#1GB max upload size
options(shiny.maxRequestSize = 1000 * 1024 ^ 2)
options(useFancyQuotes = FALSE)
options(shiny.autoreload = TRUE)
seurat.version <- packageVersion(pkg = "SeuratObject")


internetConnection <- suppressWarnings(Biobase::testBioCConnection())
source("partials.R", local = TRUE) # creates several smaller UI components
# R.utils::sourceDirectory("qc_help_pages")
source("qc_help_pages/ui_decontX_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_soupX_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_cxds_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_bcds_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_cxds_bcds_hybrid_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_doubletFinder_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_scrublet_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_dc_and_qcm_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_scDblFinder_help.R", local = TRUE) # creates several smaller UI components
# source("server_partials/server_01_data.R", local = TRUE) # functions for Data section

# Define server logic required to draw a histogram
shinyServer(function(input, output, session) {
  
  # PushBar setup
  # setup_pushbar(blur = FALSE, overlay = FALSE)
  
  # library(fs)
  # library(shinyFiles)
  
  #-----------------------------------------------------------------------------
  # MISC - Used throughout app
  #-----------------------------------------------------------------------------
  
  #reactive values object
  vals <- reactiveValues(
    counts = getShinyOption("inputSCEset"),
    original = getShinyOption("inputSCEset"),
    batchRes = NULL,
    gsvaRes = NULL,
    gsvaLimma = NULL,
    vamRes = NULL,
    vamCdf = NULL,
    vamResults = NULL,
    vamScore = NULL,
    gsvaScore = NULL,
    gsvaResults = NULL,
    visplotobject = NULL,
    enrichRes = NULL,
    celdaMod = NULL,
    celdaList = NULL,
    celdaListAll = NULL,
    celdaListAllNames = NULL,
    celdatSNE = NULL,
    celdaModuleFeature = NULL,
    dimRedPlot = NULL,
    dimRedPlot_geneExp = NULL,
    dendrogram = NULL,
    pcX = NULL,
    pcY = NULL,
    showAssayDetails = FALSE,
    hmCSPresets = list("RWB" = c("blue", "white", "red"),
                       "RdBu_r" = c("#2971b1", "#f7f6f6", "#b92732"),
                       "BrBG" = c("#995d12", "#f4f4f4", "#0c7068"),
                       "Blues" = c("#dae8f5", "#6daed4", "#0b559f"),
                       "Greens" = c("#E1F3F6", "#69C2A1", "#04702F")),
    hmCSURL = NULL,
    hmTmpColData = NULL,
    hmTmpRowData = NULL,
    hvgCalculated = list(status = FALSE, method = NULL),
    fmHMshowHide = FALSE
  )
  
  #Update all of the columns that depend on pvals columns
  updateColDataNames <- function(){
    pdataOptions <- colnames(colData(vals$counts))
    updateSelectInput(session, "soupXCluster", choices = c("None", pdataOptions))
    updateSelectInput(session, "qcSampleSelect", choices = pdataOptions)
    updateSelectInput(session, "filteredSample",
                      choices = c("none", pdataOptions))
    updateSelectInput(session, "deleterowdatacolumn",
                      choices = pdataOptions)
    updateSelectInput(session, "colorBy",
                      choices = c("No Color", "Gene Expression", pdataOptions))
    updateSelectInput(session, "shapeBy",
                      choices = c("No Shape", pdataOptions))
    updateSelectInput(session, "scMergeCT",
                      choices = c(pdataOptions))
    updateSelectInput(session, "combatCond",
                      choices = pdataOptions)
    updateSelectInput(session, "combatBioCond",
                      choices = c("None", pdataOptions))
    updateSelectInput(session, "batchCorrVar",
                      choices = pdataOptions)
    updateSelectInput(session, "batchCheckVar",
                      choices = pdataOptions)
    updateSelectInput(session, "batchCheckCond",
                      choices = c("None", pdataOptions))
    updateSelectInput(session, "clustVisCol", choices = pdataOptions)
    updateSelectInput(session, "deC1Class",
                      choices = pdataOptions)
    updateSelectInput(session, "deC2G1Col",
                      choices = pdataOptions)
    updateSelectInput(session, "deC2G2Col",
                      choices = pdataOptions)
    updateSelectInput(session, 'deCovar', choices = pdataOptions)
    updateSelectInput(session, "deHMcolData",
                      choices = pdataOptions)
    updateSelectInput(session, "deHMSplitCol",
                      choices = c('condition', pdataOptions),
                      selected = 'condition')
    updateSelectInput(session, "fmCluster", choices = pdataOptions)
    updateSelectInput(session, "fmHMcolData",
                      choices = pdataOptions)
    updateSelectInput(session, "hmCellAnn", choices = pdataOptions)
    updateSelectInput(session, "pathwayPlotVar",
                      choices = pdataOptions)
    updateSelectInput(session, "selectReadDepthCondition",
                      choices = pdataOptions)
    updateSelectInput(session, "selectCellNumCondition",
                      choices = pdataOptions)
    updateSelectInput(session, "selectSnapshotCondition",
                      choices = pdataOptions)
    updateSelectInput(session, "annotModifyChoice",
                      choices = c("none", pdataOptions))
    updateSelectInput(session, "hmCellCol",
                      choices = pdataOptions)
    updateSelectInput(session, "hmCellTextBy",
                      choices = c("Row Names", pdataOptions))
    updateSelectInput(session, 'hmAddCellLabel',
                      choices = c("Default cell IDs", pdataOptions))
    updateSelectInput(session, "ctLabelByCluster",
                      choices = pdataOptions)
    if (!is.null(hmTemp$sce)) {
      hmAnnAllColors$col <- dataAnnotationColor(hmTemp$sce, 'col')
    }
    updateSelectInput(session, "TSCANclusterName",
                      choices = c("Auto generate clusters", pdataOptions))
  }
  
  updateGeneNames <- function(){
    selectthegenes <- rownames(vals$counts)
    updateSelectizeInput(session, "colorGenes",
                         choices = selectthegenes, server = TRUE)
    updateSelectizeInput(session, "selectvisGenes",
                         choices = selectthegenes, server = TRUE)
    updateSelectizeInput(session, "enrichGenes",
                         choices = selectthegenes, server = TRUE)
    updateSelectizeInput(session, "plotTSCANDimReduceFeatures_features",
                         choices = selectthegenes, server = TRUE)
  }
  
  updateFeatureAnnots <- function(){
    
    selectRowData <- colnames(rowData(vals$counts))
    my_list <- data.frame()
    for(i in selectRowData) {
      my_list[i,1] <- paste0(i, " (e.g. ", paste(head(rowData(vals$counts)[,i], n = 3), collapse = ","), ")")
    }
    selectRowDataWithExamples <- as.character(my_list[,1])
    
    
    selectNonNARowData <- names(apply(rowData(vals$counts), 2, anyNA)[apply(rowData(vals$counts), 2, anyNA) == FALSE])
    my_list2 <- data.frame()
    for(j in selectNonNARowData) {
      my_list2[j,1] <- paste0(j, " (e.g. ", paste(head(rowData(vals$counts)[,j], n = 3), collapse = ","), ")")
    }
    selectNonNARowDataWithExamples <- as.character(my_list2[,1])
    
    Default <- paste0("Default (e.g. ", paste(head(rownames(vals$counts), n = 3), collapse = ","), ")")
    
    updateSelectInput(session, "gsByParam",
                      choices = c("rownames", selectRowData))
    updateSelectInput(session, "importFeatureDispOpt",
                      choices = c(Default,
                                  selectRowDataWithExamples))
    updateSelectInput(session, "importFeatureNamesOpt",
                      choices = c(Default,
                                  selectNonNARowDataWithExamples))
    updateSelectInput(session, "filteredFeature",
                      choices = c("none", selectRowData))
    updateSelectInput(session, "hvgPlotFeatureDisplay",
                      choices = c("Rownames (Default)",
                                  selectRowData))
    updateSelectInput(session, "fmHMFeatureDisplay",
                      choices = c("Rownames (Default)",
                                  selectRowData))
    updateSelectInput(session, "deHMrowData",
                      choices = selectRowData)
    updateSelectInput(session, "deHMSplitRow",
                      choices = c('regulation', selectRowData),
                      selected = 'regulation')
    updateSelectInput(session, "deHMrowLabel",
                      choices = c("Rownames (Default)",
                                  selectRowData))
    updateSelectInput(session, "deVolcFeatureDisplay",
                      choices = c("Rownames (Default)",
                                  selectRowData))
    updateSelectInput(session, 'deVioLabel',
                      choices = c("Rownames (Default)",
                                  selectRowData))
    updateSelectInput(session, 'deRegLabel',
                      choices = c("Rownames (Default)",
                                  selectRowData))
    updateSelectInput(session, 'tscanDEFeatureDisplay',
                      choices = c("Rownames (Default)",
                                  selectRowData))
    updateSelectInput(session, 'plotTSCANClusterDEG_featureDisplay',
                      choices = c("Rownames (Default)",
                                  selectRowData))
    updateSelectInput(session, "plotTSCANDimReduceFeatures_featureDisplay",
                      choices = c("Rownames (Default)",
                                  selectRowData))
    updateSelectInput(session, "fmHMrowData",
                      choices = selectRowData)
    updateSelectInput(session, "hmGeneCol",
                      choices = selectRowData)
    updateSelectInput(session, "hmGeneTextBy",
                      choices = c("Row Names", selectRowData))
    updateSelectInput(session, 'hmGeneAnn', choices = selectRowData)
    updateSelectInput(session, 'hmAddGeneLabel',
                      choices = c("Default feature IDs", selectRowData))
    if (!is.null(hmTemp$sce)) {
      hmAnnAllColors$row <- dataAnnotationColor(hmTemp$sce, 'row')
    }
  }
  
  updateNumSamples <- function(){
    numsamples <- ncol(vals$counts)
    updateNumericInput(session, "downsampleNum", value = numsamples,
                       max = numsamples)
  }
  
  
  updateSelectInputTag <- function(session, inputId, choices = NULL, 
                                   selected = NULL, label = "Select assay:", 
                                   tags = NULL, recommended = NULL, 
                                   showTags = TRUE, redDims = FALSE, 
                                   inSCE = vals$counts){
    choices <- expTaggedData(inSCE, tags, redDims = redDims, showTags = showTags, recommended = recommended)
    updateSelectizeInput(session = session, inputId = inputId, label = label, choices = choices, selected = selected)
  }
  
  updateAssayInputs <- function(){
    currassays <- names(assays(vals$counts))
    updateSelectInputTag(session, "dimRedAssaySelect",
                         label = "Select Input Matrix:",
                         recommended = c("transformed", "hvg"),
                         choices = expDataNames(vals$counts))
    updateSelectInputTag(session, "dimRedAssaySelect_tsneUmap",
                         label = "Select Input Matrix:",
                         recommended = c("redDims"),
                         redDims = TRUE)
    updateSelectInputTag(session, "batchCheckAssay", choices = currassays)
    updateSelectInputTag(session, "batchCheckOrigAssay", choices = currassays)
    updateSelectInputTag(session, "clustScranSNNMat",
                         label = "Select Input Matrix:",
                         choices = expDataNames(vals$counts),
                         recommended = "redDims", redDims = TRUE)
    if (is.null(input$deMethod)) {
      updateSelectInputTag(session, "deAssay",
                           tags = c("raw", "transformed", "uncategorized",
                                    "normalized", "scaled", "redDims"),
                           recommended = c("transformed"), redDims = TRUE)
    } else if (input$deMethod == "DESeq2") {
      updateSelectInputTag(session, "deAssay",
                           tags = c("raw", "uncategorized"),
                           recommended = c("raw"))
    } else {
      updateSelectInputTag(session, "deAssay",
                           tags = c("raw", "transformed", "uncategorized",
                                    "normalized", "scaled", "redDims"),
                           recommended = c("transformed"), redDims = TRUE)
    }
    if (is.null(input$fmMethod)) {
      updateSelectInputTag(session, "fmAssay", recommended = c("transformed"))
    } else if (input$fmMethod == "DESeq2") {
      updateSelectInputTag(session, "fmAssay", recommended = c("raw"))
    } else {
      updateSelectInputTag(session, "fmAssay", recommended = c("transformed"))
    }
    updateSelectInputTag(session, "fmHMAssay", choices = currassays,
                         selected = input$fmAssay)
    updateSelectInputTag(session, "pathwayAssay",
                         recommended = c("transformed", "normalized", "scaled"))
    updateSelectInputTag(session, "vamAssay",
                         recommended = c("transformed", "normalized", "scaled"))
    
    updateSelectInputTag(session, "modifyAssaySelect")
    updateSelectInputTag(session, "normalizeAssaySelect",
                         label = "Select assay to normalize:",
                         recommended = "raw")
    
    updateSelectInputTag(session, "seuratSelectNormalizationAssay",
                         choices = currassays, showTags = FALSE)
    updateSelectInputTag(session, "scanpySelectNormalizationAssay",
                         choices = currassays, showTags = FALSE)
    
    if(input$hvgMethodFS == "vst"){
      updateSelectInputTag(session, "assaySelectFS_Norm", recommended = c("raw"))
    }
    else{
      updateSelectInputTag(session, "assaySelectFS_Norm",
                           recommended = c("transformed", "normalized"))
    }
    updateSelectInputTag(session, "filterAssaySelect", choices = currassays)
    updateSelectInputTag(session, "qcAssaySelect", recommended = "raw", inSCE = vals$original)
    updateSelectInputTag(session, "celdaAssay", choices = currassays)
    updateSelectInputTag(session, "celdaAssayGS", choices = currassays)
    updateSelectInputTag(session, "celdaAssaytSNE", choices = currassays)
    updateSelectInputTag(session, "celdaAssayProbabilityMap",
                         choices = currassays)
    updateSelectInputTag(session, "celdaAssayModuleHeatmap",
                         choices = currassays)
    updateSelectInputTag(session, "depthAssay", choices = currassays)
    updateSelectInputTag(session, "cellsAssay", choices = currassays)
    updateSelectInputTag(session, "snapshotAssay", choices = currassays)
    updateSelectInputTag(session, "exportAssay", choices = currassays)
    updateSelectInputTag(session, "hmAssay", recommended = "transformed")
    updateSelectInputTag(session, "bpAssay", recommended = "transformed")
    updateSelectInputTag(session, "ctLabelAssay", choices = currassays,
                         recommended = c("transformed"))
    # batch correction assay conditions
    bc.recommended <- NULL
    method.log <- c("FastMNN", "Limma", "MNN")
    method.scale <- c("BBKNN")
    method.raw <- c("ZINBWaVE", "ComBatSeq")
    if (is.null(input$batchCorrMethods)) {
      bc.recommended <- "raw"
    } else if (input$batchCorrMethods %in% method.log) {
      bc.recommended <- c("transformed")
    } else if (input$batchCorrMethods %in% method.raw) {
      bc.recommended <- "raw"
    } else if (input$batchCorrMethods %in% method.scale) {
      bc.recommended <- "scaled"
    }
    updateSelectInputTag(session, "batchCorrAssay",
                         label = "Select Assay to Correct:",
                         choices = currassays,
                         recommended = bc.recommended)
    updateSelectInputTag(session, "AdvancedMethodSelect_Colorby",
                         label = h5("Advanced Method"),
                         choices = currassays)
    updateSelectInputTag(session, "AdvancedMethodSelect_Xaxis",
                         label = h5("Advanced Method"),
                         choices = currassays)
    updateSelectInputTag(session, "AdvancedMethodSelect_Yaxis",
                         label = h5("Advanced Method"),
                         choices = currassays)
    updateSelectInputTag(session, "TSCANassayselect", choices = currassays,
                         recommended = "transformed")
    updateSelectInputTag(session, "TSCANBranchAssaySelect",
                         choices = currassays,
                         recommended = "transformed")
    updateSelectInputTag(session, "plotTSCANDimReduceFeatures_useAssay",
                         choices = currassays,
                         recommended = "transformed")
  }
  
  updateGeneSetSelection <- function() {
    allGS <- sctkListGeneSetCollections(vals$counts)
    if (length(allGS) == 0) {
      updateSelectizeInput(session, "PathwayGeneLists",
                           choices = "Import geneset before using")
    } else {
      updateSelectizeInput(session, "PathwayGeneLists", choices = allGS)
    }
    
    updateSelectInput(session, "gsExisting", choices = c("None", allGS))
    names(allGS) <- allGS
    updateSelectInput(session, "QCMgeneSets", choices =c("None", allGS))
  }
  
  observeEvent(vals$original, {
    if (!is.null(vals$original)) {
      #if (!is.null(metadata(vals$original)$sctk$genesets)) {
      #newGSchoices <- sctkListGeneSetCollections(vals$original)
      #updateSelectInput(session, "gsExisting", choices = c("None", newGSchoices))
      #updateSelectInput(session, "QCMgeneSets", choices =c("None", newGSchoices))
      #shinyjs::show(id = "gsAddToExisting", anim = FALSE)
      #} else {
      #shinyjs::hide(id = "gsAddToExisting", anim = FALSE)
      #updateSelectInput(session, "gsExisting", choices = c("None"), selected = "None")
      #updateSelectInput(session, "QCMgeneSets", choices =c("None"), selected = "None")
      #}
      shinyjs::show(id="combineOptions")
      #gsByChoices <- c("None", "rownames", names(rowData(vals$original)))
      #updateSelectInput(session, "gsByParam", choices = gsByChoices, selected = "rownames")
    } else {
      shinyjs::hide(id="combineOptions")
    }
  })
  
  updateReddimInputs <- function(){
    currreddim <- names(reducedDims(vals$counts))
    updateSelectInput(session, "FastMNNReddim", choices = currreddim)
    updateSelectInput(session, "HarmonyReddim", choices = currreddim)
    updateSelectInput(session, "selectRedDimPlot_tsneUmap",
                      choices = currreddim)
    updateSelectInput(session, "clustVisReddim", choices = currreddim)
    updateSelectInput(session, "clustKMeansReddim", choices = currreddim)
    updateSelectInput(session, "clustSeuratReddim", choices = currreddim)
    updateSelectInput(session, "QuickAccess",
                      choices = c(currreddim, "Custom"))
    updateSelectInput(session, "ApproachSelect_Xaxis", choices = currreddim)
    updateSelectInput(session, "ApproachSelect_Yaxis", choices = currreddim)
    updateSelectInput(session, "ApproachSelect_Colorby", choices = currreddim)
    suppressWarnings({availPathwayRes <- getPathwayResultNames(vals$counts)})
    updateSelectizeInput(session, "pathwayRedDimNames",
                         choices = availPathwayRes)
    updateSelectInput(session, "TSCANReddim", choices = currreddim)
    updateSelectInput(session, "TSCANVisRedDim", choices = currreddim)
    updateSelectInput(session, "DEClusterRedDimNames", choices = currreddim)
    updateSelectInput(session, "plotTSCANClusterDEG_useReducedDim", choices = currreddim)
    updateSelectInput(session, "plotTSCANDimReduceFeatures_useReducedDim", choices = currreddim)
  }
  
  
  observeEvent(input$consoleToggle, {
    shinyjs::toggle(id = "consolePanel")
  })
  
  # Stop auto-scroll console tab
  observeEvent(input$logDataAutoScrollStatus, {
    stopAutoScroll <- paste0("clearInterval(", input$logDataAutoScrollStatus, ");")
    shinyjs::runjs(stopAutoScroll)
  })
  # js$disableTabs()
  # Close app on quit
  # session$onSessionEnded(stopApp)
  
  #-----------------------------------------------------------------------------
  # Page 1: Upload ####
  #-----------------------------------------------------------------------------
  sysname <- Sys.info()[['sysname']]
  if (sysname == "Windows") {
    roots <- getVolumes()()
  } else {
    roots <- c(home = "~/")
  }
  dirPaths <- reactiveValues(
    bDirectory = ".",
    sDirectory = ".",
    directory = ".",
    outputDirectory = "."
  )
  
  # Upload data through shiny app
  
  allImportEntries <- reactiveValues(samples=list(), id_count=0)
  
  shinyDirChoose(input, "bDirectory", roots = roots)
  shinyDirChoose(input, "sDirectory", roots = roots)
  shinyDirChoose(input, 'directory', roots = roots)
  
  output$bDirectoryPath <- renderText({
    dirPaths$bDirectory
  })
  output$sDirectoryPath <- renderText({
    dirPaths$sDirectory
  })
  output$directoryPath <- renderText({
    dirPaths$directory
  })
  
  # event listener for the base directory modal (need to populate table for sample names)
  # see https://github.com/wleepang/shiny-directory-input
  observeEvent(
    ignoreNULL = TRUE,
    eventExpr = {
      input$bDirectory
    },
    handlerExpr = {
      if ("path" %in% names(input$bDirectory)) {
        # condition prevents handler execution on initial app launch
        #path = choose.dir(default = readDirectoryInput(session, 'bDirectory'),
        #                  caption="Choose a directory")
        #updateDirectoryInput(session, 'bDirectory', value = path)
        
        vol <- roots[[input$bDirectory$root]]
        dirPaths$bDirectory <- paste0(vol, paste(unlist(input$bDirectory$path[-1]),
                                                 collapse = .Platform$file.sep))
        path <- dirPaths$bDirectory
        # clear the previous table of sample names
        prevPath <- path
        count <- 0
        for (prev in list.dirs(prevPath, recursive = FALSE)) {
          count <- count+1
          removeUI(
            selector = paste0("#sampleRow", count)
          )
        }
        # create a new table for the selected directory
        count <- 0
        if (!is.na(path)) {
          # Add Reference selection for cellRangerV2
          if (input$uploadChoice == "cellRanger2") {
            ## Identify available reference
            firstSampleDir <- list.dirs(path, recursive = FALSE)[1]
            refPath <- file.path(firstSampleDir, "outs/filtered_gene_bc_matrices")
            refList <- basename(list.dirs(refPath, recursive = FALSE))
            ## Add UI
            insertUI(
              selector = "#bDirTable",
              ui = fluidRow(
                column(
                  6,
                  selectInput("cr2_b_Ref", "Reference:", refList)
                )
              )
            )
          }
          # Add Sample Rename rows
          counts <- vector()
          for (sample in list.dirs(path, recursive = FALSE)) {
            count <- count+1
            counts <- c(counts, count)
            insertUI(
              selector = "#bDirTable",
              ui = fluidRow(
                id = paste0("sampleRow", count),
                column(6, basename(sample)),
                column(6, textAreaInput(paste0("sampleName", count), "Sample Name", resize = "none", value = basename(sample)))
              )
            )
          }
        }
      }
    }
  )
  
  # for sample directory modal
  observeEvent(
    ignoreNULL = TRUE,
    eventExpr = {
      input$sDirectory
    },
    handlerExpr = {
      #if (input$sDirectory > 0) {
      #  # condition prevents handler execution on initial app launch
      #  path = choose.dir(default = readDirectoryInput(session, 'sDirectory'),
      #                    caption="Choose a directory")
      #  updateDirectoryInput(session, 'sDirectory', value = path)
      #  if (!is.na(path)) {
      #    updateTextInput(session, "sSampleID", value = basename(path))
      #  }
      #}
      if ("path" %in% names(input$sDirectory)) {
        vol <- roots[[input$sDirectory$root]]
        dirPaths$sDirectory <- paste0(vol, paste(unlist(input$sDirectory$path[-1]),
                                                 collapse = .Platform$file.sep))
        path <- dirPaths$sDirectory
        if (!is.na(path)) {
          if (input$uploadChoice == "cellRanger2") {
            ## Identify available reference
            refPath <- file.path(path, "outs/filtered_gene_bc_matrices")
            refList <- basename(list.dirs(refPath, recursive = FALSE))
            ## Add UI
            insertUI(
              selector = "#sDirTable",
              ui = fluidRow(
                column(
                  6,
                  selectInput("cr2_s_Ref", "Reference:", refList)
                )
              )
            )
          }
          updateTextInput(session, "sSampleID", value = basename(path))
        }
      }
    }
  )
  
  observeEvent(
    ignoreNULL = TRUE,
    eventExpr = {
      input$directory
    },
    handlerExpr = {
      #if (input$directory > 0) {
      #  # condition prevents handler execution on initial app launch
      #  path = choose.dir(default = readDirectoryInput(session, 'directory'),
      #                    caption="Choose a directory")
      #  updateDirectoryInput(session, 'directory', value = path)
      #}
      if ("path" %in% names(input$directory)) {
        vol <- roots[[input$directory$root]]
        dirPaths$directory <- paste0(vol, paste(unlist(input$directory$path[-1]),
                                                collapse = .Platform$file.sep))
      }
    }
  )
  
  # event listeners for "Add Sample" buttons
  observeEvent(input$addCR2Sample, {
    showModal(importCRModal())
  })
  observeEvent(input$crOpt1, {
    removeModal()
    showModal(importCRBDir())
  })
  observeEvent(input$crOpt2, {
    removeModal()
    showModal(importCRSDir())
  })
  observeEvent(input$crOpt3, {
    removeModal()
    showModal(importCRDDir())
  })
  observeEvent(input$addCR3Sample, {
    showModal(importCRModal())
  })
  observeEvent(input$addSSSample, {
    showModal(importStarModal())
  })
  observeEvent(input$addBUSSample, {
    showModal(importModal())
  })
  observeEvent(input$addSEQSample, {
    showModal(importModal(needsDir = TRUE))
  })
  observeEvent(input$addOptSample, {
    showModal(importModal())
  })
  
  # event listener for "Remove Sample" buttons
  observeEvent(input$clearAllImport, {
    for (entry in allImportEntries$samples) {
      removeUI(selector = paste0("#", entry$id))
    }
    allImportEntries$samples <- list()
  })
  
  # base directory
  observeEvent(input$BDirOK, {
    basePath <- dirPaths$bDirectory
    # if the user doesn't specify a base directory, show the modal again with the warning message
    if (identical(basePath, character(0))) {
      showModal(importCRBDir(failed = TRUE))
    } else {
      allDirs <- list.dirs(basePath, recursive = FALSE)
      # if we are adding a new CellRangerV2 sample
      if (input$uploadChoice == "cellRanger2") {
        allUI <- vector()
        allIDs <- vector()
        count <- 0
        for (sample in allDirs) {
          count <- count + 1
          name <- input[[paste0("sampleName", count)]]
          if (!nzchar(name)) {
            name <- basename(sample)
          }
          id <- paste0("bnewSampleCR2", allImportEntries$id_count)
          entry <- list(type="cellRanger2", id=id,
                        params=list(cellRangerDirs = basePath,
                                    sampleDirs = basename(sample),
                                    sampleNames = name,
                                    reference = input$cr2_b_Ref))
          allImportEntries$samples <- c(allImportEntries$samples, list(entry))
          fluidRowStyle <- paste0(paste0("#", id), "{border-bottom: 1px solid #bababa; padding-top: .9%; padding-bottom: .5%}")
          removeBtnStyle <- paste0(paste0("#remove", id), "{padding-top: 0; padding-bottom: 0;}")
          ui_i <- fluidRow(
            id = id,
            tags$style(HTML(paste0(fluidRowStyle, removeBtnStyle))),
            column(3, basePath),
            column(3, basename(sample)),
            column(3, name),
            column(3, actionButton(paste0("remove", id), "X"))
          )
          allImportEntries$id_count <- allImportEntries$id_count + 1
          allUI <- c(allUI, list(ui_i))
          allIDs <- c(allIDs, id)
        }
      } else { # if we are adding a new CellRangerV3 sample
        allUI <- vector()
        allIDs <- vector()
        count <- 0
        for (sample in allDirs) {
          count <- count + 1
          name <- input[[paste0("sampleName", count)]]
          if (!nzchar(name)) {
            name <- basename(sample)
          }
          id <- paste0("bnewSampleCR3", allImportEntries$id_count)
          entry <- list(type="cellRanger3", id=id, params=list(cellRangerDirs = basePath, sampleDirs = basename(sample), sampleNames = name))
          allImportEntries$samples <- c(allImportEntries$samples, list(entry))
          fluidRowStyle <- paste0(paste0("#", id), "{border-bottom: 1px solid #bababa; padding-top: .9%; padding-bottom: .5%}")
          removeBtnStyle <- paste0(paste0("#remove", id), "{padding-top: 0; padding-bottom: 0;}")
          ui_i <- fluidRow(
            id = id,
            tags$style(HTML(paste0(fluidRowStyle, removeBtnStyle))),
            column(3, basePath),
            column(3, basename(sample)),
            column(3, name),
            column(3, actionButton(paste0("remove", id), "X"))
          )
          allImportEntries$id_count <- allImportEntries$id_count + 1
          allUI <- c(allUI, list(ui_i))
          allIDs <- c(allIDs, id)
        }
      }
      # insert all the new sample rows
      for (i in seq_along(allUI)) {
        insertUI(
          selector = "#newSampleImport",
          ui = allUI[i]
        )
      }
      # create event handlers for all the remove buttons
      # from: https://stackoverflow.com/questions/40038749/r-shiny-how-to-write-loop-for-observeevent
      lapply(
        X = allIDs,
        FUN = function(id_i){
          observeEvent(input[[paste0("remove", id_i)]], {
            removeUI(
              selector = paste0("#", id_i)
            )
            toRemove <- vector()
            for (entry in allImportEntries$samples) {
              if (entry$id == id_i) {
                toRemove <- c(toRemove, FALSE)
              } else {
                toRemove <- c(toRemove, TRUE)
              }
            }
            allImportEntries$samples <- allImportEntries$samples[toRemove]
          })
        }
      )
      removeModal()
    }
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
  })
  
  # event listeners for Cell Ranger import modals' OK buttons
  # sample directory
  observeEvent(input$SDirOK, {
    samplePath <- dirPaths$sDirectory
    # make sure a directory is selected
    if (identical(samplePath, character(0))) {
      showModal(importCRSDir(failed = TRUE))
    } else {
      # add the files to the appropriate reactiveValues
      if (input$uploadChoice == "cellRanger2") {
        id <- paste0("snewSampleCR2", allImportEntries$id_count)
        entry <- list(type="cellRanger2", id=id,
                      params=list(cellRangerDirs = dirname(samplePath),
                                  sampleDirs = basename(samplePath),
                                  sampleNames = input$sSampleID,
                                  reference = input$cr2_s_Ref))
        allImportEntries$samples <- c(allImportEntries$samples, list(entry))
        allImportEntries$id_count <- allImportEntries$id_count + 1
      } else {
        id <- paste0("snewSampleCR3", allImportEntries$id_count)
        entry <- list(type="cellRanger3", id=id, params=list(cellRangerDirs = paste0(dirname(samplePath), "/"), sampleDirs = basename(samplePath), sampleNames = input$sSampleID))
        allImportEntries$samples <- c(allImportEntries$samples, list(entry))
        allImportEntries$id_count <- allImportEntries$id_count + 1
      }
      # add new row to table
      addToGeneralSampleTable(input$uploadChoice, id, samplePath, input$sSampleID)
      # handler to remove the sample that was just added
      observeEvent(input[[paste0("remove", id)]],{
        removeUI(
          selector = paste0("#", id)
        )
        toRemove <- vector()
        for (entry in allImportEntries$samples) {
          if (entry$id == id) {
            toRemove <- c(toRemove, FALSE)
          } else {
            toRemove <- c(toRemove, TRUE)
          }
        }
        allImportEntries$samples <- allImportEntries$samples[toRemove]
      })
      removeModal()
    }
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # data directory
  observeEvent(input$DDirOK, {
    dataPath <- dirPaths$directory
    if ((!nzchar(input$dSampleID)) || (identical(dataPath, character(0)))) {
      showModal(importCRDDir(failed = TRUE))
    } else {
      if (input$uploadChoice == "cellRanger2") {
        id <- paste0("dnewSampleCR2", allImportEntries$id_count)
        entry <- list(type="cellRanger2", id=id,
                      params=list(dataDir = dataPath,
                                  sampleName = input$dSampleID))
        allImportEntries$samples <- c(allImportEntries$samples, list(entry))
        allImportEntries$id_count <- allImportEntries$id_count + 1
      } else {
        id <- paste0("dnewSampleCR3", allImportEntries$id_count)
        entry <- list(type="cellRanger3", id=id, params=list(dataDir = dataPath, sampleName = input$dSampleID))
        allImportEntries$samples <- c(allImportEntries$samples, list(entry))
        allImportEntries$id_count <- allImportEntries$id_count + 1
      }
      # add new row to table
      addToGeneralSampleTable(input$uploadChoice, id, dataPath, input$dSampleID)
      observeEvent(input[[paste0("remove", id)]],{
        removeUI(
          selector = paste0("#", id)
        )
        toRemove <- vector()
        for (entry in allImportEntries$samples) {
          if (entry$id == id) {
            toRemove <- c(toRemove, FALSE)
          } else {
            toRemove <- c(toRemove, TRUE)
          }
        }
        allImportEntries$samples <- allImportEntries$samples[toRemove]
      })
      removeModal()
    }
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # event handler for pressing OK on the import Star modal
  observeEvent(input$modalStarOk, {
    basePath <- dirPaths$directory
    curFiles <- list()
    if ((!nzchar(input$sampleName)) || (identical(basePath, character(0)))) {
      showModal(importStarModal(failed = TRUE))
    } else {
      entry <- list()
      id <- paste0("newSampleSS", allImportEntries$id_count)
      entry <- list(type="starSolo", id = id, params=list(STARsoloDirs = basePath, samples = input$sampleName, STARsoloOuts = input$geneFolder))
      allImportEntries$samples <- c(allImportEntries$samples, list(entry))
      allImportEntries$id_count <- allImportEntries$id_count+1
      addToGeneralSampleTable(input$uploadChoice, id, basePath, input$sampleName)
      observeEvent(input[[paste0("remove", id)]],{
        removeUI(
          selector = paste0("#", id)
        )
        toRemove <- vector()
        for (entry in allImportEntries$samples) {
          if (entry$id == id) {
            toRemove <- c(toRemove, FALSE)
          } else {
            toRemove <- c(toRemove, TRUE)
          }
        }
        allImportEntries$samples <- allImportEntries$samples[toRemove]
      })
      removeModal()
    }
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # event handler for pressing OK on the import modal
  observeEvent(input$modalOk, {
    basePath <- dirPaths$directory
    curFiles <- list()
    if ((!nzchar(input$sampleName)) || (identical(basePath, character(0)))) {
      showModal(importModal(failed = TRUE))
    } else {
      entry <- list()
      if (input$uploadChoice == "busTools") {
        id <- paste0("newSampleBUS", allImportEntries$id_count)
        entry <- list(type="busTools", id = id, params=list(BUStoolsDirs = basePath, samples = input$sampleName))
        allImportEntries$samples <- c(allImportEntries$samples, list(entry))
        allImportEntries$id_count <- allImportEntries$id_count+1
      } else if (input$uploadChoice == "seqc") {
        id <- paste0("newSampleSEQ", allImportEntries$id_count)
        entry <- list(type="seqc", id = id, params=list(seqcDirs = basePath, prefix = input$sampleID, samples = input$sampleName))
        updateTextInput(session, "sampleID", value = "")
        allImportEntries$samples <- c(allImportEntries$samples, list(entry))
        allImportEntries$id_count <- allImportEntries$id_count+1
      } else if (input$uploadChoice == "optimus") {
        id <- paste0("newSampleOpt", allImportEntries$id_count)
        entry <- list(type="optimus", id = id, params=list(OptimusDirs = basePath, samples = input$sampleName))
        allImportEntries$samples <- c(allImportEntries$samples, list(entry))
        allImportEntries$id_count <- allImportEntries$id_count+1
      }
      addToGeneralSampleTable(input$uploadChoice, id, basePath, input$sampleName)
      observeEvent(input[[paste0("remove", id)]],{
        removeUI(
          selector = paste0("#", id)
        )
        toRemove <- vector()
        for (entry in allImportEntries$samples) {
          if (entry$id == id) {
            toRemove <- c(toRemove, FALSE)
          } else {
            toRemove <- c(toRemove, TRUE)
          }
        }
        allImportEntries$samples <- allImportEntries$samples[toRemove]
      })
      removeModal()
    }
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # Event handler to import a file input from CR3
  observeEvent(input$addFilesImport_custom, {
    id <- paste0("newSampleFiles", allImportEntries$id_count)
    entry <- list(type="cellRanger3_files", id = id, params=list(assayFile = input$countsfile_custom$datapath, annotFile = input$annotFile_custom$datapath,
                                                     featureFile = input$featureFile_custom$datapath,
                                                     summaryFile = input$summaryFile_custom$datapath,
                                                     sampleName = input$sampleNameCR,
                                                     assayName = "counts"))
    allImportEntries$samples <- c(allImportEntries$samples, list(entry))
    allImportEntries$id_count <- allImportEntries$id_count+1
    assayFileCol <- ""
    annotFileCol <- ""
    featureFileCol <- ""
    summaryFileCol <- ""
    sampleFileName <- ""
    if (!is.null(input$countsfile_custom$datapath)) {
      assayFileCol <- paste0("Assay: ", input$countsfile_custom$datapath)
    }
    if (!is.null(input$annotFile_custom$datapath)) {
      annotFileCol <- paste0("Annotation: ", input$annotFile_custom$datapath)
    }
    if (!is.null(input$featureFile_custom$datapath)) {
      featureFileCol <- paste0("Features: ", input$featureFile_custom$datapath)
    }
    if (!is.null(input$summaryFile_custom$datapath)) {
      summaryFileCol <- paste0("Metrics: ", input$summaryFile_custom$datapath)
    }
    if (!is.null(input$sampleNameCR)) {
      sampleFileName <- paste0("Sample Name: ", input$sampleNameCR)
    }

    locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol, summaryFileCol, sampleFileName), collapse = "\n")

    addToGeneralSampleTable("files", id, locCol, input$sampleNameCR)

    observeEvent(input[[paste0("remove", id)]],{
      removeUI(
        selector = paste0("#", id)
      )
      toRemove <- vector()
      for (entry in allImportEntries$samples) {
        if (entry$id == id) {
          toRemove <- c(toRemove, FALSE)
        } else {
          toRemove <- c(toRemove, TRUE)
        }
      }
      allImportEntries$samples <- allImportEntries$samples[toRemove]
    })
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # Event handler to import a file input from starSolo
  observeEvent(input$addFilesImport_custom_starSolo, {
    id <- paste0("newSampleFiles", allImportEntries$id_count)
    entry <- list(type="starSolo_files", id = id, params=list(assayFile = input$countsfile_custom_starSolo$datapath, annotFile = input$annotFile_custom_starSolo$datapath,
                                                                 featureFile = input$featureFile_custom_starSolo$datapath,
                                                                 sampleName = input$sampleNameSS,
                                                                 assayName = "counts"))
    allImportEntries$samples <- c(allImportEntries$samples, list(entry))
    allImportEntries$id_count <- allImportEntries$id_count+1
    assayFileCol <- ""
    annotFileCol <- ""
    featureFileCol <- ""
    summaryFileCol <- ""
    sampleFileName <- ""
    
    if (!is.null(input$countsfile_custom_starSolo$datapath)) {
      assayFileCol <- paste0("Assay: ", input$countsfile_custom_starSolo$datapath)
    }
    if (!is.null(input$annotFile_custom_starSolo$datapath)) {
      annotFileCol <- paste0("Annotation: ", input$annotFile_custom_starSolo$datapath)
    }
    if (!is.null(input$featureFile_custom_starSolo$datapath)) {
      featureFileCol <- paste0("Features: ", input$featureFile_custom_starSolo$datapath)
    }
    if (!is.null(input$sampleNameSS)) {
      sampleFileName <- paste0("Sample Name: ", input$sampleNameSS)
    }
    
    locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol, summaryFileCol, sampleFileName), collapse = "\n")
    
    addToGeneralSampleTable("files", id, locCol, input$sampleNameSS)
    
    observeEvent(input[[paste0("remove", id)]],{
      removeUI(
        selector = paste0("#", id)
      )
      toRemove <- vector()
      for (entry in allImportEntries$samples) {
        if (entry$id == id) {
          toRemove <- c(toRemove, FALSE)
        } else {
          toRemove <- c(toRemove, TRUE)
        }
      }
      allImportEntries$samples <- allImportEntries$samples[toRemove]
    })
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # Event handler to import a file input from busTools
  observeEvent(input$addFilesImport_custom_busTools, {
    id <- paste0("newSampleFiles", allImportEntries$id_count)
    entry <- list(type="busTools_files", id = id, params=list(assayFile = input$countsfile_custom_busTools$datapath, annotFile = input$annotFile_custom_busTools$datapath,
                                                              featureFile = input$featureFile_custom_busTools$datapath,
                                                              sampleName = input$sampleNameBT,
                                                              assayName = "counts"))
    allImportEntries$samples <- c(allImportEntries$samples, list(entry))
    allImportEntries$id_count <- allImportEntries$id_count+1
    assayFileCol <- ""
    annotFileCol <- ""
    featureFileCol <- ""
    summaryFileCol <- ""
    sampleFileName <- ""
    
    if (!is.null(input$countsfile_custom_busTools$datapath)) {
      assayFileCol <- paste0("Assay: ", input$countsfile_custom_busTools$datapath)
    }
    if (!is.null(input$annotFile_custom_busTools$datapath)) {
      annotFileCol <- paste0("Annotation: ", input$annotFile_custom_busTools$datapath)
    }
    if (!is.null(input$featureFile_custom_busTools$datapath)) {
      featureFileCol <- paste0("Features: ", input$featureFile_custom_busTools$datapath)
    }
    if (!is.null(input$sampleNameSS)) {
      sampleFileName <- paste0("Sample Name: ", input$sampleNameBT)
    }
    
    locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol, summaryFileCol), collapse = "\n")
    
    addToGeneralSampleTable("files", id, locCol, input$sampleNameBT)
    
    observeEvent(input[[paste0("remove", id)]],{
      removeUI(
        selector = paste0("#", id)
      )
      toRemove <- vector()
      for (entry in allImportEntries$samples) {
        if (entry$id == id) {
          toRemove <- c(toRemove, FALSE)
        } else {
          toRemove <- c(toRemove, TRUE)
        }
      }
      allImportEntries$samples <- allImportEntries$samples[toRemove]
    })
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # Event handler to import a file input from optimus
  observeEvent(input$addFilesImport_custom_optimus, {
    id <- paste0("newSampleFiles", allImportEntries$id_count)
    entry <- list(type="optimus_files", id = id, params=list(matrixLocation = input$matrix_custom_optimus$datapath,
                                                             colIndexLocation = input$colIndex_custom_optimus$datapath,
                                                             rowIndexLocation = input$rowIndex_custom_optimus$datapath,
                                                             cellMetricsLocation = input$cellMetrics_custom_optimus$datapath,
                                                             geneMetricsLocation = input$geneMetrics_custom_optimus$datapath,
                                                             emptyDropsLocation = input$emptyDrops_custom_optimus$datapath,
                                                             sampleName = input$sampleNameOP,
                                                              assayName = "counts"))
    allImportEntries$samples <- c(allImportEntries$samples, list(entry))
    allImportEntries$id_count <- allImportEntries$id_count+1
    matrixFileCol <- ""
    colIndexFileCol <- ""
    rowIndexFileCol <- ""
    cellMetricsFileCol <- ""
    geneMetricsFileCol <- ""
    emptyDropsFileCol <- ""
    sampleFileName <- ""
    
    if (!is.null(input$matrix_custom_optimus$datapath)) {
      matrixFileCol <- paste0("Matrix: ", input$matrix_custom_optimus$datapath)
    }
    if (!is.null(input$colIndex_custom_optimus$datapath)) {
      colIndexFileCol <- paste0("colIndex: ", input$colIndex_custom_optimus$datapath)
    }
    if (!is.null(input$rowIndex_custom_optimus$datapath)) {
      rowIndexFileCol <- paste0("rowIndex: ", input$rowIndex_custom_optimus$datapath)
    }
    if (!is.null(input$cellMetrics_custom_optimus$datapath)) {
      cellMetricsFileCol <- paste0("cellMetrics: ", input$cellMetrics_custom_optimus$datapath)
    }
    if (!is.null(input$geneMetrics_custom_optimus$datapath)) {
      geneMetricsFileCol <- paste0("geneMetrics: ", input$geneMetrics_custom_optimus$datapath)
    }
    if (!is.null(input$emptyDrops_custom_optimus$datapath)) {
      emptyDropsFileCol <- paste0("emptyDrops: ", input$emptyDrops_custom_optimus$datapath)
    }
    if (!is.null(input$sampleNameOP)) {
      sampleFileName <- paste0("Sample Name: ", input$sampleNameOP)
    }

    
    locCol <- paste(c(matrixFileCol, colIndexFileCol, rowIndexFileCol, cellMetricsFileCol, geneMetricsFileCol, emptyDropsFileCol, sampleFileName), collapse = "\n")
    
    addToGeneralSampleTable("files", id, locCol, input$sampleNameOP)
    
    observeEvent(input[[paste0("remove", id)]],{
      removeUI(
        selector = paste0("#", id)
      )
      toRemove <- vector()
      for (entry in allImportEntries$samples) {
        if (entry$id == id) {
          toRemove <- c(toRemove, FALSE)
        } else {
          toRemove <- c(toRemove, TRUE)
        }
      }
      allImportEntries$samples <- allImportEntries$samples[toRemove]
    })
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # Event handler to import a file input from seqc
  observeEvent(input$addFilesImport_custom_seqc, {
    id <- paste0("newSampleFiles", allImportEntries$id_count)
    entry <- list(type="seqc_files", id = id, params=list(readCountsLocation = input$readCounts_custom_seqc$datapath,
                                                          moleculeCountsLocation = input$moleculeCounts_custom_seqc$datapath,
                                                          barcodesLocation = input$barcodes_custom_seqc$datapath,
                                                          genesLocation = input$genes_custom_seqc$datapath,
                                                          sampleName = input$sampleNameSC,
                                                          assayName = "counts"))
    allImportEntries$samples <- c(allImportEntries$samples, list(entry))
    allImportEntries$id_count <- allImportEntries$id_count+1
    matrixFileCol <- ""
    molFileCol <- ""
    barcodesFileCol <- ""
    genesFileCol <- ""
    sampleFileName <- ""
    
    if (!is.null(input$readCounts_custom_seqc$datapath)) {
      matrixFileCol <- paste0("Matrix: ", input$readCounts_custom_seqc$datapath)
    }
    if (!is.null(input$moleculeCounts_custom_seqc$datapath)) {
      molFileCol <- paste0("molCounts: ", input$moleculeCounts_custom_seqc$datapath)
    }
    if (!is.null(input$barcodes_custom_seqc$datapath)) {
      barcodesFileCol <- paste0("barcodes: ", input$barcodes_custom_seqc$datapath)
    }
    if (!is.null(input$genes_custom_seqc$datapath)) {
      genesFileCol <- paste0("genes: ", input$genes_custom_seqc$datapath)
    }
    if (!is.null(input$sampleNameSC)) {
      sampleFileName <- paste0("Sample Name: ", input$sampleNameSC)
    }

    locCol <- paste(c(matrixFileCol, molFileCol, barcodesFileCol, genesFileCol, sampleFileName), collapse = "\n")
    
    addToGeneralSampleTable("files", id, locCol, input$sampleNameSC)
    
    observeEvent(input[[paste0("remove", id)]],{
      removeUI(
        selector = paste0("#", id)
      )
      toRemove <- vector()
      for (entry in allImportEntries$samples) {
        if (entry$id == id) {
          toRemove <- c(toRemove, FALSE)
        } else {
          toRemove <- c(toRemove, TRUE)
        }
      }
      allImportEntries$samples <- allImportEntries$samples[toRemove]
    })
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })

  # Event handler to import a file input
  observeEvent(input$addFilesImport, {
    id <- paste0("newSampleFiles", allImportEntries$id_count)
    entry <- list(type="files", id = id, params=list(assayFile = input$countsfile$datapath, annotFile = input$annotFile$datapath,
                                                     featureFile = input$featureFile$datapath, assayName = input$inputAssayType))
    allImportEntries$samples <- c(allImportEntries$samples, list(entry))
    allImportEntries$id_count <- allImportEntries$id_count+1
    assayFileCol <- ""
    annotFileCol <- ""
    featureFileCol <- ""
    if (!is.null(input$countsfile$datapath)) {
      assayFileCol <- paste0("Assay: ", input$countsfile$datapath)
    }
    if (!is.null(input$annotFile$datapath)) {
      annotFileCol <- paste0("Annotation: ", input$annotFile$datapath)
    }
    if (!is.null(input$featureFile$datapath)) {
      featureFileCol <- paste0("Features: ", input$featureFile$datapath)
    }
    
    locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol), collapse = "\n")
    
    addToGeneralSampleTable("files", id, locCol, input$inputAssayType)
    
    observeEvent(input[[paste0("remove", id)]],{
      removeUI(
        selector = paste0("#", id)
      )
      toRemove <- vector()
      for (entry in allImportEntries$samples) {
        if (entry$id == id) {
          toRemove <- c(toRemove, FALSE)
        } else {
          toRemove <- c(toRemove, TRUE)
        }
      }
      allImportEntries$samples <- allImportEntries$samples[toRemove]
    })
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # Event handler to import an example input
  observeEvent(input$addExampleImport, {
    id <- paste0("newSampleExample", allImportEntries$id_count)
    entry <- list(type="example", id = id, params=list(dataset = input$selectExampleData))
    allImportEntries$samples <- c(allImportEntries$samples, list(entry))
    allImportEntries$id_count <- allImportEntries$id_count+1
    
    scRNAseqDatasets <- c("fluidigm_pollen", "allen_tasic", "NestorowaHSCData")
    tenxPbmcDatasets <- c("pbmc3k", "pbmc4k", "pbmc6k", "pbmc8k", "pbmc33k", "pbmc68k")
    locCol <- ""
    if (input$selectExampleData %in% scRNAseqDatasets) {
      locCol <- "scRNA"
    } else {
      locCol <- "TENx"
    }
    
    addToGeneralSampleTable("example", id, locCol, input$selectExampleData)
    
    
    observeEvent(input[[paste0("remove", id)]],{
      removeUI(
        selector = paste0("#", id)
      )
      toRemove <- vector()
      for (entry in allImportEntries$samples) {
        if (entry$id == id) {
          toRemove <- c(toRemove, FALSE)
        } else {
          toRemove <- c(toRemove, TRUE)
        }
      }
      allImportEntries$samples <- allImportEntries$samples[toRemove]
    })
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  # Event handler to import an RDS input
  observeEvent(input$addRDSImport, {
    id <- paste0("newSampleRDS", allImportEntries$id_count)
    entry <- list(type="rds", id = id, params=list(rdsFile=input$rdsFile$datapath))
    allImportEntries$samples <- c(allImportEntries$samples, list(entry))
    allImportEntries$id_count <- allImportEntries$id_count+1
    
    addToGeneralSampleTable("rds", id, input$rdsFile$datapath, "")
    
    observeEvent(input[[paste0("remove", id)]],{
      removeUI(
        selector = paste0("#", id)
      )
      toRemove <- vector()
      for (entry in allImportEntries$samples) {
        if (entry$id == id) {
          toRemove <- c(toRemove, FALSE)
        } else {
          toRemove <- c(toRemove, TRUE)
        }
      }
      allImportEntries$samples <- allImportEntries$samples[toRemove]
    })
    updateCollapse(session = session, "importUI", open = "2. Create dataset:",
                   style = list("1. Add sample to import:" = "success"))
    
