inst/phewas_app/server.R

library(shiny)
library(shinyjs)
library(dplyr)
library(DWHtools2)
library(multimodalPhewas)
library(ggplot2)
library(plotly)
library(DT)
library(future)

options(shiny.sanitize.errors = FALSE)
options(shiny.maxRequestSize=3000*1024^2) # maximum upload size = 3GB
#local = .local
#bio_file = .bio_file
#concepts_file = .concepts_file
#cim_file = .icd_file
data(scale1)
data(anabio_loinc)

if(local) {
  # load(bio_file)
  # load(icd_file)
  # load(concepts_file)
  # 
  # concepts_local <- concepts
  # cim_local <- cim
  # bio_local <- bio
}

shinyServer(function(input, output,session) {
  
  cores = future::availableCores()
  
  if(local) {
    shinyjs::hide("loading-content")
    shinyjs::show("files_upload")
    shinyjs::show("analysis_parameters")
  }

   icd_prefix = 'CIM10:'
   shinyjs::disable("report")
   shinyjs::disable("result_data")

  num_cohorts = ""
  results <- function() {}
  
  if(!local) {
    config <- getConfig(config_file)
  }
  
  concepts_col_remove = list( "-CI_inf", "-CI_sup", "-cases0","-control0")
  bio_col_remove = list( "-CI_inf", "-CI_sup", "-cases0","-control0", "-fit")
  cim_col_remove = list( "-CI_inf", "-CI_sup", "-cases0","-control0", "-fit")
  


  if(!local) {
    cohorts <- DWHtools2::get_cohorts_list(username = config$username, only_num = FALSE,config = config)
    
    num_cohorts <- reactive( {
      cohorts_for_select <- list()
      num_cohorts <- cohorts$NUM_COHORTE
      names(num_cohorts) <- paste0(cohorts$TITRE_COHORTE,' (',cohorts$N_PATIENTS,' patients)')
      
      num_cohorts
      
    })
    
    observeEvent(num_cohorts(), {
      shinyjs::hide("loading-content")
      if(!local) shinyjs::show("patients_selection")
      shinyjs::show("analysis_parameters")
      
      
    })
    
  } else {
    cohorts <- data.frame(NUM_COHORTE = c(1,2), TITRE_COHORTE = c('RETT', 'RETT controls'), N_PATIENTS = c(12, 24))
  }
  
  
  
  output$controls_cases <- renderUI(selectInput('cases','cases', num_cohorts()))

  
   output$control_type <- renderUI(if(!is.null(num_cohorts())) {
     radioButtons('control_type',label = "Type of controls", choices= list(`Automatic matching`='match', `Cohort` = "cohort" ))
   })
  
  if(!local) {
    controls_num_temp_list = reactive({
      num_cohorts()[num_cohorts() != input$cases]
    })
    
  }


  output$biotab <-renderUI(renderTab(input$boot, 'results_bio', 'dumbbell_bio', input$plot_height ))

  output$conceptstab <-renderUI(renderTab(input$boot, 'results_concepts', 'dumbbell_concepts',  input$plot_height ))
  
  output$cimtab <- renderUI(renderTab(input$boot, 'results_cim', 'dumbbell_cim' , input$plot_height))
  
  renderTab <- function(boot, manhattan, dumbbell, plot_height) {
    
    
    if (boot == 'TRUE') {

      return((fluidRow(
        column(6,
               plotly::plotlyOutput(outputId = manhattan, width = 'auto', height = plot_height)
               ),
        column(6,
               plotly::plotlyOutput(outputId = dumbbell, width = 'auto', height = plot_height)
               )
      )))
    } else {

      return((fluidRow(
        column(12,
               plotly::plotlyOutput(outputId = manhattan, width = 'auto', height =  plot_height)
        )
      )))
    }
  }
  
  
  output$controls_trigger <- renderUI(if(input$control_type == 'cohort') { 
    if (!local) {
      
      if (!is.null(controls_num_temp_list())) {
        selectInput('controls','controls', controls_num_temp_list())
      } 
      
    }
    
  })
  
  if (!local) {
    observe(if (input$control_type == 'cohort' & is.null(controls_num_temp_list())) 
    {
      showNotification("There is no other cohort available in the results table", type = 'warning')
    })
  }

  
  output$match_save <- renderUI(if(input$control_type == 'match') { 
    
  
      radioButtons('match_save',label = "Save matched cohort", choices= list(`No` = FALSE, `Yes`=TRUE ))
    
