@@ -652,10 +652,7 @@ plotTSCANClusterPseudo <- function(inSCE, useCluster, useReducedDim = "UMAP",

   by.cluster <- scuttle::aggregateAcrossCells(inSCE, ids = clusters)

   line.data <- TSCAN::reportEdges(by.cluster, mst = results$mst,

                                   clusters = NULL, use.dimred = useReducedDim)

-  line.data.sub <- line.data[grepl(paste0("^", useCluster, "--"),

-                                   line.data$edge) |

-                               grepl(paste0("--", useCluster, "$"),

-                                     line.data$edge),]

+  line.data.sub <- .getClustersLineData(line.data, useCluster)

   # Get Branch pseudotime

   tscan.pseudo <- TSCAN::orderCells(results$maptscan, mst = results$mst,

@@ -694,6 +691,9 @@ plotTSCANClusterPseudo <- function(inSCE, useCluster, useReducedDim = "UMAP",

 #' @param inSCE Input \linkS4class{SingleCellExperiment} object.

 #' @param useCluster Choose a cluster used for identifying DEG with

 #' \code{\link{runTSCANClusterDEAnalysis}}. Required.

+#' @param pathIndex Specifies one of the branching paths from \code{useCluster}

+#' and plot the top DEGs on this path. Ususally presented by the terminal

+#' cluster of a path. By default \code{NULL} plot top DEGs of all paths.

 #' @param useReducedDim A single character for the matrix of 2D embedding.

 #' Should exist in \code{reducedDims} slot. Default \code{"UMAP"}.

 #' @param topN Integer. Use top N genes identified. Default \code{9}.

@@ -802,55 +802,50 @@ plotTSCANDimReduceFeatures <- function(

   combinePlot = c("all", "none"))

 {

   results <- getTSCANResults(inSCE, analysisName = "Pseudotime")

-  if (!is.null(useCluster) && length(useCluster) != 1)

-    stop("`useCluster`: can only use one cluster.")

   combinePlot <- match.arg(combinePlot)

   clusters <- colData(inSCE)$TSCAN_clusters

   by.cluster <- scuttle::aggregateAcrossCells(inSCE, ids = clusters)

   line.data <- TSCAN::reportEdges(by.cluster, mst = results$mst,

                                   clusters = NULL, use.dimred = useReducedDim)

   if (!is.null(useCluster)) {

-    line.data <- line.data[grepl(paste0("^", useCluster, "--"),

-                                 line.data$edge) |

-                             grepl(paste0("--", useCluster, "$"),

-                                   line.data$edge),]

+    line.data <- .getClustersLineData(line.data, useCluster)

     inSCE <- inSCE[, clusters %in% useCluster]

   }

   if (by == "rownames") by <- NULL

   plotList <- list()

-  if (!is.na(features)) {

-    for (f in features) {

-      g <- plotSCEDimReduceFeatures(inSCE, feature = f,

-                                    useAssay = useAssay,

-                                    featureLocation = by,dim1 = 1, dim2 = 2,

-                                    featureDisplay = featureDisplay,

-                                    reducedDimName = useReducedDim,

-                                    title = f) +

-        ggplot2::geom_line(data = line.data,

-                           ggplot2::aes_string(x = colnames(line.data)[2],

-                                               y = colnames(line.data)[3],

-                                               group = "edge"),

-                           inherit.aes = FALSE)

-      # Text labeling code credit: scater

-      reddim <- as.data.frame(SingleCellExperiment::reducedDim(inSCE,

-                                                               useReducedDim))

-      text_out <- scater::retrieveCellInfo(inSCE, "TSCAN_clusters",

-                                           search = "colData")

-      by_text_x <- vapply(split(reddim[,1], text_out$val),

-                          stats::median, FUN.VALUE = 0)

-      by_text_y <- vapply(split(reddim[,2], text_out$val),

-                          stats::median, FUN.VALUE = 0)

-      g <- g + ggrepel::geom_text_repel(

-        data = data.frame(x = by_text_x,

-                          y = by_text_y,

-                          label = names(by_text_x)),

-        mapping = ggplot2::aes_string(x = "x",

-                                      y = "y",

-                                      label = "label"),

-        inherit.aes = FALSE

-      )

-      plotList[[f]] <- g

-    }

+  features <- stats::na.omit(features)