  })
  
  observeEvent(input$backToStepOne, {
    updateCollapse(session = session, "importUI",
                   open = "1. Add sample to import:")
    updateRadioButtons(session = session, "uploadChoice", selected = "cellRanger3")
  })
  
  # Event handler for "Upload" button on import page
  observeEvent(input$uploadData,  withConsoleMsgRedirect(
    msg = "Please wait while data is being imported. See console log for progress.",
    {
      if (length(allImportEntries$samples) == 0) {
        stop("You have not selected any samples to import.")
      }

      sceObj <- importMultipleSources(allImportEntries)
      
      if (input$combineSCEChoice == "addToExistingSCE") {
        if(!is.null(vals$original)) {
          sceList <- list(vals$original, sceObj)
          vals$original <- combineSCE(sceList = sceList,
                                      by.r = NULL,
                                      by.c = Reduce(intersect, lapply(sceList, function(x) { colnames(colData(x))})),
                                      combined = TRUE)
        } else {
          vals$original <- sceObj
        }
      } else {
        vals$original <- sceObj
      }
      # clear table and empty reactive
      for (entry in allImportEntries$samples) {
        removeUI(selector = paste0("#", entry$id))
      }
      allImportEntries$samples <- list()
      
      # Add sample variable if it was not included
      if (!"sample" %in% names(colData(vals$original)) &&
          !"Sample" %in% names(colData(vals$original))) {
        sampleVar <- "sample"
        # Let the sample name of all cells be "sample"
        colData(vals$original)$sample = sampleVar
      } else if ("sample" %in% names(colData(vals$original))) {
        sampleVar <- "sample"
      } else {
        sampleVar <- "Sample"
      }
      
      if (!is.null(vals$original)) {
        vals$counts <- vals$original
        #store assayType information in the metadata
        # if (!"assayType" %in% names(metadata(vals$counts))) {
        #   vals$counts <- expSetDataTag(
        #     inSCE = vals$counts,
        #     assayType = "raw",
        #     assays = assayNames(vals$counts))
        # }
        if (any(duplicated(rownames(vals$counts)))) {
          warning("Duplicated rownames detected, making them unique...")
          vals$counts <- dedupRowNames(vals$counts)
        }
        # add feature ids (from rownames) to rowData if rowData is empty
        if (ncol(rowData(vals$counts)) < 1){
          if(!is.null(rownames(vals$counts))){
            rowData(vals$counts) <- S4Vectors::DataFrame(feature_id = rownames(vals$counts))
          }
        }
        # ToDo: Remove these automatic updates and replace with
        # observeEvents functions that activate upon the tab selection
        updateColDataNames()
        updateSelectInput(session, "qcSampleSelect", selected = sampleVar)
        updateFeatureAnnots()
        updateNumSamples()
        updateAssayInputs()
        updateGeneNames()
        updateReddimInputs()
        shinyjs::show(id="annotationData")
        js$enableTabs()
        updateGeneSetSelection()
      } else {
        shinyalert::shinyalert("Error!", "The data upload failed!",
                               type = "error")
      }
      vals$gsvaRes <- NULL
      vals$vamRes <- NULL
      vals$vamResults <- NULL
      vals$gsvaResults <- NULL
      vals$gsvaLimma <- NULL
      vals$vamScore <- NULL
      vals$gsvaScore <- NULL
      vals$visplotobject <- NULL
      vals$enrichRes <- NULL
      vals$dimRedPlot <- NULL
      vals$dimRedPlot_geneExp <- NULL
      vals$dendrogram <- NULL
      vals$pcX <- NULL
      vals$pcY <- NULL
      vals$batchRes <- NULL
      vals$hvgCalculated <- list(status = FALSE, method = NULL)
      dbList <- getMSigDBTable()
      geneSetDBChoices <- formatGeneSetDBChoices(dbIDs = dbList$ID, dbCats = dbList$Category_Description)
      updateCheckboxGroupInput(session, 'geneSetDB', choices = geneSetDBChoices)
      
      updateSeuratUIFromRDS(vals$counts)
      cleanGSTable()
      updateHVGMetricSelection()
      updateHVGListSelection()
      updateDEAnalysisNames()
      updateEnrichRAnalysisNames()
      updateFeatureDisplaySelect()
      updateTSCANUICollapse()
      # TODO: There are more things that need to be cleaned when uploading new
      # dataset, including any plots, tables that are origined from the old
      # datasets. Otherwise, errors may pop out when Shiny listens to the new
      # object but cannot find the old result.
      updateCollapse(session = session, "importUI",
                     open = "3. Data summary:",
                     close = "2. Create dataset:",
                     style = list("2. Create dataset:" = "success"))
      
      callModule(module = nonLinearWorkflow, id = "nlw-import", parent = session,
                 qcf = TRUE)
    }))
  
  updateSeuratUIFromRDS <- function(inSCE){
    if(!is.null(metadata(inSCE)$seurat$plots)){
      showNotification(HTML("Computation from Seurat Report detected in the input object, therefore the toolkit will now populate the Seurat tab with computated data & plots for further inspection. Click on the button below to directly go the the Seurat tab of the toolkit now! <br><br>"),
                       type = "message", duration = 0, action = actionBttn(
                         inputId = "goToSeurat",
                         label = "Go to Seurat Curated Workflow",
                         style = "bordered",
                         color = "royal",
                         size = "s",
                         icon = icon("arrow-right")
                       ), id = "goSeuratNotification")
      
      #Normalize Data
      shinyjs::enable(selector = "#SeuratUI > div[value='Normalize Data']")
      updateCollapse(session = session, "SeuratUI", style = list("Normalize Data" = "success"))
      normalizeParams <- metadata(vals$counts)$seurat$sctk$report$normalizeParams
      updateSelectInput(session, "normalization_method", selected = normalizeParams$normalizationMethod)
      updateTextInput(session, "scale_factor", value = normalizeParams$scaleFactor)
      
      #Scale Data
      shinyjs::enable(selector = "div[value='Scale Data']")
      updateCollapse(session = session, "SeuratUI", style = list("Scale Data" = "success"))
      scaleParams <- metadata(vals$counts)$seurat$sctk$report$scaleParams
      updateSelectInput(session, "model.use", selected = scaleParams$model)
      
      #HVG
      hvgParams <- metadata(vals$counts)$seurat$sctk$report$hvgParams
      output$plot_hvg <- renderPlotly({
        isolate({
          plotly::ggplotly(plotSeuratHVG(vals$counts, labelPoints = hvgParams$labelPoints))
        })
      })
      shinyjs::enable(selector = "#SeuratUI > div[value='Highly Variable Genes']")
      updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "success"))
      updateSelectInput(session, "hvg_method", selected = hvgParams$hvgMethod)
      updateTextInput(session, "hvg_no_features", value = hvgParams$hvgNumber)
      updateTextInput(session, "hvg_no_features_view", value = hvgParams$labelPoints)
      
      #DR
      pcaParams <- metadata(vals$counts)$seurat$sctk$report$pcaParams
      shinyjs::enable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
      updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "success"))
      
      removeTab(inputId = "seuratPCAPlotTabset", target = "PCA Plot")
      removeTab(inputId = "seuratPCAPlotTabset", target = "Elbow Plot")
      removeTab(inputId = "seuratPCAPlotTabset", target = "JackStraw Plot")
      removeTab(inputId = "seuratPCAPlotTabset", target = "Heatmap Plot")
      
      shinyjs::show(selector = ".seurat_pca_plots")
      
      appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "PCA Plot",
                                                          panel(heading = "PCA Plot",
                                                                plotlyOutput(outputId = "plot_pca")
                                                          )
      ), select = TRUE)
      appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Elbow Plot",
                                                          panel(heading = "Elbow Plot",
                                                                plotlyOutput(outputId = "plot_elbow_pca")
                                                          )
      ))
      appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "JackStraw Plot",
                                                          panel(heading = "JackStraw Plot",
                                                                plotlyOutput(outputId = "plot_jackstraw_pca")
                                                          )
      ))
      appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Heatmap Plot",
                                                          panel(heading = "Heatmap Plot",
                                                                panel(heading = "Plot Options",
                                                                      fluidRow(
                                                                        column(6,
                                                                               pickerInput(inputId = "picker_dimheatmap_components_pca", label = "Select principal components to plot:", choices = c(), options = list(`actions-box` = TRUE, size = 10, `selected-text-format` = "count > 3"), multiple = TRUE)
                                                                        ),
                                                                        column(6,
                                                                               sliderInput(inputId = "slider_dimheatmap_pca", label = "Number of columns for the plot: ", min = 1, max = 4, value = 2)
                                                                        )
                                                                      ),
                                                                      actionButton(inputId = "plot_heatmap_pca_button", "Plot")
                                                                ),
                                                                panel(heading = "Plot",
                                                                      shinyjqui::jqui_resizable(plotOutput(outputId = "plot_heatmap_pca"), options = list(maxWidth = 700))
                                                                )
                                                          )
      ))
      
      
      # output$plot_pca <- renderPlotly({
      #   plotly::ggplotly(metadata(inSCE)$seurat$plots$pca)
      # })
      
      output$plot_pca <- renderPlotly({
        isolate({
          plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts, useReduction = "pca"))
        })
      })
      
      
      #updateNumericInput(session = session, inputId = "pca_significant_pc_counter", value = singleCellTK:::.computeSignificantPC(vals$counts))
      # output$plot_elbow_pca <- renderPlotly({
      #   metadata(inSCE)$seurat$plots$elbow
      # })
      
      #update parameters from seurat report
      output$plot_elbow_pca <- renderPlotly({
        isolate({
          plotly::ggplotly(plotSeuratElbow(inSCE = vals$counts))
        })
      })
      
      output$pca_significant_pc_output <- renderText({
        isolate({
          paste("<p>Number of significant components suggested by ElbowPlot: <span style='color:red'>", pcaParams$significant_PC," </span> </p> <hr>")
        })
      })
      
      # output$plot_jackstraw_pca <- renderPlotly({
      #   plotly::ggplotly(metadata(inSCE)$seurat$plots$jackstraw)
      # })
      
      output$plot_jackstraw_pca <- renderPlotly({
        isolate({
          plotly::ggplotly(plotSeuratJackStraw(vals$counts))
        })
      })
      
      
      # output$plot_heatmap_pca <- renderPlot({
      #   metadata(inSCE)$seurat$plots$heatmap
      # })
      
      updateTextInput(session, "pca_no_components", value = pcaParams$nPCs)
      updateMaterialSwitch(session, "pca_compute_jackstraw", value = TRUE)
      updateNumericInput(session, "pca_significant_pc_counter", value = pcaParams$significant_PC)
      
      pcHeatmapParams <- metadata(inSCE)$seurat$plots$heatmap
      pcHeatmapParams$inSCE <- vals$counts
      output$plot_heatmap_pca <- renderPlot({
        isolate({
          do.call("runSeuratHeatmap", pcHeatmapParams)
        })
      })
      
      updatePickerInput(session = session, inputId = "picker_dimheatmap_components_pca", choices = singleCellTK:::.getComponentNames(vals$counts@metadata$seurat$count_pc, "PC"))
      
      
      
      #2D-Embedding
      shinyjs::enable(selector = "#SeuratUI > div[value='2D-Embedding']")
      updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "success"))
      
      # output$plot_tsne <- renderPlotly({
      #   metadata(inSCE)$seurat$plots$tsne
      # })
      #
      # output$plot_umap <- renderPlotly({
      #   metadata(inSCE)$seurat$plots$umap
      # })
      
      output$plot_tsne <- renderPlotly({
        isolate({
          plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "tsne"))
        })
      })
      
      output$plot_umap <- renderPlotly({
        isolate({
          plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "umap"))
        })
      })
      
      
      #Clustering
      clusterParams <- metadata(vals$counts)$seurat$sctk$report$clusterParams
      shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
      updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "success"))
      
      removeTab(inputId = "seuratClusteringPlotTabset", target = "PCA Plot")
      removeTab(inputId = "seuratClusteringPlotTabset", target = "ICA Plot")
      removeTab(inputId = "seuratClusteringPlotTabset", target = "tSNE Plot")
      removeTab(inputId = "seuratClusteringPlotTabset", target = "UMAP Plot")
      
      appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "PCA Plot",
                                                                 panel(heading = "PCA Plot",
                                                                       plotlyOutput(outputId = "plot_pca_clustering")
                                                                 )
      ), select = TRUE
      
      )
      
      output$plot_pca_clustering <- renderPlotly({
        plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "pca", showLegend = TRUE))
      })
      
      appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "tSNE Plot",
                                                                 panel(heading = "tSNE Plot",
                                                                       plotlyOutput(outputId = "plot_tsne_clustering")
                                                                 )
      )
      )
      
      output$plot_tsne_clustering <- renderPlotly({
        plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "tsne", showLegend = TRUE))
      })
      
      appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "UMAP Plot",
                                                                 panel(heading = "UMAP Plot",
                                                                       plotlyOutput(outputId = "plot_umap_clustering")
                                                                 )
      )
      )
      
      output$plot_umap_clustering <- renderPlotly({
        plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "umap", showLegend = TRUE))
      })
      
      shinyjs::show(selector = ".seurat_clustering_plots")
      
      updateNumericInput(session, "resolution_clustering", value = clusterParams$resolution)
      
      
      #Find Markers
      shinyjs::enable(selector = "#SeuratUI > div[value='Find Markers']")
      updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "success"))
      
      shinyjs::show(selector = ".seurat_findmarker_table")
      shinyjs::show(selector = ".seurat_findmarker_jointHeatmap")
      shinyjs::show(selector = ".seurat_findmarker_plots")
      
      removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Ridge Plot")
      removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Violin Plot")
      removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Feature Plot")
      removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Dot Plot")
      removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Heatmap Plot")
      
      appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "2D Embedding (Feature Plot)",
                                                                 panel(heading = "Feature Plot",
                                                                       shinyjqui::jqui_resizable(
                                                                         plotOutput(outputId = "findMarkerFeaturePlot")
                                                                       )
                                                                 )
      )
      )
      appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Dot Plot",
                                                                 panel(heading = "Dot Plot",
                                                                       shinyjqui::jqui_resizable(
                                                                         plotOutput(outputId = "findMarkerDotPlot")
                                                                       )
                                                                 )
      )
      )
      appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Ridge Plot",
                                                                 panel(heading = "Ridge Plot",
                                                                       shinyjqui::jqui_resizable(
                                                                         plotOutput(outputId = "findMarkerRidgePlot")
                                                                       )
                                                                 )
      )
      )
      appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Violin Plot",
                                                                 panel(heading = "Violin Plot",
                                                                       shinyjqui::jqui_resizable(
                                                                         plotOutput(outputId = "findMarkerViolinPlot")
                                                                       )
                                                                 )
      )
      )
      appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Heatmap Plot",
                                                                 panel(heading = "Heatmap Plot",
                                                                       fluidRow(
                                                                         column(12, align = "center",
                                                                                panel(
                                                                                  plotOutput(outputId = "findMarkerHeatmapPlot")
                                                                                )
                                                                         )
                                                                       )
                                                                 )
      )
      
      )
      
      showTab(inputId = "seuratFindMarkerPlotTabset", target = "Joint Heatmap Plot")
      updateTabsetPanel(session = session, inputId = "seuratFindMarkerPlotTabset", selected = "Ridge Plot")
      shinyjs::show(selector = ".seurat_findmarker_plots")
      
      groupHeatmapParams <- metadata(vals$counts)$seurat$plots$groupHeatmapParams
      groupHeatmapParams$inSCE <- vals$counts
      output$findMarkerHeatmapPlotFull <- renderPlot({
        isolate({
          do.call("plotSeuratGenes", groupHeatmapParams)
        })
      })
      
      output$findMarkerHeatmapPlotFullTopText <- renderUI({
        h6(paste("Heatmap plotted across all groups against genes with adjusted p-values <", input$seuratFindMarkerPValAdjInput))
      })
      
      updateSelectInput(session, "seuratFindMarkerSelectPhenotype", choices = colnames(colData(vals$counts)), selected = metadata(vals$counts)$seurat$plots$group)
      
      
      vals$fts <- callModule(
        module = filterTableServer,
        id = "filterSeuratFindMarker",
        dataframe = metadata(vals$counts)$seurat$plots$top9,
        topText = "You can view the marker genes in the table below and apply custom filters to filter the table accordingly. A joint heatmap for all the marker genes available in the table is plotted underneath the table. Additional visualizations are plotted for select genes which can be selected by clicking on the rows of the table."
      )
      
      #Downstream Analysis
      shinyjs::show(selector = "div[value='Downstream Analysis']")
      updateCollapse(session = session, "SeuratUI", style = list("Downstream Analysis" = "info"))
    }
  }
  
  observeEvent(input$goToSeurat,{
    updateTabsetPanel(session, "navbar",
                      selected = "Seurat")
    removeNotification(id = "goSeuratNotification", session = session)
  })
  
  observeEvent(input$importFeatureDipSet, {
    if (!is.null(vals$counts)) {
      withBusyIndicatorServer("importFeatureDipSet", {
        selected <- NULL
        
        if (!stringr::word(input$importFeatureDispOpt, 1) == "Default") {
          featureName <- word(input$importFeatureDispOpt, 1)
          selected <- featureName
        }
        if (!stringr::word(input$importFeatureNamesOpt, 1) == "Default") {
          featureID <- word(input$importFeatureNamesOpt, 1)
          rownames(vals$counts) <- rowData(vals$counts)[[featureID]]
        }
        vals$counts <- setSCTKDisplayRow(vals$counts, selected)
        updateFeatureDisplaySelect(selected = selected)
      })
    }
  })
  
  updateFeatureDisplaySelect <- function(selected = NULL, updateOptions = FALSE)
  {
    if (is.null(selected)) {
      if (!is.null(vals$counts))
        selected <- metadata(vals$counts)$featureDisplay
      if (is.null(selected))
        selected <- "Rownames (Default)"
    }
    updateSelectInput(session, "hvgPlotFeatureDisplay", selected = selected)
    updateSelectInput(session, "fmHMFeatureDisplay", selected = selected)
    updateSelectInput(session, "deHMrowLabel", selected = selected)
    updateSelectInput(session, "deVolcFeatureDisplay", selected = selected)
    updateSelectInput(session, "deVioLabel", selected = selected)
    updateSelectInput(session, "deRegLabel", selected = selected)
    updateSelectInput(session, "tscanDEFeatureDisplay", selected = selected)
    updateSelectInput(session, "plotTSCANClusterDEG_featureDisplay",
                      selected = selected)
    updateSelectInput(session, "plotTSCANDimReduceFeatures_featureDisplay",
                      selected = selected)
  }
  
  #-----------#
  # Gene Sets ####
  #-----------#
  numGS <- reactiveValues(id_count = 0)
  
  addToGSTable <- function(nameCol, locCol) {
    numGS$id_count <- numGS$id_count + 1
    id <- paste0("geneSet", numGS$id_count)
    fluidRowStyle <- paste0(paste0("#", id), "{border-bottom: 1px solid #bababa; padding-top: .9%; padding-bottom: .5%}")
    insertUI(
      selector = "#newGSImport",
      ui = fluidRow(
        id = id,
        tags$style(HTML(fluidRowStyle)),
        column(3, nameCol),
        column(9, locCol),
      )
    )
  }
  
  vals$defaultQCGS <- c("None" = "none",
                        "Human Mitochondrial Genes (Ensembl)" = "he",
                        "Human Mitochondrial Genes (Symbol)" = "hs",
                        "Mouse Mitochondrial Genes (Ensembl)" = "me",
                        "Mouse Mitochondrial Genes (Symbol)" = "ms")
  cleanGSTable <- function() {
    for (i in seq(numGS$id_count)) {
      removeUI(
        selector = paste0("#geneSet", i)
      )
    }
    numGS$id_count <- 0
    if (!is.null(vals$counts)) {
      existGS <- sctkListGeneSetCollections(vals$counts)
      if (length(existGS) > 0) {
        for (i in existGS) {
          addToGSTable(i, "SCE Object")
        }
        updateSelectInput(session, "gsExisting", choices = c("None", existGS))
        names(existGS) <- existGS
        updateSelectInput(session, "QCMgeneSets", choices =c("None", existGS),
                          selected = "None")
        shinyjs::show(id = "gsAddToExisting", anim = FALSE)
      } else {
        shinyjs::hide(id = "gsAddToExisting", anim = FALSE)
      }
    } else {
      updateSelectInput(session, "gsExisting", choices = "None")
      updateSelectInput(session, "QCMgeneSets", choices = "None")
      shinyjs::hide(id = "gsAddToExisting", anim = FALSE)
    }
  }
  
  handleGSPasteOption <- function(byParam) {
    if (!nzchar(input$geneSetText)) {
      shinyjs::show(id = "gsUploadError", anim = FALSE)
    } else if ((!nzchar(input$gsCollectionNameText)) && (input$gsExisting == "None")) {
      shinyjs::show(id = "gsUploadError", anim = FALSE)
    } else {
      shinyjs::hide(id = "gsUploadError", anim = FALSE)
      setList <- formatGeneSetList(input$geneSetText)
      if (nzchar(input$gsCollectionNameText)) {
        vals$counts <- importGeneSetsFromList(vals$counts,
                                              setList,
                                              by = byParam,
                                              collectionName = input$gsCollectionNameText)
        addToGSTable(input$gsCollectionNameText, "Paste-In")
      } else if (input$gsExisting != "None") {
        vals$counts <- importGeneSetsFromList(vals$counts,
                                              setList,
                                              by = byParam,
                                              collectionName = input$gsExisting)
        addToGSTable(input$gsExisting, "Paste-In")
      }
    }
  }
  
  observeEvent(input$uploadGS, withConsoleMsgRedirect(
    msg = "Please wait while gene sets are being imported. See console log for progress.",
    {
      byParam = NULL
      if (input$gsByParam != "None") {
        byParam <- input$gsByParam
      }
      if (input$geneSetSourceChoice == "gsGMTUpload") {
        if (is.null(input$geneSetGMT)) {
          shinyjs::show(id = "gsUploadError", anim = FALSE)
        } else if (!nzchar(input$gsCollectionNameGMT)){
          shinyjs::show(id = "gsUploadError", anim = FALSE)
        } else {
          shinyjs::hide(id = "gsUploadError", anim = FALSE)
          
          vals$counts <- importGeneSetsFromGMT(vals$counts,
                                               input$geneSetGMT$datapath,
                                               by = byParam,
                                               collectionName = input$gsCollectionNameGMT)
          addToGSTable(input$gsCollectionNameGMT, input$geneSetGMT$datapath)
        }
        
      } else if (input$geneSetSourceChoice == "gsDBUpload") {
        if (is.null(input$geneSetDB)) {
          shinyjs::show(id = "gsUploadError", anim = FALSE)
        } else {
          shinyjs::hide(id = "gsUploadError", anim = FALSE)
          vals$counts <- importGeneSetsFromMSigDB(vals$counts,
                                                  input$geneSetDB,
                                                  by = byParam)
          for(i in input$geneSetDB){
            # Handling multiple selections from the checkboxInput
            addToGSTable(i, "Database")
          }
        }
        
      } else if (input$geneSetSourceChoice == "gsMito") {
        vals$counts <- importMitoGeneSet(vals$counts,
                                         reference = input$geneSetMitoSpecies,
                                         id = input$geneSetMitoID,
                                         by = byParam,
                                         collectionName = input$geneSetMitoName)
        addToGSTable(input$geneSetMitoName, "SCTK Curated Geneset")
      } else if (input$geneSetSourceChoice == "gsPasteUpload") {
        handleGSPasteOption(byParam)
      }
      updateGeneSetSelection()
      
      shinyjs::show(id = "gsAddToExisting", anim = FALSE)
    }))
  
  #----#
  # QC #####
  #----#
  # Hide and show parameters for QC functions
  shinyjs::onclick("QCMetrics", shinyjs::toggle(id = "QCMetricsParams",
                                                anim = FALSE), add = TRUE)
  shinyjs::onclick("decontX", shinyjs::toggle(id = "decontXParams",
                                              anim = FALSE), add = TRUE)
  shinyjs::onclick("soupX", shinyjs::toggle(id = "soupXParams",
                                            anim = FALSE), add = TRUE)
  shinyjs::onclick("scDblFinder", shinyjs::toggle(id = "scDblFinderParams",
                                                  anim = FALSE), add = TRUE)
  shinyjs::onclick("cxds", shinyjs::toggle(id = "cxdsParams",
                                           anim = FALSE), add = TRUE)
  shinyjs::onclick("bcds", shinyjs::toggle(id = "bcdsParams",
                                           anim = FALSE), add = TRUE)
  shinyjs::onclick("cxds_bcds_hybrid", shinyjs::toggle(id = "cxds_bcds_hybridParams",
                                                       anim = FALSE), add = TRUE)
  shinyjs::onclick("scrublet", shinyjs::toggle(id = "scrubletParams",
                                               anim = FALSE), add = TRUE)
  shinyjs::onclick("doubletFinder", shinyjs::toggle(id = "doubletFinderParams",
                                                    anim = FALSE), add = TRUE)
  
  qc_choice_list <- list("scDblFinder", "cxds", "bcds",
                         "cxds_bcds_hybrid", "decontX", "soupX", "QCMetrics", "scrublet", "doubletFinder")
  # holds all the input ids for the QC algorithm parameters by algorithm name
  qc_input_ids <- list(scDblFinder = list(nNeighbors="DCnNeighbors", simDoublets="DCsimDoublets"),
                       
                       cxds = list(ntop="CXntop", binThresh="CXbinThresh", verb="CXverb", retRes="CXretRes"),#, estNdbl="CXestNdbl"),
                       
                       bcds = list(ntop="BCntop", srat="BCsrat", nmax="BCnmax", verb="BCverb", retRes="BCretRes", varImp="BCvarImp"),#, estNdbl="BCestNdbl"),
                       
                       cxds_bcds_hybrid = list(cxdsArgs=list(ntop="CX2ntop", binThresh="CX2binThresh", retRes="CX2retRes"),
                                               bcdsArgs=list(ntop="BC2ntop", srat="BC2srat", nmax="BC2nmax", retRes="BC2retRes", varImp="BC2varImp"),
                                               verb="CXBCverb"),#, estNdbl="CXBCestNdbl"),
                       
                       decontX = list(maxIter="DXmaxIter", estimateDelta="DXestimateDelta", convergence="DXconvergence",
                                      iterLogLik="DXiterLogLik", varGenes="DXvarGenes", dbscanEps="DXdbscanEps", verbose="DXverbose"),
                       soupX = list(cluster="soupXCluster", tfidfMin="soupXTfidfMin", soupQuantile="soupXQuantile", maxMarkers="soupXMaxMarkers", rhoMaxFDR="soupXRhoMaxFDR",
                                    priorRho="soupXPriorRho", priorRhoStdDev="soupXPriorRhoStdDev", forceAccept="soupXForceAccept", adjustMethod="soupXAdjustMethod",
                                    roundToInt="soupXRoundToInt", tol="soupXTol", pCut="soupXPCut"),
                       doubletFinder = list(seuratNfeatures="DFseuratNfeatures", seuratRes="DFseuratRes", formationRate="DFformationRate", verbose="DFverbose"),
                       scrublet = list(simDoubletRatio="SsimDoubletRatio", nNeighbors="SnNeighbors", minDist="SminDist", expectedDoubletRate="SexpectedDoubletRate",
                                       stdevDoubletRate='SstdevDoubletRate', syntheticDoubletUmiSubsampling="SsyntheticDoubletUmiSubsampling",
                                       useApproxNeighbors="SuseApproxNeighbors", distanceMetric="SdistanceMetric", getDoubletNeighborParents="SgetDoubletNeighborParents", minCounts="SminCounts",
                                       minCells="SminCells", minGeneVariabilityPctl="SminGeneVariabilityPctl", logTransform="SlogTransform", meanCenter="SmeanCenter",
                                       normalizeVariance="SnormalizeVariance", nPrinComps="SnPrinComps", tsneAngle="StsneAngle", tsnePerplexity="StsnePerplexity", verbose="Sverbose")
  )
  # to keep track of whether an algo has already been run
  qc_algo_status = reactiveValues(scDblFinder=NULL, cxds=NULL, bcds=NULL, cxds_bcds_hybrid=NULL, decontX=NULL, soupX=NULL,
                                  QCMetrics=NULL, scrublet=NULL, doubletFinder=NULL)
  
  qc_plot_ids = reactiveValues(scDblFinder="DCplots", cxds="CXplots", bcds="BCplots", cxds_bcds_hybrid="CXBCplots", decontX="DXplots",
                               soupX="SoupXPlots", QCMetrics="QCMplots", scrublet="Splots", doubletFinder="DFplots")
  
  
  # event handlers to open help pages for each qc algorithm
  observeEvent(input$DXhelp, {
    showModal(decontXHelpModal())
  })
  observeEvent(input$SoupXhelp, {
    showModal(soupXHelpModal())
  })
  observeEvent(input$CXhelp, {
    showModal(cxdsHelpModal())
  })
  observeEvent(input$BChelp, {
    showModal(bcdsHelpModal())
  })
  observeEvent(input$CXBChelp, {
    showModal(cxdsBcdsHybridHelpModal())
  })
  observeEvent(input$DFhelp, {
    showModal(doubletFinderHelpModal())
  })
  observeEvent(input$Shelp, {
    showModal(scrubletHelpModal())
  })
  observeEvent(input$DChelp, {
    showModal(scDblFinderHelpModal())
  })
  observeEvent(input$QCMhelp, {
    showModal(QCMHelpModal())
  })
  observeEvent(input$QCImportGS, {
    showTab(inputId = "navbar",
            target = "Import Gene Sets",
            select = TRUE,
            session = session)
  })
  
  
  # format the parameters for decontX
  prepDecontXParams <- function(paramsList) {
    inputIds <- qc_input_ids[["decontX"]]
    dxParams <- list()
    # put in all the params from the list (the straightforward ones)
    for (key in names(inputIds)) {
      dxParams[[key]] = input[[inputIds[[key]]]]
    }
    
    # put in the delta params (c-bind the two priors)
    dxParams[["delta"]] <- c(input$DXnativePrior, input$DXcontPrior)
    
    # add to master params list
    paramsList[["decontX"]] = dxParams
    return(paramsList)
  }
  
  # format the parameters for SoupX
  prepSoupXParams <- function(paramsList) {
    inputIds <- qc_input_ids[["soupX"]]
    soupXParams <- list()
    # put in all the params from the list (the straightforward ones)
    for (key in names(inputIds)) {
      soupXParams[[key]] = input[[inputIds[[key]]]]
    }
    
    soupXParams[["contaminationRange"]] <- c(input$soupXContRangeLow,
                                             input$soupXContRangeHigh)
    if (soupXParams[["cluster"]] == "None") {
      soupXParams[["cluster"]] <- NULL
    }
    # add to master params list
    paramsList[["soupX"]] = soupXParams
    return(paramsList)
  }
  
  # format the parameters for doubletFinder
  prepDoubletFinderParams <- function(paramsList) {
    inputIds <- qc_input_ids[["doubletFinder"]]
    dfParams <- list()
    # put in all the params from the list (the straightforward ones)
    for (key in names(inputIds)) {
      dfParams[[key]] = input[[inputIds[[key]]]]
    }
    
    # put in the seuratPcs param (range from 1 to given value)
    dfParams[["seuratPcs"]] <- 1:input$DFseuratPcs
    
    # add to master params list
    paramsList[["doubletFinder"]] = dfParams
    return(paramsList)
  }
  
  qcInputExists <- function() {
    for (algo in qc_choice_list) {
      if (isTRUE(input[[algo]])) {
        return(TRUE)
      }
    }
    return(FALSE)
  }
  
  updateQCPlots <- function() {
    # get selected sample from run QC section
    if (!is.null(vals$original)) {
      qcSample <- input$qcSampleSelect
      if (qcSample == "None") {
        qcSample <- NULL
      } else {
        qcSample <- colData(vals$original)[,input$qcSampleSelect]
      }
      # build list of selected algos
      algoList = list()
      for (algo in qc_choice_list) {
        if (isTRUE(input[[algo]])) {
          algoList <- c(algoList, algo)
        }
      }
      # only run runUMAP if there are no reducedDimNames
      # redDimName <- input$qcPlotRedDim
      # show the tabs for the result plots  output[[qc_plot_ids[[a]]]]
      
      showQCResTabs(vals, algoList, qc_algo_status, qc_plot_ids)
      arrangeQCPlots(vals$original, input, output, algoList,
                     colData(vals$original)[[input$qcSampleSelect]], qc_plot_ids,
                     qc_algo_status, input$QCUMAPName)
      
      uniqueSampleNames = unique(colData(vals$original)[[input$qcSampleSelect]])
      for (algo in algoList) {
        qc_algo_status[[algo]] <- list(self="done")
        if (length(uniqueSampleNames) > 1) {
          for (s in uniqueSampleNames) {
            qc_algo_status[[algo]][[s]] = TRUE
          }
        }
      }
    }
  }
  
  observeEvent(input$runQC, withConsoleMsgRedirect(
    msg = "Please wait while QC metrics are being calculated. See console log for progress.",
    {
      if (!qcInputExists()) {
        insertUI(
          selector = "#qcPageErrors",
          ui = wellPanel(id = "noSelected", tags$b("Please select at least one algorithm.", style = "color: red;"))
        )
      } else if (is.null(vals$original)) {
        insertUI(
          selector = "#qcPageErrors",
          ui = wellPanel(id = "noSCE", tags$b("Please upload a sample first.", style = "color: red;"))
        )
      } else if (is.null(input$qcAssaySelect)) {
        insertUI(
          selector = "#qcPageErrors",
          ui = wellPanel(id = "noQCAssay", tags$b("Please select an assay.", style = "color: red;"))
        )
      } else {
        removeUI(
          selector = "#noSelected"
        )
        removeUI(
          selector = "#noSCE"
        )
        removeUI(
          selector = "#noQCAssay"
        )
        useAssay <- input$qcAssaySelect
        qcSample <- colData(vals$original)[,input$qcSampleSelect]
        if (length(qcSample)==1 && qcSample == "None") {
          qcSample <- NULL
        }
        # Handle mitochondrial gene set selection
        mgsRef <- NULL
        mgsId <- NULL
        mgsLoc <- NULL
        if (input$QCMito != "None") {
          if (input$QCMito == "he") {
            # Import Human Mito Ensembl
            mgsRef <- "human"
            mgsId <- "ensembl"
          } else if (input$QCMito == "hs") {
            # Import Human Mito Symbol
            mgsRef <- "human"
            mgsId <- "symbol"
          } else if (input$QCMito == "me") {
            # Import Mouse Mito Ensembl
            mgsRef <- "mouse"
            mgsId <- "ensembl"
          } else if (input$QCMito == "ms") {
            # Import Mouse Mito Symbol
            mgsRef <- "mouse"
            mgsId <- "symbol"
          }
          mgsLoc <- "rownames"
        }
        # Handle another genesetCollection selection
        qcCollName <- NULL
        if (input$QCMgeneSets != "None") {
          qcCollName <- input$QCMgeneSets
        }
        algoList = list()
        paramsList <- list()
        for (algo in qc_choice_list) {
          if (isTRUE(input[[algo]])) {
            algoList <- c(algoList, algo)
            # use the specific prep functions for decontX, SoupX and doubletFinder
            if (algo == "decontX") {
              paramsList <- prepDecontXParams(paramsList)
              next
            }
            if (algo == "soupX") {
              paramsList <- prepSoupXParams(paramsList)
              next
            }
            if (algo == "doubletFinder") {
              paramsList <- prepDoubletFinderParams(paramsList)
              next
            }
            # everything else can go through the rest of the loop
            inputIds <- qc_input_ids[[algo]]
            algoParams <- list()
            for (key in names(inputIds)) {
              if(typeof(inputIds[[key]]) == "list") {
                paramSubList <- list()
                for (key2 in names(inputIds[[key]])) {
                  paramSubList[[key2]] <- input[[inputIds[[key]][[key2]]]]
                }
                algoParams[[key]] = paramSubList
              } else {
                algoParams[[key]] = handleEmptyInput(input[[inputIds[[key]]]])
              }
            }
            paramsList[[algo]] = algoParams
          }
        }
        # run selected cell QC algorithms
        vals$original <- runCellQC(inSCE = vals$original,
                                   algorithms = algoList,
                                   sample = qcSample,
                                   collectionName = qcCollName,
                                   mitoRef = mgsRef,
                                   mitoIDType = mgsId,
                                   mitoGeneLocation = mgsLoc,
                                   useAssay = input$qcAssaySelect,
                                   paramsList = paramsList)
        # Only copy the newly generated colData variables to vals$counts, but
        # not replacing the vals$counts. vals$counts might have already become
        # a subset.
        vals$counts <- passQCVar(vals$original, vals$counts, algoList)
        updateColDataNames()
        updateAssayInputs()
        # redDimList <- strsplit(reducedDimNames(vals$original), " ")
        # run runUMAP if doublet/ambient RNA detection conducted
        #umap generated during soupX, skip for now
        if (length(intersect(c("scDblFinder", "cxds", "bcds",
                               "cxds_bcds_hybrid", "decontX", #"soupX",
                               "scrublet", "doubletFinder"), algoList)) > 0) {
          message(paste0(date(), " ... Running 'UMAP'"))
          vals$original <- runUMAP(inSCE = vals$original,
                                   sample = qcSample,
                                   useAssay = input$qcAssaySelect,
                                   useReducedDim = NULL,
                                   nNeighbors = input$UnNeighbors,
                                   nIterations = input$UnIterations,
                                   alpha = input$Ualpha,
                                   minDist = input$UminDist,
                                   spread = input$Uspread,
                                   initialDims = input$UinitialDims,
                                   reducedDimName = input$QCUMAPName,
                                   seed = input$Useed)
          
        }
        message(paste0(date(), " ... QC Complete"))
        updateQCPlots()
        # Show downstream analysis options
        callModule(module = nonLinearWorkflow, id = "nlw-qcf", parent = session,
                   nbc = TRUE, cw = TRUE, cv = TRUE)
      }
      delay(500, removeNotification(id = "qcNotification"))
    }))
  
  #-----------#
  # FILTERING #####
  #-----------#
  shinyjs::onclick("colGT", shinyjs::toggle(id = "filterThreshGT",
                                            anim = FALSE), add = TRUE)
  shinyjs::onclick("colLT", shinyjs::toggle(id = "filterThreshLT",
                                            anim = FALSE), add = TRUE)
  filteringParams <- reactiveValues(params = list(), id_count = 0)
  rowFilteringParams <- reactiveValues(params = list(), id_count = 0)
  
  observeEvent(input$addFilteringParam, {
    if (!is.null(vals$original)) {
      showModal(filteringModal(colNames = names(colData(vals$original))))
    }
  })
  
  observeEvent(input$addRowFilteringParam, {
    if (!is.null(vals$original) &&
        !is.null(names(assays(vals$original)))) {
      showModal(rowFilteringModal(assayInput = names(assays(vals$original))))
    }
  })
  
  observeEvent(input$filterColSelect, {
    # prep the modal - remove the threshold div and hide the categorical option
    shinyjs::hide("convertFilterType")
    removeUI(selector = "#newThresh")
    removeUI(selector = "div:has(>> #convertToCat)")
    # check if column contains numerical values
    isNum <- is.numeric(vals$original[[input$filterColSelect]][0])
    if (length(vals$original[[input$filterColSelect]]) > 0) {
      if (isTRUE(isNum)) {
        # (from partials) insertUI for choosing greater than and less than params
        addFilteringThresholdOptions(vals$original[[input$filterColSelect]])
        # if less than 25 unique categories, give categorical option
        if (length(unique(vals$original[[input$filterColSelect]])) < 25) {
          insertUI(
            selector = "#convertFilterType",
            ui = checkboxInput("convertToCat", "Convert to categorical filter?")
          )
          shinyjs::show("convertFilterType")
        }
        
      } else { # if non-numerical values, create checkbox input
        insertUI(
          selector = "#filterCriteria",
          ui = tags$div(id="newThresh",
                        checkboxGroupInput("filterThresh", "Please select which columns to keep:",
                                           choices = as.vector(unique(vals$original[[input$filterColSelect]])),
                        ),
          )
        )
      }
    } else { # if no values in column, show error
      insertUI(
        selector = "#filterCriteria",
        ui = tags$div(id="newThresh", tags$b("This column does not have any filtering criteria", style = "color: red;"))
      )
    }
  })
  
  observeEvent(input$convertToCat, {
    if (!is.null(input$filterColSelect)) {
      removeUI(selector = "#newThresh")
      if (input$convertToCat) {
        insertUI(
          selector = "#filterCriteria",
          ui = tags$div(id="newThresh",
                        checkboxGroupInput("filterThresh", "Please select which columns to keep:",
                                           choices = as.vector(unique(vals$original[[input$filterColSelect]])),
                        )
          )
        )
      } else {
        addFilteringThresholdOptions(vals$original[[input$filterColSelect]])
        if (length(unique(vals$original[[input$filterColSelect]])) < 25) {
          shinyjs::show("convertFilterType")
        }
      }
    }
  })
  
  observeEvent(input$filterAssaySelect, {
    removeUI(selector = "#newThresh")
    insertUI(
      selector = "#rowFilterCriteria",
      ui = tags$div(id="newThresh",
                    numericInput("filterThreshX", "Keep features with this many counts:", 0),
                    numericInput("filterThreshY", "In at least this many cells:", 0),
      )
    )
    
  })
  
  observeEvent(input$filtModalOK, {
    if (is.null(input$filterThresh) && is.null(input$filterThreshGT) && is.null(input$filterThreshLT)) {
      showModal(filteringModal(failed=TRUE, colNames = names(colData(vals$original))))
    } else {
      id <- paste0("filteringParam", filteringParams$id_count)
      # figure out which options the user selected
      criteriaGT <- NULL
      criteriaLT <- NULL
      categoricalCol = FALSE
      if (isTRUE(input$colGT)) {
        criteriaGT = input$filterThreshGT
      }
      if (isTRUE(input$colLT)) {
        criteriaLT = input$filterThreshLT
      }
      if (!is.null(input$filterThresh)) {
        categoricalCol = TRUE
      }
      if (isTRUE(input$colLT) && isTRUE(input$colGT)) {
        if (criteriaGT > criteriaLT) {
          insertUI(
            selector = "#filterCrErrors",
            ui = wellPanel(id = "voidRange",
                           tags$b("Please set a valid range.",
                                  style = "color: red;"))
          )
          return()
        }
      }
      # new row in parameters table
      addToColFilterParams(name = input$filterColSelect,
                           categorial = categoricalCol,
                           criteria = input$filterThresh,
                           criteriaGT = criteriaGT,
                           criteriaLT = criteriaLT,
                           id = id,
                           paramsReactive = filteringParams)
      threshStr <- ""
      if (isTRUE(categoricalCol)) {
        threshStr <- paste(input$filterThresh, collapse = ', ')
      } else {
        if (is.null(criteriaGT)) {
          threshStr <- sprintf("< %.5f", input$filterThreshLT)
        } else if (is.null(criteriaLT)) {
          threshStr <- sprintf("> %.5f", input$filterThreshGT)
        } else {
          threshStr <- sprintf("> %.5f & < %.5f", input$filterThreshGT, input$filterThreshLT)
        }
      }
      make3ColTableRow("#newFilteringParams", id, input$filterColSelect, threshStr)
      observeEvent(input[[paste0("remove", id)]],{
        removeUI(
          selector = paste0("#", id)
        )
        toRemove <- vector()
        for (entry in filteringParams$params) {
          if (entry$id == id) {
            toRemove <- c(toRemove, FALSE)
          } else {
            toRemove <- c(toRemove, TRUE)
          }
        }
        filteringParams$params <- filteringParams$params[toRemove]
      })
      removeModal()
    }
  })
  
  observeEvent(input$rowFiltModalOK, {
    if ((is.null(input$filterThreshX)) || (is.null(input$filterThreshY)) || (is.null(input$filterAssaySelect))) {
      showModal(rowFilteringModal(failed=TRUE, assayInput = names(assays(vals$original))))
    } else {
      id <- paste0("rowFilteringParam", rowFilteringParams$id_count)
      # new row in parameters table
      threshStr <- sprintf("> %i counts in > %i cells", input$filterThreshX, input$filterThreshY)
      
      addToRowFilterParams(input$filterAssaySelect, input$filterThreshX, input$filterThreshY, id, rowFilteringParams)
      make3ColTableRow("#newRowFilteringParams", id, input$filterAssaySelect, threshStr)
      observeEvent(input[[paste0("remove", id)]],{
        removeUI(
          selector = paste0("#", id)
        )
        toRemove <- vector()
        for (entry in rowFilteringParams$params) {
          if (entry$id == id) {
            toRemove <- c(toRemove, FALSE)
          } else {
            toRemove <- c(toRemove, TRUE)
          }
        }
        rowFilteringParams$params <- rowFilteringParams$params[toRemove]
      })
      removeModal()
    }
  })
  
  observeEvent(input$clearAllFilters, {
    for (entry in filteringParams$params) {
      removeUI(selector = paste0("#", entry$id))
    }
    filteringParams$params <- list()
  })
  
  observeEvent(input$clearAllRowParams, {
    for (entry in rowFilteringParams$params) {
      removeUI(selector = paste0("#", entry$id))
    }
    rowFilteringParams$params <- list()
  })
  
  filterSCE <- function(inSCE, colFilter, rowFilter) {
    if (!is.null(colFilter)) {
      # handle column filtering (pull out the criteria strings first)
      colInput <- formatFilteringCriteria(colFilter$params)
      if (length(colInput) > 0) {
        inSCE <- subsetSCECols(inSCE, colData = colInput)
      }
    }
    if (!is.null(rowFilter)) {
      # handle row filtering (enter information as rows first, then pull out 
      # criteria strings)
      rowInput <- formatFilteringCriteria(rowFilter$params)
      if (length(rowInput) > 0) {
        inSCE <- addRowFiltersToSCE(inSCE, rowFilter)
        temp <- subsetSCERows(inSCE, rowData = rowInput, returnAsAltExp = FALSE)
        if (nrow(temp) == 0) {
          stop("This filter will clear all rows. Filter has not been applied.")
        } else {
          inSCE <- temp
        }
      }
    }
    return(inSCE)
  }
  
  observeEvent(input$filterSCE, withConsoleMsgRedirect(
    msg = "Please wait while data is being filtered. See console log for progress.",
    {
      vals$counts <- filterSCE(vals$original, filteringParams, rowFilteringParams)
      shinyjs::show(id="filteringSummary")
      updateColDataNames()
      updateReddimInputs()
      updateFeatureAnnots()
      updateAssayInputs()
      # TODO: When new subset is being created and maybe replacing previous
      # vals$counts, please find if any of the downstream UI need to be updated
      # Show downstream analysis options
      shinyjs::show(selector = ".nlw-qcf")
    }))
  
  #Render summary table
  output$beforeFiltering <- renderTable({
    req(vals$original)
    if ("sample" %in% names(colData(vals$counts))) {
      sampleVar <- "sample"
    } else if ("Sample" %in% names(colData(vals$counts))) {
      sampleVar <- "Sample"
    } else {
      sampleVar <- NULL
    }
    # Setting 'useAssay=NULL' assumes that the first assay is the one to count
    singleCellTK::summarizeSCE(inSCE = vals$original,
                               useAssay = NULL,
                               sampleVariableName = sampleVar)
  }, striped = TRUE, border = TRUE, align = "c", spacing = "l")
  
  output$afterFiltering <- renderTable({
    req(vals$counts)
    if ("sample" %in% names(colData(vals$counts))) {
      sampleVar <- "sample"
    } else if ("Sample" %in% names(colData(vals$counts))) {
      sampleVar <- "Sample"
    } else {
      sampleVar <- NULL
    }
    # Setting 'useAssay=NULL' assumes that the first assay is the one to count
    singleCellTK::summarizeSCE(inSCE = vals$counts,
                               useAssay = NULL,
                               sampleVariableName = sampleVar)
  }, striped = TRUE, border = TRUE, align = "c", spacing = "l")
  
