validate_dataframe <- function(df, validation_rules = c("structure", "peak_headers", "metadata")
) {
  expected_headers <- c(
    "Alignment ID", "Average Rt(min)", "Average Mz", "Metabolite name",
    "Adduct type", "Post curation result", "Fill %", "MS/MS assigned",
    "Reference RT", "Reference m/z", "Formula", "Ontology", "INCHIKEY",
    "SMILES", "Annotation tag (VS1.0)", "RT matched", "m/z matched",
    "MS/MS matched", "Comment", "Manually modified for quantification",
    "Manually modified for annotation", "Isotope tracking parent ID", 
    "Isotope tracking weight number", "RT similarity", "m/z similarity",
    "Simple dot product", "Weighted dot product", "Reverse dot product",
    "Matched peaks count", "Matched peaks percentage", "Total score",
    "S/N average", "Spectrum reference file name", "MS1 isotopic spectrum",
    "MS/MS spectrum"
  )
  # Input check
  if (!is.data.frame(df)) {
    stop("Input must be a dataframe")
  }
  
  if (!is.character(validation_rules)) {
    stop("validation_rules must be a character vector")
  }
  
  if (!all(validation_rules %in% c("structure", "peak_headers", "metadata"))) {
    stop("Invalid validation_rules specified")
  }
  
  validation_results <- list()
  
  # Check data structure  
  if ("structure" %in% validation_rules) {
    if (nrow(df) < 5) {
      validation_results$insufficient_rows <- 
        "Data has less than 5 rows. At least 5 rows are required."
    }
    if (ncol(df) < 34) {
      validation_results$insufficient_cols <- 
        "Missing 34 columns required for peak information."
    }
  }
  
  # Check peak information headers (row 5)
  if ("peak_headers" %in% validation_rules) {
    if (is.null(expected_headers)) {
      stop("expected_headers required for peak_headers validation")
    }
    
    if (nrow(df) >= 5) {
      headers <- df[5, 1:min(34, ncol(df))]
      missing_headers <- expected_headers[1:length(headers)][headers != expected_headers[1:length(headers)]]
      
      if (length(missing_headers) > 0) {
        validation_results$incorrect_headers <- 
          paste("Headers in row 5 differ from expected headers:",
                paste(missing_headers, collapse = ", "))
      }
    }
  }
  
  # Check metadata (rows 1-2, columns 35+)
  if ("metadata" %in% validation_rules && ncol(df) > 34) {
    # Check for Sample in columns 36+
    if (ncol(df) > 35) {
      file_types <- df[2, 36:ncol(df)]
      if (!"Sample" %in% file_types) {
        validation_results$no_sample <- 
          "'Sample' not found in columns 36 onward"
      }
    }
  }
  
  return(validation_results)
}




server <- function(input, output,session) {
  options(shiny.maxRequestSize=150*1024^100)
  originaldir <- reactiveValues(datapath = getwd()) # directry of shiny R script
  global <- reactiveValues(datapath = getwd()) # directory of file path in lipidomics tab
  col = reactiveValues(col = col)
  validation_status <- reactiveVal(FALSE)
  svg_path <- paste(getwd(),"/svg",sep = "")
  host <- session$request$HTTP_HOST　%>% 
  gsub("http:", "https:", .)
   output$downloadData <- downloadHandler(
     filename = function() {
       "Demo_data.zip"  # ダウンロード時のファイル名
     },
     content = function(file) {
       file.copy("./pathwaymap/demodata.zip", file)
    },
    contentType = "application/zip"
    )
    observeEvent(input$filetype,{
    if(input$filetype =="Sample in rows"){
      shinyjs::show("ontfile")
    }else{
      shinyjs::hide("ontfile")
    }
	if(input$ClassorMol =="TRUE"){
      shinyjs::show("X1")
	  shinyjs::show("X2")
    }else{
      shinyjs::hide("X1")
	  shinyjs::hide("X2")
    }
	})
	
	observeEvent(input$ClassorMol,{
	if(input$ClassorMol =="TRUE"){
      shinyjs::show("X1")
	  shinyjs::show("X2")
    }else{
      shinyjs::hide("X1")
	  shinyjs::hide("X2")
    }
	})
	
 observeEvent(input$file1, {	
 tryCatch({
    originaldata <- read.csv(input$file1$datapath, header = F, check.names = F, fileEncoding = "UTF-8-BOM")
  }, error = function(e) {
    showNotification(paste("Failed to load the file", e$message), type = "error")
  })
  if(input$filetype == "Sample in rows"){
   
