                 detail = 'This may take a while...',
                 value = 0, {
                   data=cyto@fcs.data[cyto@event.use,]
                   incProgress(1/10,message="Run SamSPECTRAL...")
                   ClusterID=cytosee_SamSPECTRAL(data = data,colsToUse=cyto@channel.use,
                                                 sigma=input$SamSPECTRAL_sigma,
                                                 separation.factor=input$SamSPECTRAL_separation)
                   ClusterID=as.data.frame(ClusterID)
                   ClusterID[which(is.na(ClusterID)),]="Unknow"
                   data=cbind(ClusterID,cyto@fcs.data[cyto@event.use,][,cyto@channel.use])
                   cyto@label<<-ClusterID
                   Cluster_id <- unique(cyto@label)
                   cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = Cluster_id)
                   cyto@ClusterID<<-ClusterID
                   cyto@clust_method<<-"SamSPECTRAL"
                   incProgress(1/10,message="Build DMT...")
                   cyto@dmt<<-DMT(data)
                   incProgress(1/10,message="Build MST...")
                   if(input$pre_mst_method=="flowSOM"){
                     file=flowFrame(exprs = as.matrix(cyto@fcs.data[cyto@event.use,]))
                     fsom=BuildSOM(ReadInput(file),colsToUse = cyto@channel.use)
                     MST=BuildMST(fsom)
                   }
                   cyto@mst<<-MST
                   incProgress(6/10,message="Reduce the Dimension...")
                   cyto@dim.red<<-cytosee_reduce_dim(cyto@fcs.data[cyto@event.use,][,cyto@channel.use],method = input$pre_reduce_method)
                   incProgress(1/10,message="Complete!")
                 })
    hideElement(selector = ".Step2",animType ="fade",anim = TRUE,time = 0.3)
    showElement(selector = ".Step3",animType ="slide",anim = TRUE,time = 1)
  })


  ##### Step 3 Labeling and Visualization #####

  ### Label the clusters ###
  output$alias<-renderUI({
    if(is.null(cyto@dmt)){
      return()
    }
    markers=c()
    for(i in c(1:length(cyto@dmt)-1)){
      re=unlist(strsplit(cyto@dmt[[i+1]]$label,"[<||>]"))
      markers[i]=re[1] #in order to delet the root
    }
    markers=unique(markers)
    taglist=list()
    j=1

    for(i in c(1:length(markers))){
      marker_id=sprintf("M_%s",j)
      tag=fluidRow(
        column(
          width=6,
          textInput(inputId =marker_id,
                    label = markers[i],
                    value = markers[i],
                    width = 150)
        )
      )
      taglist=c(taglist,list(tag))
      j=j+1
      #eval(parse(text=sprintf("select_marker=c(input$%s,select_marker)",i)))
    }
    div(
      style='overflow-y:auto;
      overflow-x:hidden;
      height:500px',
      taglist
    )
  })

  ##### label button #####
  observeEvent(input$label_button,{
    if(is.null(cyto@dmt)){
      return()
    }
    markers=c()
    for(i in c(1:length(cyto@dmt)-1)){
      re=unlist(strsplit(cyto@dmt[[i+1]]$label,"[<||>]"))
      markers[i]=re[1] #in order to delet the root
    }
    markers=unique(markers)
    select_marker=list()
    j=1
    for(i in markers){
      marker_id=sprintf("M_%s",j)
      if(eval(parse(text = sprintf("input$%s !=''",marker_id)))){
        eval(parse(text=sprintf("select_marker=c(select_marker,input$%s)",marker_id)))
      }
      else{
        eval(parse(text=sprintf("select_marker=c(select_marker,%s)",NA)))
      }
      j=j+1
    }

    ### data transfom for labeling ###
    marker2=unlist(markers)
    if(is.null(select_marker)){
      alert("Input area shouldn't be empty!")
      return()
    }
    else{
      manual_des=unlist(select_marker)
    }

    marker_change=c()
    for(i in c(1:length(marker2))){
      marker_change[marker2[i]]=manual_des[i]
    }