  #Render summary table
  output$summarycontents <- DT::renderDataTable({
    req(vals$counts)
    if ("sample" %in% names(colData(vals$counts))) {
      sampleVar <- "sample"
    } else if ("Sample" %in% names(colData(vals$counts))) {
      sampleVar <- "Sample"
    } else {
      sampleVar <- NULL
    }
    # Setting 'useAssay=NULL' assumes that the first assay is the one to count
    singleCellTK::summarizeSCE(inSCE = vals$counts,
                               useAssay = NULL,
                               sampleVariableName = sampleVar)
  })
  
  observeEvent(input$filteredSample, {
    output$filterSampleOptions <- renderUI({
      isolate({
        if (input$filteredSample != "none")({
          if (length(unique(colData(vals$counts)[, input$filteredSample])) < 100){
            L <- vector("list", 3)
            L[[1]] <- renderText("Select samples to keep")
            L[[2]] <- wellPanel(style = "overflow-y:scroll; max-height: 100px",
                                list(checkboxGroupInput("filterSampleChoices",
                                                        label = NULL,
                                                        choices = unique(colData(vals$counts)[, input$filteredSample]))),
                                tags$h5(tags$i("Note: the Reset button is in 'Delete Outliers' tab above."))
            )
            L[[3]] <- list(withBusyIndicatorUI(actionButton("runFilterSample", "Filter")))
            return(L)
          } else {
            L <- list(renderText("Annotation must have fewer than 100 options"))
            return(L)
          }
        }) else {
          L <- list()
        }
      })
    })
  })
  
  # Delete Data ####
  
  output$reducedDimsList <- renderUI({
    req(vals$counts)
    if (!is.null(vals$counts) &&
        length(names(reducedDims(vals$counts))) > 0){
      panel(heading = "ReducedDims",
            checkboxGroupInput(
              inputId = "checkboxRedDimToRemove",
              label = NULL,
              choices = names(reducedDims(vals$counts))
            )
      )
    }
  })
  
  output$assaysList <- renderUI({
    req(vals$counts)
    if (!is.null(vals$counts)){
      panel(heading = "Assays",
            checkboxGroupInput(
              inputId = "checkboxAssaysToRemove",
              label = NULL,
              choices = assayNames(vals$counts)
            )
      )
    }
  })
  
  output$rowDataList <- renderUI({
    req(vals$counts)
    if (!is.null(vals$counts)
        && length(colnames(rowData(vals$counts))) > 0){
      panel(heading = "Row Annotation",
            checkboxGroupInput(
              inputId = "checkboxRowDataToRemove",
              label = NULL,
              choices = colnames(rowData(vals$counts))
            )
      )
    }
  })
  
  output$colDataList <- renderUI({
    req(vals$counts)
    if (!is.null(vals$counts)
        && length(colnames(colData(vals$counts))) > 0){
      panel(heading = "Column Annotation",
            checkboxGroupInput(
              inputId = "checkboxColDataToRemove",
              label = NULL,
              choices = colnames(colData(vals$counts))
            )
      )
    }
  })
  
  output$altExpList <- renderUI({
    req(vals$counts)
    if (!is.null(vals$counts)
        && length(altExpNames(vals$counts)) > 0){
      panel(heading = "Subsets",
            checkboxGroupInput(
              inputId = "checkboxAltExpToRemove",
              label = NULL,
              choices = altExpNames(vals$counts)
            )
      )
    }
  })
  
  observeEvent(input$delRedDim, withConsoleMsgRedirect(
    msg = "Please wait while selected data is being removed. See console log for progress.",
    {
      
      req(vals$counts)
      if(length(input$checkboxAssaysToRemove) > 0){
        for(i in seq(input$checkboxAssaysToRemove)){
          expData(vals$counts, input$checkboxAssaysToRemove[i]) <- NULL
          vals$counts <- expDeleteDataTag(vals$counts, input$checkboxAssaysToRemove[i])
          message(paste0(date(), " ... Removed '", input$checkboxAssaysToRemove[i], "' assay."))
        }
      }
      if(length(input$checkboxRedDimToRemove) > 0){
        for(i in seq(input$checkboxRedDimToRemove)){
          reducedDim(vals$counts, input$checkboxRedDimToRemove[i]) <- NULL
          message(paste0(date(), " ... Removed '", input$checkboxRedDimToRemove[i], "' redDim."))
        }
      }
      if(length(input$checkboxRowDataToRemove) > 0){
        for(i in seq(input$checkboxRowDataToRemove)){
          rowData(vals$counts)[[input$checkboxRowDataToRemove[i]]] <- NULL
          message(paste0(date(), " ... Removed '", input$checkboxRowDataToRemove[i], "' feature annotation."))
        }
      }
      if(length(input$checkboxColDataToRemove) > 0){
        for(i in seq(input$checkboxColDataToRemove)){
          colData(vals$counts)[[input$checkboxColDataToRemove[i]]] <- NULL
          message(paste0(date(), " ... Removed '", input$checkboxColDataToRemove[i], "' sample annotation."))
        }
      }
      if(length(input$checkboxAltExpToRemove) > 0){
        for(i in seq(input$checkboxAltExpToRemove)){
          altExps(vals$counts)[[input$checkboxAltExpToRemove[i]]] <- NULL
          message(paste0(date(), " ... Removed '", input$checkboxAltExpToRemove[i], "' subset."))
        }
      }
      updateAssayInputs()
      updateReddimInputs()
      updateFeatureAnnots()
      updateColDataNames()
    }))
  
  # Normalization ####
  
  observeEvent(input$customNormalizeAssayMethodSelect, {
    if(input$customNormalizeAssayMethodSelect == "LogNormalize"
       || input$customNormalizeAssayMethodSelect == "CLR"
       || input$customNormalizeAssayMethodSelect == "SCTransform"
       || input$customNormalizeAssayMethodSelect == "logNormCounts"){
      updateAwesomeCheckbox(
        session = session,
        inputId = "customNormalizeOptionsTransform",
        value = FALSE
      )
    }
  })
  
  output$normalizationDataTagUI <- renderUI({
    req(vals$counts)
    tag <- ""
    if(input$normalizeAssayMethodSelect != "custom"){
      if(input$normalizeAssayMethodSelect
         %in% c("LogNormalize", "SCTransform", "CLR", "logNormCounts", "NormalizeTotal")){
        tag <- "transformed"
      }
      else{
        tag <- "normalized"
      }
      if(input$normalizationScale){
        tag <- "scaled"
      }
    }
    else{
      if(input$customNormalizeOptionsNormalize){
        if(input$customNormalizeAssayMethodSelect
           %in% c("LogNormalize", "SCTransform", "CLR", "logNormCounts")){
          tag <- "transformed"
        }
        else{
          tag <- "normalized"
        }
      }
      if(input$customNormalizeOptionsTransform){
        tag <- "transformed"
      }
      if(input$customNormalizeOptionsScale){
        tag <- "scaled"
      }
    }
    return(tag)
  })
  
  output$normalizationNormalizeSelectedMethodUI <- renderUI({
    req(vals$counts)
    if(input$normalizeAssayMethodSelect != "custom"){
      h5(input$normalizeAssayMethodSelect)
    }
    else{
      NULL
    }
    
  })
  
  observeEvent(input$modifyAssay, withConsoleMsgRedirect(
    msg = "Please wait while data is being normalized. See console log for progress.",
    {
      req(vals$counts)
      if (!(input$modifyAssaySelect %in% names(assays(vals$counts)))) {
        stop("Assay does not exist!")
      } else if (input$modifyAssayOutname == "") {
        stop("Assay name cannot be empty!")
      } else if (input$modifyAssayOutname %in% names(assays(vals$counts))) {
        stop("Assay name already exists! Use another assay name!")
      } else if(is.na(input$trimUpperValueAssay)
                || is.na(input$trimLowerValueAssay)){
        stop("Upper or lower trim value cannot be empty!")
      } else {
        checkedOptions <- c(input$customNormalizeOptionsNormalize,
                            input$customNormalizeOptionsTransform,
                            input$customNormalizeOptionsPsuedocounts,
                            input$customNormalizeOptionsScale,
                            input$customNormalizeOptionsTrim)
        
        if(!any(checkedOptions)){
          stop("Must select at least one option!")
        }
        
        #Setting initial parameters
        normalizeMethod <- NULL
        transformMethod <- NULL
        pseudocountsBefore <- NULL
        pseudocountsAfter <- NULL
        doScale <- input$customNormalizeOptionsScale
        trimOptions <- NULL
        
        if(input$customNormalizeOptionsNormalize)
          normalizeMethod <- input$customNormalizeAssayMethodSelect
        if(input$customNormalizeOptionsTransform)
          transformMethod <- input$customNormalizeTransformOptions
        if(input$customNormalizePseudoOptionsBefore)
          pseudocountsBefore <- input$customNormalizePseudoValueBefore
        if(input$customNormalizePseudoOptionsAfter)
          pseudocountsAfter <- input$customNormalizePseudoValueAfter
        if(input$customNormalizeOptionsTrim)
          trimOptions <- c(input$trimUpperValueAssay, input$trimLowerValueAssay)
        
        outAssayName <- input$modifyAssayOutname
        useAssay <- input$modifyAssaySelect
        
        args <- list(
          inSCE = vals$counts,
          useAssay = useAssay,
          outAssayName = outAssayName,
          normalizationMethod = normalizeMethod,
          scale = doScale,
          transformation = transformMethod,
          pseudocountsBeforeNorm = pseudocountsBefore,
          pseudocountsBeforeTransform = pseudocountsAfter,
          trim = trimOptions
        )
        
        message(paste0(date(), " ... Starting normalization/transformation with selected assay: '", useAssay, "'."))
        
        vals$counts <- do.call("runNormalization", args)
        
        message(paste0(date(), " ... Ended normalization/transformation."))
        # Show downstream analysis options
        callModule(module = nonLinearWorkflow, id = "nlw-nbc", parent = session,
                   dr = TRUE, fs = TRUE)
      }
    }
  ))
  
  observeEvent(input$normalizeAssay, withConsoleMsgRedirect(
    msg = "Please wait while data is being normalized. See console log for progress.",
    {
      req(vals$counts)
      if(!(input$normalizeAssaySelect %in% expDataNames(vals$counts))){
        stop("Selected assay does not exist!")
      } else if(input$normalizeAssayOutname == ""){
        stop("Assay Name cannot be empty!")
      } else if(input$normalizeAssayOutname %in% expDataNames(vals$counts)){
        stop("Your selected Assay Name already exists! Try another Assay Name!")
      } else if(input$normalizeAssaySelect == ""){
        stop("Please select an assay before proceeding with normalization!")
      } else if(is.na(as.numeric(input$normalizationScaleFactor))){
        stop("Scaling factor must be a numeric non-empty value!")
      } else{
        #Setting initial parameters
        normalizeMethod <- input$normalizeAssayMethodSelect
        doScale <- input$normalizationScale
        trimOptions <- NULL
        scaleFactor <- input$normalizationScaleFactor
        
        if(doScale && input$normalizationTrim)
          trimOptions <- c(input$normalizationTrimUpper,
                           input$normalizationTrimLower)
        
        outAssayName <- input$normalizeAssayOutname
        useAssay <- input$normalizeAssaySelect
        
        args <- list(
          inSCE = vals$counts,
          useAssay = useAssay,
          outAssayName = outAssayName,
          normalizationMethod = normalizeMethod,
          scale = doScale,
          seuratScaleFactor = scaleFactor,
          trim = trimOptions
        )
        
        message(date(), " ... Starting normalization with selected assay: '",
                useAssay, "'.")
        
        vals$counts <- do.call("runNormalization", args)
        
        message(paste0(date(), " ... Ended normalization."))
        updateAssayInputs()
        # Show downstream analysis options
        callModule(module = nonLinearWorkflow, id = "nlw-nbc", parent = session,
                   dr = TRUE, fs = TRUE)
      }
    }
  ))
  
  observeEvent(input$normalizeAssayMethodSelect, {
    if(input$normalizeAssayMethodSelect == "LogNormalize") {
      updateTextInput(session = session, inputId = "normalizeAssayOutname",
                      value = "SeuratLogNormalize")
    } else if(input$normalizeAssayMethodSelect == "CLR"){
      updateTextInput(session = session, inputId = "normalizeAssayOutname",
                      value = "SeuratCLR")
    } else if(input$normalizeAssayMethodSelect == "RC"){
      updateTextInput(session = session, inputId = "normalizeAssayOutname",
                      value = "SeuratRC")
    } else if(input$normalizeAssayMethodSelect == "CPM"){
      updateTextInput(session = session, inputId = "normalizeAssayOutname",
                      value = "ScaterCPMCounts")
    } else if(input$normalizeAssayMethodSelect == "logNormCounts"){
      updateTextInput(session = session, inputId = "normalizeAssayOutname",
                      value = "ScaterLogNormCounts")
    } else if(input$normalizeAssayMethodSelect == "SCTransform"){
      updateTextInput(session = session, inputId = "normalizeAssayOutname",
                      value = "SeuratSCTransform")
    } else if(input$normalizeAssayMethodSelect == "NormalizeTotal"){
      updateTextInput(session = session, inputId = "normalizeAssayOutname",
                      value = "ScanpyNormalizeTotal")
    }
  })
  
  #-----------------------------------------------------------------------------
  # Page 3: dimRed ####
  #-----------------------------------------------------------------------------
  
  output$dimRedNameUI <- renderUI({
    defaultText <- paste(input$dimRedAssaySelect, input$dimRedPlotMethod,
                         sep = '_')
    textInput('dimRedNameInput', "reducedDim Name:", defaultText)
  })
  
  output$dimRedNameUI_tsneUmap <- renderUI({
    defaultText <- paste(input$dimRedAssaySelect_tsneUmap, input$dimRedPlotMethod_tsneUmap,
                         sep = '_')
    textInput('dimRedNameInput_tsneUmap', "reducedDim Name:", defaultText)
  })
  
  observeEvent(input$updateHeatmap_dimRed, {
    req(vals$counts)
    if (!is.null(input$picker_dimheatmap_components_dimRed)) {
      if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
        output$plot_heatmap_dimRed <- renderPlot({
          isolate({
            singleCellTK:::.plotHeatmapMulti(
              plots = vals$counts@metadata$seurat$heatmap_dimRed,
              components = input$picker_dimheatmap_components_dimRed,
              nCol = input$slider_dimheatmap_dimRed)
          })
        })
      }
      else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
        output$plot_heatmap_dimRed <- renderPlot({
          isolate({
            singleCellTK:::.plotHeatmapMulti(
              plots = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed,
              components = input$picker_dimheatmap_components_dimRed,
              nCol = input$slider_dimheatmap_dimRed)
          })
        })
      }
    }
    session$sendCustomMessage("close_dropDownDimRedHeatmap", "")
  })
  
  observeEvent(input$closeDropDownDimRedHeatmap, {
    session$sendCustomMessage("close_dropDownDimRedHeatmap", "")
  })
  
  observeEvent(input$runDimred, withConsoleMsgRedirect(
    msg = "Please wait while dimensionality reduction is being computed. See console log for progress.",
    {
      req(vals$counts)
      vals$runDimred$dimRedAssaySelect <- input$dimRedAssaySelect
      if (vals$runDimred$dimRedAssaySelect %in% altExpNames(vals$counts)) {
        dimRedUseAltExp <- vals$runDimred$dimRedAssaySelect
      } else {
        dimRedUseAltExp <- NULL
      }
      if (input$dimRedNameInput == "" || is.null(input$dimRedNameInput)){
        stop("Please enter a reducedDim name")
      }
      dimrednamesave <- gsub(" ", "_", input$dimRedNameInput)
      if (dimrednamesave %in% reducedDimNames(vals$counts)) {
        stop("Specified reducedDim name already exist")
      }
      if (is.na(input$dimRedNumberDims) ||
          input$dimRedNumberDims < 2) {
        stop("Must specify a valid number of components for output")
      }
      useFeatureSubset <- input$dimRedHVGSelect
      if (input$dimRedHVGSelect == "None") {
        useFeatureSubset <- NULL
      }
      seed <- input$seed_dimRed
      if (is.na(input$seed_dimRed)) {
        seed <- NULL
      }
      vals$counts <- runDimReduce(
        inSCE = vals$counts,
        useAssay = vals$runDimred$dimRedAssaySelect,
        useAltExp = dimRedUseAltExp,
        method = input$dimRedPlotMethod,
        useFeatureSubset = useFeatureSubset,
        scale = input$dimRedScale,
        nComponents = input$dimRedNumberDims,
        reducedDimName = dimrednamesave,
        seed = seed)
      updateReddimInputs()
      updateAssayInputs()
      # Show downstream analysis options
      callModule(module = nonLinearWorkflow, id = "nlw-dr", parent = session,
                 cl = TRUE, cv = TRUE)
      
      message(paste0(date(), " ... Ending Dimensionality Reduction."))
      
      redDim <- reducedDim(vals$counts, dimrednamesave)
      if(input$dimRedPlotMethod == "scaterPCA"){
        new_pca <- CreateDimReducObject(
          embeddings = redDim,
          assay = "RNA",
          loadings = attr(redDim, "rotation"),
          stdev = as.numeric(attr(redDim, "percentVar")),
          key = "PC_")
      }
      else if(input$dimRedPlotMethod == "scanpyPCA"){
        new_pca <- CreateDimReducObject(
          embeddings = redDim,
          assay = "RNA",
          loadings = attr(redDim, "rotation"),
          stdev = as.numeric(attr(redDim, "percentVar")),
          key = "PC_")
      }
      
      removeTab(inputId = "dimRedPCAICA_plotTabset", target = "Component Plot")
      removeTab(inputId = "dimRedPCAICA_plotTabset", target = "Elbow Plot")
      removeTab(inputId = "dimRedPCAICA_plotTabset", target = "Heatmap Plot")
      removeTab(inputId = "dimRedPCAICA_plotTabset", target = "JackStraw Plot")
      
      shinyjs::show(selector = ".dimRedPCAICA_plotTabset_class")
      
      if(input$computeElbowPlot
         && input$dimRedPlotMethod != "seuratICA"){
        appendTab(
          inputId = "dimRedPCAICA_plotTabset",
          tabPanel(
            title = "Elbow Plot",
            panel(
              #heading = "Elbow Plot",
              plotlyOutput(outputId = "plotDimRed_elbow")
            )
          ),
          select = TRUE
        )
        message(paste0(date(), " ... Generating Elbow Plot."))
        if (input$dimRedPlotMethod == "seuratPCA"){
          if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
            output$plotDimRed_elbow <- renderPlotly({
              plotSeuratElbow(inSCE = vals$counts, )
            })
          } else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
            output$plotDimRed_elbow <- renderPlotly({
              plotSeuratElbow(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]])
            })
          }
        } else {
          if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
            output$plotDimRed_elbow <- renderPlotly({
              plotSeuratElbow(inSCE = vals$counts,
                              externalReduction = new_pca)
            })
          } else if(input$dimRedAssaySelect %in% expDataNames(vals$counts)){
            output$plotDimRed_elbow <- renderPlotly({
              plotSeuratElbow(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
                              externalReduction = new_pca)
            })
          }
        }
      }
      
      if(input$computeHeatmapPlot){
        appendTab(
          inputId = "dimRedPCAICA_plotTabset",
          tabPanel(
            title = "Heatmap Plot",
            tags$script("Shiny.addCustomMessageHandler('close_dropDownDimRedHeatmap', function(x){
                  $('html').click();
                });"),
            panel(
              fluidRow(
                column(4, dropdown(
                  fluidRow(actionBttn(inputId = "closeDropDownDimRedHeatmap", label = NULL, style = "simple", color = "danger", icon = icon("times"), size = "xs"), align = "right"),
                  selectizeInput(inputId = "picker_dimheatmap_components_dimRed",
                                 label = "Select principal components to plot:",
                                 choices = c(),
                                 multiple = TRUE),
                  numericInput(
                    inputId = "slider_dimheatmap_dimRed",
                    label = "Number of columns for the plot: ",
                    min = 1,
                    max = 4,
                    value = 3
                  ),
                  actionBttn(
                    inputId = "updateHeatmap_dimRed",
                    label = "Update",
                    style = "bordered",
                    color = "primary",
                    size = "sm"
                  ),
                  inputId = "dropDownDimRedHeatmap",
                  icon = icon("cog"),
                  status = "primary",
                  circle = FALSE,
                  inline = TRUE
                )),
                column(6, fluidRow(h6("Heatmaps of the top features correlated with each selected component"), align = "center"))
              ),
              hr(),
              br(),
              shinyjqui::jqui_resizable(
                plotOutput(outputId = "plot_heatmap_dimRed"),
                options = list(maxWidth = 700)
              )
            )
          )
        )
        message(paste0(date(), " ... Generating Heatmaps."))
        if (input$dimRedPlotMethod == "seuratPCA") {
          if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
            vals$counts@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
              inSCE = vals$counts,
              useAssay = input$dimRedAssaySelect,
              dims = 1:input$dimRedNumberDims,
              nfeatures = input$dimRedNFeaturesHeatmap,
              reduction = "pca"
            )
            output$plot_heatmap_dimRed <- renderPlot({
              singleCellTK:::.plotHeatmapMulti(vals$counts@metadata$seurat$heatmap_dimRed, seq(6), 3)
            })
          }
          else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
            altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
              inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
              useAssay = vals$runDimred$dimRedAssaySelect,
              dims = 1:input$dimRedNumberDims,
              nfeatures = input$dimRedNFeaturesHeatmap,
              reduction = "pca"
            )
            output$plot_heatmap_dimRed <- renderPlot({
              singleCellTK:::.plotHeatmapMulti(altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed, seq(6), 3)
            })
          }
        }
        else if(input$dimRedPlotMethod == "seuratICA"){
          if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
            vals$counts@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
              inSCE = vals$counts,
              useAssay = input$dimRedAssaySelect,
              dims = 1:input$dimRedNumberDims,
              nfeatures = input$dimRedNFeaturesHeatmap,
              reduction = "ica"
            )
            output$plot_heatmap_dimRed <- renderPlot({
              singleCellTK:::.plotHeatmapMulti(vals$counts@metadata$seurat$heatmap_dimRed, seq(6), 3)
            })
          }
          else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
            altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
              inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
              useAssay = vals$runDimred$dimRedAssaySelect,
              dims = 1:input$dimRedNumberDims,
              nfeatures = input$dimRedNFeaturesHeatmap,
              reduction = "ica"
            )
            output$plot_heatmap_dimRed <- renderPlot({
              singleCellTK:::.plotHeatmapMulti(altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed, seq(6), 3)
            })
          }
        }
        else{
          if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
            vals$counts@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
              inSCE = vals$counts,
              useAssay = input$dimRedAssaySelect,
              dims = 1:input$dimRedNumberDims,
              nfeatures = input$dimRedNFeaturesHeatmap,
              externalReduction = new_pca
            )
            output$plot_heatmap_dimRed <- renderPlot({
              singleCellTK:::.plotHeatmapMulti(vals$counts@metadata$seurat$heatmap_dimRed, seq(6), 3)
            })
          }
          else if(input$dimRedAssaySelect %in% expDataNames(vals$counts)){
            altExps(vals$counts)[[input$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
              inSCE = altExps(vals$counts)[[input$dimRedAssaySelect]],
              useAssay = input$dimRedAssaySelect,
              dims = 1:input$dimRedNumberDims,
              nfeatures = input$dimRedNFeaturesHeatmap,
              externalReduction = new_pca
            )
            output$plot_heatmap_dimRed <- renderPlot({
              singleCellTK:::.plotHeatmapMulti(altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed, seq(6), 3)
            })
          }
        }
        compPrefix <- "PC"
        if(input$dimRedPlotMethod == "seuratICA"){
          compPrefix <- "IC"
        }
        compChoices <- rep(paste0(compPrefix, seq(input$dimRedNumberDims)))
        updateSelectizeInput(
          session = session,
          inputId = "picker_dimheatmap_components_dimRed",
          choices = compChoices,
          selected = compChoices[seq(6)]
        )
      }
      
      appendTab(
        inputId = "dimRedPCAICA_plotTabset",
        tabPanel(
          title = "Component Plot",
          panel(
            tags$script("Shiny.addCustomMessageHandler('close_dropDownDimRedComponentPlot', function(x){$('html').click();});"),
            fluidRow(
              column(
                4,
                dropdown(
                  fluidRow(
                    column(
                      12,
                      fluidRow(
                        actionBttn(inputId = "closeDropDownDimRedComponentPlot",
                                   label = NULL, style = "simple",
                                   color = "danger",
                                   icon = icon("times"), size = "xs"),
                        align = "right"),
                      selectizeInput(
                        inputId = "plotDimRed_pca_selectRedDim",
                        label = "Select reducedDim:",
                        choices = reducedDimNames(vals$counts)
                      ),
                      numericInput(inputId = "plotDimRed_pca_dimX",
                                   label = "Select component for X-axis:",
                                   value = 1),
                      numericInput(inputId = "plotDimRed_pca_dimY",
                                   label = "Select component for Y-axis:",
                                   value = 2),
                      actionBttn(
                        inputId = "updateRedDimPlot_pca",
                        label = "Update",
                        style = "bordered",
                        color = "primary",
                        size = "sm"
                      )
                    )
                  ),
                  inputId = "dropDownDimRedComponentPlot",
                  icon = icon("cog"),
                  status = "primary",
                  circle = FALSE,
                  inline = TRUE
                )),
              column(
                6,
                fluidRow(h6("Scatterplot of cells on selected components from a dimensionality reduction"), align = "center"))
            ),
            hr(),
            br(),
            plotlyOutput(outputId = "plotDimRed_pca")
          )
        )
      )
      
      message(paste0(date(), " ... Plotting PCA/ICA."))
      output$plotDimRed_pca <- renderPlotly({
        plotly::ggplotly(
          plotDimRed(
            inSCE = vals$counts,
            useReduction = dimrednamesave,
            xAxisLabel = paste0(input$dimRedPlotMethod, "_1"),
            yAxisLabel = paste0(input$dimRedPlotMethod, "_2"))
        )
      })
      
      if(input$computeJackstrawPlot
         && input$dimRedPlotMethod != "seuratICA"){
        appendTab(inputId = "dimRedPCAICA_plotTabset", tabPanel(title = "JackStraw Plot",
                                                                panel(heading = "JackStraw Plot",
                                                                      shinyjqui::jqui_resizable(plotOutput(outputId = "plot_jackstraw_dimRed"))
                                                                )
        ))
        if (input$dimRedPlotMethod == "seuratPCA"){
          message(paste0(date(), " ... Generating JackStraw Plot."))
          if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
            vals$counts <- runSeuratJackStraw(inSCE = vals$counts,
                                              useAssay = input$dimRedAssaySelect,
                                              dims = input$dimRedNumberDims)
            output$plot_jackstraw_dimRed <- renderPlot({
              plotSeuratJackStraw(inSCE = vals$counts, dims = input$dimRedNumberDims)
            })
          }
          else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
            altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]] <- runSeuratJackStraw(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
                                                                                           useAssay = vals$runDimred$dimRedAssaySelect,
                                                                                           dims = input$dimRedNumberDims)
            output$plot_jackstraw_dimRed <- renderPlot({
              plotSeuratJackStraw(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]], dims = input$dimRedNumberDims)
            })
          }
        }
        else{
          message(paste0(date(), " ... Generating JackStraw Plot."))
          if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
            vals$counts <- runSeuratJackStraw(inSCE = vals$counts,
                                              useAssay = input$dimRedAssaySelect,
                                              dims = input$dimRedNumberDims,
                                              externalReduction = new_pca)
            output$plot_jackstraw_dimRed <- renderPlot({
              plotSeuratJackStraw(inSCE = vals$counts,
                                  dims = input$dimRedNumberDims)
            })
          }
          else if(input$dimRedAssaySelect %in% expDataNames(vals$counts)){
            altExps(vals$counts)[[input$dimRedAssaySelect]] <- runSeuratJackStraw(inSCE = altExps(vals$counts)[[input$dimRedAssaySelect]],
                                                                                  useAssay = input$dimRedAssaySelect,
                                                                                  dims = input$dimRedNumberDims,
                                                                                  externalReduction = new_pca)
            output$plot_jackstraw_dimRed <- renderPlot({
              plotSeuratJackStraw(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
                                  dims = input$dimRedNumberDims)
            })
          }
        }
      }
    }
  ))
  
  observeEvent(input$updateRedDimPlot_pca,{
    req(vals$counts)
    output$plotDimRed_pca <- renderPlotly({
      isolate({
        plotly::ggplotly(
          plotDimRed(
            inSCE = vals$counts,
            useReduction = input$plotDimRed_pca_selectRedDim,
            xDim = input$plotDimRed_pca_dimX,
            yDim = input$plotDimRed_pca_dimY,
            xAxisLabel = paste0(input$dimRedPlotMethod, "_", input$plotDimRed_pca_dimX),
            yAxisLabel = paste0(input$dimRedPlotMethod, "_", input$plotDimRed_pca_dimY))
        )
      })
    })
    session$sendCustomMessage("close_dropDownDimRedComponentPlot", "")
  })
  
  observeEvent(input$closeDropDownDimRedComponentPlot, {
    req(vals$counts)
    session$sendCustomMessage("close_dropDownDimRedComponentPlot", "")
  })
  
  observeEvent(input$dimRedAssaySelect_tsneUmap, {
    req(vals$counts)
    if (!is.null(input$dimRedAssaySelect_tsneUmap)) {
      if (input$dimRedAssaySelect_tsneUmap %in% reducedDimNames(vals$counts)) {
        shinyjs::disable("reductionMethodUMAPTSNEDimRed")
        shinyjs::disable("logNorm_tsneUmap")
        shinyjs::disable("hvg_tsneUmap")
        shinyjs::disable("scale_tsneUmap")
        shinyjs::disable("pca_tsneUmap")
        updateCheckboxInput(session, "logNorm_tsneUmap", value = FALSE)
        updateCheckboxInput(session, "scale_tsneUmap", value = FALSE)
        updateCheckboxInput(session, "pca_tsneUmap", value = FALSE)
      } else {
        shinyjs::enable("reductionMethodUMAPTSNEDimRed")
        shinyjs::enable("logNorm_tsneUmap")
        shinyjs::enable("hvg_tsneUmap")
        shinyjs::enable("scale_tsneUmap")
        shinyjs::enable("pca_tsneUmap")
        updateCheckboxInput(session, "scale_tsneUmap", value = TRUE)
        updateCheckboxInput(session, "pca_tsneUmap", value = TRUE)
      }
    } else {
      shinyjs::enable("reductionMethodUMAPTSNEDimRed")
    }
  })
  
  observeEvent(input$runDimred_tsneUmap, withConsoleMsgRedirect(
    msg = "Please wait while 2D embeddings are being created. See console log for progress.",
    {
      req(vals$counts)
      message(date(), " ... Starting Dimensionality Reduction with: '",
              input$dimRedPlotMethod_tsneUmap, "'.")
      vals$runDimred$dimRedAssaySelect_tsneUmap <- input$dimRedAssaySelect_tsneUmap
      if (vals$runDimred$dimRedAssaySelect_tsneUmap %in% reducedDimNames(vals$counts)) {
        embedUseAssay <- NULL
        embedUseRedDim <- vals$runDimred$dimRedAssaySelect_tsneUmap
        embedUseAltExp <- NULL
      } else if (vals$runDimred$dimRedAssaySelect_tsneUmap %in% altExpNames(vals$counts)) {
        embedUseAssay <- vals$runDimred$dimRedAssaySelect_tsneUmap
        embedUseRedDim <- NULL
        embedUseAltExp <- vals$runDimred$dimRedAssaySelect_tsneUmap
      } else if (vals$runDimred$dimRedAssaySelect_tsneUmap %in% assayNames(vals$counts)) {
        embedUseAssay <- vals$runDimred$dimRedAssaySelect_tsneUmap
        embedUseRedDim <- NULL
        embedUseAltExp <- NULL
      }
      if (input$dimRedNameInput_tsneUmap == "" ||
          is.null(input$dimRedNameInput_tsneUmap)){
        stop("Please enter a reducedDim name!")
      }
      if (input$dimRedNameInput_tsneUmap %in% names(reducedDims(vals$counts))){
        stop("A reducedDim with name '", input$dimRedNameInput_tsneUmap,
             "' is already stored in the object. Please specify a ",
             "different name for this reducedDim.")
      }
      dimrednamesave <- gsub(" ", "_", input$dimRedNameInput_tsneUmap)
      useFeatureSubset <- input$hvg_tsneUmap
      if (input$hvg_tsneUmap == "None") {
        useFeatureSubset <- NULL
      }
      if (input$dimRedPlotMethod_tsneUmap == "rTSNE"){
        vals$counts <- runDimReduce(
          inSCE = vals$counts,
          useAssay = embedUseAssay,
          useReducedDim = embedUseRedDim,
          useAltExp = embedUseAltExp,
          method = "rTSNE",
          logNorm = input$logNorm_tsneUmap,
          useFeatureSubset = useFeatureSubset,
          center = input$scale_tsneUmap,
          scale = input$scale_tsneUmap,
          pca = input$pca_tsneUmap,
          initialDims = input$dimRedNumberDims_tsneUmap,
          theta = input$thetaTSNE,
          reducedDimName = dimrednamesave,
          perplexity = input$perplexityTSNE,
          nIterations = input$iterTSNE,
          seed = input$seed__tsneUmap
        )
      } else if(input$dimRedPlotMethod_tsneUmap == "seuratTSNE"){
        if (!is.null(embedUseRedDim)) {
          vals$counts <- runDimReduce(
            inSCE = vals$counts,
            useAssay = embedUseAssay,
            useReducedDim = embedUseRedDim,
            useAltExp = embedUseAltExp,
            method = "seuratTSNE",
            reducedDimName = dimrednamesave,
            dims = input$dimRedNumberDims_tsneUmap,
            perplexity = input$perplexityTSNE,
            seed = input$seed__tsneUmap
          )
        } else {
          vals$counts <- runDimReduce(
            inSCE = vals$counts,
            useAssay = embedUseAssay,
            useReducedDim = embedUseRedDim,
            useAltExp = embedUseAltExp,
            method = "seuratTSNE",
            useFeatureSubset = useFeatureSubset,
            reducedDimName = dimrednamesave,
            dims = input$dimRedNumberDims_tsneUmap,
            perplexity = input$perplexityTSNE,
            useReduction = input$reductionMethodUMAPTSNEDimRed,
            seed = input$seed__tsneUmap
          )
        }
      } else if(input$dimRedPlotMethod_tsneUmap == "seuratUMAP"){
        if (!is.null(embedUseRedDim)) {
          vals$counts <- runDimReduce(
            inSCE = vals$counts,
            useAssay = embedUseAssay,
            useReducedDim = embedUseRedDim,
            useAltExp = embedUseAltExp,
            method = "seuratUMAP",
            reducedDimName = dimrednamesave,
            dims = input$dimRedNumberDims_tsneUmap,
            minDist = input$minDistUMAPDimRed,
            nNeighbors = input$nNeighboursUMAPDimRed,
            spread = input$spreadUMAPDimRed,
            seed = input$seed__tsneUmap
          )
        } else {
          vals$counts <- runDimReduce(
            inSCE = vals$counts,
            useAssay = embedUseAssay,
            useReducedDim = embedUseRedDim,
            useAltExp = embedUseAltExp,
            method = "seuratUMAP",
            useFeatureSubset = useFeatureSubset,
            reducedDimName = dimrednamesave,
            dims = input$dimRedNumberDims_tsneUmap,
            minDist = input$minDistUMAPDimRed,
            nNeighbors = input$nNeighboursUMAPDimRed,
            spread = input$spreadUMAPDimRed,
            useReduction = input$reductionMethodUMAPTSNEDimRed,
            seed = input$seed__tsneUmap
          )
        }
      } else if(input$dimRedPlotMethod_tsneUmap == "scaterUMAP") {
        if (is.na(input$alphaUMAP)) {
          stop("Learning rate (alpha) must be a numeric non-empty value!")
        }
        vals$counts <- runDimReduce(
          inSCE = vals$counts,
          useAssay = embedUseAssay,
          useReducedDim = embedUseRedDim,
          useAltExp = embedUseAltExp,
          method = "scaterUMAP",
          logNorm = input$logNorm_tsneUmap,
          useFeatureSubset = useFeatureSubset,
          scale = input$scale_tsneUmap,
          pca = input$pca_tsneUmap,
          initialDims = input$dimRedNumberDims_tsneUmap,
          reducedDimName = dimrednamesave,
          nNeighbors = input$neighborsUMAP,
          nIterations = input$iterUMAP,
          minDist = input$mindistUMAP,
          alpha = input$alphaUMAP,
          spread = input$spreadUMAP,
          seed = input$seed__tsneUmap
        )
      } else if(input$dimRedPlotMethod_tsneUmap == "scanpyUMAP"){
        vals$counts <- runDimReduce(
          inSCE = vals$counts,
          useAssay = embedUseAssay,
          useReducedDim = embedUseRedDim,
          useAltExp = embedUseAltExp,
          method = "scanpyUMAP",
          reducedDimName = dimrednamesave
        )
      } else if(input$dimRedPlotMethod_tsneUmap == "scanpyTSNE"){
        vals$counts <- runDimReduce(
          inSCE = vals$counts,
          useAssay = embedUseAssay,
          useReducedDim = embedUseRedDim,
          useAltExp = embedUseAltExp,
          method = "scanpyTSNE",
          reducedDimName = dimrednamesave
        )
      }
      updateReddimInputs()
      updateAssayInputs()
      # Show downstream analysis options
      callModule(module = nonLinearWorkflow, id = "nlw-dr", parent = session,
                 cl = TRUE, cv = TRUE)
      
      message(paste0(date(), " ... Ending Dimensionality Reduction."))
      updateSelectizeInput(session, "selectRedDimPlot_tsneUmap",
                           choices = reducedDimNames(vals$counts),
                           selected = dimrednamesave,
                           server = TRUE)
      message(paste0(date(), " ... Plotting 2D-Embedding."))
      output$plotDimRed_tsneUmap <- renderPlotly({
        isolate({
          plotly::ggplotly(plotDimRed(
            inSCE = vals$counts,
            useReduction = dimrednamesave,
            xAxisLabel = paste0(input$dimRedPlotMethod_tsneUmap,"_1"),
            yAxisLabel = paste0(input$dimRedPlotMethod_tsneUmap,"_2")
          ))
        })
      })
    }
  ))
  
  observeEvent(input$updateRedDimPlot_tsneUmap,{
    req(vals$counts)
    output$plotDimRed_tsneUmap <- renderPlotly({
      isolate({
        plotly::ggplotly(plotDimRed(
          inSCE = vals$counts,
          useReduction = input$selectRedDimPlot_tsneUmap,
          xAxisLabel = paste0(input$selectRedDimPlot_tsneUmap,"_1"),
          yAxisLabel = paste0(input$selectRedDimPlot_tsneUmap,"_2")
        ))
      })
    })
    
    session$sendCustomMessage("close_dropDownDimRedEmbedding", "")
  })
  
  observeEvent(input$closeDropDownDimRedEmbedding,{
    session$sendCustomMessage("close_dropDownDimRedEmbedding", "")
  })
  
  #-----------------------------------------------------------------------------
  # Page 3: Clustering ####
  #-----------------------------------------------------------------------------
  
  observeEvent(input$clustAlgo, {
    if(input$clustAlgo %in% seq(7)){
      # Scran SNN
      updateTextInput(session, "clustName", value = "scran_snn_cluster")
      enable("clustName")
    } else if(input$clustAlgo %in% seq(8, 10)){
      # K-Means
      updateTextInput(session, "clustName", value = "kmeans_cluster")
      enable("clustName")
    } else if(input$clustAlgo %in% seq(11, 13)){
      algoList <- list('11' = "louvain",
                       '12' = "multilevel", '13' = "SLM")
      algo <- algoList[[as.character(input$clustAlgo)]]
      updateTextInput(session, "clustName",
                      value = paste0("Seurat", "_", algo, "_",
                                     "Resolution", input$clustSeuratRes))
      disable("clustName")
    } else if(input$clustAlgo %in% seq(14, 15)){
      algoList <- list('14' = "louvain",
                       '15' = "leiden")
      algo <- algoList[[as.character(input$clustAlgo)]]
      updateTextInput(session, "clustName",
                      value = paste0("Scanpy", "_", algo, "_", input$clustSeuratRes))
    }
  })
  
  observeEvent(input$clustSeuratRes, {
    if (input$clustAlgo %in% seq(11, 13)) {
      algoList <- list('11' = "louvain",
                       '12' = "multilevel", '13' = "SLM")
      algo <- algoList[[as.character(input$clustAlgo)]]
      updateTextInput(session, "clustName",
                      value = paste0("Seurat", "_", algo, "_",
                                     "Resolution", input$clustSeuratRes))
      disable("clustName")
    }
  })
  
  clustResults <- reactiveValues(names = NULL)
  
  observeEvent(input$clustRun, withConsoleMsgRedirect(
    msg = "Please wait while clustering algorithm is being computed. See console log for progress.",
    {
      req(vals$counts)
      if (input$clustName == "") {
        stop("Cluster name should not be empty.")
      }
      saveClusterName = gsub(" ", "_", input$clustName)
      if (input$clustAlgo %in% seq(7)) {
        # Scran SNN
        if (is.na(input$clustScranSNNK)) {
          stop("K must be a numeric non-empty value!")
        }
        if (is.na(input$clustScranSNNd)) {
          stop("Number of components must be a numeric non-empty value!")
        }
        algoList <- list('1' = "louvain", '2' = "leiden", '3' = "walktrap",
                         '4' = "infomap", '5' = "fastGreedy",
                         '6' = "labelProp", '7' = "leadingEigen")
        algo <- algoList[[as.character(input$clustAlgo)]]
        params = list(inSCE = vals$counts,
                      clusterName = saveClusterName,
                      k = input$clustScranSNNK,
                      weightType = input$clustScranSNNType,
                      algorithm = algo)
        matType <- getTypeByMat(vals$counts, input$clustScranSNNMat)
        if (is.null(matType)) {
          return()
        } else if (length(matType) == 1) {
          if (matType == "assay") {
            params$useAssay = input$clustScranSNNMat
            params$nComp = input$clustScranSNNd
            plotReddim <- NULL
          } else if (matType == "reducedDim") {
            params$useReducedDim = input$clustScranSNNMat
            updateSelectInput(session, "clustVisReddim",
                              selected = input$clustScranSNNMat)
            plotReddim <- input$clustScranSNNMat
          } else if (matType == "altExp") {
            params$useAltExp = input$clustScranSNNMat
            params$altExpAssay = input$clustScranSNNMat
            params$nComp = input$clustScranSNNd
            plotReddim <- NULL
          }
        } else if (length(matType) == 2 &&
                   matType[1] == "reducedDim") {
          # Using reddims saved in altExp
          params$useAltExp = matType[2]
          params$altExpRedDim = input$clustScranSNNMat
          updateSelectInput(session, "clustVisReddim",
                            selected = input$clustScranSNNMat)
        }
        if (algo == 'leiden') {
          params$resolution_parameter <- input$clustScranSNNLeidenReso
          params$objective_function <- input$clusterScranSNNLeidenObjFunc
        }
        if (algo == "walktrap") {
          params$steps <- input$clustScranSNNWalktrapStep
        }
        vals$counts <- do.call(runScranSNN, params)
      } else if (input$clustAlgo %in% seq(8, 10)) {
        # K-Means
        if (input$clustKMeansReddim == "") {
          stop("Must select a reducedDim! If none available, compute one in the Dimensionality Reduction tab.")
        }
        if (is.na(input$clustKMeansN)) {
          stop("Number of clusters/centers must be a numeric non-empty value!")
        }
        if (is.na(input$clustKMeansNIter)) {
          stop("Max number of iterations must be a numeric non-empty value!")
        }
        if (is.na(input$clustKMeansNStart)) {
          stop("Number of random sets must be a numeric non-empty value!")
        }
        algoList <- list('8' = "Hartigan-Wong",
                         '9' = "Lloyd", '10' = "MacQueen")
        algo <- algoList[[as.character(input$clustAlgo)]]
        vals$counts <- runKMeans(inSCE = vals$counts,
                                 useReducedDim = input$clustKMeansReddim,
                                 nCenters = input$clustKMeansN,
                                 nIter = input$clustKMeansNIter,
                                 nStart = input$clustKMeansNStart,
                                 algorithm = algo,
                                 clusterName = saveClusterName)
        updateSelectInput(session, "clustVisReddim",
                          selected = input$clustKMeansReddim)
        plotReddim <- input$clustKMeansReddim
      } else if (input$clustAlgo %in% seq(11, 13)) {
        # Seurat
        if(input$clustSeuratReddim == ""){
          stop("Must select a reducedDim! If none available, compute one in the Dimensionality Reduction tab.")
        }
        if(is.na(input$clustSeuratDims)){
          stop("Number of dimensions must be a numeric non-empty value!")
        }
        if(is.na(input$clustSeuratRes)){
          stop("Resolution must be a numeric non-empty value!")
        }
        reddim <- reducedDim(vals$counts, input$clustSeuratReddim)
        rownames(reddim) <- gsub("_", "-", rownames(reddim))
        if ("percentVar" %in% names(attributes(reddim))) {
          stdev <- as.numeric(attr(reddim, "percentVar"))
          new_pca <- CreateDimReducObject(embeddings = reddim, assay = "RNA",
                                          stdev = stdev, key = "PC_")
        } else {
          new_pca <- CreateDimReducObject(embeddings = reddim, assay = "RNA",
                                          key = "PC_")
        }
        if (input$clustSeuratDims > ncol(reddim)) {
          warning("More dimensions specified in dims than have been computed")
          dims <- ncol(reddim)
        } else {
          dims <- input$clustSeuratDims
        }
        useAssay <- assayNames(vals$counts)[1]
        algoList <- list('11' = "louvain",
                         '12' = "multilevel", '13' = "SLM")
        algo <- algoList[[as.character(input$clustAlgo)]]
        vals$counts <- runSeuratFindClusters(inSCE = vals$counts,
                                             useAssay = useAssay,
                                             useReduction = "pca",
                                             externalReduction = new_pca,
                                             dims = dims,
                                             algorithm = algo,
                                             groupSingletons = input$clustSeuratGrpSgltn,
                                             resolution = input$clustSeuratRes)
        updateSelectInput(session, "clustVisReddim",
                          selected = input$clustSeuratReddim)
        plotReddim <- input$clustSeuratReddim
      }
      else if (input$clustAlgo %in% seq(14, 15)){
        algoList <- list('14' = "louvain",
                         '15' = "leiden")
        algo <- algoList[[as.character(input$clustAlgo)]]
        useAssay <- assayNames(vals$counts)[1] # change this
        vals$counts <- runScanpyFindClusters(inSCE = vals$counts, 
                                             useAssay = useAssay, 
                                             algorithm = algo, 
                                             dims = input$clustScanpyDims, 
                                             resolution = input$clustScanpyRes, 
                                             nNeighbors = input$clustScanpyNeighbors, 
                                             niterations = input$clustScanpyIter, 
                                             use_weights = input$clustScanpyWeights, 
                                             cor_method = input$clustScanpyCorrMethod,
                                             colDataName = input$clustName,
                                             useReduction = input$clustKMeansReddim)
        updateSelectInput(session, "clustVisReddim",
                          selected = input$clustKMeansReddim)
        plotReddim <- input$clustKMeansReddim
      }
      updateColDataNames()
      clustResults$names <- c(clustResults$names, saveClusterName)
      updateSelectInput(session, "clustVisRes", choices = clustResults$names)
      if (!is.null(plotReddim)) {
        output$clustVisPlot <- renderPlotly({
          isolate({
            plotSCEDimReduceColData(inSCE = vals$counts,
                                    colorBy = saveClusterName,
                                    conditionClass = "factor",
                                    reducedDimName = plotReddim,
                                    labelClusters = TRUE,
                                    dim1 = 1, dim2 = 2,
                                    legendTitle = saveClusterName)
          })
        })
      }
      # Show downstream analysis options
      callModule(module = nonLinearWorkflow, id = "nlw-cl", parent = session,
                 de = TRUE, fm = TRUE, pa = TRUE, cv = TRUE, tj = TRUE)
    }
  ))
  
  observeEvent(input$closeDropDownClust, {
    session$sendCustomMessage("close_dropDownClust", "")
  })
  
  observeEvent(input$clustPlot, {
    req(vals$counts)
    choice <- NULL
    if (input$clustVisChoicesType == 1) {
      # Use result
      if (is.null(input$clustVisRes) ||
          input$clustVisRes == "") {
        shinyalert::shinyalert("Error!", "Select the clusters to plot",
                               type = "error")
      }
      choice <- input$clustVisRes
    } else if (input$clustVisChoicesType == 2) {
      # Use colData
      if (is.null(input$clustVisCol) ||
          input$clustVisCol == "") {
        shinyalert::shinyalert("Error!", "Select the clusters to plot",
                               type = "error")
      }
      choice <- input$clustVisCol
    }
    if (is.null(input$clustVisReddim) || input$clustVisReddim == "") {
      shinyalert::shinyalert("Error!",
                             "No reduction selected. Select one or run dimension reduction first",
                             type = "error")
    }
    if (!is.null(choice) && choice != "" &&
        !is.null(input$clustVisReddim) && input$clustVisReddim != "") {
      output$clustVisPlot <- renderPlotly({
        isolate({
          plotSCEDimReduceColData(inSCE = vals$counts,
                                  colorBy = choice,
                                  conditionClass = "factor",
                                  reducedDimName = input$clustVisReddim,
                                  labelClusters = TRUE,
                                  dim1 = 1, dim2 = 2,
                                  legendTitle = choice)
        })
      })
    }
    session$sendCustomMessage("close_dropDownClust", "")

  })
  