  }else if(input$filetype == "MS-DIAL export"){
  validateresults <- validate_dataframe(originaldata, validation_rules = c("structure", "peak_headers", "metadata"))
      if (length(validateresults) == 0) {
      validation_status(TRUE)
      shinyalert(
        "Success",
        "All validation checks passed successfully!",
        type = "success"
      )
    } else {
      validation_status(FALSE)
error_message <- "<div style='text-align: left;'><strong>Validation Errors:</strong><br><br>"
    
    # 各エラーメッセージの処理
    for (error_name in names(validateresults)) {
      error_message <- paste0(
        error_message,
        "<span style='color: #dc3545;'>• ",  # 赤色のブレット
        switch(error_name,
               "insufficient_cols" = "<strong>Column Error:</strong> ",
               "incorrect_headers" = "<strong>Header Error:</strong> ",
               "no_sample" = "<strong>Sample Error:</strong> "),
        validateresults[[error_name]],
        "</span><br><br>"
      )
    }
    error_message <- paste0(error_message, "</div>")
    
    # エラーメッセージの表示
    shinyalert(
      "Validation Failed",
      html = TRUE,
      text = error_message,
      type = "error",
      size = "l",
      closeOnEsc = TRUE,
      closeOnClickOutside = TRUE,
      showConfirmButton = TRUE,
      confirmButtonText = "OK",
      timer = FALSE
    )

    }
  }
	observeEvent(input$submit,{
  tryCatch({
  if(input$filetype == "Sample in rows"){
    
    Lipidomedata <- processSampleInRows(originaldata, session, input)[[1]]
    lipidont <- read.csv(input$ontfile$datapath, sep = ",", check.names = FALSE)
    metadata <- processSampleInRows(originaldata, session, input)[[2]]
    alitable <- read.csv(input$ontfile$datapath, check.names = FALSE, fileEncoding = "UTF-8-BOM")
    colnames(alitable) <- c("Metabolite name","Ontology")
    letchoice <- c(colnames(Lipidomedata)[!colnames(Lipidomedata) %in% colnames(metadata)],"Others")
    moldata <- processSampleInRowstomoldata(originaldata, session, input)[[1]]
  }else if(input$filetype == "MS-DIAL export"){
  
  Lipidomedata <- processMSDIALExport(originaldata, session, input)[[1]]
  metadata <- processMSDIALExport(originaldata, session, input)[[2]]
  alitable <- process_alignment_file(originaldata)[[1]] %>% select(c(1,2))
  moldata <- processMSDIALExporttomoldata(originaldata, session, input)[[1]] 
  }
  if(length(input$transcriptomefile) !=0){
    transcriptome <- read.csv(input$transcriptomefile$datapath,check.names = F,fileEncoding ="UTF-8-BOM") %>% t()
    colnames(transcriptome) <- transcriptome[1,]
    transcriptome <- transcriptome[-1,] %>% data.frame()
    transcriptome[,-c(1)] <- apply(transcriptome[,-c(1)],2,as.numeric) %>% data.frame()
    transcriptome <-  rownames_to_column(transcriptome,"name")
    transcriptome[,2] <- as.character(transcriptome[,2])
  }
  
  if(length(input$file1) !=0 & length(input$transcriptomefile) != 0){
    
    Ensembl <- gconvert(query = colnames(transcriptome)[-c(1:2)] , organism = "mmusculus",#sapiens
                       target="ENSG", mthreshold = Inf, filter_na = TRUE) %>% select(input,target)
    transcriptome <- transcriptome %>% select(-2)
    data <- inner_join(Lipidomedata,transcriptome,by =c("name"))
	moldata <- inner_join(moldata,transcriptome,by =c("name"))
  }
  else{
    data <- Lipidomedata
  }
  }, error = function(e) {
    showNotification(paste("Error:Invalid CSV file format. Please ensure your file is a properly formatted CSV.", e$message), type = "error")
  })
  
  metainfocol <- ncol(metadata)
  lipidclassproperties <- read_csv("./pathwaymap/lipidclassproperties.csv")
  
  processAndUpdateInputs(data, session, metadata, metainfocol)
  processAndUpdateInputs2(moldata, session, metadata, metainfocol)
  shinyalert(
        "Success",
        "GO to Plot tab",
        type = "success"
      )
  observeEvent(input$y,{
    targetclass <- filter(alitable,Ontology %in% input$y)
    shiny::updateSelectInput(session, "mol", selected = targetclass$`Metabolite name`[1], choices = targetclass$`Metabolite name`)
  })