    Cluster_marker=c()
    Cluster_m2c=c()
    cnt=0
    for(i in c(1:length(cyto@dmt))){
      if(length(cyto@dmt[[i]]$clus)==1){
        tmp=cyto@dmt[[i]]
        str=""
        while(tmp$id!=0){
          str=paste0(tmp$label,",",str)
          tmp=cyto@dmt[[tmp$previd+1]]
        }
        cnt=cnt+1
        Cluster_marker[cnt]=str
        Cluster_m2c[cnt]=cyto@dmt[[i]]$clus
      }
    }
    for(i in c(1:length(marker_change))){
      Cluster_marker=gsub(pattern = names(marker_change[i]),replacement = marker_change[i],Cluster_marker,fixed = TRUE)
    }
    re=gsub(pattern = " ",replacement = "",Cluster_marker)
    re=gsub(pattern = "<.*?,",replacement = "<",re)
    re=gsub(pattern = ">.*?,",replacement = ">",re)

    for(s in c(1:length(marker_change))){
      word=marker_change[s]
      for(i in c(1:length(re))){
        store=re[i]
        plus=0
        minus=0

        while(length(grep(pattern = paste0(word,"<"),x = store,perl = FALSE))!=0 || length(grep(pattern = paste0(word,">"),x = store,perl = FALSE))!=0 ){
          word_p=paste0(word,">")
          word_m=paste0(word,"<")

          if(length(grep(word_p,store))!=0){
            store=sub(pattern=word_p,replacement="",store,perl = FALSE)
            plus=plus+1
          }
          if(length(grep(word_m,store))!=0){
            store=sub(pattern = word_m,replacement="",store,perl = FALSE)
            minus=minus+1
          }
        }
        if(plus==0&&minus==0){
          next()
        }
        if(plus-minus>=2){
          re[i]=paste0(store,word,"++")
        }
        else if(plus-minus==1){
          re[i]=paste0(store,word,"+")
        }
        else if(plus==minus){
          re[i]=paste0(store,word,"+")
        }
        else if(plus-minus==-1){
          re[i]=paste0(store,word,"-")
        }
        else{
          re[i]=paste0(store,word,"--")
        }
      }
    }
    CL_label=list()
    tryCatch({
      withProgress(
        message = "Run LocCL",
        value=0,{
          for(i in 1:length(re)){
            incProgress(1/length(re))
            result=LocCL(MarkerList = re[i],MaxHitsPht = input$CL_MaxHitsPht)
            ClusterID=Cluster_m2c[i]
            CL_label[[i]]=list("result"=result,"ClusterID"=ClusterID)
          }
        })
      cyto@CL_label<<-CL_label
      cyto@autoLabel<<-TRUE
      hideElement(id="change_marker_name")
      showElement(id="celltype_tree_div")
      },
      error=function(e){
        alert("Something wrong with your query markers!")
        CL_label<-list()
        return()
    }
    )
  })

  ##### Skip 1 button #####
  r_marker_value=reactiveValues(data=c())
  observeEvent(input$skip1,{
    Cluster_id <- unique(cyto@label)
    cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = Cluster_id)
    updateTabItems(session,"result_display",selected="ReportTable")
  })

  ##### Marker_line #####
  output$Marker_line<-renderEChart({
    cl_average_vec<-getAvg(fcsData=cyto@fcs.data[cyto@event.use,][,cyto@channel.use],ClusterID=cyto@label)
    ClusterID<-c()
    Channels<-c()
    Value<-c()
    for(i in 1:length(cl_average_vec)){
      for(j in 1:length(cl_average_vec[,1])){
        ClusterID<-c(row.names(cl_average_vec[j,]),ClusterID)
        Channels<-c(colnames(cl_average_vec)[i],Channels)
        Value<-c(cl_average_vec[j,i],Value)
      }
    }
    Line<-data.frame(ClusterID,Channels,Value)
    echartr(Line,x=Channels,y = Value,series = ClusterID,type = "curve") %>%
      setLegend(pos=6) %>%
      setTheme('macarons') %>%
      setToolbox(language = "en") %>%
      setTitle(title = "Marker expression level in each clusters",pos = 12)
  })