  #-----------------------------------------------------------------------------
  # Trajectory Analysis####
  #-----------------------------------------------------------------------------
  
  updateTSCANUICollapse <- function() {
    if (!is.null(vals$counts)) {
      tscanResult <- metadata(vals$counts)$sctk$Traj$TSCAN$Pseudotime
      if (is.null(tscanResult)) {
        shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Path']")
        shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Branched Cluster']")
        shinyjs::disable(selector = "div[value='Plot feature expression on trajectory']")
      } else {
        shinyjs::enable(selector = "div[value='Identify Genes Differentially Expressed For Path']")
        shinyjs::enable(selector = "div[value='Identify Genes Differentially Expressed For Branched Cluster']")
        shinyjs::enable(selector = "div[value='Plot feature expression on trajectory']")
      }
    } else {
      shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Path']")
      shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Branched Cluster']")
      shinyjs::disable(selector = "div[value='Plot feature expression on trajectory']")
    }
  }
  ###################################################
  ###  Run STEP 1: TSCAN
  ###################################################
  
  observeEvent(input$TSCANRun, withConsoleMsgRedirect(
    msg = "Please wait while pseudotime is being computed. See console log for progress.",
    {
      req(vals$counts)
      if (input$TSCANReddim == "") {
        stop("Must select a reducedDim! If none available, compute one in the Dimensionality Reduction tab.")
      }
      cluster <- input$TSCANclusterName
      if (cluster == "Auto generate clusters") cluster <- NULL
      vals$counts <- runTSCAN(inSCE = vals$counts,
                              useReducedDim = input$TSCANReddim,
                              cluster = cluster,
                              seed = handleEmptyInput(input$seed_TSCAN))
      
      output$TSCANPlot <- renderPlot({
        isolate({
          plotTSCANResults(inSCE = vals$counts,
                           useReducedDim = input$TSCANReddim)
        })
      })
      results <- getTSCANResults(vals$counts, analysisName = "Pseudotime")
      terminalNodes <- colnames(results$pseudo)
      terminalNodesList <- results$pathIndexList
      updatePickerInput(session, "pathIndexx",
                        choices = terminalNodes,
                        choicesOpt = list(content=terminalNodesList),
                        selected = NULL)
      clusterNamesList <- sort(unique(colData(vals$counts)$TSCAN_clusters))
      updatePickerInput(session, "useClusterForPlotGene",
                        choices = clusterNamesList,
                        selected = NULL)
      updatePickerInput(session, "plotTSCANDimReduceFeatures_useCluster",
                        choices = clusterNamesList)
      updateSelectInput(session, "TSCANUseCluster",
                        choices = results$branchClusters)
      updateCollapse(session = session, "TSCANUI",
                     style = list(`Calculate Pseudotime Values` = "success"))
      updateTSCANUICollapse()
    }
  ))
  
  #plot results
  observeEvent(input$TSCANPlot, {
    req(vals$counts)
    output$TSCANPlot <- renderPlot({
      isolate({
        plotTSCANResults(inSCE = vals$counts,
                         useReducedDim = input$TSCANVisRedDim)
      })
    })
    updateSelectInput(session, "plotTSCANClusterDEG_useReducedDim",
                      selected = input$TSCANVisRedDim)
    updateSelectInput(session, "plotTSCANDimReduceFeatures_useReducedDim",
                      selected = input$TSCANVisRedDim)
    session$sendCustomMessage("close_dropDownTSCAN", "")
  })
  
  ###################################################
  ###  Run STEP 2: Identify expressive genes
  ###################################################
  observeEvent(input$pathIndexx, {
    req(vals$counts)
    results <- getTSCANResults(vals$counts, analysisName = "Pseudotime")
    choices <- results$pathClusters[[input$pathIndexx]]
    updatePickerInput(session, "discardCluster", choices = choices,
                      selected = NULL,
                      options = list(
                        `none-selected-text` = "No cluster discarded"
                      ))
  })
  
  observeEvent(input$runTSCANDEG, withConsoleMsgRedirect(
    msg = "Please wait while DE genes are being found for path. See console log for progress.",
    {
      req(vals$counts)
      vals$counts <- runTSCANDEG(inSCE = vals$counts,
                                 pathIndex = input$pathIndexx,
                                 useAssay = input$TSCANassayselect,
                                 discardCluster = input$discardCluster)
      
      message(paste0(date(), " ... Expressive Genes Identified"))
      message(paste0(date(), " ... Updating heatmap"))
      output$heatmapPlot <- renderPlot({
        isolate({
          plotTSCANPseudotimeHeatmap(inSCE = vals$counts,
                                     pathIndex = input$pathIndexx)
        })
      })
      
      message(paste0(date(), " ... Updating up-regulated genes"))
      output$UpregGenesPlot <- renderPlot({
        isolate({
          plotTSCANPseudotimeGenes(inSCE = vals$counts,
                                   pathIndex = input$pathIndexx,
                                   direction = "increasing")
        })
      })
      
      message(paste0(date(), " ... Updating down-regulated genes"))
      output$DownregGenesPlot <- renderPlot({
        isolate({
          plotTSCANPseudotimeGenes(inSCE = vals$counts,
                                   pathIndex = input$pathIndexx,
                                   direction = "decreasing")
        })
      })
      
      all.results <- getTSCANResults(vals$counts, analysisName = "DEG")
      updateSelectInput(session, "tscanDEexpPathIndex",
                        choices = names(all.results),
                        selected = input$pathIndexx)
      updateCollapse(
        session = session, "TSCANUI",
        style = list("Identify Genes Differentially Expressed For Path" = "success")
      )
      callModule(module = nonLinearWorkflow, id = "nlw-Traj", parent = session,
                 de = TRUE, pa = TRUE)
    }
  ))
  
  observeEvent(input$tscanDEPlot, withConsoleMsgRedirect(
    msg = "Please wait while TSCAN DE plots are being updated. See console log for progress",
    {
      req(vals$counts)
      if (input$tscanDEFeatureDisplay == "Rownames (Default)") {
        featureDisplay <- NULL
      } else {
        featureDisplay <- input$tscanDEFeatureDisplay
      }
      message(paste0(date(), " ... Updating heatmap"))
      output$heatmapPlot <- renderPlot({
        isolate({
          plotTSCANPseudotimeHeatmap(inSCE = vals$counts,
                                     pathIndex = input$tscanDEexpPathIndex,
                                     topN = input$tscanDEHMTopGenes,
                                     featureDisplay = featureDisplay)
        })
      })
      
      message(paste0(date(), " ... Updating up-regulated genes"))
      output$UpregGenesPlot <- renderPlot({
        isolate({
          plotTSCANPseudotimeGenes(inSCE = vals$counts,
                                   pathIndex = input$tscanDEexpPathIndex,
                                   direction = "increasing",
                                   topN = input$tscanDERegTopGenes,
                                   featureDisplay = featureDisplay)
        })
      })
      
      message(paste0(date(), " ... Updating down-regulated genes"))
      output$DownregGenesPlot <- renderPlot({
        isolate({
          plotTSCANPseudotimeGenes(inSCE = vals$counts,
                                   pathIndex = input$tscanDEexpPathIndex,
                                   direction = "decreasing",
                                   topN = input$tscanDERegTopGenes,
                                   featureDisplay = featureDisplay)
        })
      })
      
      session$sendCustomMessage("close_dropDownTscanDE", "")
    }
  ))
  
  ###################################################
  ###  Run STEP 3: Identify DE genes in specific cluster
  ###################################################
  
  observeEvent(input$findDEGenes, withConsoleMsgRedirect(
    msg = "Please wait while DE genes are being found for branched cluster. See console log for progress.",
    {
      req(vals$counts)
      vals$counts <- runTSCANClusterDEAnalysis(inSCE = vals$counts,
                                               useCluster = input$TSCANUseCluster,
                                               useAssay = input$TSCANBranchAssaySelect,
                                               fdrThreshold = input$fdrThreshold_TSCAN)
      
      clusterAnalysisNames <- names(getTSCANResults(vals$counts,
                                                    analysisName = "ClusterDEAnalysis"))
      
      results <- getTSCANResults(vals$counts,
                                 analysisName = "ClusterDEAnalysis",
                                 pathName = input$TSCANUseCluster)
      pathChoices <- colnames(results$terminalNodes)
      
      updateSelectInput(session, "plotTSCANClusterDEG_useCluster",
                        choices = clusterAnalysisNames,
                        selected = input$TSCANUseCluster)
      
      #plot cluster deg by default
      message(paste0(date(), " ... Plotting top DEG expression"))
      output$tscanCLusterDEG <- renderPlot({
        isolate({
          plotTSCANClusterDEG(inSCE = vals$counts,
                              useCluster = input$TSCANUseCluster,
                              pathIndex = pathChoices[1],
                              topN = 4,
                              useReducedDim = input$TSCANVisRedDim)
        })
      })
      
      #print list of DE genes by default
      message(paste0(date(), " ... List of DE genes retrieved"))
      df <- as.data.frame(results$DEgenes[[1]])
      output$tscanCLusterDEGTable <- DT::renderDataTable({
        isolate({
          DT::datatable(
            df,
            options = list(scrollX = TRUE)
          )
        })
      })
      
      #plot cluster pseudo values by default
      message(paste0(date(), " ... Plotting pseudotime of branches for cluster"))
      
      output$tscanCLusterPeudo <- renderPlot({
        isolate({
          plotTSCANClusterPseudo(inSCE = vals$counts,
                                 useCluster = input$TSCANUseCluster,
                                 useReducedDim = input$plotTSCANClusterDEG_useReducedDim)
        })
      })
      
      updateCollapse(session = session, "TSCANUI",
                     style = list("Identify Genes Differentially Expressed For Branched Cluster" = "success"))
      callModule(module = nonLinearWorkflow, id = "nlw-Traj", parent = session,
                 de = TRUE, pa = TRUE)
    }
  ))
  
  # Plot Top DEG expression on cluster
  observeEvent(input$plotTSCANClusterDEG_useCluster, {
    req(vals$counts)
    results <- getTSCANResults(vals$counts,
                               analysisName = "ClusterDEAnalysis",
                               pathName = input$plotTSCANClusterDEG_useCluster)
    choices <- colnames(results$terminalNodes)
    choicesOpt <- list(content = results$pathIndexList)
    updatePickerInput(session, "plotTSCANClusterDEG_pathIndex",
                      choices = choices, choicesOpt = choicesOpt,
                      selected = NULL)
  })
  
  observeEvent(input$plotTSCANClusterDEG, withConsoleMsgRedirect(
    msg = "Please wait while cluster DEG visualization is being updated. See console log for progress.",
    {
      req(vals$counts)
      results <- getTSCANResults(vals$counts,
                                 analysisName = "ClusterDEAnalysis",
                                 pathName = input$plotTSCANClusterDEG_useCluster)
      req(results)
      if (input$plotTSCANClusterDEG_featureDisplay == "Rownames (Default)") {
        featureDisplay <- "rownames"
      } else {
        featureDisplay <- input$plotTSCANClusterDEG_featureDisplay
      }
      
      if (nrow(results$DEgenes[[input$plotTSCANClusterDEG_pathIndex]]) == 0) {
        shinyalert(text = "No significant feature identified for the selected path.",
                   type = "warning")
      }
      message(date(), " ... Updating UMAP with feature expression")
      plot <- plotTSCANClusterDEG(inSCE = vals$counts,
                                  useCluster = input$plotTSCANClusterDEG_useCluster,
                                  pathIndex = input$plotTSCANClusterDEG_pathIndex,
                                  useReducedDim = input$plotTSCANClusterDEG_useReducedDim,
                                  topN = handleEmptyInput(input$plotTSCANClusterDEG_topN, type = "numeric"),
                                  featureDisplay = featureDisplay)
      
      output$tscanCLusterDEG <- renderPlot({
        isolate({
          plot
        })
      })
      
      message(date(), " ... Updating DEG table")
      df <- as.data.frame(results$DEgenes[[input$plotTSCANClusterDEG_pathIndex]])
      output$tscanCLusterDEGTable <- DT::renderDataTable({
        isolate({
          DT::datatable(
            df,
            options = list(scrollX = TRUE)
          )
        })
      })
      
      message(date(), " ... Updating UMAP with pseudotime")
      output$tscanCLusterPeudo <- renderPlot({
        isolate({
          plotTSCANClusterPseudo(inSCE = vals$counts,
                                 useCluster = input$plotTSCANClusterDEG_useCluster,
                                 useReducedDim = input$plotTSCANClusterDEG_useReducedDim)
        })
      })
      
      session$sendCustomMessage("close_dropDownTscanClusterDEG", "")
    })
  )
  
  ###################################################
  ###  Run STEP 4: Plot gene of interest
  ###################################################
  observeEvent(input$plotTSCANDimReduceFeatures, withConsoleMsgRedirect(
    msg = "Please wait when the expression of selected features are being plotted. See console log for progress.",
    {
      req(vals$counts)
      if (is.null(input$plotTSCANDimReduceFeatures_features)) {
        stop("Must select at least one feature.")
      }
      if (input$plotTSCANDimReduceFeatures_featureDisplay == "Rownames (Default)") {
        featureDisplay <- "rownames"
      } else {
        featureDisplay <- input$plotTSCANDimReduceFeatures_featureDisplay
      }
      useCluster <- input$plotTSCANDimReduceFeatures_useCluster
      output$TscanDimReduceFeatures <- renderPlot({
        isolate({
          plotTSCANDimReduceFeatures(inSCE = vals$counts,
                                     features = input$plotTSCANDimReduceFeatures_features,
                                     useReducedDim = input$plotTSCANDimReduceFeatures_useReducedDim,
                                     useAssay = input$plotTSCANDimReduceFeatures_useAssay,
                                     useCluster = useCluster,
                                     featureDisplay = featureDisplay)
        })
      })
      updateCollapse(session = session, "TSCANUI",
                     style = list("Plot feature expression on trajectory" = "success"))
      
    }
  ))
  
  ##############################################################
  
  observeEvent(input$closeDropDownTSCAN,{
    session$sendCustomMessage("close_dropDownTSCAN", "")
  })
  
  observeEvent(input$closeDropDownTscanDE,{
    session$sendCustomMessage("close_dropDownTscanDE", "")
  })
  
  observeEvent(input$closeDropDownTscanClusterDEG,{
    session$sendCustomMessage("close_dropDownTscanClusterDEG", "")
  })
  
  #-----------------------------------------------------------------------------
  # Page 3.2: Celda ####
  #-----------------------------------------------------------------------------
  
  observeEvent(input$navbar, {
    if(!is.null(vals$counts)){
      if(input$navbar == "CeldaWorkflow"){
        updateSelectInput(session, "celdaassayselect", choices = c(names(assays(vals$counts))))
      }
    }
  })
  
  modsplit <- reactiveVal()
  cellsplit <- reactiveVal(NULL)
  
  observeEvent(input$celdamodsplit, withConsoleMsgRedirect(
    msg = "Please wait while recursive module split is being computed. See console log for progress.",
    {
      req(vals$counts)
      removeTab(inputId = "celdaModsplitTabset", target = "Perplexity Plot")
      removeTab(inputId = "celdaModsplitTabset", target = "Perplexity Difference Plot")
      appendTab(inputId = "celdaModsplitTabset", tabPanel(title = "Rate of perplexity change",
                                                          panel(heading = "RPC Plot",
                                                                plotlyOutput(outputId = "plot_modsplit_perpdiff", height = "auto")
                                                          )
      ), select = TRUE)
      appendTab(inputId = "celdaModsplitTabset", tabPanel(title = "Perplexity Plot",
                                                          panel(heading = "Perplexity Plot",
                                                                plotlyOutput(outputId = "plot_modsplit_perp", height = "auto")
                                                          )
      ))
      if (input$celdafeatureselect == "None"){
        vals$counts <- selectFeatures(vals$counts, minCount = input$celdarowcountsmin,
                                      minCell = input$celdacolcountsmin, useAssay = input$celdaassayselect)
      }else if(input$celdafeatureselect == "runSeuratFindHVG"){
        vals$counts <- runSeuratNormalizeData(vals$counts, useAssay = input$celdaassayselect)
        vals$counts <- runSeuratFindHVG(vals$counts, useAssay = "seuratNormData",
                                        method = input$celdaseurathvgmethod, hvgNumber = input$celdafeaturenum)
        
        g <- getTopHVG(vals$counts, method = input$celdaseurathvgmethod, n = input$celdafeaturenum)
        altExp(vals$counts, "featureSubset") <- vals$counts[g, ]
        
        vals$counts <- selectFeatures(vals$counts[g, ], minCount = input$celdarowcountsmin,
                                      minCell = input$celdacolcountsmin, useAssay = input$celdaassayselect, altExpName = "featureSubset")
      }else if(input$celdafeatureselect == "Scran_modelGeneVar"){
        if (!("ScaterLogNormCounts" %in% names(assays(vals$counts)))){
          vals$counts <- scater::logNormCounts(vals$counts, name = "ScaterLogNormCounts",
                                               exprs_values = input$celdaassayselect)
        }
        vals$counts <- scranModelGeneVar(vals$counts, assayName = "ScaterLogNormCounts")
        g <- getTopHVG(vals$counts, method = "modelGeneVar", n = input$celdafeaturenum)
        altExp(vals$counts, "featureSubset") <- vals$counts[g, ]
        
        vals$counts <- selectFeatures(vals$counts[g, ], minCount = input$celdarowcountsmin,
                                      minCell = input$celdacolcountsmin, useAssay = input$celdaassayselect, altExpName = "featureSubset")
      }
      #counts(altExp(vals$counts)) <- as.matrix(counts(altExp(vals$counts)))
      updateNumericInput(session, "celdaLselect", min = input$celdaLinit, max = input$celdaLmax, value = input$celdaLinit)
      modsplit(recursiveSplitModule(vals$counts, useAssay = input$celdaassayselect, altExpName = "featureSubset",  initialL = input$celdaLinit, maxL = input$celdaLmax))
      output$plot_modsplit_perpdiff <- renderPlotly({plotRPC(modsplit(), sep = 10)})
      output$plot_modsplit_perp <- renderPlotly({plotGridSearchPerplexity(modsplit())})
      
      
      shinyjs::enable(
        selector = ".celda_modsplit_plots a[data-value='Perplexity Plot']")
      shinyjs::enable(
        selector = ".celda_modsplit_plots a[data-value='Perplexity Diff Plot']")
      shinyjs::show(selector = ".celda_modsplit_plots")
      message(paste0(date(), " ... Module Splitting Complete"))
      
      shinyjs::show(id = "celdaLselect")
      shinyjs::show(id = "celdaLbtn")
    }
  ))
  
  observeEvent(input$celdaLbtn, {
    vals$counts <- subsetCeldaList(modsplit(), params = list(L = input$celdaLselect))
    showNotification("Number of Feature Modules Selected.")
    updateCollapse(session = session, "CeldaUI", style = list("Identify Number of Feature Modules" = "success"))
    shinyjs::enable(selector = "div[value='Identify Number of Cell Clusters']")
  })
  
  output$celdaKplots <- renderUI({
    if (!is.null(vals$counts)){
      if (!is.null(cellsplit())){
        clusterlist <- runParams(cellsplit())$K
        plot_output_list <- lapply(runParams(cellsplit())$K, function(i){
          plotname <- paste0("Cluster", i)
          tabPanel(title = sprintf("Cluster %s", i),
                   panel(heading = sprintf("Cluster %s", i),
                         plotlyOutput(plotname)
                   )
          )
        })
        myTabs <- lapply(clusterlist, tabPanel)
        do.call(tabsetPanel, plot_output_list)
      }
    }
  })
  
  observeEvent(input$celdacellsplit, withConsoleMsgRedirect(
    msg = "Please wait while recursive split cell is being computed. See console log for progress.",
    {
      req(vals$counts)
      cellsplit(recursiveSplitCell(vals$counts, useAssay = input$celdaassayselect, initialK = input$celdaKinit, maxK = input$celdaKmax,
                                   yInit = celdaModules(vals$counts)))
      temp_umap <- celdaUmap(vals$counts)
      output$plot_cellsplit_perpdiff <- renderPlotly({plotRPC(cellsplit(), sep = 10)})
      output$plot_cellsplit_perp <- renderPlotly({plotGridSearchPerplexity(cellsplit())})
      
      for (i in runParams(cellsplit())$K){
        local({
          my_i <- i
          plotname <- paste0("Cluster", my_i)
          celdamod <- subsetCeldaList(cellsplit(), params = list(K = my_i))
          output[[plotname]] <- renderPlotly(plotDimReduceCluster(celdamod,
                                                                  dim1= reducedDim(altExp(temp_umap), "celda_UMAP")[, 1],
                                                                  dim2 = reducedDim(altExp(temp_umap), "celda_UMAP")[, 2],
                                                                  labelClusters = TRUE))
        })
      }
      shinyjs::show(selector = ".celda_cellsplit_plots")
      message(paste0(date(), " ... Cell Clustering Complete"))
      updateNumericInput(session, "celdaKselect", min = input$celdaKinit, max = input$celdaKmax, value = input$celdaKinit)
      shinyjs::show(id = "celdaKselect")
      shinyjs::show(id = "celdaKbtn")
    }
  ))
  
  observeEvent(input$celdaKbtn, {
    vals$counts <- subsetCeldaList(cellsplit(), params = list(K = input$celdaKselect))
    showNotification("Number of Cell Clusters Selected.")
    updateCollapse(session = session, "CeldaUI", style = list("Identify Number of Cell Clusters" = "success"))
    shinyjs::enable(
      selector = "div[value='Visualization']")
    updateNumericInput(session, "celdamodheatmapnum", min = 1, max = input$celdaLselect, value = 1)
    # Show downstream analysis options
    callModule(module = nonLinearWorkflow, id = "nlw-celda", parent = session,
               de = TRUE, pa = TRUE)
    
  })
  
  output$celdaheatmapplt <- renderPlot({plot(celdaHeatmap(vals$counts))})
  output$celdaprobmapplt <- renderPlot({celdaProbabilityMap(vals$counts)})
  
  
  observeEvent(input$CeldaUmap, withConsoleMsgRedirect(
    msg = "Please wait while UMAP is being computed. See console log for progress.",
    {
      req(vals$counts)
      vals$counts <- celdaUmap(vals$counts,
                               useAssay = input$celdaassayselect,
                               maxCells = input$celdaUMAPmaxCells,
                               minClusterSize = input$celdaUMAPminClusterSize,
                               seed = input$celdaUMAPSeed,
                               minDist = input$celdaUMAPmindist,
                               spread = input$celdaUMAPspread,
                               nNeighbors = input$celdaUMAPnn)
      output$celdaumapplot <- renderPlotly({plotDimReduceCluster(vals$counts, reducedDimName = "celda_UMAP", xlab = "UMAP_1",
                                                                 ylab = "UMAP_2", labelClusters = TRUE)})
      message(paste0(date(), " ... UMAP Complete"))
      colData(vals$counts)$celda_clusters <- celdaClusters(vals$counts)
      updateColDataNames()
      shinyjs::enable("CeldaTsne")
    }
  ))
  
  observeEvent(input$CeldaTsne, withConsoleMsgRedirect(
    msg = "Please wait while tSNE is being computed. See console log for progress.",
    {
      req(vals$counts)
      vals$counts <- celdaTsne(vals$counts,
                               useAssay = input$celdaassayselect,
                               maxCells = input$celdatSNEmaxCells,
                               minClusterSize = input$celdatSNEminClusterSize,
                               perplexity = input$celdatSNEPerplexity,
                               maxIter = input$celdatSNEmaxIter,
                               seed = input$celdatSNESeed)
      output$celdatsneplot <- renderPlotly({
        isolate({
          plotDimReduceCluster(vals$counts, reducedDimName = "celda_tSNE",
                               xlab = "tSNE_1", ylab = "tSNE_2",
                               labelClusters = TRUE)
        })
      })
      message(paste0(date(), " ... tSNE Complete"))
    }
  ))
  
  observeEvent(input$celdamodheatmapbtn,{
    output$celdamodheatmapplt <- renderPlot({moduleHeatmap(vals$counts, topCells= input$celdamodheatmaptopcells, featureModule = input$celdamodheatmapnum)})
    output$celdamodprobplt <- renderPlot({plotDimReduceModule(vals$counts, modules =  input$celdamodheatmapnum, reducedDimName = "celda_UMAP")})
    showNotification("Module heatmap complete.")
  })
  
  observe({
    if(!is.null(vals$counts)){
      #If data is uploaded in data tab, enable first tab i.e. Normalization tab in Seurat workflow
      shinyjs::enable(
        selector = "div[value='Identify Number of Feature Modules']")
    }else{
      #If no data uploaded in data tab, disabled all tabs and plots.
      
      #Disable tabs
      shinyjs::disable(
        selector = "div[value='Identify Number of Feature Modules']")
      shinyjs::disable(
        selector = "div[value='Identify Number of Cell Clusters']")
      shinyjs::disable(
        selector = "div[value='Visualization']")
      
      #Disable plots inside Modsplit subtab
      shinyjs::disable(
        selector = ".celda_modsplit_plots a[data-value='Perplexity Plot']")
      shinyjs::disable(
        selector = ".celda_modsplit_plots a[data-value='Perplexity Diff Plot']")
    }
  })
  
  #-----------------------------------------------------------------------------
  # Page 3.3: Cell Viewer ####
  #-----------------------------------------------------------------------------
  #-+-+-+-+-+-For Functional Panel collapse##############
  shinyjs::onclick("cv_button1", shinyjs::toggle(id = "cv_collapse1",
                                                 anim = TRUE), add = TRUE)
  shinyjs::onclick("cv_button2", shinyjs::toggle(id = "cv_collapse2",
                                                 anim = TRUE), add = TRUE)
  shinyjs::onclick("cv_button3", shinyjs::toggle(id = "cv_collapse3",
                                                 anim = TRUE), add = TRUE)
  shinyjs::addClass(id = "cv_button1", class = "btn-block")
  shinyjs::addClass(id = "cv_button2", class = "btn-block")
  shinyjs::addClass(id = "cv_button3", class = "btn-block")
  colorbrewer_list <- rownames(RColorBrewer::brewer.pal.info)
  color_table <- RColorBrewer::brewer.pal.info %>% data.frame()
  color_seqdiv <- rownames(color_table[which(color_table$category == "div"
                                             |color_table$category == "seq"),])
  
  #-+-+-+-+-+-For Input Observe##############
  observeEvent(input$navbar,{
    if (input$navbar == "CellViewer"){
      # is there an error or not
      if (is.null(vals$counts)){
        shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
      }else{
        gene_list <- rownames(vals$counts)
        annotation_list <- names(colData(vals$counts))
        annotation_list2 <- list()
        for (i in 1:length(annotation_list)){
          if(!all.is.numeric(vals$counts[[annotation_list[i]]])){
            annotation_list2$Categorical <- c(annotation_list2$Categorical, annotation_list[i])
          }else{
            annotation_list2$Numeric <- c(annotation_list2$Numeric, annotation_list[i])
          }
        }
        annotation_list <- annotation_list2
        rm(annotation_list2)
        updateSelectizeInput(session, "GeneSelect_Assays_Xaxis",
                             choices = c(gene_list), server = TRUE)
        updateSelectInput(session, "AnnotationSelect_Xaxis",
                          choices = c(annotation_list))
        updateSelectizeInput(session, "GeneSelect_Assays_Yaxis",
                             choices = c(gene_list), server = TRUE)
        updateSelectInput(session, "AnnotationSelect_Yaxis",
                          choices = c(annotation_list))
        updateSelectizeInput(session, "GeneSelect_Assays_Colorby",
                             choices = c(gene_list), server = TRUE)
        updateSelectInput(session, "AnnotationSelect_Colorby",
                          choices = c(annotation_list))
        updateSelectizeInput(session, "adjustgroupby", label = NULL, choices = c("None", annotation_list))
        updateSelectizeInput(session,"adjustbrewer", label = "Color Palettes:",
                             choices = c("RdYlBu",color_seqdiv))
      }
    }
    
    # if(input$navbar == "Feature Selection & Dimensionality Reduction"){
    #   gene_list <- rownames(vals$counts)
    #   updateSelectizeInput(session, "scatterFSGenes",
    #                        choices = c(gene_list),
    #                        server = TRUE)
    # }
  })
  
  hide_TypeSelect <- reactiveVal("hide")
  hide_bins <- reactiveVal()
  
  observeEvent(input$viewertabs, {
    if(!is.null(vals$counts)) {
      if(!is.null(reducedDims(vals$counts))) {
        approach_list <- names(reducedDims(vals$counts))
        if (input$viewertabs != "Scatter Plot") {
          updateSelectInput(session, "QuickAccess",
                            choices = c("Custom"))
          shinyjs::delay(5,shinyjs::disable("QuickAccess"))
          
          updateSelectInput(session, "TypeSelect_Xaxis",
                            choices = c("None", "Cell Annotation"))
          updateSelectInput(session, "TypeSelect_Yaxis",
                            choices = c("Expression Assays", "Cell Annotation"))
          updateSelectInput(session, "TypeSelect_Colorby",
                            selected = "Single Color")
          updateSelectInput(session, "adjustgroupby",
                            selected = "None")
          updatePrettyToggle(session, "checkColorbinning",
                             value = FALSE)
          hide_TypeSelect("hide")
          shinyjs::delay(5,shinyjs::disable("TypeSelect_Colorby"))
          shinyjs::delay(5,shinyjs::disable("adjustgroupby"))
          
          shinyjs::delay(5, shinyjs::disable("adjustlegendtitle"))
          shinyjs::delay(5, shinyjs::disable("adjustlegendtitlesize"))
          shinyjs::delay(5, shinyjs::disable("adjustlegendsize"))
        } else {
          updateSelectInput(session, "QuickAccess",
                            choices = c("", approach_list, "Custom"))
          shinyjs::delay(5,shinyjs::enable("QuickAccess"))
          
          updateSelectInput(session, "TypeSelect_Xaxis",
                            choices = c("Reduced Dimensions", "Expression Assays", "Cell Annotation"))
          updateSelectInput(session, "TypeSelect_Yaxis",
                            choices = c("Reduced Dimensions", "Expression Assays", "Cell Annotation"))
          updateSelectInput(session, "TypeSelect_Colorby",
                            selected = "Single Color")
          updateSelectInput(session, "adjustgroupby",
                            selected = "None")
          updatePrettyToggle(session, "checkColorbinning",
                             value = FALSE)
          hide_TypeSelect("hide")
          shinyjs::delay(5,shinyjs::enable("TypeSelect_Colorby"))
          shinyjs::delay(5,shinyjs::enable("adjustgroupby"))
          shinyjs::delay(5, shinyjs::enable("adjustlegendtitle"))
          if (!is.null(input$adjustgridlines) &
              isFALSE(input$adjustgridlines)) {
            shinyjs::delay(5, shinyjs::enable("adjustlegendtitlesize"))
            shinyjs::delay(5, shinyjs::enable("adjustlegendsize"))
          }
        }
        
        if (input$viewertabs != "Bar Plot") {
          shinyjs::delay(5, shinyjs::enable("adjustalpha"))
          shinyjs::delay(5, shinyjs::enable("adjustsize"))
        } else {
          shinyjs::delay(5, shinyjs::disable("adjustalpha"))
          shinyjs::delay(5, shinyjs::disable("adjustsize"))
        }
      }
    }
  })
  
  observeEvent(input$adjustgridlines, {
    req(vals$counts)
    if (!is.null(input$adjustgridlines)) {
      if (isTRUE(input$adjustgridlines)) {
        shinyjs::delay(5, shinyjs::disable("adjustlegendtitlesize"))
        shinyjs::delay(5, shinyjs::disable("adjustlegendsize"))
        shinyjs::delay(5, shinyjs::disable("adjustaxissize"))
        shinyjs::delay(5, shinyjs::disable("adjustaxislabelsize"))
      } else {
        if (input$viewertabs == "Scatter Plot") {
          shinyjs::delay(5, shinyjs::enable("adjustlegendtitlesize"))
          shinyjs::delay(5, shinyjs::enable("adjustlegendsize"))
        }
        shinyjs::delay(5, shinyjs::enable("adjustaxissize"))
        shinyjs::delay(5, shinyjs::enable("adjustaxislabelsize"))
      }
    }
  })
  
  
  #-+-+-+-+-+-For Advanced Input Observe##############
  ###ApproachSelect to DimensionSelect X-Axis
  observeEvent(input$ApproachSelect_Xaxis, {
    if (!is.null(vals$counts)){
      len <- length(SingleCellExperiment::reducedDims(vals$counts))
      if (!is.null(input$ApproachSelect_Xaxis) & len > 0){
        Df <- data.frame(SingleCellExperiment::reducedDim(vals$counts,input$ApproachSelect_Xaxis))
        xs <- colnames(Df)
        updateSelectInput(session, "ColumnSelect_Xaxis", choices = c(xs))
        rm(Df)
      }
    }
  })
  ###ApproachSelect to DimensionSelect Y-Axis
  observeEvent(input$ApproachSelect_Yaxis, {
    if (!is.null(vals$counts)){
      len <- length(SingleCellExperiment::reducedDims(vals$counts))
      if (!is.null(input$ApproachSelect_Yaxis) & len > 0){
        Df2 <- data.frame(SingleCellExperiment::reducedDim(vals$counts,input$ApproachSelect_Yaxis))
        xs2 <- colnames(Df2)
        xs2 <- sort(xs2, decreasing = TRUE)
        updateSelectInput(session, "ColumnSelect_Yaxis", choices = c(xs2))
        rm(Df2)
      }
    }
  })
  ###ApproachSelect to DimensionSelect Colorby
  observeEvent(input$ApproachSelect_Colorby, {
    if (!is.null(vals$counts)){
      len <- length(SingleCellExperiment::reducedDims(vals$counts))
      if (!is.null(input$ApproachSelect_Colorby) & len > 0){
        Df3 <- data.frame(SingleCellExperiment::reducedDim(vals$counts,input$ApproachSelect_Colorby))
        xs3 <- colnames(Df3)
        prefix <- input$ApproachSelect_Colorby
        suffix <- seq(1:length(xs3))
        columns <- paste(prefix, suffix, sep = "_")
        updateSelectInput(session, "ColumnSelect_Colorby", choices = c(columns))
        rm(Df3)
      }
    }
  })
  
  #-+-+-+-+-+-Observe Color by###################################################
  ###Observe Radio Button Select Value Type
  # input$AnnotationSelect_Colorby,
  
  observe({
    # All inputs to listen for
    input$TypeSelect_Colorby
    input$AnnotationSelect_Colorby
    
    #Reduced Dimensions
    input$ApproachSelect_Colorby
    input$ColumnSelect_Colorby
    
    #Expression Assay
    input$AdvancedMethodSelect_Colorby
    input$GeneSelect_Assays_Colorby
    
    if(input$TypeSelect_Colorby == 'Cell Annotation'){
      ###If Cell Annotation is not numeric
      if(!is.numeric(colData(vals$counts)@listData[[input$AnnotationSelect_Colorby]])){
        updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
                           choices = c("Categorical", "Continuous"),
                           selected = "Categorical")
        hide_TypeSelect("hide")
      }else if(is.integer(colData(vals$counts)@listData[[input$AnnotationSelect_Colorby]])
               &length(levels(as.factor(colData(vals$counts)@listData[[input$AnnotationSelect_Colorby]])))<=25){
        updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
                           choices = c("Categorical", "Continuous"),
                           selected = "Categorical")
        hide_TypeSelect("show")
      }else{
        updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
                           choices = c("Categorical", "Continuous"),
                           selected = "Continuous")
        hide_TypeSelect("hide")
      }
    } else if(input$TypeSelect_Colorby == 'Reduced Dimensions'){
      updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
                         choices = c("Categorical", "Continuous"),
                         selected = "Continuous")
      hide_TypeSelect("hide")
    } else if(input$TypeSelect_Colorby == "Expression Assays"){
      updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
                         choices = c("Categorical", "Continuous"),
                         selected = "Continuous")
      hide_TypeSelect("hide")
    } else {
      # single color
      updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
                         choices = c("Categorical", "Continuous"),
                         selected = "Categorical")
      hide_TypeSelect("hide")
    }
  })
  
  observeEvent(input$SelectColorType,{
    if(input$SelectColorType == "Categorical"){
      hide_bins("hide")
    }else{
      hide_bins("show")
    }
  })
  
  output$hide_typebtns <- renderText({
    hide_TypeSelect()
  })
  
  outputOptions(output, "hide_typebtns", suspendWhenHidden = FALSE)
  
  output$hide_bins <- renderText({
    hide_bins()
  })
  
  outputOptions(output, "hide_bins", suspendWhenHidden = FALSE)
  
  numColors <- NULL
  colorLabels <- NULL
  ### Observe input changes that should trigger categorical color generator
  observeEvent(c(input$SelectColorType, input$TypeSelect_Colorby, input$AnnotationSelect_Colorby, input$colorTheme), {
    if (input$TypeSelect_Colorby == "Single Color") {
      shinyjs::hide("categoricalColorConditional")
      shinyjs::hide("continuousColorConditional")
    }
    if (input$SelectColorType == "Categorical" && input$TypeSelect_Colorby != "Single Color") {
      if(input$TypeSelect_Colorby == "Cell Annotation") {
        req(vals$counts, input$AnnotationSelect_Colorby)
        labels = sort(unique(SingleCellExperiment::colData(vals$counts)[, input$AnnotationSelect_Colorby]))
        if (length(labels) <=25 ) {
          colorLabels <<- labels
          numColors <<- length(labels)
          defaultColors <- discreteColorPalette(numColors, input$colorTheme)
          output$categoricalColorUI <- renderUI({
            lapply(1:numColors, function(i){
              colourInput(inputId=paste0(i, "_color"), label=labels[i], value=defaultColors[i], showColour="background")
            })
          })
          shinyjs::show("categoricalColorConditional")
          shinyjs::hide("continuousColorConditional")
        }
      }
    } else if (input$SelectColorType == "Continuous" && input$TypeSelect_Colorby != "Single Color") {
      shinyjs::hide("categoricalColorConditional")
      shinyjs::show("continuousColorConditional")
    }
  })
  
  testvar <- reactive({
    event.data <- event_data("plotly_selected")
    #curveNumber pointNumber x y
    #pointnumbers <- event.data[["pointNumber"]]
  })
  
  observeEvent(input$subsetCelda, {
    if(!is.null(vals$counts)){
      copy <- vals$counts
      if(!("featureSubset" %in% altExpNames(copy))){
        copy <- selectFeatures(copy)
      }
      selection <- event_data("plotly_selected")
      copy2 <- subsetSCECols(copy, index = testvar()$pointNumber)
      clustersog <- celdaClusters(copy)[testvar()$pointNumber]
      #copy2 <- subsetSCECols(copy, index = testvar())
      maxclust <- max(celdaClusters(copy))
      #celdaClusters(copy) <- 0
      #celdaClusters(copy) <- celdaClusters(copy2)
      #celdaClusters(copy)[testvar()$pointNumber] <- maxclust + 1
      #celdaClusters(copy)[colnames(copy) %in% colnames(copy2)] <- clustersog
      #celdaClusters(copy)[testvar()] <- 2
      saveRDS(copy, "celdasubsettest.RDS")
    }
    #subset <- testvar()
  })
  
  observeEvent(input$subsetCelda2, {
    if(!is.null(vals$counts)){
      copy <- vals$counts
      if(!("featureSubset" %in% altExpNames(copy))){
        copy <- selectFeatures(copy)
      }
      selection <- event_data("plotly_selected")
      #copy2 <- subsetSCECols(copy, index = testvar())
      #maxclust <- max(celdaClusters(copy))
      celdaClusters(copy) <- 1
      celdaClusters(copy)[testvar()$pointNumber] <- 2
      saveRDS(copy, "celdasubsettest2.RDS")
    }
    #subset <- testvar()
  })
  
  output$testprint <- renderText({as.character(testvar())})
  
  #-+-+-+-+-+-cellviewer prepare step1: choose data. (next steps included)###########################################################
  #cellviewer <- eventReactive(input$runCellViewer,{
  observeEvent(input$runCellViewer,{
    colors <- c()
    if (!is.null(numColors) && input$SelectColorType == 'Categorical') {
      for (i in 1: numColors) {
        colors[i] <- input[[ paste0(i,"_color")]]
      }
      names(colors) = colorLabels
    }
    #-+-+-+-+-+-cellviewer prepare3 : prepare Axis Label Name#####################
    ###Xaxis label name
    if (!is.null(input$adjustxlab) &
        input$adjustxlab != "") {
      xname <- input$adjustxlab
    } else {
      if (input$QuickAccess != "Custom" &
          input$QuickAccess != "") {
        # reddim selected
        xname <- paste0(input$QuickAccess, 1)
      } else if (input$TypeSelect_Xaxis == 'Reduced Dimensions') {
        xname <- paste0(input$ApproachSelect_Xaxis, "_",
                        substr(input$ColumnSelect_Xaxis,
                               str_length(input$ColumnSelect_Xaxis),
                               str_length(input$ColumnSelect_Xaxis)))
      } else if (input$TypeSelect_Xaxis == 'Expression Assays') {
        xname <- input$GeneSelect_Assays_Xaxis
      } else if (input$TypeSelect_Xaxis == "Cell Annotation") {
        xname <- input$AnnotationSelect_Xaxis
      } else {
        xname <- ""
      }
    }
    xname <- gsub("-", "_", xname)
    ###Yaxis label name
    if (!is.null(input$adjustylab) &
        input$adjustylab != "") {
      yname <- input$adjustylab
    } else {
      if (input$QuickAccess != "Custom" &
          input$QuickAccess != "") {
        # reddim selected
        yname <- paste0(input$QuickAccess, 2)
      } else if (input$TypeSelect_Yaxis == 'Reduced Dimensions') {
        yname <- paste0(input$ApproachSelect_Yaxis, "_",
                        substr(input$ColumnSelect_Yaxis,
                               str_length(input$ColumnSelect_Yaxis),
                               str_length(input$ColumnSelect_Yaxis)))
      } else if (input$TypeSelect_Yaxis == 'Expression Assays') {
        yname <- input$GeneSelect_Assays_Yaxis
      } else {
        yname <- input$AnnotationSelect_Yaxis
      }
    }
    yname <- gsub("-", "_", yname)
    ###Legend name
    if (input$TypeSelect_Colorby != 'Pick a Color') {
      if (input$TypeSelect_Colorby == 'Reduced Dimensions' && input$adjustlegendtitle == "") {
        legendname <- paste0(input$ApproachSelect_Colorby,"_",substr(input$ColumnSelect_Colorby,
                                                                     str_length(input$ColumnSelect_Colorby),str_length(input$ColumnSelect_Colorby)))
      } else if (input$TypeSelect_Colorby == 'Expression Assays' && input$adjustlegendtitle == "") {
        legendname <- input$GeneSelect_Assays_Colorby
      } else if (input$adjustlegendtitle == "") {
        legendname <- input$AnnotationSelect_Colorby
      } else {
        legendname <- input$adjustlegendtitle
      }
    }
    legendname <- gsub("-", "_", legendname)
    #-+-+-+-+-+-cellviewer prepare4 : choose group by and create plotly function###################
    pltVars <- list()
    if(input$viewertabs == "Violin/Box Plot" || input$viewertabs == "Bar Plot"){
      if(input$TypeSelect_Xaxis == "None"){
        pltVars$groupby <- NULL
      }else if(input$TypeSelect_Xaxis == "Expression Assays"){
        pltVars$groupby <- input$GeneSelect_Assays_Xaxis
      }else if(input$TypeSelect_Xaxis == "Cell Annotation"){
        pltVars$groupby <- input$AnnotationSelect_Xaxis
      }
    }else if(input$adjustgroupby != "None"){
      pltVars$groupby <- input$adjustgroupby
    }else{
      pltVars$groupby <- NULL
    }
    if (input$checkColorbinning == TRUE && input$SelectColorType == "Continuous"){
      pltVars$bin <- input$adjustColorbinning
    }else{
      pltVars$bin <- NULL
    }
    if (input$SelectColorType == "Categorical"){
      pltVars$class <- "factor"
    }else{
      pltVars$class <- "numeric"
    }
    
    if(input$adjustgridlines == TRUE){
      pltVars$defTheme <- FALSE
    }else{
      pltVars$defTheme <- TRUE
    }
    
    if(input$viewertabs == "Scatter Plot"){
      #### Prepare Custom plotting matrix axis ####
      if (input$QuickAccess == "Custom") {
        # X Axis
        message("CellViewer: Custom plotting mode, making up the axis")
        if (input$TypeSelect_Xaxis == "Expression Assays") {
          message("X axis: Using expression of ", input$GeneSelect_Assays_Xaxis,
                  " from ", input$AdvancedMethodSelect_Xaxis)
          xvec <- expData(vals$counts, input$AdvancedMethodSelect_Xaxis)[input$GeneSelect_Assays_Xaxis,]
        } else if (input$TypeSelect_Xaxis == "Reduced Dimensions") {
          message("X axis: Using dimension reduction ", input$ColumnSelect_Xaxis,
                  " from ", input$ApproachSelect_Xaxis)
          xvec <- reducedDim(vals$counts, input$ApproachSelect_Xaxis)[,input$ColumnSelect_Xaxis]
        } else if (input$TypeSelect_Xaxis == 'Cell Annotation') {
          message("X axis: Using cell annotation ",
                  input$AnnotationSelect_Xaxis)
          xvec <- vals$counts[[input$AnnotationSelect_Xaxis]]
        }
        # Y Axis
        if (input$TypeSelect_Yaxis == "Expression Assays") {
          message("Y axis: Using expression of ", input$GeneSelect_Assays_Yaxis,
                  " from ", input$AdvancedMethodSelect_Yaxis)
          yvec <- expData(vals$counts, input$AdvancedMethodSelect_Yaxis)[input$GeneSelect_Assays_Yaxis,]
        } else if (input$TypeSelect_Yaxis == "Reduced Dimensions") {
          message("Y axis: Using dimension reduction ", input$ColumnSelect_Yaxis,
                  " from ", input$ApproachSelect_Yaxis)
          yvec <- reducedDim(vals$counts, input$ApproachSelect_Yaxis)[,input$ColumnSelect_Yaxis]
        } else if (input$TypeSelect_Yaxis == 'Cell Annotation') {
          message("Y axis: Using cell annotation ",
                  input$AnnotationSelect_Yaxis)
          yvec <- vals$counts[[input$AnnotationSelect_Yaxis]]
        }
        # Merge and insert to reducedDim(sce, "Custom")
        customMat <- matrix(c(xvec, yvec), nrow = length(xvec))
        colnames(customMat) <- c(xname, yname)
        rownames(customMat) <- names(xvec)
        reducedDim(vals$counts, "Custom") <- customMat
      }
      if(input$TypeSelect_Colorby == "Single Color"){
        a <- plotSCEScatter(vals$counts, reducedDimName = input$QuickAccess,
                            xlab = xname, ylab = yname, title = input$adjusttitle, groupBy = pltVars$groupby,
                            transparency = input$adjustalpha, dotSize = input$adjustsize, combinePlot = "none",
                            axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
                            legendSize = input$adjustlegendsize, legendTitleSize = input$adjustlegendtitlesize,
                            conditionClass = pltVars$class, defaultTheme = as.logical(pltVars$defTheme))
      }else if(input$TypeSelect_Colorby == "Expression Assays"){
        a <- plotSCEDimReduceFeatures(vals$counts, features = input$GeneSelect_Assays_Colorby,
                                      reducedDimName = input$QuickAccess, useAssay = input$AdvancedMethodSelect_Colorby,
                                      xlab = xname, ylab = yname, legendTitle = legendname, title = input$adjusttitle,
                                      groupBy = pltVars$groupby, bin = pltVars$bin, transparency = input$adjustalpha,
                                      colorLow = input$lowColor, colorMid = input$midColor, colorHigh = input$highColor,
                                      dotSize = input$adjustsize, combinePlot = "none", axisSize = input$adjustaxissize,
                                      axisLabelSize = input$adjustaxislabelsize, legendSize = input$adjustlegendsize,
                                      legendTitleSize = input$adjustlegendtitlesize)
      } else if (input$TypeSelect_Colorby == "Cell Annotation") {
        a <- plotSCEDimReduceColData(vals$counts,reducedDimName = input$QuickAccess,xlab = xname,ylab = yname,
                                     colorBy = input$AnnotationSelect_Colorby,groupBy = pltVars$groupby,legendTitle = legendname,
                                     title = input$adjusttitle,bin = pltVars$bin,transparency = input$adjustalpha,colorScale = colors,
                                     colorLow = input$lowColor, colorMid = input$midColor, colorHigh = input$highColor, dotSize = input$adjustsize,
                                     combinePlot = "none",axisSize = input$adjustaxissize,axisLabelSize = input$adjustaxislabelsize,
                                     legendSize = input$adjustlegendsize,legendTitleSize = input$adjustlegendtitlesize,conditionClass = pltVars$class)
      }else if(input$TypeSelect_Colorby == "Reduced Dimensions"){
        a <- plotSCEScatter(vals$counts, reducedDimName = input$QuickAccess, slot = "reducedDims",
                            annotation = input$ColumnSelect_Colorby, transparency = input$adjustalpha,
                            colorLow = input$lowColor, colorMid = input$midColor, colorHigh = input$highColor,
                            groupBy = pltVars$groupby, title = input$adjusttitle, legendTitle = legendname,
                            xlab = xname, ylab = yname, dotSize = input$adjustsize, bin = pltVars$bin,
                            combinePlot = "none", axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
                            legendSize = input$adjustlegendsize, legendTitleSize = input$adjustlegendtitlesize)
      }
    }else if(input$viewertabs == "Bar Plot"){
      if(input$TypeSelect_Yaxis == "Expression Assays"){
        a <- plotSCEBarAssayData(vals$counts, title = input$adjusttitle, xlab = xname, ylab = yname,
                                 useAssay = input$AdvancedMethodSelect_Yaxis, groupBy = pltVars$groupby,
                                 feature = input$GeneSelect_Assays_Yaxis,
                                 combinePlot = "none", axisSize = input$adjustaxissize,
                                 axisLabelSize = input$adjustaxislabelsize, defaultTheme = as.logical(pltVars$defTheme))
      }else if(input$TypeSelect_Yaxis == "Cell Annotation"){
        a <- plotSCEBarColData(vals$counts, title = input$adjusttitle, xlab = xname, ylab = yname,
                               coldata = input$AnnotationSelect_Yaxis, groupBy = pltVars$groupby,
                               combinePlot = "none",
                               axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
                               defaultTheme = as.logical(pltVars$defTheme))
      }
    }else if(input$viewertabs == "Violin/Box Plot"){
      if(isTRUE(input$vlnboxcheck)){
        vln <- TRUE
        bx <- FALSE
      }else if(isFALSE(input$vlnboxcheck)){
        vln <- FALSE
        bx <- TRUE
      }
      if(input$TypeSelect_Yaxis == "Expression Assays"){
        a <- plotSCEViolinAssayData(vals$counts, violin = vln, box = bx, xlab = xname, ylab = yname,
                                    useAssay = input$AdvancedMethodSelect_Yaxis, title = input$adjusttitle,
                                    feature = input$GeneSelect_Assays_Yaxis, groupBy = pltVars$groupby,
                                    transparency = input$adjustalpha, dotSize = input$adjustsize, combinePlot = "none",
                                    axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
                                    defaultTheme = as.logical(pltVars$defTheme))
      }else if(input$TypeSelect_Yaxis == "Cell Annotation"){
        a <- plotSCEViolinColData(vals$counts, title = input$adjusttitle, xlab = xname, ylab = yname,
                                  coldata = input$AnnotationSelect_Yaxis, violin = vln, box = bx,
                                  groupBy = pltVars$groupby, transparency = input$adjustalpha,
                                  dotSize = input$adjustsize, combinePlot = "none", axisSize = input$adjustaxissize,
                                  axisLabelSize = input$adjustaxislabelsize, defaultTheme = as.logical(pltVars$defTheme))
      }
    }
    if (input$TypeSelect_Colorby == "Single Color"){
      a$layers[[1]]$aes_params$colour <- input$Col
    }
    if (isTRUE(input$adjustgridlines)){
      a <- a + ggplot2::theme_bw()
    }
    a <- plotly::ggplotly(a)
    output$scatter <- renderPlotly({
      plotly::subplot(plotlist = a, titleX = TRUE, titleY = TRUE)
    })
  })
  