  #observeEvent(input$Colorpicker,{
  #print("boton")
  #  ShowtestModaldialog4(data,input,col$col)
  #}
  #)
  observeEvent(input$exportgraph,{
    ShowtestModaldialog2(data,input,col$col)
  }
  )
  observeEvent(input$Colorpicker, {
    ShowtestModaldialog(data,input,col$col)
  })
  
    observeEvent(input$saveConfirm, {
    removeModal()
    showNotification("保存が完了しました", type = "success")
  })

  observeEvent(input$w,{
    updateOrderInput(session, "levels",
                     items = c(unique(as.matrix(select(data,input$w)))),
                     item_class = "success")
    shiny::updateSelectInput(session, "q", selected = c(unique(as.matrix(select(data,input$w))))[1], choices = c(unique(as.matrix(select(data,input$w)))))
    col$col <<- rainbow_hcl(length(unique(as.matrix(select(data,input$w)))))
    col$col <<- setNames(col$col,unique(as.matrix(select(data,input$w))))
  })
  
  observeEvent(input$levels,{
    if (!is.null(input$levels) && any(input$levels != "")) {
      dataa <<- mutate(data, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
      moldataa <<- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
      
    }
  })
  
  observe({
    if (length(grep("selectcolor", names(input), value = TRUE)) != 0) {
      col$col <<-  process_select_color_input(input,data)
    }
  })
  
    # 可能な比較の組み合わせを生成
  possible_comparisons <- reactive({
    groups <- unique(data[[input$w]])
    combs <- combn(groups, 2, simplify = FALSE)
    setNames(
      lapply(combs, function(x) paste(x, collapse = "vs")),
      lapply(combs, function(x) paste(x, collapse = " vs "))
    )
  })
  
  # 比較選択用のUI生成
  output$comparison_choices <- renderUI({
    checkboxGroupInput("comparisons", "Select comparisons:",
                      choices = possible_comparisons(),
                      selected = possible_comparisons()[1])
  })
  
  # Select All ボタンの処理
  observeEvent(input$select_all, {
    updateCheckboxGroupInput(session, "comparisons",
                           selected = possible_comparisons())
  })
  
  # Clear All ボタンの処理
  observeEvent(input$clear_all, {
    updateCheckboxGroupInput(session, "comparisons",
                           selected = character(0))
  })
  
  # 統計検定の実行
  stat_test <- reactive({
    req(input$comparisons, input$test_method)
    
    # 検定関数の選択
    test_func <- get(input$test_method, asNamespace("rstatix"))
    
    # 結果を格納するデータフレーム
    results <- data.frame()
    
    for(comp in input$comparisons) {
      # グループを分割
      groups <- strsplit(comp, "vs")[[1]]
      
      # データのサブセット作成
      subset_data <- data %>%
        dplyr::filter(!!sym(input$w) %in% groups)
      
      # 検定実行
      test_result <- test_func(subset_data, as.formula(paste(input$y, "~", input$w))) %>%
        adjust_pvalue(method = input$p_adjust) %>%
        add_significance()
      
      results <- rbind(results, test_result)
    }
    
    results
  })

  stat_testmol <- reactive({
    req(input$comparisons, input$test_method)
    
    # 検定関数の選択
    test_func <- get(input$test_method, asNamespace("rstatix"))
    
    # 結果を格納するデータフレーム
    results <- data.frame()
    
    for(comp in input$comparisons) {
      # グループを分割
      groups <- strsplit(comp, "vs")[[1]]
      
      # データのサブセット作成
      subset_data <- moldata %>%
        dplyr::filter(!!sym(input$w) %in% groups)
      
      # 検定実行
      test_result <- test_func(subset_data, as.formula(paste(paste("`",input$mol,"`",sep =""), "~", input$w))) %>%
        adjust_pvalue(method = input$p_adjust) %>%
        add_significance()
      
      results <- rbind(results, test_result)
    }
    
    results
  })
  