    
  })
  
  output$match_save_title <- renderUI(if(input$control_type == 'match' & !is.null(input$match_save) ) { 
    
      if (input$match_save) {
        
        default_title = paste0(cohorts$TITRE_COHORTE[cohorts$NUM_COHORTE == input$cases], ' - ', Sys.time(), ' controls for multiphewas' )
        textInput('match_save_title', 'Name for the matching cohort', value = default_title)
        
      }
    
  })
  
  

    
  
  
  # Get cases ------------
  cases <-reactive(
  {
    
    progress_cases <- shiny::Progress$new(session, min = 0, max = 4)
    on.exit(progress_cases$close())
    progress_cases$set(message = "get data", value = 0)
    
    if(!local) {
      data_ <- get_data_from_num(cohorte_num = input$cases,
                                 num_type = 'cohorte',
                                 cohort = 'cases', 
                                 neg = FALSE,
                                 progress = progress_cases, 
                                 icd_prefix = icd_prefix,
                                 config = config, step_p = 0) 
      data_
    }

  
  })
  
  # ----------
  
  # Get controls -----------
  control_cohort <- reactive({
    
    progress_match <- shiny::Progress$new(session, min = 0, max = 4)
    on.exit(progress_match$close())
    progress_match$set(message = "Matching", value = 0)
    
    if(!local) {
      
      if (input$control_type == 'match') {
        
        control_cohort <- match_patients(cohorte_num = input$cases, 
                                         num_type = 'cohorte', 
                                         birth_range = input$birth_range, 
                                         concepts_range=input$concept_range, n_match=input$n_match, 
                                         match_save = as.logical(input$match_save), 
                                         match_save_title= input$match_save_title, 
                                         config = config
        )
        
      } else if (input$control_type == 'cohort') {
        control_cohort = list(control_num = input$controls, control_num_type = 'cohorte')
      }
      control_cohort
    }
    
    

  
    
  })
  
  
  controls <-reactive(
    {
      progress_controls <- shiny::Progress$new(session, min = 0, max = 4)
      on.exit(progress_controls$close())
      progress_controls$set(message = "get data", value = 0)
      
      if(!local) {
        data_ <- get_data_from_num(cohorte_num = control_cohort()$control_num,
                                   num_type = control_cohort()$control_num_type,
                                   cohort = 'control', 
                                   neg = FALSE,
                                   progress = progress_controls, 
                                   icd_prefix = icd_prefix,
                                   config = config, step_p = 0) 
        data_
        
      }



    
    })
  # --------------
  
  # Extract data ----------
  patients <- reactive({
    
    if(!local) {
      patients <- controls()$patients[,names(controls()$patients) %in% names(cases()$patients)] %>%
        rbind(cases()$patients)
      #save(patients, file= '~/Sync/multimodalPhewas/patients_mecp2.RData')
    } else {
      
      if (is.null(input$patients_csv)) {
        patients <- NULL
      } else {
        patients <- read.csv(input$patients_csv$datapath, stringsAsFactors = F)
      }
      
    }
    str(patients)
    patients

  })

  concepts <- reactive({
    if(!local) {
      if (!is.null(cases()$concepts)) {
        concepts <- rbind(cases()$concepts, controls()$concepts)
        concepts <- concepts %>% dplyr::filter(PHENOTYPE == 1 | GENOTYPE == 1, !is.na(PARENT_LABEL))
        
        
      }
      }else {
        
        if (is.null(input$concepts_csv)) {
          concepts <- NULL
        } else {
          concepts <- read.csv(input$concepts_csv$datapath, stringsAsFactors = F)
        }
        
      }
    
      concepts <- merge(concepts, subset(patients(), select = c('PATIENT_NUM','SEX', 'BIRTH_YEAR')), by = 'PATIENT_NUM')
      concepts <- preprocess_umls_concepts(concepts, nb_occurrences = input$nb_occurrences_concept)

      concepts
     
    })
  
  bio <- reactive({
 
    if(!local) {
      if (!is.null(cases()$bio)) {
        bio <- rbind(cases()$bio, controls()$bio)
      }
      } else {
        if (is.null(input$bio_csv)) {
          bio <- NULL
        } else {
          bio <- read.csv(input$bio_csv$datapath, stringsAsFactors = F)
        }
      }
      str(bio)
      bio <- merge(bio, subset(patients(), select = c('PATIENT_NUM','SEX', 'BIRTH_YEAR')), by = 'PATIENT_NUM')
      bio <- preprocess_bio_concepts(bio, type = 'encounter', nb_occurrences =input$nb_occurrences_bio, map_loinc = ifelse(local, FALSE, TRUE))

      bio
      
    })
 
  
  cim <- reactive({
    
    if (!local) {
      if (!is.null(cases()$cim)) {
        cim <- rbind(cases()$cim, controls()$cim)
        