  # helper function for making checkbox in CL label datatable
  shinyInput_CL = function(FUN, len, id, ...){
    inputs = character(len)
    for (i in seq_len(len)) {
      inputs[i] = as.character(FUN(paste0(id, i),paste0(cyto@CL_label[[i]]$result$Labels),...))
    }
    inputs
  }

  # helper function for making checkbox in Cluster report datatable
  shinyInput_Cluster = function(FUN, len, id,Label, ...){
    inputs = character(len)
    for (i in seq_len(len)) {
      inputs[i] = as.character(FUN(paste0(id, i),paste0(Label[i]),...))
    }
    inputs
  }


  # helper function for reading checkbox in datatable
  shinyValue = function(id, len) {
    unlist(lapply(seq_len(len), function(i) {
      value = input[[paste0(id, i)]]
      if (is.null(value))
        NA
      else
        value
    }))
  }

  ##### Clusters_tabel #####
  output$Clusters_table<-DT::renderDataTable({
    Cluster_id = unique(cyto@label)
    size = c()
    for(i in 1:length(Cluster_id[,1])){
      size = c(size,length(cyto@label[which(cyto@label==Cluster_id[i,]),]))
    }
    table=data.frame(Cluster_id,size,"Label Choose"=shinyInput_CL(
      selectInput,
      nrow(Cluster_id),
      "selecter_",
      width = "390px",
      label = NULL
    )
   )
    data.frame(table)
  }, selection = list(mode='single',selected=1), server = FALSE, escape = FALSE,rownames = FALSE, options = list(
    dom = "rpit",
    paging = FALSE,
    scrollY=650,
    preDrawCallback=JS('function() { Shiny.unbindAll(this.api().table().node()); }'),
    drawCallback=JS("function() { Shiny.bindAll(this.api().table().node()); } ")
  ))

  ##### Cell type tree builder #####
  output$CellType_tree <-renderVisNetwork({
    id=input$Clusters_table_rows_selected
    if(is.null(id)){
      return("you can choose a cluster to display")
    }
    Nodes<-cyto@CL_label[[id]]$result$Nodes
    Links<-cyto@CL_label[[id]]$result$Links
    visNetwork(Nodes, Links) %>%
      visEdges(arrows = "from",color="#cccccc") %>%
      visNodes(size=18) %>%
      visOptions(highlightNearest = list(enabled=TRUE,algorithm="hierarchical",
                                         degree=list(from=0,to=50))) %>%
      visHierarchicalLayout(treeSpacing=300,nodeSpacing=150,
                            levelSeparation = 100,direction = "DU",
                            blockShifting = FALSE,sortMethod = "directed")
  })

  ##### Skip #####
  observeEvent(input$Skip,{
    Cluster_id <- unique(cyto@label)
    cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = Cluster_id)
    updateTabItems(session,"result_display",selected="ReportTable")
  })


  ##### Next button #####
  observeEvent(input$Next,{
    Cluster_id <- unique(cyto@label)
    cyto@ID2CL <<- data.frame(ID=Cluster_id,CD_Label = shinyValue("selecter_", nrow(Cluster_id)))
    updateTabItems(session,"result_display",selected="ReportTable")
  })


  ##### Project_report #####
  output$Project_report<-DT::renderDataTable({
    projectName=cyto@projectname
    cell_num<-length(cyto@fcs.data[cyto@event.use,][,1])
    cluster_num<-length(unique(cyto@label)[,1])
    transform_method<-cyto@transform_method
    channels<-paste(colnames(cyto@fcs.data[cyto@event.use,])[cyto@channel.use],collapse = ",\t")
    eventused<-length(cyto@event.use)
    clust_method<-cyto@clust_method
    preprocess<-cyto@preprocess
    AutoLabel<-cyto@autoLabel
    table<-data.frame(c("ProjectName","Cell num","num of clusters","Channels","events used","Transform method","Clust Method","Preprocess Method","AutoLabel"),
                      c(projectName,cell_num,cluster_num,channels,eventused,transform_method,clust_method,preprocess,AutoLabel)
    )
    colnames(table)=c("Characters","Values")