#if(input$pvaluecheck == TRUE){
  # サマリー統計の表示

   output$stat_results <- renderPrint({
    req(stat_test())
    results <- stat_test() 
    cat("Results:\n")
    print(summary(results))
  })
#}  
  observeEvent(input$levels,{
    observe({
      if (!is.null(input$mol) && input$mol != "") {
        if(!is.null(input$levels) && any(input$levels != "")){
          
          output$plottest <- renderPlot({
            
            if(input$mydrop == "box"){
              process_boxplot(input,input$y,dataa,col$col,stat_test())
            }
            else if(input$mydrop == "bar"){
              process_barplot(input,input$y,dataa,col$col,stat_test())
            }
            else if(input$mydrop == "violin"){
              process_violinplot(input,input$y,dataa,col$col,stat_test())
            }
            else if(input$mydrop == "polar"){
              process_polarplot(input,input$y,dataa,col$col,stat_test())
            }
            else if(input$mydrop == "coding"){
              process_dotplot(input,input$y,dataa,col$col,stat_test())
            }
          }
          )
          output$mappingraph <- renderPlot({
            
            if(input$mydrop == "box"){
              process_boxplot_diagram(input,input$y,dataa,col$col,stat_test())
            }
            else if(input$mydrop == "bar"){
              process_barplot_diagram(input,input$y,dataa,col$col,stat_test())
            }
            else if(input$mydrop == "violin"){
              process_violinplot_diagram(input,input$y,dataa,col$col,stat_test())
            }
            else if(input$mydrop == "polar"){
              process_polar_diagram(input,input$y,dataa,col$col,stat_test())
            }
            else if(input$mydrop == "coding"){
              process_dotplot_diagram(input,input$y,dataa,col$col,stat_test())
            }
          }
          )
          
          observeEvent(input$mol,{
            
            output$plottest2 <- renderPlot({
              if(input$mydrop == "box"){
                process_boxplot(input, paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
              }
              else if(input$mydrop == "bar"){
                process_barplot(input, paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
              }
              else if(input$mydrop == "violin"){
                process_violinplot(input,paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
              }
              else if(input$mydrop == "polar"){
                process_polarplot(input,paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
              }
              else if(input$mydrop == "coding"){
                process_dotplot(input,paste("`",input$mol,"`",sep =""),moldataa,col$col,stat_testmol())
              }
              
            })
          })
		   }
      }
    })
  })
  
  graph_json <- reactive({
    if (input$pathwaytype == "Global pathway") {
      read_graph_json("./pathwaymap/globalpathway.cyjs") %>% gsub("localhost:9000", paste0(host,"/api"), .)
    } else if (input$pathwaytype == "Ceramide pathway") {
      read_graph_json("./pathwaymap/ceramidepathway.cyjs") %>% gsub("localhost:9000", paste0(host,"/api"), .)
    } else if (input$pathwaytype == "Remodeling pathway") {
      read_graph_json("./pathwaymap/remodeling.cyjs") %>% gsub("localhost:9000", paste0(host,"/api"), .)
    }
  })
  
  style_file_path <- "./pathwaymap/nodestyle1.js"
  styles_xml_path <- "./pathwaymap/styles.xml"
  # CYJSファイルのダウンロード
  output$exportCYJS <- downloadHandler(
    filename = function() {
      paste0(input$pathwaytype, "_", format(Sys.Date(), "%Y%m%d"), ".cyjs")
    },
    content = function(file) {
      writeLines(graph_json, file)
    },
    contentType = "application/json"
  )
  
  # styles.xmlファイルのダウンロード
  output$exportStyles <- downloadHandler(
    filename = function() {
      "styles.xml"
    },
    content = function(file) {
      file.copy(styles_xml_path, file)
    },
    contentType = "application/xml"
  )
  #observeEvent(input$sidebarCollapse, {
  #  toggleClass(id = "content", class = "active")
  #})
  
   observeEvent(input$toggle_sidebar, {
    shinyjs::runjs("toggleSidebar()")
  })
  observeEvent(input$actionplottest,{
  observeEvent(input$pathway, {
    output$graphContainer <- renderUI({
      cyjShinyOutput("cyjShinytest", height = "90%", width = "90%")
    })
  })
          output$cyjShinytest <- renderCyjShiny({
            #p <- input$pathway           
            #if (p != 0) {
              if (input$pathwaytype == "Global pathway") {
                graph_json <<- paste(readLines("./pathwaymap/globalpathway.cyjs"), collapse = "") %>% gsub("localhost:9000", paste0(host,"/api"), .)
              } else if (input$pathwaytype == "Ceramide pathway") {
                graph_json <<- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse = "") %>% gsub("localhost:9000", paste0(host,"/api"), .)
              } else if (input$pathwaytype == "Remodeling pathway") {
                graph_json <<- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse = "") %>% gsub("localhost:9000", paste0(host,"/api"), .)
              }
              
              test <- fromJSON(graph_json)
              test <- as.data.frame(test$elements$nodes)
              
              if (input$viewacyllevel == TRUE) {
                cyjShiny(graph_json, layoutName = "preset", styleFile = "./pathwaymap/nodestyle1.js")
              } else {
                cyjShiny(graph_json, layoutName = "preset", styleFile = "./pathwaymap/nodestyle2.js")
              }
           # } else {
              