+  for (f in features) {

+    # Should not enter the loop if features is length zero after NA omit

+    g <- plotSCEDimReduceFeatures(inSCE, feature = f,

+                                  useAssay = useAssay,

+                                  featureLocation = by,dim1 = 1, dim2 = 2,

+                                  featureDisplay = featureDisplay,

+                                  reducedDimName = useReducedDim,

+                                  title = f) +

+      ggplot2::geom_line(data = line.data,

+                         ggplot2::aes_string(x = colnames(line.data)[2],

+                                             y = colnames(line.data)[3],

+                                             group = "edge"),

+                         inherit.aes = FALSE)

+    # Text labeling code credit: scater

+    reddim <- as.data.frame(SingleCellExperiment::reducedDim(inSCE,

+                                                             useReducedDim))

+    text_out <- scater::retrieveCellInfo(inSCE, "TSCAN_clusters",

+                                         search = "colData")

+    by_text_x <- vapply(split(reddim[,1], text_out$val),

+                        stats::median, FUN.VALUE = 0)

+    by_text_y <- vapply(split(reddim[,2], text_out$val),

+                        stats::median, FUN.VALUE = 0)

+    g <- g + ggrepel::geom_text_repel(

+      data = data.frame(x = by_text_x,

+                        y = by_text_y,

+                        label = names(by_text_x)),

+      mapping = ggplot2::aes_string(x = "x",

+                                    y = "y",

+                                    label = "label"),

+      inherit.aes = FALSE, na.rm = TRUE,

+    )

+    plotList[[f]] <- g

   }

   if (combinePlot == "all") {

     if (length(plotList) > 0)

@@ -858,3 +853,22 @@ plotTSCANDimReduceFeatures <- function(

   }

   return(plotList)

 }

+

+#' Extract edges that connects to the clusters of interest

+#' @param line.data data.frame of three columns. First column should be named

+#' "edge", the other two are coordinates of one of the vertices that this

+#' edge connects.

+#' @param useCluster Vector of clusters, that are going to be the vertices for

+#' edge finding.

+#' @return data.frame, subset rows of line.data

+.getClustersLineData <- function(line.data, useCluster) {

+  idx <- vapply(useCluster, function(x){

+    grepl(paste0("^", x, "--"), line.data$edge) |

+      grepl(paste0("--", x, "$"), line.data$edge)

+  }, logical(nrow(line.data)))

+  # `idx` returned was c("matrix", "array") class. Each column is a logical

+  # vector for edge selection of each cluster.

+  # Next, do "or" for each row.

+  idx <- rowSums(idx) > 0

+  line.data[idx,]

+}

@@ -8,9 +8,10 @@ nonLinearWorkflowUI <- function(id)

 }

 # Server

-nonLinearWorkflow <- function(input, output, session, parent, 

+nonLinearWorkflow <- function(input, output, session, parent,

                               de = FALSE,

-                              cv = FALSE, 

+                              fm = FALSE,

+                              cv = FALSE,

                               pa = FALSE,

                               tj = FALSE,

                               qcf = FALSE,

@@ -21,12 +22,13 @@ nonLinearWorkflow <- function(input, output, session, parent,

                               cl = FALSE)

 {

   ns <- session$ns

-  

+

   output$ui <- renderUI({

     bsCollapse(

       open = "Next Steps",

-      bsCollapsePanel("Next Steps", 

+      bsCollapsePanel("Next Steps",

                       uiOutput(ns("de")),

+                      uiOutput(ns("fm")),

                       uiOutput(ns("pa")),

                       uiOutput(ns("tj")),

                       uiOutput(ns("qcf")),

@@ -38,53 +40,60 @@ nonLinearWorkflow <- function(input, output, session, parent,

                       uiOutput(ns("cv")),

                       style = "success")

       )

-    

+

   })

-  

+

   if(de){

     output$de <- renderUI({

       panel(

-        heading = "Differential Expression & Marker Selection",

-        h5("Compute and visualize marker genes for each cluster using one of the many integrated statistical frameworks for differential expression:"),

-        actionButton(inputId = ns("goMS"), label = "Go to Marker Selection!"),

-        hr(),

+        heading = "Differential Expression",

         h5("Discover quantitative changes between experimental conditions using one of the many integrated statistical frameworks for differential expression:"),

-        actionButton(inputId = ns("goDE"), label = "Go to Differential Expression!")