        
      }
      } else {
        if (is.null(input$cim_csv)) {
          cim <- NULL
        } else {
          cim <- read.csv(input$cim_csv$datapath, stringsAsFactors = F)
        }
      }
     
      cim <- merge(cim, subset(patients(), select = c('PATIENT_NUM','SEX', 'BIRTH_YEAR')), by = 'PATIENT_NUM')
      cim <- preprocess_cim(cim, scale1,   icd_prefix = icd_prefix, nb_occurrences = input$nb_occurrences_icd)

      cim
     
    })
  
  # -------------
  
  
  # Compute PheWAS ----------
  
  result_concepts <- eventReactive(eventExpr = (input$submit_loc),{
    
    shinyjs::disable("report")
    shinyjs::disable("result_data")

    if ('free_text' %in% input$sources & (!local | !is.null(input$concepts_csv))) {
      progress<- shiny::Progress$new(session, min = 0, max = 2)
      on.exit(progress$close())
      progress$set(message = "Concepts", value = 0)
      
      str(concepts())
      res <- compute_phewas(concepts(),patients(), 
                            concepts_min_occur = input$concepts_min_occur,
                            with_boot= as.logical(input$boot), perm = input$boot_perm, fdr_threshold = input$fdr_threshold,
                            , progress = progress, cores = cores)
        
      

      shinyjs::enable("report")
      shinyjs::enable("result_data")
      
      res
      
    }
   
   

  })
  
  
  result_bio <- eventReactive(eventExpr = input$submit_loc,{
    
    shinyjs::disable("report")
    shinyjs::disable("result_data")
 
    if('bio' %in% input$sources & (!local | !is.null(input$bio_csv))) {
      progress<- shiny::Progress$new(session, min = 0, max = 2)
      on.exit(progress$close())
      progress$set(message = "Bio", value = 0)
      
      
     
        bio_sup <- compute_bio_phewas(bio(), patients(), direction = "SUP", bio_min_occur = 1, 
                                      zero_controls_add = 2, patients_min_concept = NULL, analysis_type = input$bio_family, 
                                      with_boot = as.logical(input$boot), perm = input$boot_perm,
                                      fdr_threshold = input$fdr_threshold,progress= progress, cores = cores )
        bio_inf <- compute_bio_phewas(bio(), patients(), direction = "INF", bio_min_occur = 1, 
                                      zero_controls_add = 2, patients_min_concept = NULL, analysis_type = input$bio_family,
                                      with_boot = as.logical(input$boot), perm = input$boot_perm , fdr_threshold = input$fdr_threshold
                                      ,progress= progress, cores = cores)
        

      
      res <- list()
      res$result <- rbind(bio_sup$result, bio_inf$result)
      res$boot <- bio_sup$boot
      

      
      shinyjs::enable("report")
      shinyjs::enable("result_data")
      
      res
    }
    

  })
  
  result_cim <- eventReactive(eventExpr = input$submit_loc,{
    
    shinyjs::disable("report")
    shinyjs::disable("result_data")
    
    if('icd' %in% input$sources & (!local | !is.null(input$cim_csv))) {
      progress<- shiny::Progress$new(session, min = 0, max = 2)
      on.exit(progress$close())
      progress$set(message = "ICD", value = 0)
      
      
        res <- compute_cim_phewas(cim(), patients(), cim_min_occur = input$concepts_min_occur, 
                                  with_boot = as.logical(input$boot), perm = input$boot_perm, fdr_threshold = input$fdr_threshold,
                                  progress = progress, cores = cores)
        