            #}
          })
		  
		  
		  
          observeEvent(input$getSelectedNodes, ignoreInit=TRUE, {
            output$selectedNodesDisplay <- renderText({" "})
            getSelectedNodes(session)
          })
          
          
          output$corselect <- renderPlotly({
            if(input$pathwaytype == "Global pathway"){
              graph_json <<- paste(readLines("./pathwaymap/globalpathway.cyjs"), collapse="") %>% gsub("localhost:9000", paste0(host,"/api"), .)
            }else if(input$pathwaytype == "Ceramide pathway"){
              graph_json <<- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse="") %>% gsub("localhost:9000", paste0(host,"/api"), .)
            }else if(input$pathwaytype == "Remodeling pathway"){
              graph_json <<- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
            }
            test <- fromJSON(graph_json)
            test <- as.data.frame(test$elements$nodes)
            test1 <- test[test$data$id %in% unlist(input$selectedNodes),]
            dataa <- mutate(data, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
            
            if(length(test1$data$shared_name) == 2){
            moldataa <- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
            targetclass <- filter(alitable,Ontology %in% test1$data$shared_name)
              if(! test1$data$shared_name[1] %in% colnames(data)) {
                a <- Ensembl[Ensembl$target %in% test1$data$Ensembl_ID[1],]
                test1$data$shared_name[1] <- a$input
              }
              if(! test1$data$shared_name[2] %in% colnames(data)) {
                b <- Ensembl[Ensembl$target %in% test1$data$Ensembl_ID[2],]
                test1$data$shared_name[2] <- b$input
              }
			  if(input$ClassorMol == FALSE){
			  cor_value <- cor(dataa[,test1$data$shared_name[1]], dataa[,test1$data$shared_name[2]],method = "spearman")
              g <- ggplot(dataa,aes_string(test1$data$shared_name[1],test1$data$shared_name[2],fill = input$w,size = input$size))+geom_point(shape =21,color = "black")+
    scale_fill_manual(values = unlist(col$col)) + ggtitle(paste("r = ",round(cor_value, 2)))
			  }else{
			  moldataa <- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
	cor_value <- cor(moldataa[,input$X1], moldataa[,input$X2],method = "spearman")
			  g <- ggplot(moldataa,aes_string(paste("`",input$X1,"`",sep =""),paste("`",input$X2,"`",sep =""),fill = input$w,size = input$size))+geom_point(shape =21,color = "black")+
    scale_fill_manual(values = unlist(col$col))+ ggtitle(paste("r = ",round(cor_value, 2)))
			  }
			  plotly::ggplotly(g)
            }
            
          })
		  output$textOutput <- renderText({
		  if(input$ClassorMol == FALSE){
    "Select two nodes from the network to analyze their correlation."}
	else{
	"Select two features to analyze their correlation."
	}
  })
		  


		  
		     observeEvent(input$selectedNodes, {
      if(input$pathwaytype == "Global pathway"){
        graph_json <<- paste(readLines("./pathwaymap/globalpathway.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
      }else if(input$pathwaytype == "Ceramide pathway"){
        graph_json <<- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
      }else if(input$pathwaytype == "Remodeling pathway"){
        graph_json <<- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
      }
      test <- fromJSON(graph_json)
      test <- as.data.frame(test$elements$nodes)
      test1 <- test[test$data$id %in% unlist(input$selectedNodes),]
      moldataa <- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
      targetclass <- filter(alitable,Ontology %in% test1$data$shared_name)
        shiny::updateSelectInput(session, "selectmol", selected = targetclass$`Metabolite name`[1], choices = targetclass$`Metabolite name`)
      },ignoreNULL = TRUE)
	  