  #-----------------------------------------------------------------------------
  # Page 3.4: Heatmap ####
  #-----------------------------------------------------------------------------
  
  hmTemp <- reactiveValues(
    sce = NULL,
    cellIndex = NULL,
    geneIndex = NULL,
    colDataName = NULL,
    rowDataName = NULL,
    colSplitBy = NULL,
    rowSplitBy = NULL,
    cellTableCol = NULL,
    geneTableCol = NULL,
    colColorPresets = list(),
    rowColorPresets = list()
  )
  
  observeEvent(vals$counts, {
    if(!is.null(vals$counts)){
      hmTemp$sce <- vals$counts
    }
  })
  
  # Heatmap: Import Analysis ####
  
  
  observeEvent(input$hmImportRun, {
    if(!is.null(vals$counts)){
      if(!is.null(input$hmImport) &&
         input$hmImport == "Differential Expression"){
        if(!is.null(input$hmImpDEG)){
          result <- metadata(vals$counts)$diffExp[[input$hmImpDEG]]
          useAssay <- result$useAssay
          updateSelectInput(session, "hmAssay", selected = useAssay)
          method <- result$method
          # Cell side
          addColData <- data.frame(row.names = colnames(vals$counts))
          idx <- rep(NA, ncol(vals$counts))
          idx[result$select$ix1] <- result$groupNames[1]
          idx[result$select$ix2] <- result$groupNames[2]
          hmTemp$cellIndex <- which(!is.na(idx))
          conditionColName <- paste(method, input$hmImpDEG, "condition",
                                    sep = '_')
          addColData[[conditionColName]] <- factor(idx,
                                                   levels = result$groupNames)
          colData(hmTemp$sce) <- cbind(colData(hmTemp$sce), addColData)
          hmTemp$cellTableCol <- conditionColName
          hmTemp$colDataName <- conditionColName
          hmTemp$colSplitBy <- conditionColName
          hmTemp$colColorPresets[[conditionColName]] <- c('red', 'cyan',
                                                          'white')
          names(hmTemp$colColorPresets[[conditionColName]]) <-
            c(result$groupNames, "NA")
          # Gene side
          addRowData <- data.frame(row.names = rownames(vals$counts))
          deg <- result$result
          deg <- deg[stats::complete.cases(deg),]
          logFCColName <- paste(method, input$hmImpDEG, "Log2FC",
                                sep = '_')
          FDRColName <- paste(method, input$hmImpDEG, "FDR",
                              sep = '_')
          addRowData[deg$Gene, logFCColName] <- deg$Log2_FC
          addRowData[deg$Gene, FDRColName] <- deg$FDR
          regColName <- paste(method, input$hmImpDEG, "regulation",
                              sep = '_')
          degUp <- deg[deg$Log2_FC > 0,]
          degDown <- deg[deg$Log2_FC < 0,]
          addRowData[degUp$Gene, regColName] <- "up"
          addRowData[degDown$Gene, regColName] <- "down"
          addRowData[[regColName]] <- factor(addRowData[[regColName]],
                                             levels = c('up', 'down'))
          rowData(hmTemp$sce) <- cbind(rowData(hmTemp$sce), addRowData)
          hmTemp$geneTableCol <- c(regColName, logFCColName, FDRColName)
          hmTemp$geneIndex <- which(rownames(vals$counts) %in% deg$Gene)
          hmTemp$rowDataName <- regColName
          hmTemp$rowSplitBy <- regColName
          hmTemp$rowColorPresets[[regColName]] <- c('red', 'cyan', 'white')
          names(hmTemp$rowColorPresets[[regColName]]) <- c('up', 'down', 'NA')
        }
      } else if (!is.null(input$hmImport) &&
                 input$hmImport == "Find Marker"){
        markerTable <- metadata(vals$counts)$findMarker
        if(!is.null(markerTable) &&
           dim(markerTable)[1] > 0){
          markerTable <- markerTable[stats::complete.cases(markerTable),]
          # Cell side
          cluster <- colnames(markerTable)[5]
          hmTemp$cellIndex <- seq_len(ncol(hmTemp$sce))
          hmTemp$colDataName <- cluster
          hmTemp$cellTableCol <- cluster
          hmTemp$colSplitBy <- cluster
          # Gene side
          dup.gene <- unique(markerTable$Gene[duplicated(markerTable$Gene)])
          for(g in dup.gene){
            deg.gix <- markerTable$Gene == g
            deg.gtable <- markerTable[deg.gix,]
            toKeep <- which.max(deg.gtable$Log2_FC)
            toRemove <- which(deg.gix)[-toKeep]
            markerTable <- markerTable[-toRemove,]
          }
          hmTemp$geneIndex <- which(rownames(vals$counts) %in% markerTable$Gene)
          addRowData <- data.frame(row.names = rownames(vals$counts))
          addRowData[markerTable$Gene, "Marker_for_Cluster"] <- markerTable[,5]
          addRowData[markerTable$Gene, "findMarker_Log2FC"] <-
            markerTable$Log2_FC
          addRowData[markerTable$Gene, "findMarker_FDR"] <- markerTable$FDR
          rowData(hmTemp$sce) <- cbind(rowData(hmTemp$sce), addRowData)
          hmTemp$geneTableCol <- c("Marker_for_Cluster",
                                   "findMarker_Log2FC",
                                   "findMarker_FDR")
          hmTemp$rowDataName <- "Marker_for_Cluster"
          hmTemp$rowSplitBy <- "Marker_for_Cluster"
          hmTemp$rowColorPresets$Marker_for_Cluster <-
            hmAnnAllColors$col[[cluster]]
        }
      }
    }
  })
  # Heatmap: Subsetting Cells####
  output$hmCellColUI <- renderUI({
    if(!is.null(vals$counts)){
      selectInput(
        'hmCellCol',
        "Columns to display",
        names(colData(hmTemp$sce)), multiple = TRUE, width = '550px',
        selected = hmTemp$cellTableCol)
    }
  })
  
  output$hmCellColTable <- DT::renderDataTable({
    if(!is.null(vals$counts)){
      df <- as.data.frame(colData(hmTemp$sce))
      rowNameCol <- data.frame(Row_Names = colnames(vals$counts))
      df <- cbind(rowNameCol, df)
      rownames(df) <- NULL
      DT::datatable(
        df,
        filter = 'top', options = list(stateSave = TRUE, scrollX = TRUE)
      )
    }
  }, server = TRUE)
  
  hmCellColTable_proxy <- DT::dataTableProxy("hmCellColTable")
  
  observeEvent(input$hmCellCol, {
    colNames <- c('Row_Names', names(colData(hmTemp$sce)))
    showIdx <- which(colNames %in% input$hmCellCol)
    showIdx <- c(1, showIdx)
    DT::showCols(hmCellColTable_proxy, showIdx, reset = TRUE)
  })
  
  observeEvent(input$hmCellColTable_state, {
    DT::selectRows(hmCellColTable_proxy, hmTemp$cellIndex)
  })
  
  observeEvent(input$hmCellColTable_rows_selected, {
    hmTemp$cellIndex <- input$hmCellColTable_rows_selected
  })
  
  observeEvent(input$hmCellColTable_addAll, {
    DT::selectRows(hmCellColTable_proxy,
                   sort(unique(c(input$hmCellColTable_rows_selected,
                                 input$hmCellColTable_rows_all))))
    hmTemp$cellIndex <- sort(unique(c(input$hmCellColTable_rows_selected,
                                      input$hmCellColTable_rows_all)))
  })
  
  observeEvent(input$hmCellColTable_clear, {
    DT::selectRows(hmCellColTable_proxy, NULL)
    hmTemp$cellIndex <- NULL
  })
  
  observeEvent(input$hmCellCol, {
    hmTemp$cellTableCol <- input$hmCellCol
  })
  
  output$hmCellNEnteredUI <- renderUI({
    inputList <- str_trim(scan(text = input$hmCellText,
                               sep='\n', what = 'character', quiet = TRUE))
    uniqInput <- unique(inputList)
    nInput <- length(uniqInput)
    if(!is.null(vals$counts) && nInput > 0){
      if(!is.null(input$hmCellTextBy) && input$hmCellTextBy == 'Row Names'){
        BY <- NULL
      } else {
        BY <- input$hmCellTextBy
      }
      matched <- retrieveSCEIndex(vals$counts, uniqInput, 'cell',
                                  by = BY, exactMatch = input$hmCellTextEM,
                                  firstMatch = input$hmCellTextFM)
      nMatched <- length(matched)
    } else {
      nMatched <- 0
    }
    
    p(paste0(nInput, " unique input, ", nMatched, "matched."))
  })
  
  observeEvent(input$hmCellAddFromText, {
    if(!is.null(vals$counts)){
      inputList <- str_trim(scan(text = input$hmCellText,
                                 sep='\n', what = 'character', quiet = TRUE))
      uniqInput <- unique(inputList)
      if(length(uniqInput) > 0){
        if(!is.null(input$hmCellTextBy) && input$hmCellTextBy == 'Row Names'){
          BY <- NULL
        } else {
          BY <- input$hmCellTextBy
        }
        newIdx <- retrieveSCEIndex(vals$counts, uniqInput, 'cell',
                                   by = BY, exactMatch = input$hmCellTextEM,
                                   firstMatch = input$hmCellTextFM)
        DT::selectRows(hmCellColTable_proxy,
                       sort(unique(c(input$hmCellColTable_rows_selected,
                                     newIdx))))
      }
    }
  })
  
  output$hmCellSumUI <- renderUI({
    nCell <- length(hmTemp$cellIndex)
    if(nCell == 0){
      p("No cells selected, going to use them all", style = 'margin-top: 5px;')
    } else {
      p(paste0("Totally ", nCell, " cells selected."),
        style = 'margin-top: 5px;')
    }
  })
  
  # Heatmap: Subsetting Genes ####
  output$hmGeneColUI <- renderUI({
    if(!is.null(vals$counts)){
      selectInput(
        'hmGeneCol',
        "Columns to display",
        names(rowData(hmTemp$sce)), multiple = TRUE, width = '550px',
        selected = hmTemp$geneTableCol)
    }
  })
  
  output$hmGeneColTable <- DT::renderDataTable({
    if(!is.null(vals$counts)){
      df <- as.data.frame(rowData(hmTemp$sce))
      rowNameCol <- data.frame(Row_Names = rownames(vals$counts))
      df <- cbind(rowNameCol, df)
      rownames(df) <- NULL
      DT::datatable(
        df,
        filter = 'top', options = list(stateSave = TRUE, scrollX = TRUE)
      )
    }
  }, server = TRUE)
  
  hmGeneColTable_proxy <- DT::dataTableProxy("hmGeneColTable")
  
  observeEvent(input$hmGeneCol, {
    colNames <- c('Row_Names', names(rowData(hmTemp$sce)))
    showIdx <- which(colNames %in% input$hmGeneCol)
    showIdx <- c(1, showIdx)
    DT::showCols(hmGeneColTable_proxy, showIdx, reset = TRUE)
  })
  
  observeEvent(input$hmGeneColTable_state, {
    DT::selectRows(hmGeneColTable_proxy, hmTemp$geneIndex)
  })
  
  observeEvent(input$hmGeneColTable_rows_selected, {
    hmTemp$geneIndex <- input$hmGeneColTable_rows_selected
  })
  
  observeEvent(input$hmGeneColTable_addAll, {
    DT::selectRows(hmGeneColTable_proxy,
                   sort(unique(c(input$hmGeneColTable_rows_selected,
                                 input$hmGeneColTable_rows_all))))
    hmTemp$geneIndex <- sort(unique(c(input$hmGeneColTable_rows_selected,
                                      input$hmGeneColTable_rows_all)))
  })
  
  observeEvent(input$hmGeneColTable_clear, {
    DT::selectRows(hmGeneColTable_proxy, NULL)
    hmTemp$geneIndex <- NULL
  })
  
  observeEvent(input$hmGeneCol, {
    hmTemp$geneTableCol <- input$hmGeneCol
  })
  
  output$hmGeneNEnteredUI <- renderUI({
    inputList <- str_trim(scan(text = input$hmGeneText,
                               sep='\n', what = 'character', quiet = TRUE))
    uniqInput <- unique(inputList)
    nInput <- length(uniqInput)
    if(!is.null(vals$counts) && nInput > 0){
      if(!is.null(input$hmGeneTextBy) && input$hmGeneTextBy == 'Row Names'){
        BY <- NULL
      } else {
        BY <- input$hmGeneTextBy
      }
      matched <- retrieveSCEIndex(vals$counts, uniqInput, 'gene',
                                  by = BY, exactMatch = input$hmGeneTextEM,
                                  firstMatch = input$hmGeneTextFM)
      nMatched <- length(matched)
    } else {
      nMatched <- 0
    }
    
    p(paste0(nInput, " unique input, ", nMatched, "matched."))
  })
  
  observeEvent(input$hmGeneAddFromText, {
    if(!is.null(vals$counts)){
      inputList <- str_trim(scan(text = input$hmGeneText,
                                 sep='\n', what = 'character', quiet = TRUE))
      uniqInput <- unique(inputList)
      if(length(uniqInput) > 0){
        if(!is.null(input$hmGeneTextBy) && input$hmGeneTextBy == 'Row Names'){
          BY <- NULL
        } else {
          BY <- input$hmGeneTextBy
        }
        newIdx <- retrieveSCEIndex(vals$counts, uniqInput, 'gene',
                                   by = BY, exactMatch = input$hmGeneTextEM,
                                   firstMatch = input$hmGeneTextFM)
        DT::selectRows(hmGeneColTable_proxy,
                       sort(unique(c(input$hmGeneColTable_rows_selected,
                                     newIdx))))
      }
    }
  })
  
  output$hmGeneSumUI <- renderUI({
    nGene <- length(hmTemp$geneIndex)
    if(nGene == 0){
      p("No features selected, going to use them all",
        style = 'margin-top: 5px;')
    } else {
      p(paste0("Totally ", nGene, " features selected."),
        style = 'margin-top: 5px;')
    }
  })
  
  # Heatmap: Annotation color assignment ####
  
  output$hmCellAnnUI <- renderUI({
    if(!is.null(vals$counts)){
      classes <- names(colData(hmTemp$sce))
      selectInput('hmCellAnn', 'Add cell annotation', classes,
                  multiple = TRUE, selected = hmTemp$colDataName)
    }
  })
  
  output$hmGeneAnnUI <- renderUI({
    if(!is.null(vals$counts)){
      classes <- names(rowData(hmTemp$sce))
      selectInput('hmGeneAnn', 'Add feature annotation', classes,
                  multiple = TRUE, selected = hmTemp$rowDataName)
    }
  })
  
  observeEvent(input$hmCellAnn, {
    hmTemp$colDataName <- input$hmCellAnn
  })
  observeEvent(input$hmGeneAnn, {
    hmTemp$rowDataName <- input$hmGeneAnn
  })
  
  hmAnnAllColors <- reactiveValues(
    col = NULL,
    row = NULL
  )
  
  generateAnnColAssUI <- function(colname, axis){
    if(axis == "row"){
      data <- as.vector(rowData(hmTemp$sce)[[colname]])
    } else if(axis == 'col'){
      data <- as.vector(colData(hmTemp$sce)[[colname]])
    }
    nUniq <- length(as.vector(unique(data[!is.na(data)])))
    if(colname %in% names(hmTemp[[paste0(axis, "ColorPresets")]])){
      cats = names(hmTemp[[paste0(axis, "ColorPresets")]][[colname]])
      fluidRow(style = "padding-left:20px;",
               h4(colname),
               lapply(seq_along(cats), function(i) {
                 column(
                   width = 3,
                   colourpicker::colourInput(
                     inputId = paste0('hm', axis, colname, cats[i]),
                     label = cats[i],
                     value = hmTemp[[paste0(axis, "ColorPresets")]][[colname]][[cats[i]]]
                   )
                 )
               })
      )
    } else if(nUniq > 12){
      if(is.numeric(data)){
        fluidRow(style = "padding-left:20px;",
                 h4(colname),
                 p(paste0("Numeric annotation with ", nUniq, " unique values detected. Please choose the type of legend.")),
                 radioButtons(
                   inputId = paste0('hm', axis, colname, 'type'),
                   label = NULL,
                   choices = c('Categorical', 'Continuous'),
                   inline = TRUE
                 ),
                 conditionalPanel(
                   condition = paste0("input.hm", axis, colname, "type == 'Categorical'"),
                   p("Since more than 12 unique values detected, discrete colors will be assigned for this class")
                 ),
                 conditionalPanel(
                   condition = paste0("input.hm", axis, colname, "type == 'Continuous'"),
                   p("We generate a gradient color legend for continuous annotation value"),
                   column(
                     width = 6,
                     colourpicker::colourInput(
                       inputId = paste0('hm', axis, colname, 'High'),
                       label = 'High Value'
                     )
                   ),
                   column(
                     width = 6,
                     colourpicker::colourInput(
                       inputId = paste0('hm', axis, colname, 'Low'),
                       label = 'Low Value'
                     )
                   )
                 ),
        )
      } else {
        fluidRow(style = "padding-left:20px;", h4(colname),
                 p(paste0("Totally ", nUniq, " unique values in this class of annotation, which is too many to provide manual selection. Coloring will be provided by default."))
        )
      }
      
    } else if(nUniq >= 1 && nUniq <= 12){
      cats <- as.character(unique(data))
      fluidRow(style = "padding-left:20px;",
               h4(colname),
               lapply(seq_along(cats), function(i) {
                 if(!is.na(cats[i])){
                   column(
                     width = 3,
                     colourpicker::colourInput(
                       inputId = paste0('hm', axis, colname, cats[i]),
                       label = cats[i],
                       value = hmAnnAllColors[[axis]][[colname]][[cats[i]]]
                     )
                   )
                 } else {
                   column(
                     width = 3,
                     colourpicker::colourInput(
                       inputId = paste0('hm', axis, colname, cats[i]),
                       label = "NA",
                       value = #FFFFFF
                     )
                   )
                 }
               })
      )
    } else {
      fluidRow(style = "padding-left:20px;",
               h4(colname),
               p("No effective category found for the class.")
      )
    }
  }
  
  observeEvent(input$hmCellAnn, {
    if(!is.null(input$hmCellAnn)){
      output$hmCellAnnAssUI <- renderUI({
        panel(
          lapply(input$hmCellAnn, generateAnnColAssUI, axis = 'col')
        )
      })
    }
  })
  
  observeEvent(input$hmGeneAnn, {
    if(!is.null(input$hmGeneAnn)){
      output$hmGeneAnnAssUI <- renderUI({
        panel(
          lapply(input$hmGeneAnn, generateAnnColAssUI, axis = 'row')
        )
      })
    }
  })
  
  observe({
    for (i in names(hmTemp$colColorPresets)){
      if (i %in% hmTemp$colDataName){
        for (j in names(hmTemp$colColorPresets[[i]])){
          if(!is.null(input[[paste0('hmcol', i, j)]])){
            hmTemp$colColorPresets[[i]][[j]] <- input[[paste0('hmcol', i, j)]]
          }
        }
      }
    }
  })
  observe({
    for (i in names(hmTemp$rowColorPresets)){
      if (i %in% hmTemp$rowDataName){
        for (j in names(hmTemp$rowColorPresets[[i]])){
          if(!is.null(input[[paste0('hmrow', i, j)]])){
            hmTemp$rowColorPresets[[i]][[j]] <- input[[paste0('hmrow', i, j)]]
          }
        }
      }
    }
  })
  
  # Heatmap: Others ####
  output$hmColSplitUI <- renderUI({
    selectInput(
      'hmColSplit',
      "Split columns (cell) by (Leave this for not splitting)",
      hmTemp$colDataName, multiple = TRUE, selected = hmTemp$colSplitBy
    )
  })
  
  output$hmRowSplitUI <- renderUI({
    selectInput(
      'hmRowSplit',
      "Split rows (feature) by (Leave this for not splitting)",
      hmTemp$rowDataName, multiple = TRUE, selected = hmTemp$rowSplitBy
    )
  })
  observeEvent(input$hmColSplit, {
    hmTemp$colSplitBy <- input$hmColSplit
  })
  observeEvent(input$hmRowSplit, {
    hmTemp$rowSplitBy <- input$hmRowSplit
  })
  
  output$hmTrimUI <- renderUI({
    if(!is.null(vals$counts) && !is.null(input$hmAssay)){
      # This might be slow when running with real data
      mat <- as.matrix(assay(vals$counts, input$hmAssay))
      if(isTRUE(input$hmScale)){
        mat <- as.matrix(computeZScore(mat))
      }
      sliderInput("hmTrim",  "Trim", min = floor(min(mat)),
                  max = ceiling(max(mat)), value = c(-2, 2), step = 0.5)
    }
  })
  
  # Heatmap: Color Scheme ####
  observe({
    # Palette preset coding refers:
    # https://stackoverflow.com/a/52552008/13676674
    vals$hmCSURL <- session$registerDataObj(
      name = 'uniquename1',
      data = vals$hmCSPresets,
      filter = function(data, req) {
        query <- parseQueryString(req$QUERY_STRING)
        palette <- query$palette
        cols <- data[[palette]]
        image <- tempfile()
        tryCatch({
          png(image, width = 75, height = 25, bg = 'transparent')
          par(mar = c(0, 0, 0, 0))
          barplot(rep(1, length(cols)), col = cols, axes = FALSE)
        },finally = dev.off())
        
        shiny:::httpResponse(
          200, 'image/png',readBin(image, 'raw', file.info(image)[,'size'])
        )
      }
    )
    
    updateSelectizeInput(
      session, 'hmCSPalette', server = TRUE,
      choices = names(vals$hmCSPresets),
      selected = "RWB",
      options = list(
        render = I(
          sprintf(
            "{
            option: function(item, escape) {
            return '<div><img width=\"75\" height=\"25\" ' +
            'src=\"%s&palette=' + escape(item.value) + '\" />' +
            escape(item.value) + '</div>';
            }
          }",
            vals$hmCSURL
          )
        )
      )
    )
  })
  
  observeEvent(input$hmCSPalette, {
    if(!input$hmCSPalette == ""){
      lowColor <- vals$hmCSPresets[[input$hmCSPalette]][1]
      colourpicker::updateColourInput(session, 'hmCSLow', value = lowColor)
    }
    if(!input$hmCSPalette == ""){
      mediumColor <- vals$hmCSPresets[[input$hmCSPalette]][2]
      colourpicker::updateColourInput(session, 'hmCSMedium', value = mediumColor)
    }
    if(!input$hmCSPalette == ""){
      highColor <- vals$hmCSPresets[[input$hmCSPalette]][3]
      colourpicker::updateColourInput(session, 'hmCSHigh', value = highColor)
    }
  })
  
  # Heatmap: Final run ####
  observeEvent(input$plotHeatmap, {
    if (is.null(vals$counts)){
      shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
    } else {
      # Move all plotting process into alert callback, thus auto-re-render can
      # be avoided while tuning parameters.
      shinyalert(
        title = "Confirm",
        text = "Large dataset might take time to rerun. Are you sure with the parameters?",
        type = "warning",
        showCancelButton = TRUE,
        confirmButtonText = "Plot",
        cancelButtonText = "Check Once More",
        callbackR = function(x){
          if(isTRUE(x)){
            withBusyIndicatorServer("plotHeatmap", {
              if(!is.null(hmTemp$colDataName)){
                cellAnnColor <- list()
                for(i in hmTemp$colDataName){
                  uniqs <- as.vector(unique(colData(hmTemp$sce)[[i]]))
                  uniqs[is.na(uniqs)] <- 'NA'
                  if (i %in% names(hmTemp$colColorPresets)) {
                    cellAnnColor[[i]] <- hmTemp$colColorPresets[[i]]
                  } else if (length(uniqs) <= 12) {
                    cellAnnColor[[i]] <- vector()
                    for(j in uniqs){
                      inputId <- paste0('hmcol', i, j)
                      cellAnnColor[[i]] <- c(cellAnnColor[[i]], input[[inputId]])
                    }
                    names(cellAnnColor[[i]]) <- uniqs
                  } else {
                    if(is.numeric(colData(hmTemp$sce)[[i]])){
                      if(input[[paste0('hmcol', i, 'type')]] == 'Continuous'){
                        cFun <- circlize::colorRamp2(
                          c(min(colData(hmTemp$sce)[[i]]),
                            max(colData(hmTemp$sce)[[i]])),
                          c(input[[paste0('hmcol', i, 'Low')]],
                            input[[paste0('hmcol', i, 'High')]])
                        )
                        cellAnnColor[[i]] <- cFun
                      } else {
                        c <- distinctColors(length(uniqs))
                        names(c) <- uniqs
                        cellAnnColor[[i]] <- c
                      }
                    }
                  }
                }
              } else {
                cellAnnColor <- NULL
              }
              if(!is.null(hmTemp$rowDataName)){
                geneAnnColor <- list()
                for(i in hmTemp$rowDataName){
                  uniqs <- as.vector(unique(rowData(hmTemp$sce)[[i]]))
                  if (i %in% names(hmTemp$rowColorPresets)) {
                    geneAnnColor[[i]] <- hmTemp$rowColorPresets[[i]]
                  } else if(length(uniqs) <= 12){
                    geneAnnColor[[i]] <- vector()
                    for(j in uniqs){
                      inputId <- paste0('hmrow', i, j)
                      geneAnnColor[[i]] <- c(geneAnnColor[[i]], input[[inputId]])
                    }
                    names(geneAnnColor[[i]]) <- uniqs
                  } else {
                    if(is.numeric(rowData(hmTemp$sce)[[i]])){
                      if(input[[paste0('hmrow', i, 'type')]] == 'Continuous'){
                        cFun <- circlize::colorRamp2(
                          c(min(rowData(hmTemp$sce)[[i]]),
                            max(rowData(hmTemp$sce)[[i]])),
                          c(input[[paste0('hmrow', i, 'Low')]],
                            input[[paste0('hmrow', i, 'High')]])
                        )
                        geneAnnColor[[i]] <- cFun
                      } else {
                        c <- distinctColors(length(uniqs))
                        names(c) <- uniqs
                        geneAnnColor[[i]] <- c
                      }
                    }
                  }
                }
              } else {
                geneAnnColor <- NULL
              }
              hmAddLabel <- list(cell = FALSE, gene = FALSE)
              if(!is.null(input$hmAddLabel)){
                if("1" %in% input$hmAddLabel){
                  if(input$hmAddCellLabel == "Default cell IDs"){
                    hmAddLabel$cell <- TRUE
                  } else {
                    hmAddLabel$cell <- input$hmAddCellLabel
                  }
                }
                if("2" %in% input$hmAddLabel){
                  if(input$hmAddGeneLabel == "Default feature IDs"){
                    hmAddLabel$gene <- TRUE
                  } else {
                    hmAddLabel$gene <- input$hmAddGeneLabel
                  }
                }
              }
              hmShowDendro <- c(FALSE, FALSE)
              hmShowDendro[as.numeric(input$hmShowDendro)] <- TRUE
              #if(is.null(hmTemp$rowSplitBy)){
              #  hmRowSplit <- NULL
              #} else {
              #  hmRowSplit <- hmTemp$rowSplitBy
              #}
              #if(is.null(hmTemp$colSplitBy)){
              #  hmColSplit <- NULL
              #} else {
              #  hmColSplit <- hmTemp$colSplitBy
              #}
              cs <- circlize::colorRamp2(
                c(input$hmTrim[1], mean(input$hmTrim), input$hmTrim[2]),
                c(input$hmCSLow, input$hmCSMedium, input$hmCSHigh)
              )
              output$Heatmap <- renderPlot({
                isolate({
                  plotSCEHeatmap(
                    inSCE = hmTemp$sce, useAssay = input$hmAssay, colorScheme = cs,
                    featureIndex = hmTemp$geneIndex, cellIndex = hmTemp$cellIndex,
                    rowDataName = hmTemp$rowDataName, colDataName = hmTemp$colDataName,
                    rowSplitBy = hmTemp$rowSplitBy, colSplitBy = hmTemp$colSplitBy,
                    rowLabel = hmAddLabel$gene, colLabel = hmAddLabel$cell,
                    rowDend = hmShowDendro[2], colDend = hmShowDendro[1],
                    scale = input$hmScale, trim = input$hmTrim,
                    width = unit(20, 'cm'), height = unit(20, 'cm'),
                    featureAnnotationColor = geneAnnColor,
                    cellAnnotationColor = cellAnnColor
                  )
                })
              }, height = 800)
            })
          }
        }
      )
    }
  })
  
  # Bubbleplot: Final run ####
  observeEvent(input$bpAssay, {
    req(vals$counts)
    updateSelectInput(session, "bpAssay",
                      label = "Select Assay")
  })
  
  output$bpClusterUI <- renderUI({
    req(vals$counts)
    selectInput(
      'bpCluster',
      "",
      colnames(colData(vals$counts)), multiple = FALSE, width = '550px')
  })
  
  observe({
    req(vals$counts) 
    
    if (!is.null(metadata(vals$counts)$featureDisplay) && metadata(vals$counts)$featureDisplay %in% names(rowData(vals$counts))) {
      featureDisplayValue <- metadata(vals$counts)$featureDisplay
      updateSelectizeInput(session, "bpFeatures", choices = rowData(vals$counts)[[featureDisplayValue]], server = TRUE)
    }
  })
  
  observeEvent(input$plotBubbleplot, {
    req(vals$counts)
    output$Bubbleplot <- renderPlot({
      isolate({
        plotBubble(inSCE=vals$counts, useAssay=input$bpAssay, featureNames=input$bpFeatures, 
                   displayName=input$bpRow, groupNames=input$bpCluster, title=input$bpTitle, 
                   xlab=input$bpX, ylab=input$bpY, colorLow=input$bpLow, colorHigh=input$bpHigh, scale=input$scaleBubble)
      })
    })
  })
  
  observeEvent(input$updateBubbleplot, {
    req(vals$counts)
    output$Bubbleplot <- renderPlot({
      isolate({
        plotBubble(inSCE=vals$counts, useAssay=input$bpAssay, featureNames=input$bpFeatures, 
                   displayName=input$bpRow, groupNames=input$bpCluster, title=input$bpTitle, 
                   xlab=input$bpX, ylab=input$bpY, colorLow=input$bpLow, colorHigh=input$bpHigh, scale=input$scaleBubble)
      })
    })
  })
  
  # #COG For BubblePlot
  # observeEvent(input$closeDropDownBubble, {
  #   session$sendCustomMessage("close_dropDownBubble", "")
  # }) 
  # 
  # observeEvent(input$bubblePlot, {
  #   req(vals$counts)
  #   choice <- NULL
  #   if (input$bubbleVisChoicesType == 1) {
  #     # Use result
  #     if (is.null(input$bubbleVisRes) ||
  #         input$bubbleVisRes == "") {
  #       shinyalert::shinyalert("Error!", "Select the clusters to plot",
  #                              type = "error")
  #     }
  #     choice <- input$bubbleVisRes
  #   } else if (input$bubbleVisChoicesType == 2) {
  #     # Use colData
  #     if (is.null(input$bubbleVisCol) ||
  #         input$bubbleVisCol == "") {
  #       shinyalert::shinyalert("Error!", "Select the clusters to plot",
  #                              type = "error")
  #     }
  #     choice <- input$bubbleVisCol
  #   }
  #   if (is.null(input$bubbleVisReddim) || input$bubbleVisReddim == "") {
  #     shinyalert::shinyalert("Error!",
  #                            "No reduction selected. Select one or run dimension reduction first",
  #                            type = "error")
  #   }
  #   if (!is.null(choice) && choice != "" &&
  #       !is.null(input$bubbleVisReddim) && input$bubbleVisReddim != "") {
  #     output$bubbleVisPlot <- renderPlotly({
  #       isolate({
  #         plotSCEDimReduceColData(inSCE = vals$counts,
  #                                 colorBy = choice,
  #                                 conditionClass = "factor",
  #                                 reducedDimName = input$bubbleVisReddim,
  #                                 labelClusters = TRUE,
  #                                 dim1 = 1, dim2 = 2,
  #                                 legendTitle = choice)
  #       })
  #     })
  #   }
  #   session$sendCustomMessage("close_dropDownBubble", "")
  
  #-----------------------------------------------------------------------------
  # Page 4: Batch Correction ####
  #-----------------------------------------------------------------------------
  
  observeEvent(input$closeDropDownBC, {
    session$sendCustomMessage("close_dropDownBC", "")
  })
  
  observeEvent(input$batchCorrMethods, {
    if (!is.null(vals$counts) &&
        !is.null(input$batchCorrMethods)) {
      # What type of assays are required, according their docs
      # ComBatSeq - counts
      # BBKNN - filtered, normalized, and scaled
      # fastMNN - log-expression
      # Limma - log-expression
      # MNN - log-expression
      # scanorama - normalized, log1p
      # scMerge - logcounts
      # zinbwave - counts
      bc.recommended <- NULL
      method.log <- c("FastMNN", "Limma", "MNN")
      method.scale <- c("BBKNN")
      method.raw <- c("ZINBWaVE", "ComBatSeq")
      if (is.null(input$batchCorrMethods)) {
        bc.recommended <- "raw"
      } else if (input$batchCorrMethods %in% method.log) {
        bc.recommended <- c("transformed", "normalized")
      } else if (input$batchCorrMethods %in% method.raw) {
        bc.recommended <- "raw"
      } else if (input$batchCorrMethods %in% method.scale) {
        bc.recommended <- "scaled"
      }
      updateSelectInputTag(session, "batchCorrAssay",
                           label = "Select Assay to Correct:",
                           choices = assayNames(vals$counts),
                           recommended = bc.recommended)
    }
  })
  
  output$batchCheckResUI <- renderUI({
    selectInput("batchCheckCorrName", "Corrected Matrix",
                c(names(vals$batchRes)))
  })
  
  observeEvent(input$plotBatchCheck, {
    if(!is.null(vals$counts) &&
       !is.null(input$batchCheckCorrName) &&
       input$batchCheckVar != input$batchCheckCond){
      withBusyIndicatorServer("plotBatchCheck", {
        ## Generals
        if(input$batchCheckCond == "None"){
          shapeBy <- NULL
        } else {
          shapeBy <- input$batchCheckCond
        }
        ## Original assay PCA
        oriAssayPCAName <- paste0(input$batchCheckOrigAssay, "_PCA")
        if(!oriAssayPCAName %in% names(reducedDims(vals$counts))){
          vals$counts <- scaterPCA(vals$counts,
                                   useAssay = input$batchCheckOrigAssay, useFeatureSubset = NULL,
                                   reducedDimName = oriAssayPCAName)
          updateReddimInputs()
        }
        resName <- input$batchCheckCorrName
        ## Corrected assay/altExp PCA
        if (vals$batchRes[[resName]] == 'assay'){
          corrAssayPCAName = paste0(resName, "_PCA")
          vals$counts <- scaterPCA(vals$counts, useAssay = resName, useFeatureSubset = NULL,
                                   reducedDimName = corrAssayPCAName)
          updateReddimInputs()
        } else if (vals$batchRes[[resName]] == 'altExp'){
          ae <- altExp(vals$counts, resName)
          corrAltExpPCAName <- paste0(resName, "_PCA")
          ae <- scaterPCA(ae, useAssay = resName, useFeatureSubset = NULL,
                          reducedDimName = corrAltExpPCAName)
          reducedDim(vals$counts, corrAltExpPCAName) <-
            reducedDim(ae, corrAltExpPCAName)
          updateReddimInputs()
        }
        ## Update plots
        output$batchOriVars <- renderPlot({
          isolate({
            plotBatchVariance(inSCE = vals$counts,
                              useAssay = input$batchCheckOrigAssay,
                              batch = input$batchCheckVar,
                              condition = shapeBy)
          })
        })
        output$batchOriPCA <- renderPlot({
          isolate({
            plotSCEDimReduceColData(vals$counts, colorBy = input$batchCheckVar,
                                    shape = shapeBy,
                                    reducedDimName = oriAssayPCAName,
                                    dim1 = 1, dim2 = 2,
                                    title = paste0("Original ",
                                                   input$batchCheckOrigAssay,
                                                   " PCA"))
          })
        })
        output$batchCorrVars <- renderPlot({
          isolate({
            if (vals$batchRes[[resName]] == 'reddim'){
              plotBatchVariance(inSCE = vals$counts, useReddim = resName,
                                batch = input$batchCheckVar,
                                condition = shapeBy)
            } else if (vals$batchRes[[resName]] == 'assay'){
              plotBatchVariance(inSCE = vals$counts, useAssay = resName,
                                batch = input$batchCheckVar,
                                condition = shapeBy)
            } else if (vals$batchRes[[resName]] == 'altExp'){
              plotBatchVariance(inSCE = vals$counts, useAltExp = resName,
                                batch = input$batchCheckVar,
                                condition = shapeBy)
            }
          })
        })
        output$batchCorrReddim <- renderPlot({
          isolate({
            if (vals$batchRes[[resName]] == 'reddim'){
              plotSCEDimReduceColData(vals$counts,
                                      colorBy = input$batchCheckVar,
                                      shape = shapeBy,
                                      reducedDimName = resName,
                                      conditionClass = "character",
                                      dim1 = 1, dim2 = 2,
                                      title = paste0(resName, " corrected"))
            } else if (vals$batchRes[[resName]] == 'assay'){
              plotSCEDimReduceColData(vals$counts,
                                      colorBy = input$batchCheckVar,
                                      shape = shapeBy,
                                      reducedDimName = corrAssayPCAName,
                                      conditionClass = "character",
                                      dim1 = 1, dim2 = 2,
                                      title = paste0(resName, " corrected"))
            } else if (vals$batchRes[[resName]] == 'altExp'){
              plotSCEDimReduceColData(vals$counts,
                                      colorBy = input$batchCheckVar,
                                      shape = shapeBy,
                                      reducedDimName = corrAltExpPCAName,
                                      dim1 = 1, dim2 = 2,
                                      title = paste0(resName, " corrected"))
            }
          })
        })
      })
    }
    session$sendCustomMessage("close_dropDownBC", "")
  })
  
  #observeEvent(input$BBKNNRun, withConsoleMsgRedirect(
  #  msg = "Please wait while BBKNN method for batch correction is being executed. See console log for progress.",
  #  {
  #    req(vals$counts)
  #    saveassayname <- gsub(" ", "_", input$BBKNNSaveReddim)
  #    message(date(), " ... Running BBKNN batch correction method")
  #    vals$counts <- runBBKNN(vals$counts,
  #                            useAssay = input$batchCorrAssay,
  #                            batch = input$batchCorrVar,
  #                            reducedDimName = saveassayname,
  #                            nComponents = input$BBKNNNComp)
  #    message(date(), " ... BBKNN finished")
  #    vals$batchRes[[saveassayname]] <- 'reddim'
  #    updateReddimInputs()
  #  }
  #))
  
  observeEvent(input$combatRun, withConsoleMsgRedirect(
    msg = "Please wait while CombatSeq method for batch correction is being executed. See console log for progress.",
    {
      req(vals$counts)
      #check for zeros
      if (any(rowSums(assay(vals$counts, input$batchCorrAssay)) == 0)){
        stop("Rows with a sum of zero found. Filter data to continue.")
      }
      saveassayname <- gsub(" ", "_", input$combatSaveAssay)
      if (input$combatKnownCT == "Yes") {
        cov <- input$combatCond
      } else {
        cov <- NULL
      }
      if (input$combatCTBalance == "Yes") {
        useSVA <- FALSE
      } else {
        useSVA <- TRUE
      }
      if (input$combatBioCond == "None") {
        combatBioCond <- NULL
      } else {
        combatBioCond <- input$combatBioCond
      }
      message(date(), " ... Running ComBatSeq batch correction method")
      vals$counts <- runComBatSeq(inSCE = vals$counts,
                                  useAssay = input$batchCorrAssay,
                                  batch = input$batchCorrVar,
                                  covariates = cov,
                                  bioCond = combatBioCond,
                                  useSVA = useSVA,
                                  assayName = saveassayname,
                                  shrink = input$combatShrink,
                                  shrinkDisp = input$combatShrinkDisp,
                                  nGene = input$combatNGene                                      )
      vals$batchRes[[saveassayname]] <- 'assay'
      updateAssayInputs()
      message(date(), " ... CamBatSeq finished")
    }
  ))
  
  observeEvent(input$FastMNNRun, withConsoleMsgRedirect(
    msg = "Please wait while FASTMNN method for batch correction is being executed. See console log for progress.",
    {
      req(vals$counts)
      saveassayname <- gsub(" ", "_", input$FastMNNSaveReddim)
      if(isTRUE(input$FastMNNPcInput)){
        fmnnAssay <- input$FastMNNReddim
      } else {
        fmnnAssay <- input$batchCorrAssay
      }
      message(date(), " ... Running FastMNN batch correction method")
      vals$counts <- runFastMNN(vals$counts,
                                useAssay = fmnnAssay,
                                batch = input$batchCorrVar,
                                reducedDimName = saveassayname,
                                pcInput = input$FastMNNPcInput
      )
      message(date(), " ... FastMNN finished")
      vals$batchRes[[saveassayname]] <- 'reddim'
      updateReddimInputs()
    }
  ))
  
  # TODO: Remember to follow the logging format as other batch correction chunks
  # When putting Harmony back
  # observeEvent(input$HarmonyRun, {
  #   if (is.null(vals$counts)){
  #     shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
  #   } else {
  #     withBusyIndicatorServer("HarmonyRun", {
  #       saveassayname <- gsub(" ", "_", input$HarmonySaveReddim)
  #       if(isTRUE(input$HarmonyPcInput)){
  #         useAssay <- input$HarmonyReddim
  #       } else {
  #         useAssay <- input$batchCorrAssay
  #       }
  #       if(is.na(as.numeric(input$HarmonyTheta))){
  #         stop("Theta value must be numeric.")
  #       } else {
  #         theta <- as.numeric(input$HarmonyTheta)
  #       }
  #       vals$counts <- runHarmony(vals$counts, useAssay = useAssay,
  #                                 pcInput = input$HarmonyPcInput,
  #                                 batch = input$batchCorrVar,
  #                                 reducedDimName = saveassayname,
  #                                 nComponents = input$HarmonyNComp,
  #                                 theta = theta, nIter = input$HarmonyNIter)
  #       shinyalert::shinyalert('Success!', 'Harmony completed.',
  #                              type = 'success')
  #       vals$batchRes[[saveassayname]] <- 'reddim'
  #       updateReddimInputs()
  #     })
  #   }
  # })
  
  observeEvent(input$limmaRun, withConsoleMsgRedirect(
    msg = "Please wait while Limma method for batch correction is being executed. See console log for progress.",
    {
      req(vals$counts)
      saveassayname <- gsub(" ", "_", input$limmaSaveAssay)
      message(date(), " ... Running Limma batch correction method")
      vals$counts <- runLimmaBC(vals$counts,
                                useAssay = input$batchCorrAssay,
                                batch = input$batchCorrVar,
                                assayName = saveassayname)
      message(date(), " ... Limma batch correction finished")
      vals$batchRes[[saveassayname]] <- 'assay'
      updateAssayInputs()
    }
  ))
  
  # TODO: Remember to follow the logging format as other batch correction chunks
  # When putting LIGER back
  #observeEvent(input$ligerRun, {
  #  if (is.null(vals$counts)){
  #    shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
  #  }
  #  else{
  #    withBusyIndicatorServer("ligerRun", {
  #      #check for zeros
  #      if (any(rowSums(assay(vals$counts, input$batchCorrAssay)) == 0)){
  #        shinyalert::shinyalert("Error!", "Rows with a sum of zero found. Filter data to continue.", type = "error")
  #      } else {
  #        saveassayname <- gsub(" ", "_", input$ligerSaveReddim)
  #        vals$counts <-
  #          runLIGER(inSCE = vals$counts,
  #                   useAssay = input$batchCorrAssay,
  #                   batch = input$batchCorrVar,
  #                   reducedDimName = saveassayname,
  #                   nComponents = input$ligerNComp,
  #                   lambda = input$ligerLambda,
  #                   resolution = input$ligerResolution)
  #        shinyalert::shinyalert('Success!', 'LIGER completed.',
  #                               type = 'success')
  #        vals$batchRes[[saveassayname]] <- 'reddim'
  #        updateReddimInputs()
  #      }
  #    })
  #  }
  #})
  
  observeEvent(input$MNNRun, withConsoleMsgRedirect(
    msg = "Please wait while MNN method for batch correction is being executed. See console log for progress.",
    {
      req(vals$counts)
      saveassayname <- gsub(" ", "_", input$MNNSaveAssay)
      message(date(), " ... Running MNN batch correction method")
      vals$counts <- runMNNCorrect(vals$counts,
                                   useAssay = input$batchCorrAssay,
                                   batch = input$batchCorrVar,
                                   k = input$MNNK, sigma = input$MNNSigma,
                                   assayName = saveassayname)
      message(date(), " ... MNN finished")
      vals$batchRes[[saveassayname]] <- 'assay'
      updateAssayInputs()
    }
  ))
  
  observeEvent(input$scnrmRun, withConsoleMsgRedirect(
    msg = "Please wait while Scanorama method for batch correction is being executed. See console log for progress.",
    {
      req(vals$counts)
      saveassayname <- gsub(" ", "_", input$scnrmSaveAssay)
      message(date(), " ... Running Scanorama batch correction method")
      vals$counts <- runSCANORAMA(vals$counts,
                                  useAssay = input$batchCorrAssay,
                                  batch = input$batchCorrVar,
                                  SIGMA = input$scnrmSIGMA,
                                  ALPHA = input$scnrmALPHA,
                                  KNN = input$scnrmKNN,
                                  assayName = saveassayname)
      message(date(), " ... Scanorama finished")
      vals$batchRes[[saveassayname]] <- 'assay'
      updateAssayInputs()
    }
  ))
  
  observeEvent(input$batchCorrVar, {
    req(vals$counts)
    req(input$batchCorrVar)
    nBatch <- length(unique(colData(vals$counts)[[input$batchCorrVar]]))
    output$scMergeNBatch <- renderUI({
      span(paste0("Please input ", nBatch, " integer(s), separated by ','."))
    })
  })
  
  observeEvent(input$scMergeRun, withConsoleMsgRedirect(
    msg = "Please wait while scMerge method for batch correction is being executed. See console log for progress.",
    {
      req(vals$counts)
      saveassayname <- gsub(" ", "_", input$scMergeSaveAssay)
      if(input$scMergeSEGOpt == 1){
        seg <- NULL
      } else if(input$scMergeSEGOpt == 2){
        data("SEG")
        seg <- SEG[[input$scMergeSEGSpecies]]
      } else {
        seg <- str_trim(scan(text = input$scMergeSEGCustom,
                             sep='\n', what = 'character'))
      }
      if(isTRUE(input$scMergeAutoKmk)){
        kmk <- NULL
      } else {
        kmk <- scan(text = input$scMergeUserKmk, sep=',')
      }
      message(date(), " ... Running scMerge batch correction method")
      vals$counts <- runSCMerge(inSCE = vals$counts,
                                useAssay = input$batchCorrAssay,
                                batch = input$batchCorrVar,
                                cellType = input$scMergeCT,
                                seg = seg, kmeansK = kmk,
                                assayName = saveassayname
      )
      message(date(), " ... scMerge finished")
      vals$batchRes[[saveassayname]] <- 'assay'
      updateAssayInputs()
    }
  ))
  
  output$Srt3IntNAnchUI <- renderUI({
    if(!is.null(vals$counts)){
      ngene <- nrow(vals$counts)
      tagList(
        
        numericInput('Srt3IntNAnch', "Number of anchors:",
                     value = ngene, min = 30, max = ngene, step = 1),
        
        numericInput('Srt3IntKWeight', "kWeight:",
                     value = 0, min = 0, step = 1),
        
        numericInput('Srt3IntKFilter', "kFilter:",
                     value = 0, min = 0, step = 1),
        
        numericInput('Srt3IntNDims', "Number of Dimensions:",
                     value = 2, min = 2, step = 1)
        