      
      
      res$result$CAT <- phewas_categories(res$result$CODE)
      
      shinyjs::enable("report")
      shinyjs::enable("result_data")
      
      res
    }
    
    
  })
  # -----------
  
  # Manhattan output -----------
  
  manhattan_concepts <- reactive({
    req(input$plot_height)
    if(nrow(result_concepts()$result) > 0) {
    make_manhattan(result_concepts()$result, 'p.value','concepts', plot_height = input$plot_height)
  } else {
    ggplot(data.frame(x=0,y=0)) + theme_void()
  }})
  
  output$results_concepts <- plotly::renderPlotly(
    manhattan_concepts()
    )
  
  manhattan_bio <-  reactive({ 
    req(input$plot_height)
    if(nrow(result_bio()$result) > 0) {
     make_manhattan(result_bio()$result, 'p.value','bio',  plot_height =input$plot_height)
    } else {
      ggplot(data.frame(x=0,y=0)) + theme_void()
    }})
  
  output$results_bio <- plotly::renderPlotly(
    manhattan_bio()
    )
  
  manhattan_cim <-  reactive({
    req(input$plot_height)
    if(nrow(result_cim()$result) > 0) {
      make_manhattan(result_cim()$result, 'p.value','cim',  plot_height = input$plot_height)
    } else {
      ggplot(data.frame(x=0,y=0)) + theme_void()
    }})
  
  output$results_cim <- plotly::renderPlotly(
    manhattan_cim()
  )
  
  # ------------
  
  
  # Dumbbell output --------
  
  
  
  
  dumbbell_plot_bio <- reactive( {
    if(result_bio()$boot == TRUE) {
    input$plot_height 
    make_dumbbell(result_bio()$result, plot_height = input$plot_height, db_type = input$db_type)
    }
  })
  

  dumbbell_plot_cim <- reactive( {
    if(result_cim()$boot == TRUE) {
      input$plot_height
      make_dumbbell(result_cim()$result, plot_height = input$plot_height,  db_type = input$db_type)
    }
  })
  

  dumbbell_plot_concepts <- reactive( {
    if(result_concepts()$boot == TRUE) {
      input$plot_height
      make_dumbbell(result_concepts()$result, plot_height = input$plot_height , db_type = input$db_type)
    } else {
      g <- ggplot(data.frame(x=0,y=0)) + theme_void()
      ggplotly(g)
    }
      
    
  })
  
  output$dumbbell_bio <- plotly::renderPlotly(dumbbell_plot_bio())
  output$dumbbell_concepts <- plotly::renderPlotly(dumbbell_plot_concepts())
  output$dumbbell_cim <- plotly::renderPlotly(dumbbell_plot_cim())

  # --------------
  
  # Table output -----------
  
  output$table_concepts <- DT::renderDataTable(
    make_print_table(result_concepts()$result, cols_to_remove = concepts_col_remove, result_concepts()$boot)
  )
  

  #output$results_bio <- plotly::renderPlotly(make_manhattan(result_bio()$result, 'p.value','bio'))

  output$table_bio <- DT::renderDataTable(
    make_print_table(result_bio()$result, cols_to_remove = bio_col_remove, result_bio()$boot)
  )
  
 #output$results_cim <- plotly::renderPlotly(make_manhattan(result_cim()$result, 'p.value','cim', plot_height = input$plot_height))

  output$table_cim <- DT::renderDataTable(
    make_print_table(result_cim()$result, cols_to_remove = cim_col_remove, result_cim()$boot)
  )
  # ------------
  # 
  # Patients description ---------
  patients_desc <- reactive({
    patients() %>%
      group_by(group) %>%
      mutate(BIRTH_YEAR = as.numeric(BIRTH_YEAR),
             UNIQ_CONCEPTS = as.numeric(UNIQ_CONCEPTS)) %>%
      summarise(n  = n(),
                BIRTH_YEAR = median(BIRTH_YEAR, na.rm = T),
                BIRTH_YEAR_q25 = quantile(BIRTH_YEAR, probs = c(0.25)),
                BIRTH_YEAR_q75 = quantile(BIRTH_YEAR, probs = c(0.75)),
                NB_MALES = sum(SEX == 'M'),