+        actionButton(inputId = ns("goDE"), label = "Go to Differential Expression")

       )

     })

   }

-  

+

+  if(fm){

+    output$de <- renderUI({

+      panel(

+        heading = "Marker Selection",

+        h5("Compute and visualize marker genes for each cluster using one of the many integrated statistical frameworks for differential expression:"),

+        actionButton(inputId = ns("goMS"), label = "Go to Find Marker")

+      )

+    })

+  }

+

   if(cv){

     output$cv <- renderUI({

       panel(

         heading = "Cell Viewer",

         h5("Visualize data using scatter plot, violin plot, bar or box plots."),

-        actionButton(inputId = ns("goCV"), label = "Go to Cell Viewer!")

+        actionButton(inputId = ns("goCV"), label = "Go to Cell Viewer")

       )

     })

   }

-  

+

   if(pa){

     output$pa <- renderUI({

       panel(

         heading = "Pathway Analysis",

-        h5("Explore biological activity or functions with pathway analysis using either 'GSVA' or 'EnrichR' statistical frameworks:"),

-        actionButton(inputId = ns("goPathwayAnalysis"), label = "Go to Pathway Analysis!"),

-        actionButton(inputId = ns("goEnrichR"), label = "Go to EnrichR!"),

+        h5("Explore biological activity or functions with pathway analysis using 'VAM' or 'GSVA' statistical frameworks, or perform GSEA with 'enrichR':"),

+        actionButton(inputId = ns("goPathwayAnalysis"), label = "Go to Pathway Analysis"),

+        actionButton(inputId = ns("goEnrichR"), label = "Go to EnrichR"),

       )

     })

   }

-  

+

   if(tj){

     output$tj <- renderUI({

       panel(

         heading = "Trajectory Analysis",

         h5("Estimate Pseudotime path of cell population with TSCAN method:"),

-        actionButton(inputId = ns("goTSCAN"), label = "Go to Trajectory Analysis!")

+        actionButton(inputId = ns("goTSCAN"), label = "Go to Trajectory Analysis")

       )

     })

   }

-  

+

   if(qcf){

     output$qcf <- renderUI({

       panel(

@@ -95,17 +104,17 @@ nonLinearWorkflow <- function(input, output, session, parent,

       )

     })

   }

-  

+

   if(nbc){

     output$nbc <- renderUI({

       panel(

         heading = "Normalization & Batch-Correction",

         h5("Normalize the raw/filtered input data to remove biasness of technical variation from the data or additionally adjust for batch-effect if the data is processed in multiple batches."),

-        actionButton(inputId = ns("goNBC"), label = "Go to Normalization/Batch-Correction!")

+        actionButton(inputId = ns("goNBC"), label = "Go to Normalization/Batch-Correction")

       )

     })

   }

-  

+

   if(cw){

     output$cw <- renderUI({

       panel(

@@ -116,7 +125,7 @@ nonLinearWorkflow <- function(input, output, session, parent,

       )

     })

   }

-  

+

   if(dr){

     output$dr <- renderUI({

       panel(

@@ -126,7 +135,7 @@ nonLinearWorkflow <- function(input, output, session, parent,

       )

     })

   }

-  

+

   if(fs){

     output$fs <- renderUI({

       panel(

@@ -136,7 +145,7 @@ nonLinearWorkflow <- function(input, output, session, parent,

       )

     })

   }

-  

+

   if(cl){

     output$cl <- renderUI({

       panel(

@@ -146,14 +155,14 @@ nonLinearWorkflow <- function(input, output, session, parent,

       )

     })

   }

-  

+

   observeEvent(input$goCV,{

     showTab(inputId = "navbar",

             target = "CellViewer",

             select = TRUE,

             session = parent)

   })

-  

+

     observeEvent(input$goDE,{

       showTab(inputId = "navbar",

               target = "Differential Expression",

@@ -167,89 +176,89 @@ nonLinearWorkflow <- function(input, output, session, parent,

               select = TRUE,

               session = parent)

     })

-    

+

     observeEvent(input$goPathwayAnalysis,{

       showTab(inputId = "navbar",

               target = "Pathway Activity",

               select = TRUE,

               session = parent)

     })

-    

+

     observeEvent(input$goEnrichR,{

       showTab(inputId = "navbar",

               target = "EnrichR",

               select = TRUE,

               session = parent)

     })

-    

+

     observeEvent(input$goTSCAN,{

       showTab(inputId = "navbar",

               target = "TSCANWorkflow",

               select = TRUE,

               session = parent)

     })

