inst/app/R/tab_eda.R

In systemPipeR/spsBio: spsBio: A systemPipeShiny plugin for biological data visualization

## UI
edaUI <- function(id) {
  ns <- NS(id)
  tagList(
    h2("Exploratory Data Analysis"),
    fluidRow(
      tabBox(title = tagList(shiny::icon("database"), "Specifications"), id = "Tab1",width = 12,
             tabPanel("Normalization",
                      radioButtons(ns("norm"), "Normalization methods", choices = c("raw","rlog", "vst"))
                      )                   
            ),
      boxPlus(title = "Plot Types",closable = F,
              uiOutput(ns("plot_types"))
              ),
    ),
    fluidRow(title = "Plot Output",
             uiOutput(ns("PCA")),
             uiOutput(ns("GLM")),
             uiOutput(ns("TSNE")),
             uiOutput(ns("MDS")),
             uiOutput(ns("HCLUST")),
             uiOutput(ns("HMAP"))
    )
  )
}

## server

edaServer <- function(input, output, session, shared){
  ns <- session$ns
  observe({
  output$plot_types <- renderUI({
    if (input$norm == "raw") {
      fluidRow(
      checkboxInput(ns("GLM"), "GLM-PCA"),
      checkboxInput(ns("TSNE"), "t-SNE"),
      checkboxInput(ns("PCA"), "PCA"),
      checkboxInput(ns("MDS"), "MDS"),
      checkboxInput(ns("HCLUST"), "Hierarchical Clustering"),
      checkboxInput(ns("HEAT"), "Heat Map of Hierarchical Clustering")
      )
    } else if (input$norm == "rlog") {
      fluidRow(
      checkboxInput(ns("PCA"), "PCA"),
      checkboxInput(ns("MDS"), "MDS"),
      checkboxInput(ns("HCLUST"), "Hierarchical Clustering"),
      checkboxInput(ns("HEAT"), "Heat Map of Hierarchical Clustering")
      )
    } else if (input$norm == "vst") {
      fluidRow(
      checkboxInput(ns("PCA"), "PCA"),
      checkboxInput(ns("MDS"), "MDS"),
      checkboxInput(ns("HCLUST"), "Hierarchical Clustering"),
      checkboxInput(ns("HEAT"), "Heat Map of Hierarchical Clustering")
      )
    } 
  })  
  })
  
  observe({
    
    if (!is.null(shared$count_list)){ 
      ## GLM 
      output$GLM <- renderUI({
        if (input$GLM == T) {
          output$GLM_out <- renderPlotly({
            run_GLM(countDF = count_list$countDF, targets = count_list$targets, colData = count_list$colData)
          })
        } else { return(NULL) }
        boxPlus(plotlyOutput(ns("GLM_out")))
      })
      
      ## PCA
      output$PCA <- renderUI({
        if (input$PCA == T) {
          output$PCA_out <- renderPlotly({
            run_PCA(countDF = count_list$countDF, targets = count_list$targets, colData = count_list$colData, method = input$norm)
          })
        } else { return(NULL) }
        boxPlus(plotlyOutput(ns("PCA_out")))
      })
      
      ## TSNE
      output$TSNE <- renderUI({
        if (input$TSNE == T) {
          output$TSNE_out <- renderPlotly({
            run_TSNE(countDF = count_list$countDF, targets = count_list$targets)
          })
        } else { return(NULL) }
        boxPlus(plotlyOutput(ns("TSNE_out")))
      })
      
      ## MDS
      output$MDS <- renderUI({
        if (input$MDS == T) {
          output$MDS_out <- renderPlotly({
            run_MDS(countDF = count_list$countDF, targets = count_list$targets, colData = count_list$colData, method = input$norm)
          })
        } else { return(NULL) }
        boxPlus(plotlyOutput(ns("MDS_out")))
      })
      
      ## Hierarchial Clustering Dendrogram
      output$HCLUST <- renderUI({
        if (input$HCLUST == T) {
          output$HCLUST_out <- renderPlot({
            run_CLUST(countDF = count_list$countDF, targets = count_list$targets, colData = count_list$colData, method = input$norm)
          })
        } else { return(NULL) }
        boxPlus(plotOutput(ns("HCLUST_out")))
      })
      
      ## Heat Map
      output$HMAP <- renderUI({
        if (input$HEAT == T) {
          output$HEAT_out <- renderPlotly({
            run_HEAT(countDF = count_list$countDF, targets = count_list$targets, colData = count_list$colData, method = input$norm)
          })
        } else { return(NULL) }
        boxPlus(plotlyOutput(ns("HEAT_out")))
      })
      
    }
    
  })
  
}