		  output$corselect2 <- renderPlot({
        if(input$pathwaytype == "Global pathway"){
          graph_json <<- paste(readLines("./pathwaymap/globalpathway.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
        }else if(input$pathwaytype == "Ceramide pathway"){
          graph_json <<- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
        }else if(input$pathwaytype == "Remodeling pathway"){
          graph_json <<- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse="")　%>% gsub("localhost:9000", paste0(host,"/api"), .)
        }
        test <- fromJSON(graph_json)
        test <- as.data.frame(test$elements$nodes)
        test1 <- test[test$data$id %in% unlist(input$selectedNodes),]
        moldataa <- mutate(moldata, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
        targetclass <- filter(alitable,Ontology %in% test1$data$shared_name)
        if(length(test1$data$shared_name) > 0 && input$selectmol != " "){
          if(test1$data$shared_name[1] %in% colnames(data)) {
          if(input$mydrop == "box"){
			g <- process_boxplot_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
          }
          else if(input$mydrop == "bar"){
			g <- process_barplot_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
          }
          else if(input$mydrop == "violin"){
			g <- process_violinplot_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
          }
          else if(input$mydrop == "polar"){
			g <- process_polar_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
          }
          else if(input$mydrop == "coding"){

			g <- process_dotplot_diagram(input,paste("`",input$selectmol,"`",sep =""),moldataa,col$col,stat_testmol())
          }}
          plot(g)
        }
        
      })
          })

  observeEvent(input$save_pdf,{
            shinyscreenshot::screenshot(selector="#cyjShinytest")
          })
  #Creating heatmap
  observeEvent(input$actionplottest, {
             waiter::waiter_show(
               id = NULL,
               html = tagList(waiter::spin_loader(),
                              "Loading ..."),
               color = "#333e48",
               logo = "",
               image = ""
             )
			 file_list <- list.files(svg_path)
             if (length(unlist(file_list)) > 0) {
                file.remove(file.path(svg_path, file_list))
             } else {
			 }
            if(is.null(input$transcriptomefile) == FALSE){
               Ensembl <- gconvert(query = colnames(transcriptome)[-c(1:2)] , organism = "mmusculus",#sapiens
                                   target="ENSG", mthreshold = Inf, filter_na = TRUE) %>% select(input,target)
              graph_json1 <- paste(readLines("./pathwaymap/ceramidepathway.cyjs"), collapse="")  %>% fromJSON()
              graph_json2 <- paste(readLines("./pathwaymap/remodeling.cyjs"), collapse="") %>% fromJSON()
              Ensemblinmap <- c(graph_json1$elements$nodes$data$Ensembl_ID,graph_json2$elements$nodes$data$Ensembl_ID)
              Ensemblinmap <- Ensemblinmap[-which(Ensemblinmap %in% "")]
	
              geneinmap <- Ensembl[Ensembl[,2] %in% Ensemblinmap,]

              transcriptome2 <- colnames(transcriptome)[colnames(transcriptome) %in% geneinmap$input]
			  
              data <- data[,colnames(data) %in% c(colnames(data)[1:metainfocol],colnames(Lipidomedata),transcriptome2)]
			  dataa <- mutate(data, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
			  names(dataa)[match(geneinmap$input, names(dataa))] <- geneinmap$target
			  
			  } else {
			  dataa <- mutate(data, !!as.symbol(input$w) := !!as.symbol(input$w) %>% factor(levels = input$levels))
			 }
            
           # if (length(grep("selectcolor", names(input), value = TRUE)) != 0) {
           #   col <<-  process_select_color_input(input,data)
           # }
            if(input$mydrop == "box"){
              process_action_boxplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
            }
            else if(input$mydrop == "bar"){
              process_action_barplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
            }
            else if(input$mydrop == "violin"){
              process_action_violinplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
            }
            else if(input$mydrop == "polar"){
              process_action_polarplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
            }
            else if(input$mydrop == "coding"){
              process_action_dotplot(input,dataa,metainfocol,svg_path,col$col,output,data) 
            }
            # if(input$filetype == "MS-DIAL export"){
            #   exportgroupnodeheatmap(originaldata,metadata,paste(global$datapath,"",sep =""),input$w,input$levels,originaldir,input)}
            Sys.sleep(3)
             waiter::waiter_hide()
			   shinyalert(
        "Success",
        "GO to Pathway analysis tab",
        type = "success"
      )
        }
          )
  observeEvent(input$w,{
   
  output$heatmap <- renderPlot({
  if(length(unlist(input$selectedNodes)) > 0 ) {
        test <- fromJSON(graph_json)
      test <- as.data.frame(test$elements$nodes)
      test1 <- test[test$data$id %in% unlist(input$selectedNodes),]
     groupnodeheatmap(lipidclassproperties,originaldata,metadata,input$w,input$levels,test1$data$shared_name[1],input)
	 }
  })
  })
} )})}