      )
    }
  })
  
  observeEvent(input$Srt3IntRun, withConsoleMsgRedirect(
    msg = "Please wait while Seurat3 integration for batch correction is being executed. See console log for progress.",
    {
      req(vals$counts)
      saveassayname <- gsub(" ", "_", input$Srt3IntSaveAssay)
      message(date(), " ... Running Seurat3 integration method")
      vals$counts <- runSeuratIntegration(
        inSCE = vals$counts,
        batch = input$batchCorrVar,
        newAssayName = saveassayname,
        kAnchor = input$Srt3IntNAnch,
        kWeight = input$Srt3IntKWeight,
        kFilter = input$Srt3IntKFilter,
        ndims = input$Srt3IntNDims)
      message(date(), " ... Seurat3 integration finished")
      vals$batchRes[[saveassayname]] <- 'altExp'
    }
  ))
  
  output$zinbwaveNHvgUI <- renderUI({
    if(!is.null(vals$counts)){
      ngenes <- nrow(vals$counts)
      zwdefault <- min(ngenes, 1000)
      numericInput('zinbwaveNHVG', 'Number of highly variable genes to use:',
                   value = zwdefault, max = ngenes)
    }
  })
  
  output$zinbwaveEpsUI <- renderUI({
    if(!is.null(vals$counts)){
      ngenes <- nrow(vals$counts)
      zwdefault <- min(ngenes, 1000)
      numericInput('zinbwaveEps', 'Epsilon value:',
                   value = zwdefault, max = ngenes)
    }
  })
  
  observeEvent(input$zinbwaveRun, withConsoleMsgRedirect(
    msg = "Please wait while ZINBWaVE method for batch correction is being executed. See console log for progress.",
    {
      req(vals$counts)
      saveassayname <- gsub(" ", "_", input$limmaSaveAssay)
      message(date(), " ... Running ZINBWaVE batch correction method")
      vals$counts <- runZINBWaVE(vals$counts,
                                 useAssay = input$batchCorrAssay,
                                 batch = input$batchCorrVar,
                                 reducedDimName = saveassayname,
                                 epsilon = input$zinbwaveEps,
                                 nHVG = input$zinbwaveNHVG,
                                 nIter = input$zinbwaveNIter,
                                 nComponents = input$zinbwaveNComp
      )
      message(date(), " ... ZINBWaVE finished")
      vals$batchRes[[saveassayname]] <- 'reddim'
      updateReddimInputs()
    }
  ))
  
  #-----------------------------------------------------------------------------
  # Page 4.1: Feature Selection ####
  #-----------------------------------------------------------------------------
  observeEvent(input$hvgMethodFS,{
    req(vals$counts)
    updateAssayInputs()
  })
  
  updateHVGMetricSelection <- function(selected = NULL){
    metricList <- lapply(metadata(vals$counts)$sctk$runFeatureSelection,
                         function(x){x$useAssay})
    metricOptions <- names(metricList)
    toggleState("hvgMetricSelect", !is.null(metricOptions))
    toggleState("hvgNumberSelect", !is.null(metricOptions))
    toggleState("hvgSubsetName", !is.null(metricOptions))
    toggleState("hvgSubsetRun", !is.null(metricOptions))
    toggleState("hvgPlotMethod", !is.null(metricOptions))
    toggleState("updatePlotFS", !is.null(metricOptions))
    if (!is.null(metricOptions)) {
      names(metricOptions) <- paste0(names(metricList), " - ", metricList)
      updateSelectInput(session, "hvgPlotMethod", choices = metricOptions,
                        selected = selected)
      updateSelectInput(session, "hvgMetricSelect", choices = metricOptions,
                        selected = selected)
    }
  }
  
  updateHVGListSelection <- function(selected = NULL) {
    featureSubsets <- names(metadata(vals$counts)$sctk$featureSubsets)
    toggleState("hvgPlotSubsetSelect", !is.null(featureSubsets))
    toggleState("hvgPlotNLabel", !is.null(featureSubsets))
    if (!is.null(featureSubsets)) {
      if (is.null(selected)) {
        if (!is.null(featureSubsets)) {
          selected <- featureSubsets[1]
        } else {
          selected <- "None"
        }
      }
      updateSelectInput(session, "hvgPlotSubsetSelect",
                        choices = c("None", featureSubsets),
                        selected = selected)
      updateSelectInput(session, "dimRedHVGSelect",
                        choices = c("None", featureSubsets),
                        selected = selected)
      updateSelectInput(session, "hvg_tsneUmap",
                        choices = c("None", featureSubsets),
                        selected = selected)
    }
  }
  
  observeEvent(input$findHvgButtonFS, withConsoleMsgRedirect(
    msg = "Please wait while variability is being computed. See console log for progress.",
    {
      req(vals$counts)
      vals$counts <- runFeatureSelection(inSCE = vals$counts,
                                         useAssay = input$assaySelectFS_Norm,
                                         method = input$hvgMethodFS)
      updateHVGMetricSelection(selected = input$hvgMethodFS)
      output$plotFS <- renderPlot({
        isolate({
          plotTopHVG(inSCE =  vals$counts,
                     method = input$hvgMethodFS,
                     hvgNumber = 0,
                     labelsCount = 0)
        })
      })
    }
  ))
  
  observeEvent(c(input$hvgMetricSelect, input$hvgNumberSelect), {
    # Auto made default hvgListName
    subsetName <- paste0("HVG_", input$hvgMetricSelect, input$hvgNumberSelect)
    updateTextInput(session, "hvgSubsetName", value = subsetName)
  })
  
  observeEvent(input$hvgSubsetRun, withConsoleMsgRedirect(
    msg = "Please wait feature subset is being created. See console log for progress.",
    {
      req(vals$counts)
      if (input$hvgMetricSelect == "") {
        stop("Must calculate variability before selection.")
      } else if (input$hvgSubsetName == "") {
        stop("Must specify a name for the HVG list.")
      } else if (is.na(input$hvgNumberSelect)) {
        stop("Must set the number of HVGs for the selection.")
      } else {
        vals$counts <- setTopHVG(vals$counts,
                                 method = input$hvgMetricSelect,
                                 hvgNumber = input$hvgNumberSelect,
                                 featureSubsetName = input$hvgSubsetName)
        updateHVGListSelection(selected = input$hvgSubsetName)
        updateHVGMetricSelection(selected = input$hvgMetricSelect)
        updateNumericInput(session, "hvgPlotNSelect", value = input$hvgNumberSelect)
        featureDisplay <- NULL
        if (input$hvgPlotFeatureDisplay != "Rownames (Default)") {
          featureDisplay <- input$hvgPlotFeatureDisplay
        }
        # After selection, update visualization part
        output$plotFS <- renderPlot({
          isolate({
            plotTopHVG(inSCE =  vals$counts,
                       method = input$hvgMetricSelect,
                       useFeatureSubset = input$hvgSubsetName,
                       labelsCount = 20,
                       featureDisplay = featureDisplay)
          })
        })
        output$hvgOutputFS <- renderText({
          isolate({
            getTopHVG(inSCE = vals$counts,
                      useFeatureSubset = input$hvgSubsetName,
                      featureDisplay = featureDisplay)
          })
        })
      }
    }))
  
  observeEvent(input$updatePlotFS, {
    req(vals$counts)
    req(metadata(vals$counts)$sctk$runFeatureSelection)
    featureDisplay <- NULL
    if (input$hvgPlotFeatureDisplay != "Rownames (Default)") {
      featureDisplay <- input$hvgPlotFeatureDisplay
    }
    usefeatureSubset <- input$hvgPlotSubsetSelect
    if (input$hvgPlotSubsetSelect == "None") {
      usefeatureSubset <- NULL
    }
    output$plotFS <- renderPlot({
      isolate({
        plotTopHVG(inSCE =  vals$counts,
                   method = input$hvgPlotMethod,
                   useFeatureSubset = usefeatureSubset,
                   labelsCount = input$hvgPlotNLabel,
                   featureDisplay = featureDisplay)
      })
    })
    output$hvgOutputFS <- renderText({
      isolate({
        if (!is.null(usefeatureSubset)) {
          getTopHVG(inSCE = vals$counts,
                    useFeatureSubset = usefeatureSubset,
                    featureDisplay = featureDisplay)
        } else {
          ""
        }
      })
    })
    session$sendCustomMessage("close_dropDownFS", "")
  })
  
  observeEvent(input$closeDropDownFS, {
    session$sendCustomMessage("close_dropDownFS", "")
  })
  
  #-----------------------------------------------------------------------------
  # Page 5.1: Differential Expression ####
  #-----------------------------------------------------------------------------
  observeEvent(input$deMethod, {
    if (!is.null(vals$counts)) {
      if (is.null(input$deMethod)) {
        updateSelectInputTag(session, "deAssay",
                             tags = c("raw", "transformed", "uncategorized",
                                      "normalized", "scaled", "redDims"),
                             recommended = c("transformed", "normalized"),
                             redDims = TRUE)
      } else if (input$deMethod == "DESeq2") {
        updateSelectInputTag(session, "deAssay",
                             tags = c("raw", "transformed", "uncategorized",
                                      "normalized", "scaled"),
                             recommended = c("raw"))
      } else {
        updateSelectInputTag(session, "deAssay",
                             tags = c("raw", "transformed", "uncategorized",
                                      "normalized", "scaled", "redDims"),
                             recommended = c("transformed", "normalized"),
                             redDims = TRUE)
      }
    }
  })
  
  ## DE - Thresholding Vis ####
  observeEvent(input$deViewThresh, withConsoleMsgRedirect(
    msg = "Please wait while threshold is being plotted. See console log for progress.",
    {
      req(vals$counts)
      req(input$deAssay)
      shinyjs::showElement(id= "deThreshpanel")
      message(paste0(date(), " ... Plotting thresholding"))
      # MAST style sanity check for whether logged or not
      x <- expData(vals$counts, input$deAssay)
      if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
          100) {
        output$deSanityWarnThresh <- renderText("")
        isLogged <- TRUE
      } else {
        output$deSanityWarnThresh <- renderText("Selected assay seems not logged (MAST style sanity check). Forcing to plot by automatically applying log-transformation. ")
        isLogged <- FALSE
      }
      suppressMessages({
        thres.grob <- plotMASTThresholdGenes(inSCE = vals$counts,
                                             useAssay = input$deAssay,
                                             check_sanity = FALSE,
                                             isLogged = isLogged,
                                             doPlot = FALSE)
      })
      nSub <- tail(strsplit(thres.grob$childrenOrder, split = '-'),
                   n = 1)[[1]][3]
      plotHeight <- ceiling(as.numeric(nSub) / 4) * 240
      
      output$deThreshPlotDiv <- renderUI({
        div(
          style = paste0("height: ", plotHeight, "px;"),
          plotOutput("deThreshplot"))
      })
      output$deThreshplot <- renderPlot({
        grid.draw(thres.grob)
      }, height = plotHeight)
      updateActionButton(session, "deViewThresh", "Refresh")
    }
  ))
  
  observeEvent(input$deHideThresh, {
    shinyjs::hideElement(id= "deThreshpanel")
    updateActionButton(session, "deViewThresh", "View Thresholding")
  })
  
  ## DE - condition determination method1 ####
  output$deC1G1UI <- renderUI({
    if(!is.null(vals$counts) &
       !input$deC1Class == "None"){
      classCol <- colData(vals$counts)[[input$deC1Class]]
      classChoices <- sort(as.vector(unique(classCol)))
      selectInput(inputId = "deC1G1", label = "Select Condition(s)",
                  choices = classChoices, multiple = TRUE)
    } else {
      selectInput(inputId = "deC1G1", label = "Select Condition(s)",
                  choices = NULL, multiple = TRUE)
    }
  })
  
  output$deC1G2UI <- renderUI({
    if(!is.null(vals$counts) &
       !input$deC1Class == "None"){
      classCol <- colData(vals$counts)[[input$deC1Class]]
      classChoices <- sort(as.vector(unique(classCol)))
      selectInput(inputId = "deC1G2", label = "Select Condition(s)",
                  choices = classChoices, multiple = TRUE)
    } else {
      selectInput(inputId = "deC1G2", label = "Select Condition(s)",
                  choices = NULL, multiple = TRUE)
    }
  })
  
  output$deC1G1CellCheckUI <- renderUI({
    if(!is.null(input$deC1G1) &
       length(input$deC1G1) > 0){
      g1Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G1
      g1Cells <- colnames(vals$counts)[g1Idx]
      g1CellsText <- paste(g1Cells, collapse = "\n")
      textAreaInput("deC1G1CellCheck", "Cells selected:", g1CellsText,
                    height = '100px', placeholder = "Nothing selected")
    } else {
      textAreaInput("deC1G1CellCheck", "Cells selected:", NULL,
                    height = '100px', placeholder = "Nothing selected")
    }
  })
  
  output$deC1G2CellCheckUI <- renderUI({
    if(!is.null(input$deC1G2) &
       length(input$deC1G2) > 0){
      g2Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G2
      g2Cells <- colnames(vals$counts)[g2Idx]
      g2CellsText <- paste(g2Cells, collapse = "\n")
      textAreaInput("deC1G2CellCheck", "Cells selected:", g2CellsText,
                    height = '100px',
                    placeholder = "Leave unselected for all the others.")
    } else {
      textAreaInput("deC1G2CellCheck", "Cells selected:", NULL,
                    height = '100px',
                    placeholder = "Leave unselected for all the others.")
    }
  })
  
  output$deC1G1NCell <- renderUI({
    if(!is.null(input$deC1G1CellCheck)){
      if(!input$deC1G1CellCheck == ""){
        cellList <- str_trim(scan(text = input$deC1G1CellCheck,
                                  sep='\n', what = 'character', quiet = TRUE))
        cellList <- unique(cellList)
        nCell <- length(which(cellList %in% colnames(vals$counts)))
      } else {
        nCell <- 0
      }
    } else {
      nCell <- 0
    }
    msg <- paste0("Totally ", nCell, " cell(s) selected.")
    span(msg, style = 'margin-left:10px')
  })
  
  output$deC1G2NCell <- renderUI({
    if(!is.null(input$deC1G2CellCheck)){
      if(!input$deC1G2CellCheck == ""){
        cellList <- str_trim(scan(text = input$deC1G2CellCheck,
                                  sep='\n', what = 'character', quiet = TRUE))
        cellList <- unique(cellList)
        nCell <- length(which(cellList %in% colnames(vals$counts)))
      } else {
        nCell <- 0
      }
    } else {
      nCell <- 0
    }
    msg <- paste0("Totally ", nCell, " cell(s) selected.")
    span(msg, style = 'margin-left:10px')
  })
  ## DE - condition determination method2 ####
  ## condition 1 table operation vvvv
  output$deC2G1Table <- DT::renderDataTable({
    if(!is.null(vals$counts)){
      df <- lapply(colData(vals$counts),
                   function(i){
                     if(is.character(i) && !length(unique(i)) == length(i)){
                       return(as.factor(i))
                     } else if(is.integer(i) &&
                               !length(unique(i)) == length(i)){
                       return(as.factor(i))
                     } else {
                       return(i)
                     }
                   })
      df <- data.frame(df, row.names = colnames(vals$counts))
      DT::datatable(df, filter = "top", options = list(scrollX = TRUE))
    }
  }, server = TRUE)
  deC2G1Table_proxy <- DT::dataTableProxy("deC2G1Table")
  
  observeEvent(input$deC2G1Col, {
    colNames <- names(colData(vals$counts))
    showIdx <- which(colNames %in% input$deC2G1Col)
    DT::showCols(deC2G1Table_proxy, showIdx, reset = TRUE)
  })
  
  observeEvent(input$deC2G1Table_addAll, {
    DT::selectRows(deC2G1Table_proxy,
                   sort(unique(c(input$deC2G1Table_rows_selected,
                                 input$deC2G1Table_rows_all))))
  })
  
  observeEvent(input$deC2G1Table_clear, {
    DT::selectRows(deC2G1Table_proxy, NULL)
  })
  
  output$deC2G1info <- renderUI({
    nCell <- length(input$deC2G1Table_rows_selected)
    p(paste0("Totally ", nCell, " cells selected for ", input$deG1Name))
  })
  ## condition 1 table operation ^^^^
  ## condition 2 table operation vvvv
  output$deC2G2Table <- DT::renderDataTable({
    if(!is.null(vals$counts)){
      df <- lapply(colData(vals$counts),
                   function(i){
                     if(is.character(i) && !length(unique(i)) == length(i)){
                       return(as.factor(i))
                     } else if(is.integer(i) &&
                               !length(unique(i)) == length(i)){
                       return(as.factor(i))
                     } else {
                       return(i)
                     }
                   })
      df <- data.frame(df, row.names = colnames(vals$counts))
      DT::datatable(df, filter = "top", options = list(scrollX = TRUE))
    }
  }, server = TRUE)
  deC2G2Table_proxy <- DT::dataTableProxy("deC2G2Table")
  
  observeEvent(input$deC2G2Col, {
    colNames <- names(colData(vals$counts))
    showIdx <- which(colNames %in% input$deC2G2Col)
    DT::showCols(deC2G2Table_proxy, showIdx, reset = TRUE)
  })
  
  observeEvent(input$deC2G2Table_addAll, {
    DT::selectRows(deC2G2Table_proxy,
                   sort(unique(c(input$deC2G2Table_rows_selected,
                                 input$deC2G2Table_rows_all))))
  })
  
  observeEvent(input$deC2G2Table_clear, {
    DT::selectRows(deC2G2Table_proxy, NULL)
  })
  
  output$deC2G2info <- renderUI({
    nCell <- length(input$deC2G2Table_rows_selected)
    p(paste0("Totally ", nCell, " cells selected for ", input$deG2Name))
  })
  ## condition 2 table operation ^^^^
  ## DE - condition determination method3 ####
  output$deC3G1NCell <- renderUI({
    if(!is.null(input$deC3G1Cell)){
      if(!input$deC3G1Cell == ""){
        cellList <- str_trim(scan(text = input$deC3G1Cell,
                                  sep='\n', what = 'character', quiet = TRUE))
        cellList <- unique(cellList)
        nCell <- length(which(cellList %in% colnames(vals$counts)))
      } else {
        nCell <- 0
      }
    } else {
      nCell <- 0
    }
    msg <- paste0("Totally ", nCell, " valid cell name(s) entered.")
    span(msg, style = 'margin-left:10px')
  })
  
  output$deC3G2NCell <- renderUI({
    if(!is.null(input$deC3G2Cell)){
      if(!input$deC3G2Cell == ""){
        cellList <- str_trim(scan(text = input$deC3G2Cell,
                                  sep='\n', what = 'character', quiet = TRUE))
        cellList <- unique(cellList)
        nCell <- length(which(cellList %in% colnames(vals$counts)))
      } else {
        nCell <- 0
      }
    } else {
      nCell <- 0
    }
    msg <- paste0("Totally ", nCell, " valid cell name(s) entered.")
    span(msg, style = 'margin-left:10px')
  })
  
  # DE run analysis ####
  runDEfromShiny <- function(overwrite){
    if (input$deAssay %in% assayNames(vals$counts) &
        !input$deAssay %in% reducedDimNames(vals$counts)) {
      deUseAssay <- input$deAssay
      deUseReducedDim <- NULL
    } else if (!input$deAssay %in% assayNames(vals$counts) &
               input$deAssay %in% reducedDimNames(vals$counts)) {
      deUseAssay <- NULL
      deUseReducedDim <- input$deAssay
    } else {
      stop("Error in identifying input matrix")
    }
    if(input$deCondMethod == 1){
      vals$counts <- runDEAnalysis(method = input$deMethod,
                                   inSCE = vals$counts,
                                   useAssay = deUseAssay,
                                   useReducedDim = deUseReducedDim,
                                   class = input$deC1Class,
                                   classGroup1 = input$deC1G1,
                                   classGroup2 = input$deC1G2,
                                   groupName1 = input$deG1Name,
                                   groupName2 = input$deG2Name,
                                   analysisName = input$deAnalysisName,
                                   covariates = input$deCovar,
                                   fdrThreshold = input$deFDRThresh,
                                   onlyPos = input$mastPosOnly,
                                   overwrite = overwrite)
    } else if(input$deCondMethod == 2){
      vals$counts <- runDEAnalysis(method = input$deMethod,
                                   inSCE = vals$counts,
                                   useAssay = deUseAssay,
                                   useReducedDim = deUseReducedDim,
                                   index1 = input$deC2G1Table_rows_selected,
                                   index2 = input$deC2G2Table_rows_selected,
                                   groupName1 = input$deG1Name,
                                   groupName2 = input$deG2Name,
                                   analysisName = input$deAnalysisName,
                                   covariates = input$deCovar,
                                   fdrThreshold = input$deFDRThresh,
                                   onlyPos = input$dePosOnly,
                                   overwrite = overwrite)
    } else {
      g1CellList <- str_trim(scan(text = input$deC3G1Cell,
                                  sep='\n', what = 'character', quiet = TRUE))
      g1CellList <- sort(unique(g1CellList))
      g2CellList <- str_trim(scan(text = input$deC3G2Cell,
                                  sep='\n', what = 'character', quiet = TRUE))
      g2CellList <- sort(unique(g2CellList))
      vals$counts <- runDEAnalysis(method = input$deMethod,
                                   inSCE = vals$counts,
                                   useAssay = deUseAssay,
                                   useReducedDim = deUseReducedDim,
                                   index1 = g1CellList,
                                   index2 = g2CellList,
                                   groupName1 = input$deG1Name,
                                   groupName2 = input$deG2Name,
                                   analysisName = input$deAnalysisName,
                                   covariates = input$deCovar,
                                   fdrThreshold = input$deFDRThresh,
                                   onlyPos = input$dePosOnly,
                                   overwrite = overwrite)
    }
    updateDEAnalysisNames(selected = input$deAnalysisName)
    
    colSplitBy <- "condition"
    rowSplitBy <- "regulation"
    
    x <- expData(vals$counts, input$deAssay)
    if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
        100) {
      isLogged <- TRUE
    } else {
      isLogged <- FALSE
      updateCheckboxGroupInput(session, "deHMDoLog", selected = TRUE)
    }
    
    if (isTRUE(input$deHMShowRowLabel)) {
      rowLabel <- TRUE
      if (input$deHMrowLabel != "Rownames (Default)" &
          is.null(deUseReducedDim)) {
        rowLabel <- input$deHMrowLabel
      }
    } else {
      rowLabel <- FALSE
    }
    message(date(), " ... Updating DE heatmap for analysis: ",
            input$deAnalysisName)
    output$deHeatmap <- renderPlot({
      isolate({
        plotDEGHeatmap(inSCE = vals$counts,
                       useResult = input$deAnalysisName,
                       onlyPos = input$dePosOnly,
                       fdrThreshold = input$deFDRThresh,
                       colSplitBy = colSplitBy,
                       rowSplitBy = rowSplitBy,
                       doLog = !isLogged,
                       rowLabel = rowLabel)
      })
    })
    if (isFALSE(input$deVolcShowLabel)) {
      labelTopN <- FALSE
    } else if (isTRUE(input$deVolcShowLabel)) {
      labelTopN <- input$deVolcTopN
    }
    message(date(), " ... Updating DE volcano plot for analysis: ",
            input$deAnalysisName)
    output$deVolcanoPlot <- renderPlot({
      isolate({
        plotDEGVolcano(inSCE = vals$counts,
                       useResult = input$deAnalysisName,
                       log2fcThreshold = input$deVolcLog2FC,
                       labelTopN = labelTopN,
                       fdrThreshold = input$deFDRThresh)
      })
    })
    message(date(), " ... Updating DE violin plot for analysis: ",
            input$deAnalysisName)
    output$deViolinPlot <- renderPlot({
      isolate({
        plotDEGViolin(inSCE = vals$counts, useResult = input$deAnalysisName,
                      nrow = input$deVioNRow, ncol = input$deVioNCol,
                      labelBy = NULL,
                      check_sanity = FALSE, isLogged = isLogged)
      })
    })
    message(date(), " ... Updating DE regression plot for analysis: ",
            input$deAnalysisName)
    output$deRegPlot <- renderPlot({
      isolate({
        plotDEGRegression(inSCE = vals$counts,
                          useResult = input$deAnalysisName,
                          nrow = input$deRegNRow,
                          ncol = input$deRegNCol,
                          labelBy = NULL,
                          check_sanity = FALSE,
                          isLogged = isLogged)
      })
    })
  }
  
  observeEvent(input$runDE, withConsoleMsgRedirect(
    msg = "Please wait while DE analysis are being performed. See console log for progress.",
    {
      req(vals$counts)
      if (input$deAnalysisName == "" ||
          input$deG1Name == "" ||
          input$deG2Name == "") {
        stop("The name of the two conditions and the whole analysis have to be specified!")
      }
      allRes <- names(metadata(vals$counts)$diffExp)
      if(input$deAnalysisName %in% allRes){
        shinyalert(
          "Warning",
          "Entered differential experiment analysis name is already there.",
          "warning", showCancelButton = TRUE,
          confirmButtonText = "Overwrite",
          callbackR = function(x){if(isTRUE(x)){runDEfromShiny(x)}})
      } else {
        runDEfromShiny(FALSE)
      }
      # Show downstream analysis options
      callModule(module = nonLinearWorkflow, id = "nlw-de", parent = session,
                 pa = TRUE, cv = TRUE)
    }
  ))
  
  updateDEAnalysisNames <- function(selected = NULL) {
    deRes <- rev(names(metadata(vals$counts)$diffExp))
    if (!is.null(deRes)) {
      updateSelectInput(session, "deResSel", choices = deRes,
                        selected = selected)
      updateSelectInput(session, "enrDEGSelect", choices = deRes,
                        selected = selected)
      output$hmImpDEGUI <- renderUI({
        selectInput('hmImpDEG', "Import results from analysis:", deRes)
      })
    } else {
      output$hmImpDEGUI <- renderUI({
        p("Differential expression analysis not performed yet.")
      })
    }
  }
  # DE: Result visualize ####
  
  # Data table
  # output$deResult <- DT::renderDataTable({
  #   if(!is.null(input$deResSel) &&
  #      !is.null(vals$counts)){
  #     metadata(vals$counts)$diffExp[[input$deResSel]]$result
  #   }
  # }, filter = 'top')
  
  
  
  observeEvent(input$deResSelUpdate, {
    if (is.null(input$deResSel) ||
        input$deResSel == "") {
      shinyjs::disable("deDownload")
    } else {
      shinyjs::enable("deDownload")
      callModule(
        module = filterTableServer,
        id = "deResult",
        dataframe = getDEGTopTable(vals$counts, input$deResSel,
                                   log2fcThreshold = NULL),
        defaultFilterColumns = c('Log2_FC', 'FDR'),
        defaultFilterOperators = c('>', '<'),
        defaultFilterValues = c("1", "0.05"),
        initialTopN = 100,
        topText = "You can view the differentially epxressed features between the selected groups in the table below. And you can apply customized filters to filter the table accordingly."
      )
    }
  })
  
  output$deDownload <- downloadHandler(
    filename = function() {
      paste0("deResult_", input$deResSel, ".csv")
    },
    content = function(file) {
      fullTable <- metadata(vals$counts)$diffExp[[input$deResSel]]$result
      filteredTable <- fullTable[input$deResult_rows_all,]
      filteredTable <- filteredTable[rowSums(is.na(filteredTable)) != ncol(filteredTable), ]
      utils::write.csv(filteredTable, file, row.names = FALSE, )
    }
  )
  
  # Volcano plot
  
  observeEvent(input$closeDropDownDeVolcano, {
    session$sendCustomMessage("close_dropDownDeVolcano", "")
  })
  
  observeEvent(input$deVolcShowLabel, {
    if (isTRUE(input$deVolcShowLabel)) {
      enable("deVolcTopN")
      enable("deVolcFeatureDisplay")
    } else if (isFALSE(input$deVolcShowLabel)) {
      disable("deVolcTopN")
      disable("deVolcFeatureDisplay")
    }
  })
  
  observeEvent(list(input$dePlotVolcano, input$deResSelUpdate), {
    req(vals$counts)
    req(input$deResSel)
    if (isFALSE(input$deVolcShowLabel)) {
      labelTopN <- FALSE
    } else if (isTRUE(input$deVolcShowLabel)) {
      labelTopN <- input$deVolcTopN
    }
    featureDisplay <- NULL
    useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
    if (input$deVolcFeatureDisplay != "Rownames (Default)" &
        !is.null(useAssay)) {
      featureDisplay <- input$deVolcFeatureDisplay
    }
    message(date(), " ... Updating DE volcano plot for analysis: ",
            input$deResSel)
    output$deVolcanoPlot <- renderPlot({
      isolate({
        plotDEGVolcano(inSCE = vals$counts,
                       useResult = input$deResSel,
                       labelTopN = labelTopN,
                       log2fcThreshold = input$deVolcLog2FC,
                       fdrThreshold = input$deVolcFDR,
                       featureDisplay = featureDisplay
        )
      })
    })
    session$sendCustomMessage("close_dropDownDeVolcano", "")
  })
  
  # Violin plot
  output$deVioTotalUI <- renderUI({
    topN <- input$deVioNRow * input$deVioNCol
    p(as.character(topN))
  })
  
  observeEvent(input$closeDropDownDeViolin, {
    session$sendCustomMessage("close_dropDownDeViolin", "")
  })
  
  observeEvent(list(input$dePlotVio, input$deResSelUpdate), {
    if(!is.null(input$deResSel) &&
       !input$deResSel == "" &&
       !is.null(vals$counts)){
      useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
      labelBy <- NULL
      if (input$deVioLabel != "Rownames (Default)" &
          !is.null(useAssay)) {
        labelBy <- input$deVioLabel
      }
      # MAST style sanity check for whether logged or not
      if (!is.null(useAssay)) {
        x <- expData(vals$counts, useAssay)
        if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
            100) {
          output$deSanityWarnViolin <- renderText("")
          isLogged <- TRUE
        } else {
          output$deSanityWarnViolin <- renderText("Selected assay seems not logged (MAST style sanity check). Forcing to plot by automatically applying log-transformation. ")
          isLogged <- FALSE
        }
      } else {
        isLogged <- TRUE
      }
      message(date(), " ... Updating DE violin plot for analysis: ",
              input$deResSel)
      output$deViolinPlot <- renderPlot({
        isolate({
          plotDEGViolin(inSCE = vals$counts, useResult = input$deResSel,
                        #threshP = input$deVioUseThresh,
                        nrow = input$deVioNRow, ncol = input$deVioNCol,
                        labelBy = labelBy, check_sanity = FALSE,
                        isLogged = isLogged)
        })
      })
      session$sendCustomMessage("close_dropDownDeViolin", "")
    }
  })
  # Linear Regression Plot
  output$deRegTotalUI <- renderUI({
    topN <- input$deRegNRow * input$deRegNCol
    p(as.character(topN))
  })
  
  observeEvent(input$closeDropDownDeReg, {
    session$sendCustomMessage("close_dropDownDeReg", "")
  })
  
  observeEvent(list(input$dePlotReg, input$deResSelUpdate), {
    if(!is.null(input$deResSel) &&
       !input$deResSel == "" &&
       !is.null(vals$counts)){
      useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
      labelBy <- NULL
      if (input$deVioLabel != "Rownames (Default)" &
          !is.null(useAssay)) {
        labelBy <- input$deRegLabel
      }
      # MAST style sanity check for whether logged or not
      if (!is.null(useAssay)) {
        x <- expData(vals$counts, useAssay)
        if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
            100) {
          output$deSanityWarnReg <- renderText("")
          isLogged <- TRUE
        } else {
          output$deSanityWarnReg <- renderText("Selected assay seems not logged (MAST style sanity check). Forcing to plot by automatically applying log-transformation. ")
          isLogged <- FALSE
        }
      } else {
        isLogged <- TRUE
      }
      message(date(), " ... Updating DE regression plot for analysis: ",
              input$deResSel)
      output$deRegPlot <- renderPlot({
        isolate({
          plotDEGRegression(inSCE = vals$counts,
                            useResult = input$deResSel,
                            nrow = input$deRegNRow,
                            ncol = input$deRegNCol,
                            labelBy = labelBy,
                            check_sanity = FALSE,
                            isLogged = isLogged)
        })
      })
      session$sendCustomMessage("close_dropDownDeReg", "")
    }
  })
  
  # Heatmap
  output$deHMSplitColUI <- renderUI({
    otherAvail <- input$deHMcolData
    selectInput("deHMSplitCol", "Split columns by", multiple = TRUE,
                choices = c('condition', otherAvail),
                selected = 'condition')
  })
  output$deHMSplitRowUI <- renderUI({
    otherAvail <- input$deHMrowData
    selectInput("deHMSplitRow", "Split rows by", multiple = TRUE,
                choices = c('regulation', otherAvail),
                selected = 'regulation')
  })
  
  observeEvent(input$closeDropDownDeHM, {
    session$sendCustomMessage("close_dropDownDeHM", "")
  })
  
  observeEvent(list(input$dePlotHM, input$deResSelUpdate), {
    if(!is.null(input$deResSel) &&
       !input$deResSel == ""){
      deHMMinExp1 <- handleEmptyInput(input$deHMMinExp1, "numeric", NULL)
      deHMMaxExp2 <- handleEmptyInput(input$deHMMaxExp2, "numeric", NULL)
      deHMMinExpPerc1 <- handleEmptyInput(input$deHMMinExpPerc1, "numeric", NULL)
      deHMMaxExpPerc2 <- handleEmptyInput(input$deHMMaxExpPerc2, "numeric", NULL)
      useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
      rowLabel <- FALSE
      if (isTRUE(input$deHMShowRowLabel)) {
        rowLabel <- TRUE
        if (input$deHMrowLabel != "Rownames (Default)" &
            !is.null(useAssay)) {
          rowLabel <- input$deHMrowLabel
        }
      }
      message(date(), " ... Updating DE heatmap for analysis: ",
              input$deResSel)
      output$deHeatmap <- renderPlot({
        isolate({
          plotDEGHeatmap(inSCE = vals$counts,
                         useResult = input$deResSel,
                         doLog = input$deHMDoLog,
                         onlyPos = input$deHMPosOnly,
                         log2fcThreshold = input$deHMFC,
                         fdrThreshold = input$deHMFDR,
                         rowDataName = input$deHMrowData,
                         colDataName = input$deHMcolData,
                         colSplitBy = input$deHMSplitCol,
                         rowSplitBy = input$deHMSplitRow,
                         rowLabel = rowLabel)
        })
      })
      session$sendCustomMessage("close_dropDownDeHM", "")
    }
  })
  
  #-----------------------------------------------------------------------------
  # Page 5.2: Find Marker ####
  #-----------------------------------------------------------------------------
  observeEvent(input$fmMethod, {
    if (!is.null(vals$counts)) {
      if (is.null(input$fmMethod)) {
        updateSelectInputTag(session, "fmAssay", recommended = c("transformed", "normalized"))
      } else if (input$fmMethod == "DESeq2") {
        updateSelectInputTag(session, "fmAssay", recommended = c("raw"))
      } else {
        updateSelectInputTag(session, "fmAssay", recommended = c("transformed", "normalized"))
      }
    }
  })
  
  # findMarker RUN ####
  observeEvent(input$runFM, withConsoleMsgRedirect(
    msg = "Please wait while marker genes are being found. See console log for progress.",
    {
      req(vals$counts)
      fdrThreshold <- handleEmptyInput(input$fmFDR)
      vals$counts <- runFindMarker(inSCE = vals$counts,
                                   method = input$fmMethod,
                                   useAssay = input$fmAssay,
                                   cluster = input$fmCluster,
                                   covariates = input$fmCovar,
                                   fdrThreshold = fdrThreshold)
      message(date(), " ... Updating find marker heatmap")
      updateFMPlot()
      
      callModule(
        module = filterTableServer,
        id = "filterfmResTable",
        dataframe = getFindMarkerTopTable(vals$counts, log2fcThreshold = 0,
                                          minClustExprPerc = 0,
                                          maxCtrlExprPerc = 1,
                                          minMeanExpr = 0, topN = NULL),
        defaultFilterColumns = c("Log2_FC", "clusterExprPerc", "ControlExprPerc", "clusterAveExpr"),
        defaultFilterOperators = c(">", ">=", "<=", ">="),
        defaultFilterValues = c("0", "0", "1", "0"),
        initialTopN = 100,
        topText = "You can view the markers of each cluster in the table below. And you can apply customized filters to filter the table accordingly."
      )
    }
  ))
  
  # findMarker ResultTable ####
  # output$fmResTable <- DT::renderDataTable({
  #   if(!is.null(vals$counts) &&
  #      'findMarker' %in% names(metadata(vals$counts))){
  #     fullTable <- metadata(vals$counts)$findMarker
  #     fullTable[,5] <- as.factor(fullTable[,5])
  #     fullTable
  #   }
  # }, filter = "top", options = list(scrollX = TRUE))
  
  observe({
    if (!is.null(vals$counts) &&
        !is.null(metadata(vals$counts)$findMarker)) {
      shinyjs::enable("fmDownload")
    } else {
      shinyjs::disable("fmDownload")
    }
  })
  
  output$fmDownload <- downloadHandler(
    filename = function() {
      paste0("findMarkerResult_", input$fmCluster, ".csv")
    },
    content = function(file) {
      fullTable <- metadata(vals$counts)$findMarker
      filteredTable <- fullTable[input$fmResTable_rows_all,]
      utils::write.csv(filteredTable, file, row.names = FALSE)
    }
  )
  
  # findMarker Heatmap ####
  observeEvent(input$fmShowHMSetting, {
    if (isTRUE(vals$fmHMshowHide)) {
      shinyjs::hide("fmHMsettings")
      updateActionButton(session, "fmShowHMSetting", label = "Show Settings")
      vals$fmHMshowHide <- FALSE
    } else {
      shinyjs::show("fmHMsettings")
      updateActionButton(session, "fmShowHMSetting", label = "Hide Settings")
      vals$fmHMshowHide <- TRUE
    }
  })
  
  
  observeEvent(input$fmUseTopN, {
    if (!isTRUE(input$fmUseTopN)) {
      shinyjs::disable("fmTopN")
    } else {
      shinyjs::enable("fmTopN")
    }
  })
  
  observeEvent(input$closeDropDownFM, {
    session$sendCustomMessage("close_dropDownFM", "")
  })
  
  observeEvent(input$plotFM, {
    updateFMPlot()
    session$sendCustomMessage("close_dropDownFM", "")
  })
  
  updateFMPlot <- function() {
    if(!is.null(vals$counts) &&
       'findMarker' %in% names(metadata(vals$counts))){
      withBusyIndicatorServer("plotFM", {
        message(paste0(date(), " ... Updating marker heatmap"))
        
        if (isTRUE(input$fmUseTopN)
            && is.na(input$fmTopN)) {
          stop("Top N marker must be a numeric non-empty value")
        }
        if (is.na(input$fmHMFC)) {
          stop("Log2FC must be a numeric non-empty value!")
        }
        if (is.na(input$fmHMFDR)) {
          stop("FDR must be a numeric non-empty value!")
        }
        if (!isTRUE(input$fmUseTopN)) {
          topN <- NULL
        } else {
          topN <- input$fmTopN
        }
        if (input$fmHMFeatureDisplay != "Rownames (Default)") {
          rowLabel <- input$fmHMFeatureDisplay
        } else {
          rowLabel <- TRUE
        }
        # Take value before rendering plot, so that the plot doesn't auto
        # re-render while we tweak the parameter
        output$fmHeatmap <- renderPlot({
          isolate({
            plotFindMarkerHeatmap(inSCE = vals$counts,
                                  orderBy = input$fmHMOrder,
                                  log2fcThreshold = input$fmHMFC,
                                  topN = topN,
                                  fdrThreshold = input$fmHMFDR,
                                  decreasing = input$fmHMdec,
                                  rowDataName = input$fmHMrowData,
                                  colDataName = input$fmHMcolData,
                                  minClustExprPerc = input$fmHMMinClustExprPerc,
                                  maxCtrlExprPerc = input$fmHMMaxCtrlExprPerc,
                                  minMeanExpr = input$fmHMMinMeanExpr,
                                  rowLabel = rowLabel)
          })
        })
      })
    }
  }
  
  
  #-----------------------------------------------------------------------------
  # Page 6: Pathway Activity Analysis
  #-----------------------------------------------------------------------------
  
  observeEvent(input$pathwayImportGS, {
    showTab(inputId = "navbar",
            target = "Import Gene Sets",
            select = TRUE,
            session = session)
  })
  #colData for grouping the data (optional for user)
  observeEvent(input$pathway, {
    if(!is.null(vals$counts)){
      updateSelectInput(session, "pathwayPlotVar", choices = colnames(colData(vals$counts)))
    }
  })
  
  #select geneset collection name for pathway analysis
  #output$selectPathwayGeneLists <- renderUI({
  #  if (!is.null(vals$counts)){
  #    if (!is.null(metadata(vals$counts)$sctk$genesets)) {
  #      newGSchoices <- sctkListGeneSetCollections(vals$counts)
  #      selectizeInput("PathwayGeneLists", "Select Geneset Collection(s):",
  #                     choices = newGSchoices, multiple = FALSE)
  #    }
  #  } else {
  #    HTML("<h5><span style='color:red'>Must import geneset data first!</span></h5></br>")
  #  }
  #})
  
  #Run algorithm
  observeEvent(input$pathwayRun, withConsoleMsgRedirect(
    msg = "Please wait while pathway analysis are being performed. See console log for progress.",
    {
      req(vals$counts)
      if (input$PathwayGeneLists == "Import geneset before using") {
        stop("Must import geneset first.", type = "error")
      }
      if (input$pathway == "VAM") {
        vals$counts <- runVAM(inSCE = vals$counts,
                              useAssay = input$vamAssay,
                              geneSetCollectionName = input$PathwayGeneLists,
                              center = input$vamCenterParameter,
                              gamma = input$vamGammaParameter)
        scoreSelect <- paste0("VAM_", input$PathwayGeneLists, "_CDF")
      } else if (input$pathway == "GSVA") {
        vals$counts <- runGSVA(inSCE = vals$counts,
                               useAssay = input$vamAssay,
                               geneSetCollectionName = input$PathwayGeneLists)
        scoreSelect <- paste0("GSVA_", input$PathwayGeneLists, "_Scores")
        
      }
      updateAssayInputs()
      updateReddimInputs()
      availPathwayRes <- getPathwayResultNames(vals$counts)
      firstGS <- colnames(reducedDim(vals$counts, scoreSelect))[1]
      updateSelectizeInput(session, "pathwayRedDimNames",
                           choices = availPathwayRes, selected = scoreSelect)
      updateSelectizeInput(session, "pathwayPlotGS",
                           choices = colnames(reducedDim(vals$counts, scoreSelect)),
                           selected = firstGS)
      #plot results with default values intitially
      output$pathwayPlot <- renderPlot({
        isolate({
          plotPathway(inSCE = vals$counts,
                      resultName = scoreSelect,
                      geneset = firstGS,
                      groupBy = input$pathwayPlotVar,
                      boxplot = input$pathwayPlotBoxplot,
                      violin = input$pathwayPlotViolinplot,
                      dots = input$pathwayPlotDots,
                      summary = input$pathwayPlotSummary)
        })
      })
    }
  ))
  
  observeEvent(input$pathwayRedDimNames, {
    if (!is.null(vals$counts)) {
      updateSelectizeInput(session, "pathwayPlotGS",
                           choices = colnames(reducedDim(vals$counts, input$pathwayRedDimNames)))
    }
  })
  
  #plot results
  observeEvent(input$pathwayPlot, {
    output$pathwayPlot <- renderPlot({
      isolate({
        plotPathway(inSCE = vals$counts,
                    resultName = input$pathwayRedDimNames,
                    geneset = input$pathwayPlotGS,
                    groupBy = input$pathwayPlotVar,
                    boxplot = input$pathwayPlotBoxplot,
                    violin = input$pathwayPlotViolinplot,
                    dots = input$pathwayPlotDots,
                    summary = input$pathwayPlotSummary)
      })
    })
    session$sendCustomMessage("close_dropDownPathway", "")
  })
  
  observeEvent(input$closeDropDownPathway,{
    session$sendCustomMessage("close_dropDownPathway", "")
  })
  
  #disable downloadPathway button if the pathway data doesn't exist
  #isVamResult <- reactive(is.null(vals$vamResults))
  #isGsvaResult <- reactive(is.null(vals$gsvaResults))
  #observe({
  #  if (isVamResult() && isGsvaResult()) {
  #    shinyjs::disable("downloadPathway")
  #  } else {
  #    shinyjs::enable("downloadPathway")
  #  }
  #})
  
  #download pathway results
  #output$downloadPathway <- downloadHandler(
  #  filename = function() {
  #    paste("Pathway_results-", Sys.Date(), ".csv", sep = "")
  #  },
  #  content = function(file) {
  #    if(input$pathway == "VAM"){
  #      utils::write.csv(vals$vamResults, file)
  #    }
  #    else if (input$pathway == "GSVA"){
  #      utils::write.csv(vals$gsvaResults, file)
  #    }
  #  }
  #)
  
  #-----------------------------------------------------------------------------
  # Page 6.2 : Enrichment Analysis - EnrichR ####
  #-----------------------------------------------------------------------------
  
  enrichRfile <- reactive(read.csv(input$enrFile$datapath,
                                   header = input$header,
                                   sep = input$sep,
                                   quote = input$quote,
                                   row.names = 1))
  
  updateEnrichRAnalysisNames <- function(selected = NULL) {
    if (is.null(selected)) {
      selected <- input$enrAnalysisNameSel
    }
    allNames <- names(metadata(vals$counts)$sctk$runEnrichR)
    updateSelectInput(session, "enrAnalysisNameSel",
                      label = "Select analysis name:",
                      choices = allNames,
                      selected = selected)
  }
  
  update_enrDEG <- reactive({
    list(input$enrDEGSelect, input$enrDEGUpOnly, input$enrDEGlog2fc,
         input$enrDEGFDR, input$enrDEGminMean1, input$enrDEGmaxMean2,
         input$enrDEGminPerc1, input$enrDEGmaxPerc2)
  })
  
  observeEvent(ignoreInit = TRUE, update_enrDEG(), {
    req(vals$counts)
    req(input$enrDEGSelect)
    degSelect <- getDEGTopTable(vals$counts, useResult = input$enrDEGSelect,
                                labelBy = NULL, onlyPos = input$enrDEGUpOnly,
                                log2fcThreshold = input$enrDEGlog2fc,
                                fdrThreshold = input$enrDEGFDR,
                                minGroup1MeanExp = input$enrDEGminMean1,
                                maxGroup2MeanExp = input$enrDEGmaxMean2,
                                minGroup1ExprPerc = input$enrDEGminPerc1,
                                maxGroup2ExprPerc = input$enrDEGmaxPerc2)$Gene
    nGene <- length(degSelect)
    output$enrDEGText <- renderUI(p(paste0("Selected ", nGene, " DEGs. Listed below.")))
    output$enrDEGRes <- renderText({
      isolate({
        degSelect
      })
    })
  })
  
  #count_db <- reactive(length(dbs()))
  observeEvent (input$enrichRun, withConsoleMsgRedirect(
    msg = "Please wait while EnrichR is running. See console log for progress.",
    {
      req(vals$counts)
      if (!internetConnection) {
        stop("Internet connection failed.")
      }
      if (input$enrAnalysisNameSet == "" |
          is.null(input$enrAnalysisNameSet)) {
        stop("The analysis name has to be specified")
      }
      by <- "rownames"
      if (input$geneListChoice == "deg") {
        genes <- getDEGTopTable(vals$counts, useResult = input$enrDEGSelect,
                                labelBy = NULL, onlyPos = input$enrDEGUpOnly,
                                log2fcThreshold = input$enrDEGlog2fc,
                                fdrThreshold = input$enrDEGFDR,
                                minGroup1MeanExp = input$enrDEGminMean1,
                                maxGroup2MeanExp = input$enrDEGmaxMean2,
                                minGroup1ExprPerc = input$enrDEGminPerc1,
                                maxGroup2ExprPerc = input$enrDEGmaxPerc2)$Gene
      } else if (input$geneListChoice == "selectGenes"){
        genes <- input$enrichGenes
      } else if (input$geneListChoice == "geneFile"){
        req(input$enrFile)
        genes <- rownames(enrichRfile())
        message(date(), " ... Reading from file. The first three features are:")
        message(date(), "     ", paste(genes[seq(3)], collapse = ", "))
        by <- input$enrFileBy
      }
      message(date(), " ... Performing GSEA with enrichR")
      vals$counts <- runEnrichR(inSCE = vals$counts,
                                features = genes,
                                analysisName = input$enrAnalysisNameSet,
                                db = input$enrichDb,
                                by = by,
                                featureName = input$enrFeatureName)
      updateEnrichRAnalysisNames(selected = input$enrAnalysisNameSet)
    }
  ))
  
  enrChangeDBShow <- reactive({
    list(input$enrAnalysisNameSel,
         input$enrichRun)
  })
  
  observeEvent(enrChangeDBShow(), {
    req(input$enrAnalysisNameSel)
    dbs <- getEnrichRResult(vals$counts, input$enrAnalysisNameSel)$param$db
    updateSelectizeInput(session, "enrDbShow", choices = dbs,
                         selected = input$enrDbShow)
  })
  
  enrResultSel <- reactive({
    list(input$enrAnalysisNameSel,
         input$enrDbShow,
         input$enrichRun)
  })
  #create datatables
  observeEvent(enrResultSel(), {
    req(vals$counts)
    req(input$enrAnalysisNameSel)
    res <- getEnrichRResult(vals$counts, input$enrAnalysisNameSel)$result
    dbToShow <- input$enrDbShow
    if (is.null(dbToShow)) {
      dbToShow <- getEnrichRResult(vals$counts, input$enrAnalysisNameSel)$param$db
    }
    res <- res[which(res[, 1] %in% dbToShow), ]
    vals$enrichRes <- res
    #tableToShow <- res[, c(1:10)] %>%
    #  mutate(Database_selected =
    #           paste0("<a href='", res[, 11],
    #                  "' target='_blank'>",
    #                  res[, 1], "</a>"))
    tableToShow <- res
    output$enrDataTable <- DT::renderDataTable({
      DT::datatable({
        tableToShow
      },
      escape = FALSE,
      options = list(scrollX = TRUE, pageLength = 20),
      rownames = FALSE)
    })
  })
  
  #disable the downloadEnrichR button if the result doesn't exist
  isResult <- reactive(is.null(vals$enrichRes))
  observe({
    if (isResult()) {
      shinyjs::disable("downloadEnrichR")
    } else {
      shinyjs::enable("downloadEnrichR")
    }
  })
  
  output$downloadEnrichR <- downloadHandler(
    filename = function() {
      paste0("SCTK_enrichR_results_", input$enrAnalysisNameSel, "_",
             Sys.Date(), ".csv")
    },
    content = function(file) {
      utils::write.csv(vals$enrichRes, file, row.names = FALSE)
    },
    contentType = "text/csv"
  )
  