-    

+

     observeEvent(input$goQC,{

       showTab(inputId = "navbar",

               target = "QC & Filtering",

               select = TRUE,

               session = parent)

-      updateTabsetPanel(session = parent, 

-                        inputId = "QCFilterTabsetPanel", 

+      updateTabsetPanel(session = parent,

+                        inputId = "QCFilterTabsetPanel",

                         selected = "QC")

     })

-    

+

     observeEvent(input$goFilter,{

       showTab(inputId = "navbar",

               target = "QC & Filtering",

               select = TRUE,

               session = parent)

-      updateTabsetPanel(session = parent, 

-                        inputId = "QCFilterTabsetPanel", 

+      updateTabsetPanel(session = parent,

+                        inputId = "QCFilterTabsetPanel",

                         selected = "Filtering")

     })

-    

+

     observeEvent(input$goNBC,{

       showTab(inputId = "navbar",

               target = "Normalization & Batch Correction",

               select = TRUE,

               session = parent)

     })

-    

+

     observeEvent(input$goCelda,{

       showTab(inputId = "navbar",

               target = "Celda",

               select = TRUE,

               session = parent)

     })

-    

+

     observeEvent(input$goSeurat,{

       showTab(inputId = "navbar",

               target = "Seurat",

               select = TRUE,

               session = parent)

     })

-    

+

     observeEvent(input$goDR,{

       showTab(inputId = "navbar",

               target = "Feature Selection & Dimensionality Reduction",

               select = TRUE,

               session = parent)

-      updateTabsetPanel(session = parent, 

-                        inputId = "FSDimRedTabsetPanel", 

+      updateTabsetPanel(session = parent,

+                        inputId = "FSDimRedTabsetPanel",

                         selected = "Dimensionality Reduction")

     })

-    

+

     observeEvent(input$goFS,{

       showTab(inputId = "navbar",

               target = "Feature Selection & Dimensionality Reduction",

               select = TRUE,

               session = parent)

-      updateTabsetPanel(session = parent, 

-                        inputId = "FSDimRedTabsetPanel", 

+      updateTabsetPanel(session = parent,

+                        inputId = "FSDimRedTabsetPanel",

                         selected = "Feature Selection")

     })

-    

+

     observeEvent(input$goCL,{

       showTab(inputId = "navbar",

               target = "Clustering",

@@ -196,10 +196,7 @@ shinyServer(function(input, output, session) {

     updateSelectInput(session, 'deRegLabel',

                       choices = c("Rownames (Default)",

                                   selectRowData))

-    updateSelectInput(session, 'tscanDEHMFeatureDisplay',

-                      choices = c("Rownames (Default)",

-                                  selectRowData))

-    updateSelectInput(session, 'tscanDERegFeatureDisplay',

+    updateSelectInput(session, 'tscanDEFeatureDisplay',

                       choices = c("Rownames (Default)",

                                   selectRowData))

     updateSelectInput(session, 'plotTSCANClusterDEG_featureDisplay',

@@ -1093,7 +1090,8 @@ shinyServer(function(input, output, session) {

                    close = "2. Create dataset:",

                    style = list("2. Create dataset:" = "success"))

-    callModule(module = nonLinearWorkflow, id = "nlw-import", parent = session, qcf = TRUE)

+    callModule(module = nonLinearWorkflow, id = "nlw-import", parent = session,

+               qcf = TRUE)

     }))

   updateSeuratUIFromRDS <- function(inSCE){

@@ -1445,8 +1443,7 @@ shinyServer(function(input, output, session) {

     updateSelectInput(session, "deVolcFeatureDisplay", selected = selected)

     updateSelectInput(session, "deVioLabel", selected = selected)

     updateSelectInput(session, "deRegLabel", selected = selected)

-    updateSelectInput(session, "tscanDEHMFeatureDisplay", selected = selected)

-    updateSelectInput(session, "tscanDERegFeatureDisplay", selected = selected)

+    updateSelectInput(session, "tscanDEFeatureDisplay", selected = selected)

     updateSelectInput(session, "plotTSCANClusterDEG_featureDisplay",

                       selected = selected)

     updateSelectInput(session, "plotTSCANDimReduceFeatures_featureDisplay",

@@ -1906,7 +1903,8 @@ shinyServer(function(input, output, session) {

         message(paste0(date(), " ... QC Complete"))

         updateQCPlots()

         # Show downstream analysis options

-        callModule(module = nonLinearWorkflow, id = "nlw-qcf", parent = session, nbc = TRUE, cw = TRUE, cv = TRUE)

+        callModule(module = nonLinearWorkflow, id = "nlw-qcf", parent = session,

+                   nbc = TRUE, cw = TRUE, cv = TRUE)

       }

       delay(500, removeNotification(id = "qcNotification"))

   }))

@@ -2452,7 +2450,8 @@ shinyServer(function(input, output, session) {

         message(paste0(date(), " ... Ended normalization/transformation."))