                GENDER_RATIO = sum(SEX == 'M') / n(),
                NB_DISTINCT_CONCEPTS = median(UNIQ_CONCEPTS, na.rm = T),
                NB_DISTINCT_CONCEPTS_q25 = quantile(UNIQ_CONCEPTS, probs = c(0.25)),
                NB_DISTINCT_CONCEPTS_q75 = quantile(UNIQ_CONCEPTS, probs = c(0.75))) %>%
      mutate(BIRTH_YEAR = paste0(round(BIRTH_YEAR,0), ' [',round(BIRTH_YEAR_q25,0),'-',round(BIRTH_YEAR_q75,0),']'),
             GENDER_RATIO = paste0(NB_MALES,'/',n,' (',round(GENDER_RATIO,2),')'),
             NB_DISTINCT_CONCEPTS = paste0(round(NB_DISTINCT_CONCEPTS,0), ' [',round(NB_DISTINCT_CONCEPTS_q25,0),'-',round(NB_DISTINCT_CONCEPTS_q75,0),']')) %>%
      select(group, n, BIRTH_YEAR, GENDER_RATIO, NB_DISTINCT_CONCEPTS)
  })

  output$table_patients <- DT::renderDataTable(patients_desc())
  # ---------------

  
  # Dynamic report ---------
  output$report <- downloadHandler(
    # For PDF output, change this to "report.pdf"
    filename = "report.html",
    content = function(file) {
      # Copy the report file to a temporary directory before processing it, in
      # case we don't have write permissions to the current working dir (which
      # can happen when deployed).
      tempReport <- file.path(tempdir(), "report.Rmd")
      file.copy("report.Rmd", tempReport, overwrite = TRUE)
      
      # Set up parameters to pass to Rmd document
      
      # if (input$boot == 'TRUE') {
      #   db_concepts = ifelse('free_text' %in% input$sources,dumbbell_plot_concepts(), NA)
      #   db_bio =  ifelse('bio' %in% input$sources,dumbbell_plot_bio(), NA)
      #   db_cim = ifelse('bio' %in% input$sources,dumbbell_plot_cim(),NA)
      # } else {
      #   db_concepts = NA
      #   db_bio =  NA
      #   db_cim = NA
      # }
        
      cases = cohorts %>% 
        filter(NUM_COHORTE == input$cases) %>%
        mutate(name = paste0(TITRE_COHORTE,' (',N_PATIENTS,' patients)',' (NUM = ',NUM_COHORTE,')' ))
      
      if (input$control_type == 'match') {
        controls = list()
        controls$name = 'Automatic matching'
        if (input$match_save == 'TRUE') {
          controls$name = paste0(controls$name, ' (saved as "', input$match_save_title, '")')
        }
      } else {
        controls = cohorts %>% 
          filter(NUM_COHORTE == input$controls) %>%
          mutate(name = paste0(TITRE_COHORTE,' (',N_PATIENTS,' patients)',' (NUM = ',NUM_COHORTE,')' ))
      }
      
      manhattan_concepts_rep <- NA
      table_concepts_rep <- NA
      dumbbell_concepts_rep <- NA
      
      manhattan_bio_rep <- NA
      table_bio_rep <- NA
      dumbbell_bio_rep <- NA
      
      manhattan_cim_rep <- NA
      table_cim_rep <- NA
      dumbbell_cim_rep <- NA
      
      if ('icd' %in% input$sources) table_cim_rep <- make_print_table(result_cim()$result, cim_col_remove, input$boot)
      if ('icd' %in% input$sources) manhattan_cim_rep <- manhattan_cim()
      if ('icd' %in% input$sources & input$boot == 'TRUE') dumbbell_cim_rep <- dumbbell_plot_cim()
      
      if ('bio' %in% input$sources) table_bio_rep <- make_print_table(result_bio()$result, bio_col_remove, input$boot)
      if ('bio' %in% input$sources) manhattan_bio_rep <- manhattan_bio()
      if ('bio' %in% input$sources & input$boot == 'TRUE') dumbbell_bio_rep <- dumbbell_plot_bio()
      
      if ('free_text' %in% input$sources) table_concepts_rep <- make_print_table(result_concepts()$result, concepts_col_remove, input$boot)
      if ('free_text' %in% input$sources) manhattan_concepts_rep <- manhattan_concepts()
      if ('free_text' %in% input$sources & input$boot == 'TRUE') dumbbell_concepts_rep <- dumbbell_plot_concepts()
      
        params <- list(cases = cases$name,
                       controls = controls$name,
                       patients = patients_desc(), 
                       manhattan_concepts =  manhattan_concepts_rep,
                       table_concepts = table_concepts_rep, 
                       dumbbell_concepts = dumbbell_concepts_rep,
                       manhattan_bio =  manhattan_bio_rep,
                       table_bio = table_bio_rep, 
                       dumbbell_bio = dumbbell_bio_rep,
                       manhattan_cim =  manhattan_cim_rep,
                       table_cim = table_cim_rep, 
                       dumbbell_cim = dumbbell_cim_rep)
        
