R/mod_infos_dataset.R

In samWieczorek/Dapar2: Tools for the Differential Analysis of Proteins Abundance with R

#' @title   mod_infos_dataset_ui and mod_infos_dataset_server
#' @description  A shiny Module.
#' 
#' @param id shiny id
#' @param obj An instance of the class `QFeatures`.
#' 
#' @return A shiny app
#'
#' 
#' @name mod_infos_dataset
#' 
#' @example inst/extdata/examples/ex_mod_infos_dataset.R

NULL



#'
#'
#' @rdname mod_infos_dataset
#'
#' @export 
#' @importFrom shiny NS tagList 
#' @import QFeatures
#' 
mod_infos_dataset_ui <- function(id){
  ns <- NS(id)
  
  tagList(
    uiOutput(ns('title')),
    
    fluidRow(
      column(width=6,
             mod_format_DT_ui(ns('dt')),
             br(),
             br(),
             
             uiOutput(ns('samples_tab_ui'))
             
      ),
      column(width=6,
             uiOutput(ns('choose_SE_ui')),
             uiOutput(ns('show_SE_ui'))
      )
    )
  )
}





# Module Server

#' @rdname mod_infos_dataset
#' @export
#' 
#' @keywords internal
#' 
#' @importFrom tibble as_tibble
#' 
mod_infos_dataset_server <- function(id, obj){
  
  
  moduleServer(id, function(input, output, session){
    ns <- session$ns
    
    observe({
      req(obj())
      if (!inherits(obj(),'QFeatures'))
      {
        warning("'obj' is not of class 'QFeatures'")
        return(NULL)
      }
      
      mod_format_DT_server('samples_tab',
                           data = reactive({
                             req(obj())
                             data.frame(colData(obj()))
                             }),
                           full_style = reactive({req(obj())
                             list(cols = colnames(colData(obj())),
                                  vals = colnames(colData(obj()))[2],
                                  unique = unique(colData(obj())$Condition),
                                  pal = RColorBrewer::brewer.pal(3,'Dark2')[1:2])
                           })
      )
    })
    
    
    mod_format_DT_server('dt',
                         data = reactive({req(Get_QFeatures_summary())
                           tibble::as_tibble(Get_QFeatures_summary())}),
                         full_style=reactive({NULL}))
    
    
    
    
    
    output$samples_tab_ui <- renderUI({
      req(obj())
      
      tagList(
        h4("Samples"),
        mod_format_DT_ui(ns('samples_tab'))
      )
      
    })
    
    output$title <- renderUI({
      req(obj())
      name <- metadata(obj())$analysis
      tagList(
          h3("Dataset summary"),
        p(paste0("Name of analysis:",name$analysis))
)
    })
    
    
    
    output$choose_SE_ui <- renderUI({
      req(obj())
      selectInput(ns("selectInputSE"),
                  "Select a dataset for further information",
                  choices = c("None",names(experiments(obj())))
      )
    })
    
    
    Get_QFeatures_summary <- reactive({
      
      req(obj())
      nb_assay <- length(obj())
      names_assay <- unlist(names(obj()))
      pipeline <- metadata(obj())$pipelineType
      
      columns <- c("Number of assay(s)",
                   "List of assay(s)",
                   "Pipeline Type")
      
      vals <- c( if(is.null(metadata(obj())$pipelineType)) '-' else metadata(obj())$pipelineType,
                 length(obj()),
                 if (length(obj())==0) '-' else HTML(paste0('<ul>', paste0('<li>', names_assay, "</li>", collapse=""), '</ul>', collapse=""))
      )
      
      
      
      do <- data.frame(Definition= columns,
                       Value=vals
      )
      
      do
    })
    
    
    
    
    Get_SE_Summary <- reactive({
      req(obj())
      req(input$selectInputSE)
      
      if(input$selectInputSE != "None"){
        #browser()
        