         # Show downstream analysis options

-        callModule(module = nonLinearWorkflow, id = "nlw-nbc", parent = session, dr = TRUE, fs = TRUE)

+        callModule(module = nonLinearWorkflow, id = "nlw-nbc", parent = session,

+                   dr = TRUE, fs = TRUE)

       }

     }

   ))

@@ -3130,7 +3129,8 @@ shinyServer(function(input, output, session) {

       updateReddimInputs()

       updateAssayInputs()

       # Show downstream analysis options

-      callModule(module = nonLinearWorkflow, id = "nlw-dr", parent = session, cl = TRUE, cv = TRUE)

+      callModule(module = nonLinearWorkflow, id = "nlw-dr", parent = session,

+                 cl = TRUE, cv = TRUE)

       message(paste0(date(), " ... Ending Dimensionality Reduction."))

       updateSelectizeInput(session, "selectRedDimPlot_tsneUmap",

@@ -3357,7 +3357,7 @@ shinyServer(function(input, output, session) {

       }

       # Show downstream analysis options

       callModule(module = nonLinearWorkflow, id = "nlw-cl", parent = session,

-                 de = TRUE, pa = TRUE, cv = TRUE, tj = TRUE)

+                 de = TRUE, fm = TRUE, pa = TRUE, cv = TRUE, tj = TRUE)

     }

   ))

@@ -3462,8 +3462,8 @@ shinyServer(function(input, output, session) {

       updatePickerInput(session, "useClusterForPlotGene",

                         choices = clusterNamesList,

                         selected = NULL)

-      updateSelectInput(session, "plotTSCANDimReduceFeatures_useCluster",

-                        choices = c("Use all", clusterNamesList))

+      updatePickerInput(session, "plotTSCANDimReduceFeatures_useCluster",

+                        choices = clusterNamesList)

       updateSelectInput(session, "TSCANUseCluster",

                         choices = results$branchClusters)

       updateCollapse(session = session, "TSCANUI",

@@ -3481,6 +3481,10 @@ shinyServer(function(input, output, session) {

                          useReducedDim = input$TSCANVisRedDim)

       })

     })

+    updateSelectInput(session, "plotTSCANClusterDEG_useReducedDim",

+                      selected = input$TSCANVisRedDim)

+    updateSelectInput(session, "plotTSCANDimReduceFeatures_useReducedDim",

+                      selected = input$TSCANVisRedDim)

     session$sendCustomMessage("close_dropDownTSCAN", "")

   })

@@ -3498,7 +3502,7 @@ shinyServer(function(input, output, session) {

                       ))

   })

-  observeEvent(input$findExpGenes, withConsoleMsgRedirect(

+  observeEvent(input$runTSCANDEG, withConsoleMsgRedirect(

     msg = "Please wait while DE genes are being found for path. See console log for progress.",

     {

       req(vals$counts)

@@ -3516,7 +3520,7 @@ shinyServer(function(input, output, session) {

         })

       })

-      message(paste0(date(), " ... Updating upregulated genes"))

+      message(paste0(date(), " ... Updating up-regulated genes"))

       output$UpregGenesPlot <- renderPlot({

         isolate({

           plotTSCANPseudotimeGenes(inSCE = vals$counts,

@@ -3524,58 +3528,73 @@ shinyServer(function(input, output, session) {

                                    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, "tscanDEHMexpPathIndex",

-                        choices = names(all.results),

-                        selected = NULL)

-      updateSelectInput(session, "tscanDERegexpPathIndex",

+      updateSelectInput(session, "tscanDEexpPathIndex",

                         choices = names(all.results),

-                        selected = NULL)

+                        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)

+        style = list("Identify Genes Differentially Expressed For Path" = "success")

+      )

+      callModule(module = nonLinearWorkflow, id = "nlw-Traj", parent = session,

+                 de = TRUE, pa = TRUE)

     }

   ))