      
      # Knit the document, passing in the `params` list, and eval it in a
      # child of the global environment (this isolates the code in the document
      # from the code in this app).
      rmarkdown::render(tempReport, output_file = file,
                        params = params,
                        envir = new.env(parent = globalenv())
      )
    }
  )
  
  concepts_dl <- reactiveValues()
  observe({
    if(!is.null(result_concepts()$result))
      isolate(
        result_dl <<- result_concepts()$result
      )
  })
  
  bio_dl <- reactiveValues()
  observe({
    if(!is.null(result_bio()$result))
      isolate(
        bio_dl <<- result_bio()$result
      )
  })
  
  icd_dl <- reactiveValues()
  observe({
    if(!is.null(result_cim()$result))
      isolate(
        icd_dl <<- result_cim()$result
      )
  })
  
  output$result_data <- downloadHandler(
    # For PDF output, change this to "report.pdf"
    filename = "multimodalPhewas.RData",
    content = function(file) {
      
      save(concepts_dl,bio_dl,icd_dl, file = file)
      #write.csv(result()$result, file = file)
      
    }
  )
  # -------------


  output$panels = renderUI({
    tabs = list()
    input$plot_height
    i = 1
    if ('free_text' %in% input$sources) {
      tabs[[i]] <- tabPanel("UMLS concepts from free text reports", uiOutput('conceptstab'), fluidRow(column(12,DT::dataTableOutput('table_concepts'))))
      i = i + 1
      }

    if ('bio' %in% input$sources) {
      tabs[[i]] <- tabPanel("Biological test results", uiOutput('biotab'), fluidRow(column(12,DT::dataTableOutput('table_bio'))))
      i = i + 1
    }

    if ('icd' %in% input$sources) {
      tabs[[i]] <- tabPanel("Billing codes (ICD10)", uiOutput('cimtab'), fluidRow(column(12,DT::dataTableOutput('table_cim'))))
      i = i + 1
    }

    if (!local) {
      tabs[[i]] <- tabPanel("Patients Description", DT::dataTableOutput('table_patients'))
      i = i + 1
    }
    
    tabs[[i]] <- tabPanel("Parameters", 
                          tags$h3('Patients selection'),
                          tags$h4('Matching parameters'),
                          numericInput('n_match', label = 'Number of control patients to match', value = 5, min = 1, max = 20),
                          numericInput('birth_range', label = 'Range for matching on birth years (e.g. between [year-range; year+range])', value = 2, min = 1, max = 20),
                          numericInput('concept_range', label = 'Range for matching on the number of concepts (e.g. between [n_concepts - n_concepts*range ; n_concepts + n_concepts*range])', value = 1, min = 0, max = 1),
                          tags$h3('Analysis'),
                          hidden(radioButtons('bio_family',label = "Bio analysis type", 
                                                     choices= list( `Binomial`='binomial', `Poisson` = 'poisson' ))),
                          tags$h4('Minimum number of occurrences per patient'),
                          numericInput('nb_occurrences_concept', label = 'UMLS concept', value = 2, min = 1, max = 5),
                          numericInput('nb_occurrences_bio', label = 'Biological test results', value = 1, min = 1, max = 5),
                          numericInput('nb_occurrences_icd', label = 'ICD codes', value = 1, min = 1, max = 5),
                          tags$h4('Phenome-wide scan'),
                          numericInput('concepts_min_occur', label = 'Minimal number of occurrences of a concept (cohort wise)', value = 5, min = 5, max = 200),
                          tags$h4('Bootstrap'),
                          numericInput('boot_perm', label = 'Number of permutations', value = 1000, min = 10, max = 1e6),
                          numericInput('fdr_threshold', label = 'FDR corrected p-value threshold for bootstrap', value = 0.05, min = 0, max = 1),
                          tags$hr(id='line2'),
                          tags$h3(id = 'controls_title3','Visualisation parameters'),
                          radioButtons('db_type', label = 'Type of results for dumbbell plot', choices = list(`OR` = 'OR', `P value`='p.value')),
                          sliderInput("plot_height", "Plot Height", 200, 1200, 900)) 

    do.call(tabsetPanel,tabs)

  })

})
aneuraz/multiWAS documentation built on July 29, 2017, 3:39 a.m.