    DT::datatable(
      table,
      class = "hover",
      style = "default",
      rownames=FALSE,
      escape = FALSE,
      selection="none",
      options = list(dom="rpt",paging = FALSE,ordering=FALSE,searching=FALSE)
    )
  })

  ##### Cluster_report #####
  output$Cluster_report<-DT::renderDataTable({
    input$Next
    Cluster_id = unique(cyto@ClusterID)
    ID2CL=cyto@ID2CL
    inputLabel = c()
    for(i in 1:length(Cluster_id[,1])){
      inputLabel =c(inputLabel,as.character(ID2CL[which(ID2CL[,1]==Cluster_id[i,1]),2]))
    }
    size = c()
    for(i in 1:length(Cluster_id[,1])){
      size = c(size,length(cyto@label[which(cyto@label==Cluster_id[i,]),]))
    }
    table=data.frame(Cluster_id,size,"Label Input"=shinyInput_Cluster(
      textInput,
      len=nrow(Cluster_id),
      id="text_",
      Label = inputLabel,
      width = "395px",
      label = NULL
    )
   )
    data.frame(table)
  }, selection = list(selected=1,mode="single"), server = FALSE, escape = FALSE,rownames = FALSE, options = list(
    dom = "rpit",
    paging = FALSE,
    scrollY=330,
    preDrawCallback=JS('function() { Shiny.unbindAll(this.api().table().node()); }'),
    drawCallback=JS("function() { Shiny.bindAll(this.api().table().node()); } ")
  )
 )

  ##### cell_type_confirm_plot #####
  output$cell_type_confirm_plot<-renderPlot({
    clusters=unlist(cyto@label)
    unique_cluster=unique(clusters)
    clusters[which(clusters!=unique_cluster[input$Cluster_report_rows_selected])]="Others"
    data=cyto@fcs.data[cyto@event.use,][,cyto@channel.use]

    Dimension1<-data[,input$confirm_marker_x]
    Dimension2<-data[,input$confirm_marker_y]

    if(is.null(input$confirm_marker_x)){
      return()
    }

    ggplot(data = as.data.frame(data),aes(x=Dimension1,y=Dimension2))+geom_point(aes(colour=clusters))+theme_bw()+
      theme(panel.grid.major = element_blank(),
            panel.grid.minor = element_blank())+
      theme(panel.border  = element_blank())+
      theme(axis.line = element_line(colour = "black"))+
      xlab(input$confirm_marker_x)+
      ylab(input$confirm_marker_y)+
      scale_colour_manual(values = c("#3dccfb","#c2c2c2"))
  })


  ##### cell_type_confirm_plot_marker choose #####
  output$confirm_marker_choose<-renderUI({
    fluidRow(
      column(
        width = 6,
        selectInput(inputId = "confirm_marker_x",
                    label = "X",
                    selected = colnames(cyto@fcs.data[cyto@event.use,])[cyto@channel.use[1]],
                    choices = colnames(cyto@fcs.data[cyto@event.use,])[cyto@channel.use])
             ),
      column(
        width = 6,
        selectInput(inputId = "confirm_marker_y",
                    label = "Y",
                    selected = colnames(cyto@fcs.data[cyto@event.use,])[cyto@channel.use[2]],
                    choices = colnames(cyto@fcs.data[cyto@event.use,])[cyto@channel.use])
      )
    )
  })

  ##### confirm button #####
  observeEvent(input$confirm,{
    Cluster_id = unique(cyto@ClusterID)
    cyto@ID2CL<<-data.frame(ID=Cluster_id,CD_Label = shinyValue("text_", nrow(Cluster_id)))
    ID2CL<-cyto@ID2CL
    label=cyto@label
    for(i in 1:length(Cluster_id[,1])){
      label[which(label[,1]==Cluster_id[i,1]),1]<-as.character(ID2CL[which(ID2CL[,1]==Cluster_id[i,1]),2])
    }
    cyto@label<<-label
    updateTabItems(session,"result_display",selected="Visualization")
  })