-  #plot results on Expression plot

-  observeEvent(input$tscanDEHMPlot, {

-    req(vals$counts)

-    if (input$tscanDEHMFeatureDisplay == "Rownames (Default)") {

-      featureDisplay <- NULL

-    } else {

-      featureDisplay <- input$tscanDEHMFeatureDisplay

-    }

-    output$heatmapPlot <- renderPlot({

-      isolate({

-        plotTSCANPseudotimeHeatmap(inSCE = vals$counts,

-                                   pathIndex = input$tscanDEHMexpPathIndex,

-                                   topN = input$tscanDEHMTopGenes,

+  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)

+        })

       })

-    })

-    session$sendCustomMessage("close_dropDownTscanDEHM", "")

-  })

-  observeEvent(input$tscanDERegPlot, {

-    req(vals$counts)

-    if (input$tscanDERegFeatureDisplay == "Rownames (Default)") {

-      featureDisplay <- NULL

-    } else {

-      featureDisplay <- input$tscanDERegFeatureDisplay

-    }

-    output$UpregGenesPlot <- renderPlot({

-      isolate({

-        plotTSCANPseudotimeGenes(inSCE = vals$counts,

-                                 pathIndex = input$tscanDERegexpPathIndex,

-                                 direction = input$tscanDERegDirection,

-                                 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_dropDownTscanDEReg", "")

-  })

+

+      session$sendCustomMessage("close_dropDownTscanDE", "")

+    }

+  ))

   ###################################################

   ###  Run STEP 3: Identify DE genes in specific cluster

@@ -3590,29 +3609,17 @@ shinyServer(function(input, output, session) {

                                                useAssay = input$TSCANBranchAssaySelect,

                                                fdrThreshold = input$fdrThreshold_TSCAN)

-      clusterAnalysisNamesList <- names(getTSCANResults(vals$counts,

+      clusterAnalysisNames <- names(getTSCANResults(vals$counts,

                                                         analysisName = "ClusterDEAnalysis"))

-      terminalNodes<- c(colnames(getTSCANResults(vals$counts,

-                                                 analysisName = "ClusterDEAnalysis",

-                                                 pathName = input$TSCANUseCluster)$terminalNodes))

-

-      terminalNodesList <- getTSCANResults(vals$counts,

-                                           analysisName = "ClusterDEAnalysis",

-                                           pathName = "pathIndexList")

-

-      updateSelectInput(session, "plotTSCANClusterDEG_useCluster",

-                        choices = clusterAnalysisNamesList,

-                        selected = NULL)

-      updateSelectInput(session, "useListCluster",

-                        choices = clusterAnalysisNamesList,

-                        selected = NULL)

-      updateSelectInput(session, "useVisCluster",

-                        choices = clusterAnalysisNamesList,

-                        selected = NULL)

       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"))

@@ -3620,8 +3627,9 @@ shinyServer(function(input, output, session) {

         isolate({

           plotTSCANClusterDEG(inSCE = vals$counts,

                               useCluster = input$TSCANUseCluster,

+                              pathIndex = pathChoices[1],

                               topN = 4,

-                              useReducedDim = input$TSCANReddim)

+                              useReducedDim = input$TSCANVisRedDim)

         })

       })

@@ -3638,20 +3646,20 @@ shinyServer(function(input, output, session) {

       })

       #plot cluster pseudo values by default

-      message(paste0(date(), " ... Plotting pseudotime of branches for Cluster"))

+      message(paste0(date(), " ... Plotting pseudotime of branches for cluster"))

       output$tscanCLusterPeudo <- renderPlot({

         isolate({

           plotTSCANClusterPseudo(inSCE = vals$counts,

                                  useCluster = input$TSCANUseCluster,

-                                 useReducedDim = input$TSCANReddim)

+                                 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)

+      callModule(module = nonLinearWorkflow, id = "nlw-Traj", parent = session,

+                 de = TRUE, pa = TRUE)

     }

   ))

@@ -3668,78 +3676,61 @@ shinyServer(function(input, output, session) {

                       selected = NULL)

   })

-  observeEvent(input$plotTSCANClusterDEG, {

-    req(vals$counts)

-    if (input$plotTSCANClusterDEG_featureDisplay == "Rownames (Default)") {

-      featureDisplay <- "rownames"

-    } else {

-      featureDisplay <- input$plotTSCANClusterDEG_featureDisplay

-    }

-    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)

-    if (identical(plot, list())) {

-      shinyalert(text = "No significant feature identified for the selected path.",