        .se <- obj()[[input$selectInputSE]]
        #data <- experiments(obj())[[input$selectInputSE]]
        
        
        typeOfData <- metadata(.se)$typeDataset
        nLines <- nrow(.se)
        .nNA <- QFeatures::nNA(.se)
        percentMV <- round(.nNA$nNA[,'pNA'], digits = 2)
        nEmptyLines <-  length(which(.nNA$nNArows[,'pNA']==100))
        
        val <- c(typeOfData, nLines, percentMV, nEmptyLines)
        row_names <- c("Type of data",
                       "Number of lines",
                       "% of missing values",
                       "Number of empty lines")
        
        if (tolower(typeOfData) == 'peptide'){
          
          if(length(metadata(.se)$list.matAdj) > 0){
            adjMat.txt <- "<span style=\"color: lime\">OK</span>"
          } else{
            adjMat.txt <- "<span style=\"color: red\">Missing</span>"
          }
          
          if(!is.null(metadata(.se)$list.cc)){
            cc.txt <- "<span style=\"color: lime\">OK</span>"
          } else{
            cc.txt <- "<span style=\"color: red\">Missing</span>"
          }
          
          val <- c(val, adjMat.txt, cc.txt)
          row_names <- c(row_names, "Adjacency matrices", "Connex components")
        }
        
        
        do <- data.frame(Definition = row_names, 
                         Value = val,
                         row.names = row_names)
        do
        
      }
      
      
    })
    
    
    
    # output$properties_ui <- renderUI({
    #   req(input$selectInputSE)
    #   req(obj())
    # 
    #   if (input$selectInputSE != "None") {
    #     checkboxInput(ns('properties_button'), "Display details?", value=FALSE)
    #   }
    # })
    
    
    
    # observeEvent(input$selectInputSE,{
    # 
    #   if (isTRUE(input$properties_button)) {
    #     output$properties_ui <- renderUI({
    #       checkboxInput(ns('properties_button'), "Display details?", value=TRUE)
    #     })
    #   }
    #   else{ return(NULL)}
    # })
    
    
    # output$properties <- renderPrint({
    #   req(input$properties_button)
    # 
    #   if (input$selectInputSE != "None" && isTRUE(input$properties_button)) {
    # 
    #     data <- experiments(obj())[[input$selectInputSE]]
    #     metadata(data)
    #   }
    # })
    
    
    
    output$show_SE_ui <- renderUI({
      req(input$selectInputSE)
      req(obj())
      
      if (input$selectInputSE != "None") {
        
        data <- experiments(obj())[[input$selectInputSE]]
        print(class(data))
        mod_format_DT_server('dt2',
                             data = reactive({Get_SE_Summary()}),
                             full_style=reactive({NULL}))
        tagList(
          mod_format_DT_ui(ns('dt2')),
          br(),
          uiOutput(ns('info'))
        )
      }
      else {
        return(NULL)
      }
      
    })
    
    
    
    output$info <- renderUI({
      req(input$selectInputSE)
      req(obj())
      
      if (input$selectInputSE != "None") {
        
        typeOfDataset <- Get_SE_Summary()["Type of data", 2]
        pourcentage <- Get_SE_Summary()["% of missing values", 2]
        nb.empty.lines <- Get_SE_Summary()["Number of empty lines", 2]
        if (pourcentage > 0 && nb.empty.lines > 0) {
          tagList(
            tags$h4("Info"),
            if (typeOfDataset == "protein"){
              tags$p("The aggregation tool
                 has been disabled because the dataset contains
                 protein quantitative data.")
            },
            
            if (pourcentage > 0){
              tags$p("As your dataset contains missing values, you should
                 impute them prior to proceed to the differential analysis.")
            },
            if (nb.empty.lines > 0){
              tags$p("As your dataset contains lines with no values, you
                 should remove them with the filter tool
                 prior to proceed to the analysis of the data.")
            }
          )
        }
      }
    })
    
    
    
    
    NeedsUpdate <- reactive({
      req(obj())
      PROSTAR.version <- metadata(experiments(obj()))$versions$Prostar_Version
      
      if(compareVersion(PROSTAR.version,"1.12.9") != -1 && !is.na(PROSTAR.version) && PROSTAR.version != "NA") {
        return (FALSE)
      } else {
        return(TRUE)
      }
    })
    
    
  })
  
  
}