  #-----------------------------------------------------------------------------
  # Page 7: Subsampling
  #-----------------------------------------------------------------------------
  
  #Run subsampling analysis
  observeEvent(input$runSubsampleDepth, withConsoleMsgRedirect(
    msg = "Please wait while subsampler is being computed. See console log for progress.",
    {
      req(vals$counts)
      if(is.na(input$minCount)){
        stop("Minimum readcount must be a non-empty numeric value!")
      }
      if(is.na(input$minCells)){
        stop("Minimum number of cells must be a non-empty numeric value!")
      }
      if(is.na(input$iterations)){
        stop("Number of bootstrap iterations must be a non-empty numeric value!")
      }
      vals$subDepth <- downSampleDepth(originalData = vals$counts,
                                       useAssay = input$depthAssay,
                                       minCount = input$minCount,
                                       minCells = input$minCells,
                                       maxDepth = 10 ^ input$maxDepth,
                                       realLabels = input$selectReadDepthCondition,
                                       depthResolution = input$depthResolution,
                                       iterations = input$iterations)
      
      output$depthDone <- renderPlot({
        plot(apply(vals$subDepth[, , 1], 2, median)~
               seq(from = 0, to = input$maxDepth, length.out = input$depthResolution),
             lwd = 4, xlab = "log10(Total read counts)", ylab = "Number of detected genes",
             main = "Number of dected genes by sequencing depth")
        lines(apply(vals$subDepth[, , 1], 2, function(x){quantile(x, 0.25)})~
                seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
        lines(apply(vals$subDepth[, , 1], 2, function(x){quantile(x, 0.75)})~
                seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
      })
      output$minEffectDone <- renderPlot({
        plot(apply(vals$subDepth[, , 2], 2, median)~
               seq(from = 0, to = input$maxDepth, length.out = input$depthResolution),
             lwd = 4, xlab = "log10(Total read counts)", ylab = "Average significant effect size",
             ylim = c(0, 2))
        lines(apply(vals$subDepth[, , 2], 2, function(x){quantile(x, 0.25)})~
                seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
        lines(apply(vals$subDepth[, , 2], 2, function(x){quantile(x, 0.75)})~
                seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
      })
      output$sigNumDone <- renderPlot({
        plot(apply(vals$subDepth[, , 3], 2, median)~
               seq(from = 0, to = input$maxDepth, length.out = input$depthResolution),
             lwd = 4, xlab = "log10(Total read counts)", ylab = "Number of significantly DiffEx genes")
        lines(apply(vals$subDepth[, , 3], 2, function(x){quantile(x, 0.25)})~
                seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
        lines(apply(vals$subDepth[, , 3], 2, function(x){quantile(x, 0.75)})~
                seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
      })
    }
  ))
  
  observeEvent(input$runSubsampleCells, withConsoleMsgRedirect(
    msg = "Please wait while resampler is being computed. See console log for progress.",
    {
      req(vals$counts)
      if (is.na(input$minCellNum) ||
          is.na(input$maxCellNum) ||
          is.na(input$iterations) ||
          is.na(input$totalReads) ||
          is.na(input$minCount) ||
          is.na(input$minCells) ||
          is.na(input$depthResolution)) {
        stop("One or more parameter values are empty!")
      }
      if (input$useReadCount) {
        vals$subCells <- downSampleCells(originalData = vals$counts,
                                         useAssay = input$cellsAssay,
                                         realLabels = input$selectCellNumCondition,
                                         totalReads = sum(SummarizedExperiment::assay(vals$counts, input$cellsAssay)),
                                         minCellnum = input$minCellNum,
                                         maxCellnum = input$maxCellNum,
                                         minCountDetec = input$minCount,
                                         minCellsDetec = input$minCells,
                                         depthResolution = input$depthResolution,
                                         iterations = input$iterations)
      } else {
        vals$subCells <- downSampleCells(originalData = vals$counts,
                                         useAssay = input$cellsAssay,
                                         realLabels = input$selectCellNumCondition,
                                         totalReads = input$totalReads,
                                         minCellnum = input$minCellNum,
                                         maxCellnum = input$maxCellNum,
                                         minCountDetec = input$minCount,
                                         minCellsDetec = input$minCells,
                                         depthResolution = input$depthResolution,
                                         iterations = input$iterations)
      }
      output$cellsDone <- renderPlot({
        plot(apply(vals$subCells[, , 1], 2, median)~
               seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution),
             lwd = 4, xlab = "Number of virtual cells", ylab = "Number of detected genes",
             main = "Number of dected genes by cell number")
        lines(apply(vals$subCells[, , 1], 2, function(x){quantile(x, 0.25)})~
                seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
        lines(apply(vals$subCells[, , 1], 2, function(x){quantile(x, 0.75)})~
                seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
      })
      output$minEffectCells <- renderPlot({
        plot(apply(vals$subCells[, , 2], 2, median)~
               seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution),
             lwd = 4, xlab = "Number of virtual cells", ylab = "Average significant effect size",
             ylim = c(0, 2))
        lines(apply(vals$subCells[, , 2], 2, function(x){quantile(x, 0.25)})~
                seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
        lines(apply(vals$subCells[, , 2], 2, function(x){quantile(x, 0.75)})~
                seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
      })
      output$sigNumCells <- renderPlot({
        plot(apply(vals$subCells[, , 3], 2, median)~
               seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution),
             lwd = 4, xlab = "Number of vitual cells", ylab = "Number of significantly DiffEx genes")
        lines(apply(vals$subCells[, , 3], 2, function(x){quantile(x, 0.25)})~
                seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
        lines(apply(vals$subCells[, , 3], 2, function(x){quantile(x, 0.75)})~
                seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
      })
    }
  ))
  
  #Run differential power analysis
  observeEvent(input$runSnapshot, withConsoleMsgRedirect(
    msg = "Please wait while resampling snapshot is being computed. See console log for progress.",
    {
      req(vals$counts)
      if (is.na(input$numCellsSnap) ||
          is.na(input$numReadsSnap) ||
          is.na(input$iterationsSnap)) {
        stop("One or more parameter values are empty!")
      }
      vals$snapshot <- iterateSimulations(originalData = vals$counts,
                                          useAssay = input$snapshotAssay,
                                          realLabels = input$selectSnapshotCondition,
                                          totalReads = input$numReadsSnap,
                                          cells = input$numCellsSnap,
                                          iterations = input$iterationsSnap)
      vals$effectSizes <- calcEffectSizes(countMatrix = expData(vals$counts, input$snapshotAssay), condition = colData(vals$counts)[, input$selectSnapshotCondition])
      output$Snaplot <- renderPlot({
        plot(apply(vals$snapshot, 1, function(x){sum(x <= 0.05) / length(x)}) ~ vals$effectSizes,
             xlab = "Cohen's d effect size", ylab = "Detection power", lwd = 4, main = "Power to detect diffex by effect size")
      })
    }
  ))
  
  #-----------------------------------------------------------------------------
  # Page 8: Seurat Workflow
  #-----------------------------------------------------------------------------
  
  #Perform normalization
  observeEvent(input$normalize_button, withConsoleMsgRedirect(
    msg = "Please wait while data is being normalized. See console log for progress.",
    {
      req(vals$counts)
      message(paste0(date(), " ... Normalizing Data"))
      vals$counts <- runSeuratNormalizeData(inSCE = vals$counts,
                                            useAssay = input$seuratSelectNormalizationAssay,
                                            normAssayName = "seuratNormData",
                                            normalizationMethod = input$normalization_method,
                                            scaleFactor = as.numeric(input$scale_factor))
      metadata(vals$counts)$sctk$seuratUseAssay <- input$seuratSelectNormalizationAssay
      vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts)
      updateCollapse(session = session, "SeuratUI", style = list("Normalize Data" = "success"))
      shinyjs::enable(selector = "#SeuratUI > div[value='Highly Variable Genes']")
      S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
      shinyjs::hide(
        selector = "div[value='Downstream Analysis']")
      updateAssayInputs()
      message(paste0(date(), " ... Normalization Complete"))
    }))
  
  # #Perform scaling
  # observeEvent(input$scale_button, withConsoleMsgRedirect ({
  #   #shows the notification spinner and console log
  #   .loadOpen ("Please wait while data is being scaled. See console log for progress.")
  #
  #   req(vals$counts)
  #   message(paste0(date(), " ... Scaling Data"))
  #   vals$counts <- runSeuratScaleData(inSCE = vals$counts,
  #                                    useAssay = "seuratNormData",
  #                                    scaledAssayName = "seuratScaledData",
  #                                    #model = input$model.use,
  #                                    scale = input$do.scale,
  #                                    center = input$do.center,
  #                                    scaleMax = input$scale.max)
  #
  #     vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE)
  #
  #   updateCollapse(session = session, "SeuratUI", style = list("Scale Data" = "success"))
  #   shinyjs::enable(selector = "div[value='Dimensionality Reduction']")
  #   S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
  #   shinyjs::hide(
  #     selector = "div[value='Downstream Analysis']")
  #   message(paste0(date(), " ... Scaling Complete"))
  #
  #   .loadClose() #close the notification spinner and console log
  # }))
  
  #Find HVG
  observeEvent(input$find_hvg_button, withConsoleMsgRedirect(
    msg = "Please wait while high variable genes are being found. See console log for progress.",
    {
      req(vals$counts)
      message(paste0(date(), " ... Finding High Variable Genes"))
      if(input$hvg_method == "vst" || packageVersion(pkg = "SeuratObject") >= "5.0"){
        vals$counts <- runSeuratFindHVG(inSCE = vals$counts,
                                        useAssay = metadata(vals$counts)$sctk$seuratUseAssay,
                                        method = input$hvg_method,
                                        hvgNumber = as.numeric(input$hvg_no_features))
      }
      else{
        vals$counts <- runSeuratFindHVG(inSCE = vals$counts,
                                        useAssay = "seuratNormData",
                                        method = input$hvg_method,
                                        hvgNumber = as.numeric(input$hvg_no_features))
        
      }
      vals$counts <- setTopHVG(inSCE = vals$counts, 
                               method = input$hvg_method, 
                               hvgNumber = as.numeric(input$hvg_no_features), 
                               featureSubsetName = "featureSubset")
      vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, varFeatures = FALSE)
      message(paste0(date(), " ... Plotting HVG"))
      output$plot_hvg <- renderPlotly({
        isolate({
          plotly::ggplotly(plotSeuratHVG(vals$counts, input$hvg_no_features_view))
        })
      })
      updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "success"))
      shinyjs::enable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
      S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
      shinyjs::hide(
        selector = "div[value='Downstream Analysis']")
      message(paste0(date(), " ... Finding HVG Complete"))
    }
  ))
  
  #Display highly variable genes
  output$hvg_output <- renderText({
    req(vals$counts)
    if (!is.null(vals$counts@metadata$seurat$obj)) {
      if(packageVersion(pkg = "SeuratObject") >= "5.0"){
        #if (length(vals$counts@metadata$seurat$obj$RNA$"var.features") > 0) {
          isolate({
            
            singleCellTK:::.seuratGetVariableFeatures(vals$counts, input$hvg_no_features_view)
          })
        #}
      }
      else{
        if (length(slot(vals$counts@metadata$seurat$obj, "assays")[["RNA"]]@var.features) > 0) {
          isolate({
            singleCellTK:::.seuratGetVariableFeatures(vals$counts, input$hvg_no_features_view)
          })
        }
      }
      
    }
  })
  
  #Run PCA
  
  observeEvent(input$run_pca_button, withConsoleMsgRedirect(
    msg = "Please wait while PCA is being computed. See console log for progress.",
    {
      req(vals$counts)
      #remove tabs if not generated
      removeTab(inputId = "seuratPCAPlotTabset", target = "PCA Plot")
      removeTab(inputId = "seuratPCAPlotTabset", target = "Elbow Plot")
      removeTab(inputId = "seuratPCAPlotTabset", target = "JackStraw Plot")
      removeTab(inputId = "seuratPCAPlotTabset", target = "Heatmap Plot")
      
      message(paste0(date(), " ... Running PCA"))
      # For the commented line below:
      # `useFeatureSubset`, in any functions that use it, goes to util function
      # `.parseUseFeatureSubset()` which does a check for rownames(inSCE). Thus
      # incompatible with Seurat's "_"-to-"-" change. But in `runSeuratPCA/ICA`,
      # we automatically detect seurat HVG from the object when `useFeatureSubset
      # = NULL`, so no need to specify this now.
      vals$counts <- runSeuratPCA(inSCE = vals$counts,
                                  useAssay = "seuratNormData",
                                  reducedDimName = "seuratPCA",
                                  #useFeatureSubset = getSeuratVariableFeatures(vals$counts),
                                  nPCs = input$pca_no_components,
                                  seed = input$seed_PCA, 
                                  scale = TRUE)
      
      vals$counts@metadata$seurat$count_pc <- dim(convertSCEToSeurat(vals$counts)[["pca"]])[2]
      vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE)
      appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "PCA Plot",
                                                          panel(heading = "PCA Plot",
                                                                plotlyOutput(outputId = "plot_pca")
                                                          )
      ), select = TRUE)
      
      message(paste0(date(), " ... Plotting PCA"))
      
      output$plot_pca <- renderPlotly({
        isolate({
          plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
                                               useReduction = "pca",
                                               showLegend = FALSE))
        })
      })
      if (input$pca_compute_elbow) {
        appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Elbow Plot",
                                                            panel(
                                                              heading = "Elbow Plot",
                                                              plotlyOutput(outputId = "plot_elbow_pca")
                                                            )
        ))
        
        message(paste0(date(), " ... Generating Elbow Plot"))
        updateNumericInput(session = session, inputId = "pca_significant_pc_counter", value = singleCellTK:::.computeSignificantPC(vals$counts))


        output$plot_elbow_pca <- renderPlotly({
          isolate({
            plotSeuratElbow(inSCE = vals$counts,
                            significantPC = singleCellTK:::.computeSignificantPC(vals$counts))
                            
          })
        })

        output$pca_significant_pc_output <- renderText({
          isolate({
            paste("<p>Number of significant components suggested by ElbowPlot: <span style='color:red'>", singleCellTK:::.computeSignificantPC(vals$counts)," </span> </p> <hr>")

            })
        })
      }
      if (input$pca_compute_jackstraw) {
        appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "JackStraw Plot",
                                                            panel(heading = "JackStraw Plot",
                                                                  plotlyOutput(outputId = "plot_jackstraw_pca")
                                                            )
        ))
        message(paste0(date(), " ... Generating JackStraw Plot"))
        vals$counts <- runSeuratJackStraw(inSCE = vals$counts,
                                          useAssay = "seuratNormData", # scales internally
                                          dims = input$pca_no_components)
        output$plot_jackstraw_pca <- renderPlotly({
          isolate({
            plotly::ggplotly(plotSeuratJackStraw(inSCE = vals$counts,
                                                 dims = input$pca_no_components))
          })
        })
      }
      if (input$pca_compute_heatmap) {
        appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Heatmap Plot",
                                                            panel(heading = "Heatmap Plot",
                                                                  panel(heading = "Plot Options",
                                                                        fluidRow(
                                                                          column(4, dropdown(
                                                                            fluidRow(
                                                                              column(12,
                                                                                     fluidRow(actionBttn(inputId = "closeDropDownSeuratHM", label = NULL, style = "simple", color = "danger", icon = icon("times"), size = "xs"), align = "right"),
                                                                                     fluidRow(
                                                                                       column(6,
                                                                                              pickerInput(inputId = "picker_dimheatmap_components_pca", label = "Select principal components to plot:", choices = c(), options = list(`actions-box` = TRUE, size = 10, `selected-text-format` = "count > 3"), multiple = TRUE)
                                                                                       ),
                                                                                       column(6,
                                                                                              sliderInput(inputId = "slider_dimheatmap_pca", label = "Number of columns for the plot: ", min = 1, max = 4, value = 2)
                                                                                       )
                                                                                     ),
                                                                                     actionBttn(
                                                                                       inputId = "plot_heatmap_pca_button",
                                                                                       label = "Update",
                                                                                       style = "bordered",
                                                                                       color = "primary",
                                                                                       size = "sm"
                                                                                     )
                                                                              )
                                                                            ),
                                                                            inputId = "dropDownSeuratHM",
                                                                            icon = icon("cog"),
                                                                            status = "primary",
                                                                            circle = FALSE,
                                                                            inline = TRUE
                                                                          )),
                                                                          column(7, fluidRow(h6("Heatmaps of the top features correlated with each component"), align="center"))
                                                                        )
                                                                  ),
                                                                  panel(heading = "Plot",
                                                                        shinyjqui::jqui_resizable(plotOutput(outputId = "plot_heatmap_pca"), options = list(maxWidth = 700))
                                                                  )
                                                            )
        ))
        
        message(paste0(date(), "  ... Generating Heatmaps"))
        
        vals$counts@metadata$seurat$heatmap_pca <- runSeuratHeatmap(inSCE = vals$counts,
                                                                    useAssay = "seuratNormData",
                                                                    useReduction = "pca",
                                                                    dims = input$pca_no_components,
                                                                    nfeatures = input$pca_compute_heatmap_nfeatures,
                                                                    combine = FALSE,
                                                                    fast = FALSE)
        output$plot_heatmap_pca <- renderPlot({
          isolate({
            plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_pca,
                              dims = input$pca_no_components,
                              ncol = 2,
                              labels = c("PC1", "PC2", "PC3", "PC4"))
          })
        })
        updatePickerInput(session = session, inputId = "picker_dimheatmap_components_pca", choices = singleCellTK:::.getComponentNames(vals$counts@metadata$seurat$count_pc, "PC"))
      }
      updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "success"))
      
      #Enable/Disable PCA plot panels not selected for computation (ElbowPlot, JackStraw or Heatmap)
      shinyjs::enable(
        selector = ".seurat_pca_plots a[data-value='PCA Plot']")
      
      shinyjs::toggleState(
        selector = ".seurat_pca_plots a[data-value='Elbow Plot']",
        condition = input$pca_compute_elbow)
      
      shinyjs::toggleState(
        selector = ".seurat_pca_plots a[data-value='JackStraw Plot']",
        condition = input$pca_compute_jackstraw)
      
      shinyjs::toggleState(
        selector = ".seurat_pca_plots a[data-value='Heatmap Plot']",
        condition = input$pca_compute_heatmap)
      
      shinyjs::enable(
        selector = "#SeuratUI > div[value='2D-Embedding']")
      
      shinyjs::show(selector = ".seurat_pca_plots")
      
      S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
      shinyjs::hide(
        selector = "div[value='Downstream Analysis']")
      
      message(paste0(date(), " ... PCA Complete"))
    }))
  
  observeEvent(input$closeDropDownSeuratHM,{
    session$sendCustomMessage("close_dropDownSeuratHM", "")
  })
  
  #Run ICA
  
  observeEvent(input$run_ica_button, withConsoleMsgRedirect(
    msg = "Please wait while ICA is being computed. See console log for progress.",
    {
      req(vals$counts)
      #remove tabs if not generated
      removeTab(inputId = "seuratICAPlotTabset", target = "ICA Plot")
      removeTab(inputId = "seuratICAPlotTabset", target = "Heatmap Plot")
      
      message(paste0(date(), " ... Running ICA"))
      vals$counts <- runSeuratICA(inSCE = vals$counts,
                                  useAssay = "seuratNormData", # scales internally with scale = TRUE
                                  nics = input$ica_no_components,
                                  seed = input$seed_ICA, 
                                  scale = TRUE)
      
      vals$counts@metadata$seurat$count_ic <- dim(convertSCEToSeurat(vals$counts)[["ica"]])[2]
      vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE)
      
      appendTab(inputId = "seuratICAPlotTabset", tabPanel(title = "ICA Plot",
                                                          panel(heading = "ICA Plot",
                                                                plotlyOutput(outputId = "plot_ica")
                                                          )
      ), select = TRUE)
      
      message(paste0(date(), " ... Plotting ICA"))
      output$plot_ica <- renderPlotly({
        isolate({
          plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
                                               useReduction = "ica",
                                               showLegend = FALSE))
        })
      })
      if (input$ica_compute_heatmap) {
        appendTab(inputId = "seuratICAPlotTabset", tabPanel(title = "Heatmap Plot",
                                                            panel(heading = "Heatmap Plot",
                                                                  panel(heading = "Plot Options",
                                                                        fluidRow(
                                                                          column(6,
                                                                                 pickerInput(inputId = "picker_dimheatmap_components_ica", label = "Select principal components to plot:", choices = c(), options = list(`actions-box` = TRUE, size = 10, `selected-text-format` = "count > 3"), multiple = TRUE)
                                                                          ),
                                                                          column(6,
                                                                                 sliderInput(inputId = "slider_dimheatmap_ica", label = "Number of columns for the plot: ", min = 1, max = 4, value = 2)
                                                                          )
                                                                        ),
                                                                        actionButton(inputId = "plot_heatmap_ica_button", "Plot")
                                                                  ),
                                                                  panel(heading = "Plot",
                                                                        shinyjqui::jqui_resizable(plotOutput(outputId = "plot_heatmap_ica"), options = list(maxWidth = 700))
                                                                  )
                                                            )
        ))
        
        message(paste0(date(), " ... Generating Heatmaps"))
        vals$counts@metadata$seurat$heatmap_ica <- runSeuratHeatmap(inSCE = vals$counts,
                                                                    useAssay = "seuratNormData", # scales internally
                                                                    useReduction = "ica",
                                                                    dims = input$ica_no_components,
                                                                    nfeatures = input$ica_compute_heatmap_nfeatures,
                                                                    combine = FALSE,
                                                                    fast = FALSE)
        output$plot_heatmap_ica <- renderPlot({
          isolate({
            plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_ica,
                              dims = input$ica_no_components,
                              ncol = 2,
                              labels = c("IC1", "IC2", "IC3", "IC4"))
          })
        })
        updatePickerInput(session = session, inputId = "picker_dimheatmap_components_ica", choices = singleCellTK:::.getComponentNames(vals$counts@metadata$seurat$count_ic, "IC"))
      }
      updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "success"))
      
      #Enable/Disable ICA plot panels not selected for computation (Heatmap)
      shinyjs::enable(
        selector = ".seurat_ica_plots a[data-value='ICA Plot']")
      
      shinyjs::toggleState(
        selector = ".seurat_ica_plots a[data-value='Heatmap Plot']",
        condition = input$ica_compute_heatmap)
      
      shinyjs::enable(
        selector = "#SeuratUI > div[value='2D-Embedding']")
      
      shinyjs::show(selector = ".seurat_ica_plots")
      
      S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
      shinyjs::hide(
        selector = "div[value='Downstream Analysis']")
      
      message(paste0(date(), " ... ICA Complete"))
    }))
  
  #Find clusters
  
  observeEvent(input$find_clusters_button, withConsoleMsgRedirect(
    msg = "Please wait while clusters are being computed. See console log for progress.",
    {
      req(vals$counts)
      if(packageVersion(pkg = "SeuratObject") >= "5.0"){
        pathToCluster = vals$counts@metadata$seurat$obj@reductions[[input$reduction_clustering_method]]
      }
      else
        pathToCluster = vals$counts@metadata$seurat$obj@"reductions"[[input$reduction_clustering_method]]
      
      if(!is.null(pathToCluster)){
        #Remove plot tabs if generated before
        removeTab(inputId = "seuratClusteringPlotTabset", target = "PCA Plot")
        removeTab(inputId = "seuratClusteringPlotTabset", target = "ICA Plot")
        removeTab(inputId = "seuratClusteringPlotTabset", target = "tSNE Plot")
        removeTab(inputId = "seuratClusteringPlotTabset", target = "UMAP Plot")
        
        
        message(paste0(date(), " ... Clustering Dataset"))
        vals$counts <- runSeuratFindClusters(inSCE = vals$counts,
                                             useReduction = input$reduction_clustering_method,
                                             dims = input$pca_significant_pc_counter,
                                             algorithm = input$algorithm.use,
                                             groupSingletons = input$group.singletons,
                                             resolution = input$resolution_clustering)
        updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "success"))
        message(paste0(date(), " ... Finding Clusters Complete"))
        
        if(packageVersion(pkg = "SeuratObject") >= "5.0"){
          pathToUMAP = vals$counts@metadata$seurat$obj@reductions[["umap"]]
        }
        else
          pathToUMAP = vals$counts@metadata$seurat$obj@"reductions"[["umap"]]
        
        
        if(!is.null(pathToUMAP)){
          appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "UMAP Plot",
                                                                     panel(heading = "UMAP Plot",
                                                                           plotlyOutput(outputId = "plot_umap_clustering")
                                                                     )
          ), select = TRUE)
          message(paste0(date(), " ... Re-generating UMAP Plot with Cluster Labels"))
          
          output$plot_umap_clustering <- renderPlotly({
            isolate({
              plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
                                                   useReduction = "umap",
                                                   showLegend = TRUE))
            })
          })
          shinyjs::toggleState(
            selector = ".seurat_clustering_plots a[data-value='UMAP Plot']",
            condition = !is.null(pathToUMAP))
        }
        
        if(packageVersion(pkg = "SeuratObject") >= "5.0"){
          pathToTSNE = vals$counts@metadata$seurat$obj@reductions[["tsne"]]
        }
        else
          pathToTSNE = vals$counts@metadata$seurat$obj@"reductions"[["tsne"]]
        
        
        if(!is.null(pathToTSNE)){
          appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "tSNE Plot",
                                                                     panel(heading = "tSNE Plot",
                                                                           plotlyOutput(outputId = "plot_tsne_clustering")
                                                                     )
          ), select = TRUE)
          
          message(paste0(date(), " ... Re-generating tSNE Plot with Cluster Labels"))
          
          output$plot_tsne_clustering <- renderPlotly({
            isolate({
              plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
                                                   useReduction = "tsne",
                                                   showLegend = TRUE))
            })
          })
          shinyjs::toggleState(
            selector = ".seurat_clustering_plots a[data-value='tSNE Plot']",
            condition = !is.null(pathToTSNE))
        }
        
        if(packageVersion(pkg = "SeuratObject") >= "5.0"){
          pathToPCA = vals$counts@metadata$seurat$obj@reductions[["pca"]]
        }
        else
          pathToPCA = vals$counts@metadata$seurat$obj@"reductions"[["pca"]]
        
        
        if(!is.null(pathToPCA)){
          appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "PCA Plot",
                                                                     panel(heading = "PCA Plot",
                                                                           plotlyOutput(outputId = "plot_pca_clustering")
                                                                     )
          )
          )
          message(paste0(date(), " ... Re-generating PCA Plot with Cluster Labels"))
          output$plot_pca_clustering <- renderPlotly({
            isolate({
              plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
                                                   useReduction = "pca",
                                                   showLegend = TRUE))
            })
          })
          shinyjs::toggleState(
            selector = ".seurat_clustering_plots a[data-value='PCA Plot']",
            condition = !is.null(pathToPCA))
        }
        
        if(packageVersion(pkg = "SeuratObject") >= "5.0"){
          pathToICA = vals$counts@metadata$seurat$obj@reductions[["ica"]]
        }
        else
          pathToICA = vals$counts@metadata$seurat$obj@"reductions"[["ica"]]
        
        
        if(!is.null(pathToICA)){
          appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "ICA Plot",
                                                                     panel(heading = "ICA Plot",
                                                                           plotlyOutput(outputId = "plot_ica_clustering")
                                                                     )
          ))
          
          message(paste0(date(), " ... Re-generating ICA Plot with Cluster Labels"))
          output$plot_ica_clustering <- renderPlotly({
            isolate({
              plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
                                                   useReduction = "ica",
                                                   showLegend = TRUE))
            })
          })
          shinyjs::toggleState(
            selector = ".seurat_clustering_plots a[data-value='ICA Plot']",
            condition = !is.null(pathToICA))
        }
        
        shinyjs::show(selector = ".seurat_clustering_plots")
        
        #enable find marker selection
        shinyjs::enable(
          selector = "#SeuratUI > div[value='Find Markers']")
        
        shinyjs::enable(selector = "#SeuratUI > div[value='Marker Gene Plots']")
        
        #MARKER SETTINGS

        # this doesn't work for the PBMC Seurat data

        #geneChoices <- rowData(vals$counts)$Symbol_TENx
        geneChoices <- rownames(vals$counts)

        updateSelectizeInput(session, "selectGenesMarkerPlots",
                          choices = geneChoices)
        
        shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
        
        removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Ridge Plot")
        removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Violin Plot")
        removeTab(inputId = "seuratFindMarkerPlotTabset", target = "2D Embedding (Feature Plot)")
        removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Dot Plot")
        removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Heatmap Plot")
        
        
        appendTab(inputId = "seuratFindMarkerPlotTabset",
                  tabPanel(title = "2D Embedding (Feature Plot)",
                           panel(heading = "Feature Plot",
                                 shinyjqui::jqui_resizable(
                                   plotOutput(outputId = "findMarkerFeaturePlot")
                                 )
                           )
                  )
        )
        appendTab(inputId = "seuratFindMarkerPlotTabset",
                  tabPanel(title = "Dot Plot",
                           panel(heading = "Dot Plot",
                                 shinyjqui::jqui_resizable(
                                   plotOutput(outputId = "findMarkerDotPlot")
                                 )
                           )
                  )
        )
        appendTab(inputId = "seuratFindMarkerPlotTabset",
                  tabPanel(title = "Ridge Plot",
                           panel(heading = "Ridge Plot",
                                 shinyjqui::jqui_resizable(
                                   plotOutput(outputId = "findMarkerRidgePlot")
                                 )
                           )
                  )
        )
        appendTab(inputId = "seuratFindMarkerPlotTabset",
                  tabPanel(title = "Violin Plot",
                           panel(heading = "Violin Plot",
                                 shinyjqui::jqui_resizable(
                                   plotOutput(outputId = "findMarkerViolinPlot")
                                 )
                           )
                  )
        )
        appendTab(inputId = "seuratFindMarkerPlotTabset",
                  tabPanel(title = "Heatmap Plot",
                           panel(heading = "Heatmap Plot",
                                 fluidRow(
                                   column(12, align = "center",
                                          panel(
                                            plotOutput(outputId = "findMarkerHeatmapPlot")
                                          )
                                   )
                                 )
                           )
                  )
        )
        updateTabsetPanel(session = session, inputId = "seuratFindMarkerPlotTabset", selected = "2D Embedding (Feature Plot)")
        #update colData names
        updateColDataNames()
        
        S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
        shinyjs::hide(
          selector = "div[value='Downstream Analysis']")
        
        #populate updated colData items for findMarkers tab
        updateSelectInput(session = session,
                          inputId = "seuratFindMarkerSelectPhenotype",
                          choices = colnames(colData(vals$counts)),
                          selected = paste0(
                            "Seurat_", 
                            input$algorithm.use, 
                            "_Resolution", 
                            input$resolution_clustering))
        
        #populate reducDim objects from seuratObject for findMarkers tab
        updateSelectInput(session = session,
                          inputId = "seuratFindMarkerReductionMethod",
                          choices = Seurat::Reductions(convertSCEToSeurat(vals$counts)))
        
      }
      else{
        showNotification(paste0("'", input$reduction_clustering_method, "' reduction not found in input object"))
      }
    }))
  
  observeEvent(input$seuratFindMarkerSelectPhenotype, {
    if(!is.null(vals$counts)){
      updateSelectInput(
        session = session,
        inputId = "seuratFindMarkerGroup1",
        choices = unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
      )
      updateSelectInput(
        session = session,
        inputId = "seuratFindMarkerGroup2",
        choices = unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
      )
    }
  })
  
  observeEvent(input$seuratFindMarkerGroup1, {
    if(!is.null(vals$counts)){
      matchedIndex <- match(input$seuratFindMarkerGroup1,  unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]]))
      if(!is.na(matchedIndex)){
        updateSelectInput(
          session = session,
          inputId = "seuratFindMarkerGroup2",
          choices = unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])[-matchedIndex]
        )
      }
    }
  })
  
  
  observeEvent(input$seuratFindMarkerRun, withConsoleMsgRedirect(
    msg = "Please wait while marker genes are being found. See console log for progress.",
    {
      req(vals$counts)
      message(paste0(date(), " ... Finding Marker Genes"))
      
      if(input$seuratFindMarkerType == "markerAll"){
        vals$counts <- runSeuratFindMarkers(inSCE = vals$counts,
                                            allGroup = input$seuratFindMarkerSelectPhenotype,
                                            test = input$seuratFindMarkerTest,
                                            onlyPos = input$seuratFindMarkerPosOnly)
      }
      else{
        indices1 <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == input$seuratFindMarkerGroup1, arr.ind = TRUE)
        indices2 <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == input$seuratFindMarkerGroup2, arr.ind = TRUE)
        cells1 <- colnames(vals$counts)[indices1]
        cells2 <- colnames(vals$counts)[indices2]
        if(input$seuratFindMarkerType == "markerConserved"){
          vals$counts <- runSeuratFindMarkers(inSCE = vals$counts,
                                              cells1 = cells1,
                                              cells2 = cells2,
                                              group1 = input$seuratFindMarkerGroup1,
                                              group2 = input$seuratFindMarkerGroup2,
                                              conserved = TRUE,
                                              test = input$seuratFindMarkerTest,
                                              onlyPos = input$seuratFindMarkerPosOnly)
        }
        else{
          vals$counts <- runSeuratFindMarkers(inSCE = vals$counts,
                                              cells1 = cells1,
                                              cells2 = cells2,
                                              group1 = input$seuratFindMarkerGroup1,
                                              group2 = input$seuratFindMarkerGroup2,
                                              test = input$seuratFindMarkerTest,
                                              onlyPos = input$seuratFindMarkerPosOnly)
        }
      }
      shinyjs::show(selector = ".seurat_findmarker_table")
      shinyjs::show(selector = ".seurat_findmarker_jointHeatmap")
      shinyjs::show(selector = ".seurat_findmarker_plots")
      
      #df <- metadata(vals$counts)$seuratMarkers[which(metadata(vals$counts)$seuratMarkers$p_val_adj < 0.05, arr.ind = TRUE),]
      df <- metadata(vals$counts)$seuratMarkers
      seuratObject <- convertSCEToSeurat(vals$counts, normAssay = "seuratNormData")
      indices <- list()
      cells <- list()
      groups <- unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
      for(i in seq(length(groups))){
        indices[[i]] <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == groups[i], arr.ind = TRUE)
        cells[[i]] <- colnames(vals$counts)[indices[[i]]]
        cells[[i]] <- lapply(
          X = cells[[i]],
          FUN = function(t) gsub(
            pattern = "_",
            replacement = "-",
            x = t,
            fixed = TRUE)
        )
        if(seurat.version >= "5.0"){
          Idents(seuratObject, cells = unlist(cells[[i]])) <- groups[i]
          
        }
        else{
          Idents(seuratObject, cells = cells[[i]]) <- groups[i]
          
        }
      }
      
      showTab(inputId = "seuratFindMarkerPlotTabset", target = "Joint Heatmap Plot")
      shinyjs::show(selector = ".seurat_findmarker_plots")
      
      # Output the heatmap
      colnames(df)[which(startsWith(colnames(df), "avg") == TRUE)] <- "avg_log2FC"
      top10markers <- df %>% group_by(cluster1) %>% arrange(desc(avg_log2FC)) %>% slice_head(n=10)
      # Subset seuratObject to contain only cells available in selected clusters
      if(input$seuratFindMarkerType != "markerAll"){
        subsetIdents <- c(unique(top10markers$cluster1), unique(top10markers$cluster2))
        subsetIdents <- subsetIdents[subsetIdents!="all"]
        seuratObject <- subset(seuratObject, idents = subsetIdents)
      }
      seuratObject <- Seurat::ScaleData(seuratObject, features = top10markers$gene.id)
      # Plot heatmap
      output$findMarkerHeatmapPlotFull <- renderPlot({
        isolate({
          DoHeatmap(seuratObject, features = top10markers$gene.id)
        })
      })
      
      # output$findMarkerHeatmapPlotFullTopText <- renderUI({
      #   h6(paste("Heatmap plotted across all groups against genes with adjusted p-values <", input$seuratFindMarkerPValAdjInput))
      # })
      
      message(paste0(date(), " ... Find Markers Complete"))
      
      
      # Show downstream analysis options
      callModule(module = nonLinearWorkflow, id = "nlw-seurat", parent = session,
                 de = TRUE, fm = TRUE, pa = TRUE)
      
      updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "success"))
      
      updateCollapse(session = session, "SeuratUI", style = list("Downstream Analysis" = "info"))
      
      #singleCellTK:::.exportMetaSlot(vals$counts, "seuratMarkers")
      
      orderByLFCMarkers <- metadata(vals$counts)$seuratMarkers
      orderByLFCMarkers <- orderByLFCMarkers[order(-orderByLFCMarkers$avg_log2FC), ]
      vals$fts <- callModule(
        module = filterTableServer,
        id = "filterSeuratFindMarker",
        dataframe = orderByLFCMarkers,
        defaultFilterColumns = c("p_val_adj"),
        defaultFilterOperators = c("<="),
        defaultFilterValues = c("0.05"),
        topText = "You can view the marker genes in the table below and apply custom filters to filter the table accordingly. A joint heatmap for all the marker genes available in the table is plotted underneath the table. Additional visualizations are plotted for select genes which can be selected by clicking on the rows of the table."
      )
      
      shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
      
      
      # vals$fts <- callModule(
      #   module = filterTableServer,
      #   id = "filterSeuratFindMarker",
      #   dataframe = orderByLFCMarkers
      # )
    }))
  
  observeEvent(input$findMarkerHeatmapPlotFullNumericRun, withConsoleMsgRedirect(
    msg = "Please wait while heatmap is being plotted. See console log for progress.",
    {
      ##df <- metadata(vals$counts)$seuratMarkers[which(metadata(vals$counts)$seuratMarkers$p_val_adj < 0.05, arr.ind = TRUE),]
      df <- metadata(vals$counts)$seuratMarkers
      seuratObject <- convertSCEToSeurat(vals$counts, normAssay =  "seuratNormData")
      indices <- list()
      cells <- list()
      groups <- unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
      for(i in seq(length(groups))){
        indices[[i]] <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == groups[i], arr.ind = TRUE)
        cells[[i]] <- colnames(vals$counts)[indices[[i]]]
        cells[[i]] <- lapply(
          X = cells[[i]],
          FUN = function(t) gsub(
            pattern = "_",
            replacement = "-",
            x = t,
            fixed = TRUE)
        )
        if(seurat.version >= "5.0"){
          cells[[i]] = unlist(cells[[i]])
        }
        Idents(seuratObject, cells = cells[[i]]) <- groups[i]
        
      }
      colnames(df)[which(startsWith(colnames(df), "avg") == TRUE)] <- "avg_log2FC"
      topMarkers <- df %>% group_by(cluster1) %>% arrange(desc(avg_log2FC)) %>% slice_head(n=input$findMarkerHeatmapPlotFullNumeric)
      #topMarkers <- data.frame(df %>% group_by(cluster1) %>% top_n(input$findMarkerHeatmapPlotFullNumeric, avg_log2FC))
      # if(nrow(topMarkers) > (input$findMarkerHeatmapPlotFullNumeric * length(groups))){
      #   topMarkers <- data.frame(topMarkers %>% group_by(cluster1) %>% top_n(input$findMarkerHeatmapPlotFullNumeric, -p_val_adj))
      # }
      # Subset seuratObject to contain only cells available in selected clusters
      if(input$seuratFindMarkerType != "markerAll"){
        subsetIdents <- c(unique(topMarkers$cluster1), unique(topMarkers$cluster2))
        subsetIdents <- subsetIdents[subsetIdents!="all"]
        seuratObject <- subset(seuratObject, idents = subsetIdents)
      }
      seuratObject <- Seurat::ScaleData(seuratObject, features = topMarkers$gene.id)
      # Plot heatmap
      output$findMarkerHeatmapPlotFull <- renderPlot({
        isolate({
          DoHeatmap(seuratObject, features = topMarkers$gene.id)
        })
      })
    }))
  
  observe({
    req(input$selectGenesMarkerPlots)
    output$findMarkerRidgePlot <- renderPlot({
      plotSeuratGenes(
        inSCE = vals$counts,
        plotType = "ridge",
        features = input$selectGenesMarkerPlots,
        groupVariable = input$seuratFindMarkerSelectPhenotype,
        ncol = 2,
        combine = TRUE
      )
    })
    output$findMarkerViolinPlot <- renderPlot({
      plotSeuratGenes(
        inSCE = vals$counts,
        plotType = "violin",
        features = input$selectGenesMarkerPlots,
        groupVariable = input$seuratFindMarkerSelectPhenotype,
        ncol = 2,
        combine = TRUE
      )
    })
    output$findMarkerFeaturePlot <- renderPlot({
      plotSeuratGenes(
        inSCE = vals$counts,
        plotType = "feature",
        features = input$selectGenesMarkerPlots,
        groupVariable = input$seuratFindMarkerSelectPhenotype,
        ncol = 2,
        combine = TRUE,
        #useReduction = input$seuratFeatureUseReduction
      )
    })
    output$findMarkerDotPlot <- renderPlot({
      plotSeuratGenes(
        inSCE = vals$counts,
        plotType = "dot",
        features = input$selectGenesMarkerPlots,
        groupVariable = input$seuratFindMarkerSelectPhenotype
      )
    })
    output$findMarkerHeatmapPlot <- renderPlot({
      plotSeuratGenes(
        inSCE = vals$counts,
        plotType = "heatmap",
        features = input$selectGenesMarkerPlots,
        groupVariable = input$seuratFindMarkerSelectPhenotype
      )
    })
  })
  
  # observe({
  #   req(vals$fts$data)
  #   df <- vals$fts$data
  #   output$findMarkerHeatmapPlotFull <- renderPlot({
  #     plotSeuratGenes(
  #       inSCE = vals$counts,
  #       scaledAssayName = "seuratScaledData",
  #       plotType = "heatmap",
  #       features = df$gene_id,
  #       groupVariable = input$seuratFindMarkerSelectPhenotype
  #     )
  #   })
  # })
  
  
  #Update PCA/ICA message in clustering tab
  output$display_message_clustering <- renderText({
    if(input$reduction_clustering_method == "pca"){
      if(input$pca_significant_pc_counter){
        paste("<p>Analysis will be performed with <span style='color:red'>", input$pca_significant_pc_counter," components</span> from PCA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
      }
    }
    else{
      if(input$ica_significant_ic_counter){
        paste("<p>Analysis will be performed with <span style='color:red'>", input$ica_significant_ic_counter," components</span> from ICA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
      }
    }
  })
  
  #Run tSNE
  
  observeEvent(input$run_tsne_button, withConsoleMsgRedirect(
    msg = "Please wait while tSNE is being computed. See console log for progress.",
    {
      req(vals$counts)
      
      if(packageVersion(pkg = "SeuratObject") >= "5.0"){
        pathToTSNE = vals$counts@metadata$seurat$obj@reductions[[input$reduction_tsne_method]]
      }
      else
        pathToTSNE = vals$counts@metadata$seurat$obj@"reductions"[[input$reduction_tsne_method]]
      
      if(!is.null(pathToTSNE)){
        message(paste0(date(), " ... Running tSNE"))
        vals$counts <- runSeuratTSNE(inSCE = vals$counts,
                                     useReduction = input$reduction_tsne_method,
                                     reducedDimName = "seuratTSNE",
                                     dims = input$pca_significant_pc_counter,
                                     perplexity = input$perplexity_tsne,
                                     seed = input$seed_TSNE)
        vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE, tSNE = FALSE, UMAP = FALSE)

        message(paste0(date(), " ... Plotting tSNE"))
        
        output$plot_tsne <- renderPlotly({
          isolate({
            plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
                                                 useReduction = "tsne",
                                                 showLegend = FALSE))
          })
        })
        updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "success"))
        S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
        shinyjs::hide(
          selector = "div[value='Downstream Analysis']")
        message(paste0(date(), " ... tSNE Complete"))
        
      }
      else{
        showNotification(paste0("'", input$reduction_tsne_method, "' reduction not found in input object"))
      }
    }))
  
  #Update PCA/ICA message in tSNE tab
  output$display_message_tsne <- renderText({
    if(input$reduction_tsne_method == "pca"){
      if(input$pca_significant_pc_counter){
        paste("<p>Analysis will be performed with <span style='color:red'>", input$pca_significant_pc_counter," components</span> from PCA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
      }
    }
    else{
      if(input$ica_significant_ic_counter){
        paste("<p>Analysis will be performed with <span style='color:red'>", input$ica_significant_ic_counter," components</span> from ICA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
      }
    }
  })
  
  #Run UMAP
  
  observeEvent(input$run_umap_button, withConsoleMsgRedirect(
    msg = "Please wait while UMAP is being computed. See console log for progress.",
    {
      req(vals$counts)
      if(packageVersion(pkg = "SeuratObject") >= "5.0"){
        #pathToUMAP = vals$counts@metadata$seurat$obj$"reductions"[[input$reduction_umap_method]]
        pathToUMAP = vals$counts@metadata$seurat$obj@reductions[[input$reduction_umap_method]]
      }
      else
        pathToUMAP = vals$counts@metadata$seurat$obj@"reductions"[[input$reduction_umap_method]]
      
      
      if(!is.null(pathToUMAP)){
        message(paste0(date(), " ... Running UMAP"))
        vals$counts <- runSeuratUMAP(inSCE = vals$counts,
                                     useReduction = input$reduction_umap_method,
                                     reducedDimName = "seuratUMAP",
                                     dims = input$pca_significant_pc_counter,
                                     minDist = input$min_dist_umap,
                                     nNeighbors = input$n_neighbors_umap,
                                     spread = input$spread_umap,
                                     seed = input$seed_UMAP)
        vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE, tSNE = FALSE, UMAP = FALSE)

        message(paste0(date(), " ... Plotting UMAP"))
        
        output$plot_umap <- renderPlotly({
          isolate({
            plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
                                                 useReduction = "umap",
                                                 showLegend = FALSE))
          })
        })
        updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "success"))
        shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
        S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
        shinyjs::hide(
          selector = "div[value='Downstream Analysis']")
        message(paste0(date(), " ... UMAP Complete"))
        
      }
      else{
        showNotification(paste0("'", input$reduction_umap_method, "' reduction not found in input object"))
      }
    }))
  
  #Update PCA/ICA message in UMAP tab
  output$display_message_umap <- renderText({
    if(input$reduction_umap_method == "pca"){
      if(input$pca_significant_pc_counter){
        paste("<p>Analysis will be performed with <span style='color:red'>", input$pca_significant_pc_counter," components</span> from PCA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
      }
    }
    else{ #ICA to do
      if(input$ica_significant_ic_counter){
        paste("<p>Analysis will be performed with <span style='color:red'>", input$ica_significant_ic_counter," components</span> from ICA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
      }
    }
  })
  
  #Update pca significant slider maximum value with total number of computed principal components
  observe({
    req(vals$counts)
    if (!is.null(vals$counts@metadata$seurat$count_pc)) {
      updateSliderInput(session = session, inputId = "pca_significant_pc_counter", max = vals$counts@metadata$seurat$count_pc)
    }
  })
  
  #Update ica significant slider maximum value with total number of computed independent components
  observe({
    req(vals$counts)
    if (!is.null(vals$counts@metadata$seurat$count_ic)) {
      updateNumericInput(session = session, inputId = "ica_significant_ic_counter", max = vals$counts@metadata$seurat$count_ic)
    }
  })
  
  #Update tsne, umap and clustering selected number of principal components input
  observe({
    if (input$reduction_umap_method == "pca") {
      updateTextInput(session = session, inputId = "reduction_umap_count", value = input$pca_significant_pc_counter)
    }
    else if (input$reduction_umap_method == "ica") {
      updateTextInput(session = session, inputId = "reduction_umap_count", value = vals$counts@metadata$seurat$count_ic)
    }
    if (input$reduction_clustering_method == "pca") {
      updateTextInput(session = session, inputId = "reduction_clustering_count", value = input$pca_significant_pc_counter)
    }
    else if (input$reduction_clustering_method == "ica") {
      updateTextInput(session = session, inputId = "reduction_clustering_count", value = vals$counts@metadata$seurat$count_ic)
    }
    if (input$reduction_tsne_method == "pca") {
      updateTextInput(session = session, inputId = "reduction_tsne_count", value = input$pca_significant_pc_counter)
    }
    else if (input$reduction_tsne_method == "ica") {
      updateTextInput(session = session, inputId = "reduction_tsne_count", value = vals$counts@metadata$seurat$count_ic)
    }
  })
  
  #Customize heatmap (pca) with selected options
  observeEvent(input$plot_heatmap_pca_button, {
    if (!is.null(input$picker_dimheatmap_components_pca)) {
      output$plot_heatmap_pca <- renderPlot({
        isolate({
          plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_pca,
                            dims = length(input$picker_dimheatmap_components_pca),
                            ncol = input$slider_dimheatmap_pca,
                            labels = input$picker_dimheatmap_components_pca)
        })
      })
    }
    session$sendCustomMessage("close_dropDownSeuratHM", "")
  })
  
  #Customize heatmap (ica) with selected options
  observeEvent(input$plot_heatmap_ica_button, {
    if (!is.null(input$picker_dimheatmap_components_ica)) {
      output$plot_heatmap_ica <- renderPlot({
        isolate({
          plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_ica,
                            dims = length(input$picker_dimheatmap_components_ica),
                            ncol = input$slider_dimheatmap_ica,
                            labels = input$picker_dimheatmap_components_ica)
        })
      })
    }
  })
  
  
  #Disable Seurat tabs & reset collapse panel tabs
  
  # Try deleting this chunk of Seurat code and see if the image still runs
  

  # observe({
  #   if(!is.null(vals$counts)){
  #     #If data is uploaded in data tab, enable first tab i.e. Normalization tab in Seurat workflow
  #     shinyjs::enable(
  #       selector = "#SeuratUI > div[value='Normalize Data']")
  #     shinyjs::enable(
  #       selector = "#ScanpyUI > div[value='Normalize Data']")
      