-                 type = "warning")

-    } else {

+  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

         })

       })

-    }

-    session$sendCustomMessage("close_dropDownTscanClusterDEG", "")

-  })

-

-

-  # Retrieve DEG table on different branches of the cluster

-  observeEvent(input$useListCluster, {

-    req(vals$counts)

-    results <- getTSCANResults(vals$counts,

-                               analysisName = "ClusterDEAnalysis",

-                               pathName = input$useListCluster)

-    choices <- colnames(results$terminalNodes)

-    choicesOpt <- list(content = results$pathIndexList)

-    updatePickerInput(session, "clusterListPathIndex",

-                      choices = choices, choicesOpt = choicesOpt,

-                      selected = NULL)

-  })

-

-  observeEvent(input$DEClusterListPlot, {

-    req(vals$counts)

-    results <- getTSCANResults(vals$counts,

-                               analysisName = "ClusterDEAnalysis",

-                               pathName = input$useListCluster)

-    req(results)

-    df <- as.data.frame(results$DEgenes[[input$clusterListPathIndex]])

-    output$tscanCLusterDEGTable <- DT::renderDataTable({

-      isolate({

-        DT::datatable(

-          df,

-          options = list(scrollX = TRUE)

-        )

+      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)

+          )

+        })

       })

-    })

-    session$sendCustomMessage("close_dropDownTscanClusterDEGTable", "")

-  })

-

-  # Plot recomputed pseudotime on branches of the cluster

-  observeEvent(input$DEClusterPlot, {

-    req(vals$counts)

-    output$tscanCLusterPeudo <- renderPlot({

-      isolate({

-        plotTSCANClusterPseudo(inSCE = vals$counts,

-                               useCluster = input$useVisCluster,

-                               useReducedDim = input$DEClusterRedDimNames)

+      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", "")

     })

-    session$sendCustomMessage("close_dropDownTscanClusterPseudo", "")

-  })

+  )

   ###################################################

   ###  Run STEP 4: Plot gene of interest

@@ -3748,7 +3739,7 @@ shinyServer(function(input, output, session) {

     msg = "Please wait when the expression of selected features are being plotted. See console log for progress.",

     {

       req(vals$counts)

-      if (input$plotTSCANDimReduceFeatures_features == "") {

+      if (is.null(input$plotTSCANDimReduceFeatures_features)) {

         stop("Must select at least one feature.")

       }

       if (input$plotTSCANDimReduceFeatures_featureDisplay == "Rownames (Default)") {

@@ -3757,9 +3748,6 @@ shinyServer(function(input, output, session) {

         featureDisplay <- input$plotTSCANDimReduceFeatures_featureDisplay

       }

       useCluster <- input$plotTSCANDimReduceFeatures_useCluster

-      if (useCluster == "Use all") {

-        useCluster <- NULL

-      }

       output$TscanDimReduceFeatures <- renderPlot({

         isolate({

           plotTSCANDimReduceFeatures(inSCE = vals$counts,

@@ -3782,26 +3770,14 @@ shinyServer(function(input, output, session) {

     session$sendCustomMessage("close_dropDownTSCAN", "")

   })

-  observeEvent(input$closeDropDownTscanDEHM,{

-    session$sendCustomMessage("close_dropDownTscanDEHM", "")

-  })

-

-  observeEvent(input$closeDropDownTscanDEReg,{

-    session$sendCustomMessage("close_dropDownTscanDEReg", "")

+  observeEvent(input$closeDropDownTscanDE,{

+    session$sendCustomMessage("close_dropDownTscanDE", "")

   })

   observeEvent(input$closeDropDownTscanClusterDEG,{

     session$sendCustomMessage("close_dropDownTscanClusterDEG", "")

   })

-  observeEvent(input$closeDropDownTscanClusterDEGTable,{

-    session$sendCustomMessage("close_dropDownTscanClusterDEGTable", "")

-  })

-

-  observeEvent(input$closeDropDownTscanClusterPseudo,{

-    session$sendCustomMessage("close_dropDownTscanClusterPseudo", "")

-  })

-

   #-----------------------------------------------------------------------------

   # Page 3.2: Celda ####

   #-----------------------------------------------------------------------------

@@ -3939,7 +3915,8 @@ shinyServer(function(input, output, session) {

       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)

+    callModule(module = nonLinearWorkflow, id = "nlw-celda", parent = session,