  ##### previous button #####
  observeEvent(input$previous,{
    updateTabItems(session,"result_display",selected="ClusterLabel")
  })

  ##### DMT plot#####
  output$DMT<-renderVisNetwork({
    if(is.null(cyto@dmt)){
      return()
    }

    #vector for nodes
    id=c(0)
    label=c("Root")
    title=c("Root")
    size=c(length(cyto@fcs.data[cyto@event.use,][,1]))
    group=c("Root")

    #vector for links
    from=c()
    to=c()
    for(i in c(2:length(cyto@dmt))){
      if(length(cyto@dmt[i][[1]]$clus)>1){
        group=c("Others",group)
        title=c(paste0("ClusterID:","Others"),title)
      }
      else{
        group=c("Clusters",group)
        title=c(paste0("ClusterID:",cyto@dmt[i][[1]]$clus[1]),title)
      }
      label=c(cyto@dmt[i][[1]]$label,label)
      id=c(cyto@dmt[i][[1]]$id,id)
      size=c(cyto@dmt[i][[1]]$size,size)
      from=c(cyto@dmt[i][[1]]$previd,from)
      to=c(cyto@dmt[i][[1]]$id,to)
    }
    links=data.frame(from=from,to=to,length=2)
    nodes=data.frame(id=id,label=label,size=log10(size)*10+3,title=title,group=group)
    visNetwork(nodes,links, width = "100%",height = "100%") %>%
      visEdges(arrows = "to") %>%
        visOptions(highlightNearest = list(enabled=TRUE,algorithm="hierarchical",
                                           degree=list(from=100,to=0)),
                   selectedBy = list(variable="title",multiple=TRUE)) %>%
        visGroups(groupname = "Root", color ="#6f89ab") %>%
        visGroups(groupname = "Clusters", color = "#f0eccc") %>%
        visGroups(groupname = "Others", color = "#ffdca3")

    })

  ##### cell_type_marker_line #####
  output$cell_type_marker_line<-renderEChart({

    if(input$IDexchangeLabel=="ID"){
      ClusterID=cyto@ClusterID
    }
    else if(input$IDexchangeLabel=="Cell_Label"){
      ClusterID=cyto@label
    }

    cl_average_vec<-getAvg(fcsData=cyto@fcs.data[cyto@event.use,][,cyto@channel.use],ClusterID=ClusterID)
    ClusterID<-c()
    Channels<-c()
    Value<-c()
    for(i in 1:length(cl_average_vec)){
      for(j in 1:length(cl_average_vec[,1])){
        ClusterID<-c(row.names(cl_average_vec[j,]),ClusterID)
        Channels<-c(colnames(cl_average_vec)[i],Channels)
        Value<-c(cl_average_vec[j,i],Value)
      }
    }
    Line<-data.frame(ClusterID,Channels,Value)
    echartr(Line,x=Channels,y = Value,series = ClusterID,type = "curve") %>%
      setLegend(pos=3) %>%
      setTheme('macarons') %>%
      setToolbox(language = "en") %>%
      setGrid(width = 600,height = 580)
  })

 ##### MST_pie plot #####
  output$MST_pie<-renderPlot({

    if(input$IDexchangeLabel=="ID"){
        ClusterID=cyto@ClusterID
      }
      else if(input$IDexchangeLabel=="Cell_Label"){
        ClusterID=cyto@label
      }