  #     #Proceed only if sce object has metadata slot
  #     if(!is.null(vals$counts@metadata)){
  #       if(packageVersion(pkg = "SeuratObject") >= "5.0"){
  #         #Proceed only if sce object has seurat object stored in metadata slot
  #         if(!is.null(vals$counts@metadata$seurat$obj)){
  #           #If variableFeatures have been removed from sce object, reset HVG tab and reset/lock next tab
  #           if(length(vals$counts@metadata$seurat$obj$RNA$"var.features") <= 0){
  #             updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "primary"))
  #             updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
  #             shinyjs::disable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
  #           }
            
  #           #Proceed if reduction slot is present in seurat object in metadata slot
  #           if("reductions" %in% names(vals$counts@metadata$seurat$obj)){
              
  #             #If PCA and ICA both removed from sce object, reset DR tab and reset/lock next tab
  #             if(is.null(vals$counts@metadata$seurat$obj@reductions$pca)
  #                && is.null(vals$counts@metadata$seurat$obj@reductions$ica)){
  #               updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
  #               updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
  #               shinyjs::disable(selector = "#SeuratUI > div[value='2D-Embedding']")
  #             }
              
  #             #If TSNE and UMAP both removed from sce object, reset 2D-Embedding tab and reset/lock next tab
  #             if(is.null(vals$counts@metadata$seurat$obj@reductions$tsne)
  #                && is.null(vals$counts@metadata$seurat$obj@reductions$umap)){
  #               updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
  #               updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
  #               shinyjs::disable(selector = "#SeuratUI > div[value='Clustering']")
  #             }
              
  #             #If seurat cluster information removed from sce object, reset Clustering tab
  #             if(!"seurat_clusters" %in% names(vals$counts@metadata$seurat$obj@meta.data)){
  #               updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
  #               updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "primary"))
  #               shinyjs::disable(selector = "#SeuratUI > div[value='Find Markers']")
  #             }
  #           }
            
  #         }
          
          
  #       }
  #       else{
  #         #Proceed only if sce object has seurat object stored in metadata slot
  #         if(!is.null(vals$counts@metadata$seurat$obj)){
  #           # #If seuratScaledData has been removed from sce object, reset Scale Data tab and reset/lock its next tab
  #           # if(!"seuratScaledData" %in% expDataNames(vals$counts)){
  #           #   updateCollapse(session = session, "SeuratUI", style = list("Scale Data" = "primary"))
  #           #   updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
  #           #   shinyjs::disable(selector = "div[value='Dimensionality Reduction']")
  #           # }
            
  #           #If variableFeatures have been removed from sce object, reset HVG tab and reset/lock next tab
  #           if(length(slot(vals$counts@metadata$seurat$obj, "assays")[["RNA"]]@var.features) <= 0){
  #             updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "primary"))
  #             updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
  #             shinyjs::disable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
  #           }
            
  #           #Proceed if reduction slot is present in seurat object in metadata slot
  #           if("reductions" %in% slotNames(vals$counts@metadata$seurat$obj)){
              
  #             #If PCA and ICA both removed from sce object, reset DR tab and reset/lock next tab
  #             if(is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["pca"]])
  #                && is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["ica"]])){
  #               updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
  #               updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
  #               shinyjs::disable(selector = "#SeuratUI > div[value='2D-Embedding']")
  #             }
              
  #             #If TSNE and UMAP both removed from sce object, reset 2D-Embedding tab and reset/lock next tab
  #             if(is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["tsne"]])
  #                && is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["umap"]])){
  #               updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
  #               updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
  #               shinyjs::disable(selector = "#SeuratUI > div[value='Clustering']")
  #             }
              
  #             #If seurat cluster information removed from sce object, reset Clustering tab
  #             if(!"seurat_clusters" %in% names(vals$counts@metadata$seurat$obj@meta.data)){
  #               updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
  #               updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "primary"))
  #               shinyjs::disable(selector = "#SeuratUI > div[value='Find Markers']")
  #             }
  #           }
            
  #         }
          
  #       }
        
        
        
  #     }
  #   }
  #   else{
  #     #If no data uploaded in data tab, disable all tabs and plots.
      
  #     #Disable tabs
  #     shinyjs::disable(
  #       selector = "#SeuratUI > div[value='Normalize Data']")
  #     shinyjs::disable(
  #       selector = "#ScanpyUI > div[value='Normalize Data']")
      
  #     shinyjs::disable(
  #       selector = "#SeuratUI > div[value='Highly Variable Genes']")
  #     shinyjs::disable(
  #       selector = "#ScanpyUI > div[value='Highly Variable Genes']")
      
  #     shinyjs::disable(
  #       selector = "#SeuratUI > div[value='Scale Data']")
  #     shinyjs::disable(
  #       selector = "#SeuratUI > div[value='Dimensionality Reduction']")
  #     shinyjs::disable(
  #       selector = "#ScanpyUI > div[value='Dimensionality Reduction']")
      
  #     shinyjs::disable(
  #       selector = "#SeuratUI > div[value='2D-Embedding']")
  #     shinyjs::disable(
  #       selector = "#ScanpyUI > div[value='2D-Embedding']")
      
  #     shinyjs::disable(
  #       selector = "#SeuratUI > div[value='Clustering']")
  #     shinyjs::disable(
  #       selector = "#ScanpyUI > div[value='Clustering']")
      
  #     shinyjs::disable(
  #       selector = "#SeuratUI > div[value='Marker Gene Plots']")
      
  #     shinyjs::disable(
  #       selector = "#SeuratUI > div[value='Scale Data']")
  #     shinyjs::disable(
  #       selector = "#SeuratUI > div[value='Find Markers']")
  #     shinyjs::disable(
  #       selector = "#ScanpyUI > div[value='Find Markers']")
      
      
  #     #Disable plots inside PCA subtab
  #     shinyjs::disable(
  #       selector = ".seurat_pca_plots a[data-value='PCA Plot']")
  #     shinyjs::disable(
  #       selector = ".seurat_pca_plots a[data-value='Elbow Plot']")
  #     shinyjs::disable(
  #       selector = ".seurat_pca_plots a[data-value='JackStraw Plot']")
  #     shinyjs::disable(
  #       selector = ".seurat_pca_plots a[data-value='Heatmap Plot']")
      
  #     #Disable plots inside ICA subtab
  #     shinyjs::disable(
  #       selector = ".seurat_ica_plots a[data-value='ICA Plot']")
  #     shinyjs::disable(
  #       selector = ".seurat_ica_plots a[data-value='Heatmap Plot']")
      
  #     #Disabled plots inside Clustering tab
  #     shinyjs::disable(
  #       selector = ".seurat_clustering_plots a[data-value='PCA Plot']")
  #     shinyjs::disable(
  #       selector = ".seurat_clustering_plots a[data-value='ICA Plot']")
  #     shinyjs::disable(
  #       selector = ".seurat_clustering_plots a[data-value='tSNE Plot']")
  #     shinyjs::disable(
  #       selector = ".seurat_clustering_plots a[data-value='UMAP Plot']")
  #   }
  # })
  
  #-----------------------------------------------------------------------------
  # Page: Column Annotation (colData) ####
  #-----------------------------------------------------------------------------
  
  #populate colData from sce object when uploaded
  observe({
    if(!is.null(vals$counts)){
      if(!is.null(colData(vals$counts))){
        vals$columnAnnotation <- as.data.frame(colData(vals$counts))
      }
    }
  })
  
  #import colData from local storage
  observeEvent(input$importDataButton_colData, {
    withBusyIndicatorServer("importDataButton_colData",{
      if(!is.null(input$uploadFile_colData)){
        temp <- read.csv(input$uploadFile_colData$datapath, header = TRUE,sep = ",")
        if(nrow(colData(vals$counts)) == nrow(temp)){
          if(input$editorChoiceRadio_colData == "replace"){
            vals$columnAnnotation <- temp
          }
          else{
            x <- as.data.frame(colData(vals$counts))
            y <- as.data.frame(temp)
            commonCols <- intersect(colnames(x), colnames(y))
            x[, commonCols] <- y[,commonCols]
            y[, commonCols] <- NULL
            vals$columnAnnotation <- cbind(x, y)
          }
        }
        else{
          showNotification("Number of rows of the assay and the input colData must be equal", type = "error")
        }
      }
      else{
        showNotification("No file selected to upload", type = "error")
      }
    })
    
    #Render a warning message if there are unsaved changes to colData
    output$changesWarning_colData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #Render table with colData
  output$outputColumnAnnotationTable_colData <- renderUI({
    output$colOutTable <- DT::renderDataTable({
      DT::datatable(vals$columnAnnotation,
                    editable = 'cell',
                    options = list(pageLength = 5,
                                   scrollX = TRUE))
    })
    DT::dataTableOutput("colOutTable")
  })
  
  #create selectinput for selecting attribute with colnames from incoming dataset
  #create selectinput for selecting attribute value
  output$inputSelectAttribute_colData <- renderUI({
    if(!is.null(vals$columnAnnotation)){
      if(ncol(vals$columnAnnotation) > 0){
        selectInput("inputSelectAttribute_colData",
                    label = "select attribute",
                    choices = colnames(vals$columnAnnotation))
      }
    }
  })
  output$inputSelectAttributeDelete_colData <- renderUI({
    if(!is.null(vals$columnAnnotation)){
      if(ncol(vals$columnAnnotation) > 0){
        selectInput("inputSelectAttributeDelete_colData",
                    label = "select attribute to delete",
                    choices = colnames(vals$columnAnnotation))
      }
    }
  })
  
  #create selectinput for selecting column to delete
  output$inputSelectAttributeValue_colData <- renderUI({
    if(!is.null(vals$columnAnnotation) &&
       ncol(vals$columnAnnotation) > 0 &&
       !is.null(input$inputSelectAttribute_colData) &&
       input$inputSelectAttribute_colData %in% colnames(vals$columnAnnotation)){
      selectInput("inputSelectAttributeValue_colData",
                  label = "select attribute value",
                  choices = vals$columnAnnotation[, input$inputSelectAttribute_colData])
    }
  })
  
  #create selectinput for selecting merge_1 attribute
  #create selectinput for selecting merge_2 attribute
  output$inputSelectAttributeMerge1_colData <- renderUI({
    if(!is.null(vals$columnAnnotation)){
      if(ncol(vals$columnAnnotation) > 0){
        selectInput("inputSelectAttributeMerge1_colData",
                    label = "select first column",
                    choices = colnames(vals$columnAnnotation))
      }
    }
  })
  output$inputSelectAttributeMerge2_colData <- renderUI({
    if(!is.null(vals$columnAnnotation)){
      if(ncol(vals$columnAnnotation) > 0){
        selectInput("inputSelectAttributeMerge2_colData",
                    label = "select second column",
                    choices = colnames(vals$columnAnnotation))
      }
    }
  })
  
  #create selectinput for selecting fill_1 attribute
  #create selectinput for selecting fill_2 attribute
  output$inputSelectAttributeFill1_colData <- renderUI({
    if(!is.null(vals$columnAnnotation)){
      if(ncol(vals$columnAnnotation) > 0){
        selectInput("inputSelectAttributeFill1_colData",
                    label = "select attribute column",
                    choices = colnames(vals$columnAnnotation))
      }
    }
  })
  output$inputSelectAttributeFill2_colData <- renderUI({
    if(!is.null(vals$columnAnnotation)){
      if(ncol(vals$columnAnnotation) > 0){
        selectInput("inputSelectAttributeFill2_colData",
                    label = "select column to fill",
                    choices = colnames(vals$columnAnnotation))
      }
    }
  })
  
  #create selectinput for selecting attribute value for magic fill
  output$inputSelectAttributeFillvalue_colData <- renderUI({
    if(!is.null(vals$columnAnnotation)){
      if(ncol(vals$columnAnnotation) > 0){
        selectInput("inputSelectAttributeFillvalue_colData",
                    label = "select attribute value",
                    choices = vals$columnAnnotation[, match(input$inputSelectAttributeFill1_colData,
                                                            colnames(vals$columnAnnotation))])
      }
    }
  })
  
  #update criteria parameter text input when attribute value selectinput is changed
  observeEvent(input$inputSelectAttributeValue_colData, {
    updateTextInput(session = session,
                    "inputCriteria_colData",
                    value = input$inputSelectAttributeValue_colData)
  })
  
  #create selectinput for selecting attribute for clean operation
  output$inputSelectAttributeClean_colData <- renderUI({
    if(!is.null(vals$columnAnnotation)){
      if(ncol(vals$columnAnnotation) > 0){
        selectInput("inputSelectAttributeClean_colData",
                    label = "select attribute column",
                    choices = colnames(vals$columnAnnotation))
      }
    }
  })
  
  #confirm create bin button
  observeEvent(input$buttonConfirmBin_colData, {
    #getting variables
    selected_attribute <- input$inputSelectAttribute_colData
    bin_name <- input$inputBinName_colData
    selected_column_no <- match(selected_attribute, colnames(vals$columnAnnotation))
    criteria_term <- input$inputCriteria_colData
    operator_term <- input$inputOperator_colData
    
    #get df from reactive input, backup column datatypes and convert factor to character
    data <- singleCellTK:::.manageFactor(vals$columnAnnotation, operation = "backup")
    df <- data$df
    
    #perform operations
    if (operator_term == "=")
    {
      df[, selected_column_no][df[, selected_column_no] %in% criteria_term] <- bin_name
    }
    else if (operator_term == ">")
    {
      df[, selected_column_no][as.numeric(df[, selected_column_no]) > criteria_term] <- bin_name
    }
    else if (operator_term == "<")
    {
      df[, selected_column_no][as.numeric(df[, selected_column_no]) < criteria_term] <- bin_name
    }
    else if (operator_term == "<=")
    {
      df[, selected_column_no][as.numeric(df[, selected_column_no]) <= criteria_term] <- bin_name
    }
    else if (operator_term == ">=")
    {
      df[, selected_column_no][as.numeric(df[, selected_column_no]) >= criteria_term] <- bin_name
    }
    
    #restore datatypes
    data$df <- df
    data <- singleCellTK:::.manageFactor(data, operation = "restore")
    vals$columnAnnotation <- data$df
    
    output$changesWarning_colData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #confirm merge button
  observeEvent(input$buttonConfirmMerge_colData, {
    df <- vals$columnAnnotation
    colname1 <- input$inputSelectAttributeMerge1_colData
    colname2 <- input$inputSelectAttributeMerge2_colData
    df <- unite_(df, col = colname1, c(colname1, colname2),
                 sep = input$inputSelectSeparatorMerge_colData)
    
    vals$columnAnnotation <- df
    
    output$changesWarning_colData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #fill column button
  observeEvent(input$buttonConfirmFill_colData, {
    #get df from reactive input, backup column datatypes and convert factor to character
    data <- singleCellTK:::.manageFactor(vals$columnAnnotation, operation = "backup")
    df <- data$df
    
    #perform operation
    selected_attribute_1 <- input$inputSelectAttributeFill1_colData
    selected_attribute_2 <- input$inputSelectAttributeFill2_colData
    selected_column_no_1 <- match(selected_attribute_1, colnames(df))
    selected_column_no_2 <- match(selected_attribute_2, colnames(df))
    old_value <- input$inputSelectAttributeFillvalue_colData
    new_value <- input$inputReplaceText_colData
    
    for (i in 1:nrow(df))
    {
      if (df[i, selected_column_no_1] == old_value)
      {
        df[i, selected_column_no_2] <- new_value
      }
    }
    
    #restore datatypes
    data$df <- df
    data <- singleCellTK:::.manageFactor(data, operation = "restore")
    vals$columnAnnotation <- data$df
    
    output$changesWarning_colData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #confirm clean button
  observeEvent(input$buttonConfirmClean_colData, {
    #get df from reactive input, backup column datatypes and convert factor to character
    data <- singleCellTK:::.manageFactor(vals$columnAnnotation, operation = "backup")
    df <- data$df
    
    #perform operation
    selected_attribute <- input$inputSelectAttributeClean_colData
    selected_column_no <- match(selected_attribute, colnames(df))
    selected_choice <- input$inputRemovalOperation_colData
    selected_choice_no <- match(selected_choice, c("remove alphabets",
                                                   "remove digits",
                                                   "remove spaces",
                                                   "remove symbols"))
    
    if (selected_choice_no == 1)
    {
      for (i in 1:nrow(df))
      {
        df[i, selected_column_no] <- gsub("[A-z]", "", df[i, selected_column_no])
      }
      
    }
    else if (selected_choice_no == 2)
    {
      for (i in 1:nrow(df))
      {
        df[i, selected_column_no] <- gsub("[0-9]", "", df[i, selected_column_no])
      }
    }
    else if (selected_choice_no == 3)
    {
      for (i in 1:nrow(df))
      {
        df[i, selected_column_no] <- gsub(" ", "", df[i, selected_column_no])
      }
    }
    else if (selected_choice_no == 4)
    {
      for (i in 1:nrow(df))
      {
        df[i, selected_column_no] <- gsub("[[:punct:]]", "", df[i, selected_column_no])
      }
    }
    
    #restore datatypes
    data$df <- df
    data <- singleCellTK:::.manageFactor(data, operation = "restore")
    vals$columnAnnotation <- data$df
    
    output$changesWarning_colData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #add new empty column button
  observeEvent(input$buttonConfirmEmptyColumnName_colData, {
    df <- vals$columnAnnotation
    
    colname <- input$inputEmptyColumnName_colData
    df$newcolumn <- input$inputDefaultValueAddColumn_colData
    names(df)[ncol(df)] <- colname
    
    vals$columnAnnotation <- df
    
    output$changesWarning_colData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #delete column button
  observeEvent(input$buttonConfirmDeleteColumn_colData,{
    
    #getting variables
    selected_attribute <- input$inputSelectAttributeDelete_colData
    
    #get df from reactive input
    df <- vals$columnAnnotation
    
    #delete
    df[[selected_attribute]] <- NULL
    
    vals$columnAnnotation <- df
    
    output$changesWarning_colData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #restore saved/original colData
  observeEvent(input$buttonRestore_colData,{
    vals$columnAnnotation <- as.data.frame(colData(vals$counts))
    output$changesWarning_colData <- NULL
    showNotification("Changes reverted back to last checkpoint.")
  })
  
  #save changes to colData
  observeEvent(input$buttonSave_colData,{
    colData(vals$counts) <- DataFrame(vals$columnAnnotation)
    output$changesWarning_colData <- NULL
    updateColDataNames()
    showNotification("Changes saved successfully.")
  })
  
  #-----------------------------------------------------------------------------
  # Page: Row Annotation (rowData) ####
  #-----------------------------------------------------------------------------
  
  #populate colData from sce object when uploaded
  observe({
    if(!is.null(vals$counts)){
      if(!is.null(rowData(vals$counts))){
        vals$rowAnnotation <- as.data.frame(rowData(vals$counts))
      }
    }
  })
  
  #import rowData from local storage
  observeEvent(input$importDataButton_rowData, {
    withBusyIndicatorServer("importDataButton_rowData",{
      if(!is.null(input$uploadFile_rowData)){
        temp <- read.csv(input$uploadFile_rowData$datapath, header = TRUE,sep = ",")
        if(nrow(rowData(vals$counts)) == nrow(temp)){
          if(input$editorChoiceRadio_rowData == "replace"){
            vals$rowAnnotation <- temp
          }
          else{
            x <- as.data.frame(rowData(vals$counts))
            y <- as.data.frame(temp)
            commonCols <- intersect(colnames(x), colnames(y))
            x[, commonCols] <- y[,commonCols]
            y[, commonCols] <- NULL
            vals$rowAnnotation <- cbind(x, y)
          }
        }
        else{
          showNotification("Number of rows of the assay and the input rowData must be equal", type = "error")
        }
      }
      else{
        showNotification("No file selected to upload", type = "error")
      }
    })
    
    #Render a warning message if there are unsaved changes to rowData
    output$changesWarning_rowData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #Render table with rowData
  output$outputColumnAnnotationTable_rowData <- renderUI({
    output$rowOutTable <- DT::renderDataTable({
      DT::datatable(vals$rowAnnotation,
                    editable = 'cell',
                    options = list(pageLength = 5,
                                   scrollX = TRUE))
    })
    DT::dataTableOutput("rowOutTable")
  })
  
  #create selectinput for selecting attribute with colnames from incoming dataset
  #create selectinput for selecting attribute value
  output$inputSelectAttribute_rowData <- renderUI({
    if(!is.null(vals$rowAnnotation)){
      if(ncol(vals$rowAnnotation) > 0){
        selectInput("inputSelectAttribute_rowData",
                    label = "select attribute",
                    choices = colnames(vals$rowAnnotation))
      }
    }
  })
  output$inputSelectAttributeDelete_rowData <- renderUI({
    if(!is.null(vals$rowAnnotation)){
      if(ncol(vals$rowAnnotation) > 0){
        selectInput("inputSelectAttributeDelete_rowData",
                    label = "select attribute to delete",
                    choices = colnames(vals$rowAnnotation))
      }
    }
  })
  
  #create selectinput for selecting column to delete
  observeEvent(input$inputSelectAttribute_rowData, {
    if(!is.null(vals$rowAnnotation) &&
       ncol(vals$rowAnnotation) > 0 &&
       !is.null(input$inputSelectAttribute_rowData) &&
       input$inputSelectAttribute_rowData %in% colnames(vals$rowAnnotation)){
      updateSelectizeInput(session, "inputSelectAttributeValue_rowData",
                           choices = vals$rowAnnotation[, input$inputSelectAttribute_rowData],
                           server = TRUE)
    }
  })
  
  #create selectinput for selecting merge_1 attribute
  #create selectinput for selecting merge_2 attribute
  output$inputSelectAttributeMerge1_rowData <- renderUI({
    if(!is.null(vals$rowAnnotation)){
      if(ncol(vals$rowAnnotation) > 0){
        selectInput("inputSelectAttributeMerge1_rowData",
                    label = "select first column",
                    choices = colnames(vals$rowAnnotation))
      }
    }
  })
  output$inputSelectAttributeMerge2_rowData <- renderUI({
    if(!is.null(vals$rowAnnotation)){
      if(ncol(vals$rowAnnotation) > 0){
        selectInput("inputSelectAttributeMerge2_rowData",
                    label = "select second column",
                    choices = colnames(vals$rowAnnotation))
      }
    }
  })
  
  #create selectinput for selecting fill_1 attribute
  #create selectinput for selecting fill_2 attribute
  output$inputSelectAttributeFill1_rowData <- renderUI({
    if(!is.null(vals$rowAnnotation)){
      if(ncol(vals$rowAnnotation) > 0){
        selectInput("inputSelectAttributeFill1_rowData",
                    label = "select attribute column",
                    choices = colnames(vals$rowAnnotation))
      }
    }
  })
  output$inputSelectAttributeFill2_rowData <- renderUI({
    if(!is.null(vals$rowAnnotation)){
      if(ncol(vals$rowAnnotation) > 0){
        selectInput("inputSelectAttributeFill2_rowData",
                    label = "select column to fill",
                    choices = colnames(vals$rowAnnotation))
      }
    }
  })
  
  #create selectinput for selecting attribute value for magic fill
  observeEvent(input$inputSelectAttributeFill1_rowData, {
    if(!is.null(vals$rowAnnotation)){
      if(ncol(vals$rowAnnotation) > 0){
        updateSelectizeInput(session, "inputSelectAttributeFillvalue_rowData",
                             choices = vals$rowAnnotation[, match(input$inputSelectAttributeFill1_rowData,
                                                                  colnames(vals$rowAnnotation))],
                             server = TRUE)
      }
    }
  })
  
  #update criteria parameter text input when attribute value selectinput is changed
  observeEvent(input$inputSelectAttributeValue_rowData, {
    updateTextInput(session = session,
                    "inputCriteria_rowData",
                    value = input$inputSelectAttributeValue_rowData)
  })
  
  #create selectinput for selecting attribute for clean operation
  output$inputSelectAttributeClean_rowData <- renderUI({
    if(!is.null(vals$rowAnnotation)){
      if(ncol(vals$rowAnnotation) > 0){
        selectInput("inputSelectAttributeClean_rowData",
                    label = "select attribute column",
                    choices = colnames(vals$rowAnnotation))
      }
    }
  })
  
  #confirm create bin button
  observeEvent(input$buttonConfirmBin_rowData, {
    #getting variables
    selected_attribute <- input$inputSelectAttribute_rowData
    bin_name <- input$inputBinName_rowData
    selected_column_no <- match(selected_attribute, colnames(vals$rowAnnotation))
    criteria_term <- input$inputCriteria_rowData
    operator_term <- input$inputOperator_rowData
    
    #get df from reactive input, backup column datatypes and convert factor to character
    data <- singleCellTK:::.manageFactor(vals$rowAnnotation, operation = "backup")
    df <- data$df
    
    #operations
    if (operator_term == "=")
    {
      df[, selected_column_no][df[, selected_column_no] %in% criteria_term] <- bin_name
    }
    else if (operator_term == ">")
    {
      df[, selected_column_no][as.numeric(df[, selected_column_no]) > criteria_term] <- bin_name
    }
    else if (operator_term == "<")
    {
      df[, selected_column_no][as.numeric(df[, selected_column_no]) < criteria_term] <- bin_name
    }
    else if (operator_term == "<=")
    {
      df[, selected_column_no][as.numeric(df[, selected_column_no]) <= criteria_term] <- bin_name
    }
    else if (operator_term == ">=")
    {
      df[, selected_column_no][as.numeric(df[, selected_column_no]) >= criteria_term] <- bin_name
    }
    
    #restore datatypes
    data$df <- df
    data <- singleCellTK:::.manageFactor(data, operation = "restore")
    vals$rowAnnotation <- data$df
    
    output$changesWarning_rowData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #confirm merge button
  observeEvent(input$buttonConfirmMerge_rowData, {
    df <- vals$rowAnnotation
    colname1 <- input$inputSelectAttributeMerge1_rowData
    colname2 <- input$inputSelectAttributeMerge2_rowData
    df <- unite_(df, col = colname1, c(colname1, colname2),
                 sep = input$inputSelectSeparatorMerge_rowData)
    
    vals$rowAnnotation <- df
    
    output$changesWarning_rowData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #fill column button
  observeEvent(input$buttonConfirmFill_rowData, {
    #get df from reactive input, backup column datatypes and convert factor to character
    data <- singleCellTK:::.manageFactor(vals$rowAnnotation, operation = "backup")
    df <- data$df
    
    #operations
    selected_attribute_1 <- input$inputSelectAttributeFill1_rowData
    selected_attribute_2 <- input$inputSelectAttributeFill2_rowData
    selected_column_no_1 <- match(selected_attribute_1, colnames(df))
    selected_column_no_2 <- match(selected_attribute_2, colnames(df))
    old_value <- input$inputSelectAttributeFillvalue_rowData
    new_value <- input$inputReplaceText_rowData
    
    for (i in 1:nrow(df))
    {
      if (df[i, selected_column_no_1] == old_value)
      {
        df[i, selected_column_no_2] <- new_value
      }
    }
    
    #restore datatypes
    data$df <- df
    data <- singleCellTK:::.manageFactor(data, operation = "restore")
    vals$rowAnnotation <- data$df
    
    output$changesWarning_rowData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #confirm clean button
  observeEvent(input$buttonConfirmClean_rowData, {
    #get df from reactive input, backup column datatypes and convert factor to character
    data <- singleCellTK:::.manageFactor(vals$rowAnnotation, operation = "backup")
    df <- data$df
    
    #operations
    selected_attribute <- input$inputSelectAttributeClean_rowData
    selected_column_no <- match(selected_attribute, colnames(df))
    selected_choice <- input$inputRemovalOperation_rowData
    selected_choice_no <- match(selected_choice, c("remove alphabets",
                                                   "remove digits",
                                                   "remove spaces",
                                                   "remove symbols"))
    
    if (selected_choice_no == 1)
    {
      for (i in 1:nrow(df))
      {
        df[i, selected_column_no] <- gsub("[A-z]", "", df[i, selected_column_no])
      }
      
    }
    else if (selected_choice_no == 2)
    {
      for (i in 1:nrow(df))
      {
        df[i, selected_column_no] <- gsub("[0-9]", "", df[i, selected_column_no])
      }
    }
    else if (selected_choice_no == 3)
    {
      for (i in 1:nrow(df))
      {
        df[i, selected_column_no] <- gsub(" ", "", df[i, selected_column_no])
      }
    }
    else if (selected_choice_no == 4)
    {
      for (i in 1:nrow(df))
      {
        df[i, selected_column_no] <- gsub("[[:punct:]]", "", df[i, selected_column_no])
      }
    }
    
    #restore datatypes
    data$df <- df
    data <- singleCellTK:::.manageFactor(data, operation = "restore")
    vals$rowAnnotation <- data$df
    
    output$changesWarning_rowData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #add new empty column button
  observeEvent(input$buttonConfirmEmptyColumnName_rowData, {
    df <- vals$rowAnnotation
    colname <- input$inputEmptyColumnName_rowData
    df$newcolumn <- input$inputDefaultValueAddColumn_rowData
    names(df)[ncol(df)] <- colname
    
    vals$columnAnnotation <- df
    
    output$changesWarning_rowData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #delete column button
  observeEvent(input$buttonConfirmDeleteColumn_rowData,{
    
    #getting variables
    selected_attribute <- input$inputSelectAttributeDelete_rowData
    
    #get df from reactive input
    df <- vals$rowAnnotation
    
    #delete
    df[[selected_attribute]] <- NULL
    
    vals$rowAnnotation <- df
    
    output$changesWarning_rowData <- renderUI({
      HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
    })
    showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
  })
  
  #restore saved/original rowData
  observeEvent(input$buttonRestore_rowData,{
    vals$rowAnnotation <- as.data.frame(rowData(vals$counts))
    output$changesWarning_rowData <- NULL
    showNotification("Changes reverted back to last checkpoint.")
  })
  
  #save changes to rowData
  observeEvent(input$buttonSave_rowData,{
    rowData(vals$counts) <- DataFrame(vals$rowAnnotation)
    output$changesWarning_rowData <- NULL
    updateFeatureAnnots()
    showNotification("Changes saved successfully.")
  })
  
  
  #-----------------------------------------------------------------------------
  # Page Download ####
  #-----------------------------------------------------------------------------
  
  output$exportFileName <- renderUI({
    defaultName <- paste0("SCE-", strftime(Sys.time(), format = "%y%m%d_%H%M"))
    if (input$exportChoice == "rds") {
      extName <- ".rds"
    } else if (input$exportChoice == "annData") {
      extName <- ".h5ad"
    } 
    tags$div(
      div(style = "display: inline-block;vertical-align:top; width: 160px;",
          textInput("exportPrefix", label = NULL,
                    value = defaultName, placeholder = "Required!",
                    width = '160px')),
      div(
        style = "display: inline-block;vertical-align:top; width: 50px;",
        p(extName, style = "margin-top: 8px; margin-left: 2px; font-size: 16px;")
      )
      
    )
  })
  addPopover(session, 'exportAssayLabel', '', "The name of assay of interests that will be set as the primary matrix of the output AnnData.", 'right')
  
  output$exportData <- downloadHandler(
    filename = function() {
      if (input$exportChoice == "rds") {
        paste0(input$exportPrefix, ".rds")
      }
      else if (input$exportChoice == "annData"){
        paste0(input$exportPrefix, ".h5ad")
      }
    },
    content = function(file) {
      if (input$exportChoice == "rds") {
        saveRDS(vals$counts, file)
      } 
      else if (input$exportChoice == "annData") {
        zellkonverter::writeH5AD(sce = vals$counts, 
                                 file,
                                 X_name = input$exportAssay)
      }
    }
  )
  
  ##############################################################################
  # Page: Cell Type Labeling ####
  ##############################################################################
  output$ctLabelLevelUI <- renderUI({
    if (input$ctLabelRef %in% c("hpca", "bpe", "dice", "immgen", "mouse")) {
      selectInput("ctLabelLevel", "Labeling level:",
                  c("main", "fine", "ont"), "main")
    } else {
      disabled(
        selectInput("ctLabelLevel", "Labeling level (not supported):",
                    choices = NULL, selected = NULL)
      )
    }
  })
  
  observeEvent(input$ctLabelRun, withConsoleMsgRedirect(
    msg = "Please wait while cell types are being labeled. See console log for progress.",
    {
      req(vals$counts)
      if (input$ctLabelBy == "Clusters") {
        cluster <- input$ctLabelByCluster
        if (is.null(cluster) || cluster == "") {
          stop("Choose the clustering label for this condition!")
        }
      } else {
        cluster <- NULL
      }
      vals$counts <- runSingleR(vals$counts,
                                useAssay = input$ctLabelAssay,
                                useBltinRef = input$ctLabelRef,
                                level = input$ctLabelLevel,
                                featureType = input$ctLabelFeatureType,
                                labelByCluster = cluster)
      updateColDataNames()
      message(date(), " ... SingleR finished")
    }
  ))
  
  ##############################################################################
  # Page: Scanpy Curated Workflow  ####
  ##############################################################################
  
  # Run Normalization
  observeEvent(input$scanpy_normalize_button, withConsoleMsgRedirect(
    msg = "Please wait while data is being normalized. See console log for progress.",
    {
      req(vals$counts)
      message(paste0(date(), " ... Normalizing Data"))
      vals$counts <- runScanpyNormalizeData(inSCE = vals$counts,
                                            useAssay = input$scanpySelectNormalizationAssay,
                                            targetSum = input$scanpy_targetSum,
                                            maxFraction = input$scanpy_maxFraction,
                                            normAssayName = "scanpyNormData"
      )
      updateCollapse(session = session, "ScanpyUI", style = list("Normalize Data" = "success"))
      shinyjs::enable(selector = "#ScanpyUI > div[value='Highly Variable Genes']")
      updateAssayInputs()
      message(paste0(date(), " ... Normalization Complete"))
    }))
  
  # HVG
  #Find HVG
  observeEvent(input$scanpy_find_hvg_button, withConsoleMsgRedirect(
    msg = "Please wait while high variable genes are being found. See console log for progress.",
    {
      req(vals$counts)
      message(paste0(date(), " ... Finding High Variable Genes"))
      useAssay <- NULL
      
      maxDisp <- NULL
      if(input$scanpy_maxDisp_Inf){
        maxDisp <- Inf
      }
      else{
        maxDisp <- input$scanpy_maxDisp
      }
      
      vals$counts <- runScanpyFindHVG(inSCE = vals$counts,
                                      useAssay = "scanpyNormData",
                                      method = input$scanpy_hvg_method,
                                      hvgNumber = input$scanpy_hvg_no_features,
                                      minMean = input$scanpy_minMean,
                                      maxMean = input$scanpy_maxMean,
                                      minDisp = input$scanpy_minDisp,
                                      maxDisp = maxDisp)
      
      message(paste0(date(), " ... Finding HVG Complete"))
      updateCollapse(session = session, "ScanpyUI", style = list("Highly Variable Genes" = "success"))
      shinyjs::enable(selector = "#ScanpyUI > div[value='Dimensionality Reduction']")
      
      message(paste0(date(), " ... Plotting HVG"))
      output$scanpy_plot_hvg <- renderPlot({
        isolate({
          plotScanpyHVG(vals$counts)
        })
      })
      
      message(paste0(date(), " ... Finding HVG Complete"))
    }
  ))
  
  # PCA
  observeEvent(input$scanpy_run_pca_button, withConsoleMsgRedirect(
    msg = "Please wait while PCA is being computed. See console log for progress.",
    {
      req(vals$counts)
      
      message(paste0(date(), " ... Running PCA"))
      
      vals$counts <- runScanpyScaleData(inSCE = vals$counts,
                                        useAssay = "scanpyNormData")
      
      vals$counts <- runScanpyPCA(inSCE = vals$counts,
                                  useAssay = "scanpyScaledData", 
                                  method = input$scanpy_pca_method,
                                  nPCs = input$scanpy_pca_no_components,
                                  reducedDimName = "scanpyPCA",
                                  use_highly_variable = TRUE)
      
      appendTab(inputId = "scanpyPCAPlotTabset", tabPanel(title = "PCA Variance",
                                                          panel(heading = "PCA Variance",
                                                                plotOutput(outputId = "scanpy_plot_pca_variance")
                                                          )
      ), select = TRUE)
      
      appendTab(inputId = "scanpyPCAPlotTabset", tabPanel(title = "PCA Plot",
                                                          panel(heading = "PCA Plot",
                                                                plotOutput(outputId = "scanpy_plot_pca")
                                                          )
      ), select = FALSE)
      
      appendTab(inputId = "scanpyPCAPlotTabset", tabPanel(title = "PCA Gene Ranking",
                                                          panel(heading = "PCA Gene Ranking",
                                                                plotOutput(outputId = "scanpy_plot_pca_gene_ranking")
                                                          )
      ), select = FALSE)
      
      message(paste0(date(), " ... Plotting PCA"))
      
      output$scanpy_plot_pca_variance <- renderPlot({
        isolate({
          plotScanpyPCAVariance(inSCE = vals$counts, log = TRUE)
        })
      })
      
      output$scanpy_plot_pca_gene_ranking <- renderPlot({
        isolate({
          plotScanpyPCAGeneRanking(inSCE = vals$counts)
        })
      })
      
      output$scanpy_plot_pca <- renderPlot({
        isolate({
          plotScanpyPCA(inSCE = vals$counts)
        })
      })
      
      updateCollapse(session = session, "ScanpyUI", style = list("Dimensionality Reduction" = "success"))
      shinyjs::enable(
        selector = "#ScanpyUI > div[value='2D-Embedding']")
      
      shinyjs::show(selector = ".scanpy_pca_plots")
      message(paste0(date(), " ... PCA Complete"))
    }))
  
  
  # TSNE
  observeEvent(input$scanpy_run_tsne_button, withConsoleMsgRedirect(
    msg = "Please wait while tSNE is being computed. See console log for progress.",
    {
      req(vals$counts)
      message(paste0(date(), " ... Running tSNE"))
      vals$counts <- runScanpyTSNE(inSCE = vals$counts, 
                                   useReducedDim = "scanpyPCA",
                                   # useAssay = "scanpyScaledData",
                                   reducedDimName = "scanpyTSNE",
                                   dims = input$scanpy_pca_significant_pc_counter,
                                   perplexity = input$scanpy_perplexity_tsne)
      message(paste0(date(), " ... Plotting tSNE"))
      
      output$scanpy_plot_tsne <- renderPlot({
        isolate({
          plotScanpyEmbedding(inSCE = vals$counts,
                              reducedDimName = "scanpyTSNE")
        })
      })
      updateCollapse(session = session, "ScanpyUI", style = list("2D-Embedding" = "success"))
      shinyjs::enable(selector = "#ScanpyUI > div[value='Clustering']")
      message(paste0(date(), " ... tSNE Complete"))
    }))
  
  #UMAP 
  observeEvent(input$scanpy_run_umap_button, withConsoleMsgRedirect(
    msg = "Please wait while UMAP is being computed. See console log for progress.",
    {
      req(vals$counts)
      # if(!is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[[input$reduction_umap_method]])){
      message(paste0(date(), " ... Running UMAP"))
      vals$counts <- runScanpyUMAP(inSCE = vals$counts, 
                                   useReducedDim = "scanpyPCA", 
                                   # useAssay = "scanpyScaledData", #not needed when using PCA
                                   reducedDimName = "scanpyUMAP",
                                   dims = input$scanpy_pca_significant_pc_counter,  
                                   minDist = input$scanpy_min_dist_umap,
                                   nNeighbors = input$scanpy_n_neighbors_umap, 
                                   spread = input$scanpy_spread_umap,
                                   alpha = input$scanpy_spread_alpha, 
                                   gamma = input$scanpy_spread_gamma)
      
      message(paste0(date(), " ... Plotting UMAP"))
      
      output$scanpy_plot_umap <- renderPlot({
        isolate({
          plotScanpyEmbedding(inSCE = vals$counts,
                              reducedDimName = "scanpyUMAP")
        })
      })
      updateCollapse(session = session, "ScanpyUI", style = list("2D-Embedding" = "success"))
      shinyjs::enable(selector = "#ScanpyUI > div[value='Clustering']")
      message(paste0(date(), " ... UMAP Complete"))
    }))
  
  #Clustering
  observeEvent(input$scanpy_find_clusters_button, withConsoleMsgRedirect(
    msg = "Please wait while clusters are being computed. See console log for progress.",
    {
      req(vals$counts)
      #Remove plot tabs if generated before
      removeTab(inputId = "scanpyClusteringPlotTabset", target = "PCA Plot")
      removeTab(inputId = "scanpyClusteringPlotTabset", target = "ICA Plot")
      removeTab(inputId = "scanpyClusteringPlotTabset", target = "tSNE Plot")
      removeTab(inputId = "scanpyClusteringPlotTabset", target = "UMAP Plot")
      
      message(paste0(date(), " ... Clustering Dataset"))
      vals$counts <- runScanpyFindClusters(inSCE = vals$counts, 
                                           useReducedDim = "scanpyPCA",
                                           nNeighbors = input$scanpy_nNeighbors,
                                           dims = input$scanpy_pca_significant_pc_counter, 
                                           method = input$scanpy_algorithm.use,
                                           resolution = input$scanpy_resolution_clustering,
                                           niterations = -1,
                                           cor_method = input$scanpy_corr_method)
      updateCollapse(session = session, "ScanpyUI", style = list("Clustering" = "success"))
      message(paste0(date(), " ... Finding Clusters Complete"))
      
      if("scanpyTSNE" %in% reducedDimNames(vals$counts)){
        appendTab(inputId = "scanpyClusteringPlotTabset", tabPanel(title = "tSNE Plot",
                                                                   panel(heading = "tSNE Plot",
                                                                         plotOutput(outputId = "scanpy_plot_tsne_clustering")
                                                                   )
        )
        )
        
        message(paste0(date(), " ... Re-generating tSNE Plot with Cluster Labels"))
        
        output$scanpy_plot_tsne_clustering <- renderPlot({
          isolate({
            plotScanpyEmbedding(inSCE = vals$counts, 
                                reducedDimName = "scanpyTSNE",
                                color = paste0(
                                  "Scanpy_", 
                                  input$scanpy_algorithm.use, 
                                  "_", 
                                  input$scanpy_resolution_clustering))
          })
        })
      }
      
      if("scanpyUMAP" %in% reducedDimNames(vals$counts)){
        appendTab(inputId = "scanpyClusteringPlotTabset", tabPanel(title = "UMAP Plot",
                                                                   panel(heading = "UMAP Plot",
                                                                         plotOutput(outputId = "scanpy_plot_umap_clustering")
                                                                   )
        ), select = TRUE
        )
        message(paste0(date(), " ... Re-generating UMAP Plot with Cluster Labels"))
        
        output$scanpy_plot_umap_clustering <- renderPlot({
          isolate({
            plotScanpyEmbedding(
              inSCE = vals$counts,
              reducedDimName = "scanpyUMAP",
              color = paste0(
                "Scanpy_", 
                input$scanpy_algorithm.use, 
                "_", 
                input$scanpy_resolution_clustering)
            )
          })
        })
      }
      
      appendTab(inputId = "scanpyClusteringPlotTabset", tabPanel(title = "PCA Plot",
                                                                 panel(heading = "PCA Plot",
                                                                       plotOutput(outputId = "scanpy_plot_pca_clustering")
                                                                 )
      ), select = FALSE)
      
      message(paste0(date(), " ... Re-generating PCA Plot with Cluster Labels"))
      output$scanpy_plot_pca_clustering <- renderPlot({
        isolate({
          plotScanpyPCA(inSCE = vals$counts, 
                        color = paste0(
                          "Scanpy_", 
                          input$scanpy_algorithm.use, 
                          "_", 
                          input$scanpy_resolution_clustering))
        })
      })
      
      shinyjs::show(selector = ".scanpy_clustering_plots")
      #enable find marker selection
      shinyjs::enable(
        selector = "#ScanpyUI > div[value='Find Markers']")
      
      #update colData names
      updateColDataNames()
      
      #populate updated colData items for findMarkers tab
      updateSelectInput(session = session,
                        inputId = "scanpyFindMarkerSelectPhenotype",
                        choices = colnames(colData(vals$counts)),
                        selected = paste0(
                          "Scanpy_", 
                          input$scanpy_algorithm.use, 
                          "_", 
                          input$scanpy_resolution_clustering))
    }))
  
  observeEvent(input$scanpyFindMarkerRun, withConsoleMsgRedirect(
    msg = "Please wait while marker genes are being found. See console log for progress.",
    {
      req(vals$counts)
      message(paste0(date(), " ... Finding Marker Genes"))
      
      vals$counts <- runScanpyFindMarkers(inSCE = vals$counts,
                                          colDataName = input$scanpyFindMarkerSelectPhenotype,
                                          test = input$scanpyFindMarkerTest, 
                                          corr_method = input$scanpyFindMarkerCorrMethod)
      
      shinyjs::show(selector = ".scanpy_findmarker_table")
      shinyjs::show(selector = ".scanpy_findmarker_jointHeatmap")
      shinyjs::show(selector = ".scanpy_findmarker_plots")
      
      # Plot heatmap
      output$scanpy_findMarkerHeatmapPlotFull <- renderPlot({
        isolate({
          plotScanpyMarkerGenesHeatmap(vals$counts, 
                                       groupBy = input$scanpyFindMarkerSelectPhenotype,
                                       nGenes = 2)
        })
      })
      
      message(paste0(date(), " ... Find Markers Complete"))
      
      # Show downstream analysis options
      callModule(module = nonLinearWorkflow, id = "nlw-scanpy", parent = session,
                 de = TRUE, fm = TRUE, pa = TRUE)
      
      updateCollapse(session = session, "ScanpyUI", style = list("Find Markers" = "success"))
      
      removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Matrix Plot")
      removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Violin Plot")
      removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Feature Plot")
      removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Dot Plot")
      removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Heatmap Plot")
      
      appendTab(inputId = "scanpyFindMarkerPlotTabset",
                tabPanel(title = "Feature Plot",
                         panel(heading = "Feature Plot",
                               shinyjqui::jqui_resizable(
                                 plotOutput(outputId = "scanpyFindMarkerFeaturePlot")
                               )
                         )
                ), select = TRUE
      )
      
      appendTab(inputId = "scanpyFindMarkerPlotTabset",
                tabPanel(title = "Violin Plot",
                         panel(heading = "Violin Plot",
                               shinyjqui::jqui_resizable(
                                 plotOutput(outputId = "scanpyFindMarkerViolinPlot")
                               )
                         )
                )
      )
      appendTab(inputId = "scanpyFindMarkerPlotTabset",
                tabPanel(title = "Matrix Plot",
                         panel(heading = "Matrix Plot",
                               shinyjqui::jqui_resizable(
                                 plotOutput(outputId = "scanpyFindMarkerMatrixPlot")
                               )
                         )
                )
      )
      appendTab(inputId = "scanpyFindMarkerPlotTabset",
                tabPanel(title = "Dot Plot",
                         panel(heading = "Dot Plot",
                               shinyjqui::jqui_resizable(
                                 plotOutput(outputId = "scanpyFindMarkerDotPlot")
                               )
                         )
                )
      )
      appendTab(inputId = "scanpyFindMarkerPlotTabset",
                tabPanel(title = "Heatmap Plot",
                         panel(heading = "Heatmap Plot",
                               fluidRow(
                                 column(12, align = "center",
                                        panel(
                                          plotOutput(outputId = "scanpyFindMarkerHeatmapPlot")
                                        )
                                 )
                               )
                         )
                )
      )
      
      vals$ftScanpy <- callModule(
        module = filterTableServer,
        id = "filterScanpyFindMarker",
        dataframe = metadata(vals$counts)$scanpyMarkers,
        defaultFilterColumns = c("Pvalue"),
        defaultFilterOperators = c("<="),
        defaultFilterValues = c("0.05"),
        topText = "You can view the marker genes in the table below and apply custom filters to filter the table accordingly. A joint heatmap for all the marker genes available in the table is plotted underneath the table. Additional visualizations are plotted for select genes which can be selected by clicking on the rows of the table."
      )
    }))
  
  observeEvent(input$scanpy_findMarkerHeatmapPlotFullNumericRun, withConsoleMsgRedirect(
    msg = "Please wait while heatmap is being plotted. See console log for progress.",
    {
      output$scanpy_findMarkerHeatmapPlotFull <- renderPlot({
        isolate({
          plotScanpyMarkerGenesHeatmap(vals$counts, 
                                       groupBy = input$scanpyFindMarkerSelectPhenotype,
                                       nGenes = input$scanpy_findMarkerHeatmapPlotFullNumeric)
        })
      })
    }))
  
  observe({
    req(vals$ftScanpy$data)
    req(vals$ftScanpy$selectedRows)
    df <- vals$ftScanpy$data[vals$ftScanpy$selectedRows, ]
    
    output$scanpyFindMarkerFeaturePlot <- renderPlot({
      plotScanpyEmbedding(inSCE = vals$counts,
                          reducedDimName = "scanpyUMAP",
                          color = df$Gene)
    })
    output$scanpyFindMarkerViolinPlot <- renderPlot({
      plotScanpyViolin(inSCE = vals$counts,
                       features = df$Gene,
                       groupBy = input$scanpyFindMarkerSelectPhenotype,
                       useAssay = "scanpyNormData")
    })
    output$scanpyFindMarkerMatrixPlot <- renderPlot({
      plotScanpyMatrixPlot(inSCE = vals$counts, 
                           groupBy = input$scanpyFindMarkerSelectPhenotype, 
                           features = df$Gene,
                           useAssay = "scanpyNormData")
    })
    output$scanpyFindMarkerDotPlot <- renderPlot({
      plotScanpyDotPlot(inSCE = vals$counts,
                        groupBy = input$scanpyFindMarkerSelectPhenotype,
                        features = df$Gene,
                        useAssay = "scanpyNormData")
    })
    output$scanpyFindMarkerHeatmapPlot <- renderPlot({
      plotScanpyHeatmap(inSCE = vals$counts, 
                        groupBy = input$scanpyFindMarkerSelectPhenotype, 
                        features = df$Gene, 
                        useAssay = "scanpyNormData")
    })
  })
})