      MST=cyto@mst
        PlotPies(fsom = MST,cellTypes = as.matrix(ClusterID),view = "MST",
                 main = "Cluster pie MST Plot",
                 colorPalette = grDevices::colorRampPalette(c("#8DD3C7",
                                                              "#FFFFB3",
                                                              "#BEBADA",
                                                              "#FB8072",
                                                              "#80B1D3",
                                                              "#FDB462",
                                                              "#B3DE69",
                                                              "#FCCDE5",
                                                              "#D9D9D9",
                                                              "#BC80BD",
                                                              "#CCEBC5",
                                                              "#FFED6F")))
  })

  output$MST_marker<-renderPlot({
    MST=cyto@mst
    PlotMarker(MST,marker = input$MST_markers,view = "MST",
               main="Marker MST Plot",
               colorPalette = grDevices::colorRampPalette(c("#dbdcd7",
                                                            "#dddcd7",
                                                            "#e2c9cc",
                                                            "#e7627d",
                                                            "#b8235a",
                                                            "#801357",
                                                            "#3d1635",
                                                            "#1c1a27")))
  })


 ### MST pannel ###
  output$MST_panel<-renderUI({
    tagList(
      selectInput(inputId = "MST_markers",
                  label = "Markers",
                  choices = as.matrix(colnames(cyto@fcs.data[cyto@event.use,][,cyto@channel.use])),
                  selected = colnames(cyto@fcs.data[cyto@event.use,][,cyto@channel.use])[1])
    )
  })

 ##### Marker heatmap #####
  output$Marker_heatmap<-renderPlot({
    if(input$IDexchangeLabel=="ID"){
      ClusterID=cyto@ClusterID
    }
    else if(input$IDexchangeLabel=="Cell_Label"){
      ClusterID=cyto@label
    }
    cluster_av=getAvg(cyto@fcs.data[cyto@event.use,][,cyto@channel.use],ClusterID)
    pheatmap::pheatmap(cluster_av,color = colorRampPalette(c("#ffffd9","#edf8b1","#c7e9b4","#7fcdbb","#41b6c4","#1d91c0","#225ea8","#253494","#081d58"))(100))
  })


 ##### scatter plot #####
  output$scatter_plot<-renderPlot({

    if(input$red_choice=="t_SNE"){
      if(!is.null(cyto@dim.red$tsne2d)){
        data=as.data.frame(cyto@dim.red$tsne2d$Y)
      }
      else{
        return(ggplot()+ggtitle("This method is not choosen! Please try others"))
      }
    }
    else if(input$red_choice=="LargeVis"){
      if(!is.null(cyto@dim.red$largeVis)){
        data=as.data.frame(t(cyto@dim.red$largeVis$coords))
      }
      else{
        return(ggplot()+ggtitle("This method is not choosen! Please try others"))
      }
    }
    else if(input$red_choice=="PCA"){
      if(!is.null(cyto@dim.red$PCA)){
        data=as.data.frame(cyto@dim.red$PCA$scores[,c(1,2)])
      }
      else{
        return(ggplot()+ggtitle("This method is not choosen! Please try others"))
      }
    }
    Expression = cyto@fcs.data[cyto@event.use,][input$Marker_exp_sca_choice]
    Dimension1=data[,1]
    Dimension2=data[,2]
    if(input$Cluster_Marker=="Markers"){
      set=c()
      if(input$scatter_colors=="set1"){
        set=c("#ffffd9","#edf8b1","#c7e9b4","#7fcdbb","#41b6c4","#1d91c0","#225ea8","#253494","#081d58")
      }
      else if(input$scatter_colors=="set2"){
        set=c("#1d56f8","#7379f4","#fefefe","#ef4c44","#f1345c")
      }

      ggplot(data = data,aes(x=Dimension1,y=Dimension2))+geom_point(aes(colour=Expression))+theme_bw()+
        theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())+
        theme(panel.border  = element_blank())+
        theme(axis.line = element_line(colour = "black"))+
        scale_color_gradientn(colours = set)
    }
    else if(input$Cluster_Marker=="Clusters"){
      if(is.null(input$Cluster_sca_choice)){
        return()
      }
      set=c()
      if(input$scatter_colors=="set1"){
        set=c("#8DD3C7","#FFFFB3","#BEBADA","#FB8072","#80B1D3","#FDB462","#B3DE69","#FCCDE5","#D9D9D9","#BC80BD","#CCEBC5","#FFED6F")
      }
      else if(input$scatter_colors=="set2"){
        set=c("#9E0142","#D53E4F","#F46D43","#FDAE61","#FEE08B","#E6F598","#ABDDA4","#66C2A5","#3288BD","#5E4FA2")
      }
      if(input$Cluster_sca_choice=="ALL"){
        if(input$IDexchangeLabel=="ID"){
            clusters=as.factor(unlist(cyto@ClusterID))
        }
        else if(input$IDexchangeLabel=="Cell_Label"){
            clusters=as.factor(unlist(cyto@label))
        }

        ggplot(data = data,aes(x=Dimension1,y=Dimension2,colour=clusters))+geom_point()+theme_bw()+
          theme(panel.grid.major = element_blank(),
                panel.grid.minor = element_blank())+
          theme(panel.border = element_blank())+
          theme(axis.line = element_line(colour = "black"))+
          scale_colour_manual(values=colorRampPalette(set)(length(unique(clusters))))
      }
      else{
        if(input$IDexchangeLabel=="ID"){
          if(input$red_choice=="t_SNE" || input$red_choice=="LargeVis"){
            clusters=as.factor(unlist(cyto@ClusterID[cyto@dim.red$red_events,]))
          }
          else{
            clusters=as.factor(unlist(cyto@ClusterID))
          }
        }
        else if(input$IDexchangeLabel=="Cell_Label"){
          if(input$red_choice=="t_SNE" || input$red_choice=="LargeVis"){
            clusters=as.factor(unlist(cyto@label[cyto@dim.red$red_events,]))
          }
          else{
            clusters=as.factor(unlist(cyto@label))
          }
        }
        clusters=as.matrix(clusters)
        clusters[which(clusters!=input$Cluster_sca_choice)]="Others"
        clusters=as.factor(unlist(clusters))
        ggplot(data = data,aes(x=Dimension1,y=Dimension2,colour=clusters))+geom_point()+theme_bw()+
          theme(panel.grid.major = element_blank(),
                panel.grid.minor = element_blank())+
          theme(panel.border  = element_blank())+
          theme(axis.line = element_line(colour = "black"))+
          scale_colour_manual(values = c("#3dccfb","#c2c2c2"))
      }
    }
  })

 ###  markers or clusters event###
 observeEvent(input$Cluster_Marker,{
  if(input$Cluster_Marker=="Markers"){
    showElement(id="Marker_exp_sca")

    hideElement(id="Cluster_sca")
  }
  else if(input$Cluster_Marker=="Clusters"){
    showElement(id="Cluster_sca")
    hideElement(id="Marker_exp_sca")
  }
 })

 ### marker or cluster event UI ###
 output$Marker_or_Cluster=renderUI({

   if(input$IDexchangeLabel=="ID"){
     clusters=as.character(sort(unique(cyto@ClusterID)[,1]))
   }
   else if(input$IDexchangeLabel=="Cell_Label"){
     clusters=as.character(sort(unique(cyto@label)[,1]))
   }

  tagList(
    div(
      id="Cluster_sca",
      selectInput(inputId = "Cluster_sca_choice",
                  label = "you can select a cluster for displaying:",
                  choices = c("ALL",as.character(sort(unique(clusters)))),
                  selected = "ALL")
    ),
    div(
      id="Marker_exp_sca",style="display:none",
      selectInput(inputId = "Marker_exp_sca_choice",
                  label = "you can select a Marker for displaying:",
                  choices = c(sort(colnames(cyto@fcs.data[cyto@event.use,][,cyto@channel.use]))),
                  selected=cyto@fcs.data[cyto@event.use,][,cyto@channel.use][1])
      )
    )
  })

 ##### download Handler #######
 output$DownloadRdata <- downloadHandler(
   filename = function(){
     paste(cyto@projectname, ".RData", sep="")
   },
   content = function(file){
     save(cyto,file = file)
   }
 )

 #output$Img <- downloadHandler()
 }
   runApp(list(ui=ui, server = server),launch.browser=T)
}