Bug fixes

global.R

@@ -7,10 +7,11 @@ source('modules/dispTrackStats.R')
 source('modules/trajPlot.R')
 source('modules/trajRibbonPlot.R')
 source('modules/trajPsdPlot.R')
-source('modules/boxPlot.R')
-source('modules/tabAUC.R')
+source('modules/aucPlot.R')
+source('modules/distPlot.R')
 source('modules/clDistPlot.R')
 source('modules/tabScatter.R')
-source('modules/tabBoxPlot.R')
+source('modules/tabDist.R')
+source('modules/tabAUC.R')
 source('modules/tabClHier.R')
 source('modules/tabClHierSpar.R')
\ No newline at end of file

modules/auxfunc.R

@@ -129,16 +129,16 @@ l.col.pal.dend.2 = list(
 
 # Clustering algorithms ----
 
-s.cl.linkage = c("average",
-                 "complete",
+s.cl.linkage = c("complete",
+                 "average",
                  "single",
                  "centroid",
                  "ward.D",
                  "ward.D2",
                  "mcquitty")
 
-s.cl.spar.linkage = c("average",
-                      "complete", 
+s.cl.spar.linkage = c("complete",
+                      "average",
                       "single",
                       "centroid")
 
@@ -191,10 +191,9 @@ help.text.short = c(
   'Normalise with respect to this time span.',                                                                                     #12
   'Calculate fold-change and z-score using the median and Median Absolute Deviation, instead of the mean and sd.',                 #13
   'Normalise to mean/median of selected time calculated globally, per group, or for individual time series.',                      #14
-  'Instead of the value at a selected time point, y-axis can display a difference between values at time points on y- and x-axis.',#15
+  'Instead of the value at a selected time point, y-axis can display a difference between values at two selected time points.',    #15
   'Add a line with linear regression and regions of 95% confidence interval.',                                                     #16
-  'A number of time points left & right of selected time points; use the mean/min/max of values from these time points for the scatterplot.', #17
-  'Operations to perform on values at time points selected in the field above.'                                                    #18
+  'A number of time points left & right of selected time points; use the mean of values from these time points for the scatterplot.' #17
 )
 
 # Functions for data processing ----
@@ -809,7 +808,7 @@ LOCggplotScat = function(dt.arg,
                         trend.arg = T,
                         ci.arg = 0.95) {
   
-  p.tmp = ggplot(dt.arg, aes(x = x, y = y)) +
+  p.tmp = ggplot(dt.arg, aes(x = x, y = y, label = id)) +
     geom_point(alpha = alpha.arg)
 
   if (trend.arg) {

modules/clDistPlot.R

@@ -65,7 +65,7 @@ modClDistPlot = function(input, output, session, in.data, in.cols = NULL, in.fna
     
     p.out = p.out + 
       scale_y_continuous(labels = percent) +
-      ylab("Percentage of time-series\n") +  
+      ylab("Percentage of time series\n") +  
       xlab("Groups") +  
       LOCggplotTheme(in.font.base = PLOTFONTBASE, 
                      in.font.axis.text = PLOTFONTAXISTEXT, 

modules/distPlot.R

+#
+# Time Course Inspector: Shiny app for plotting time series data
+# Author: Maciej Dobrzynski
+#
+# This module is for plotting distrubutions at selected time points as a choice of box/violin/dot-plots
+# Assumes in.data contains columns:
+# realtime
+# y
+# group
+# id
+
+# UI ----
+modDistPlotUI =  function(id, label = "Plot distributions") {
+  ns <- NS(id)
+  
+  tagList(
+    fluidRow(
+      column(
+        4,
+        checkboxInput(ns("chBplotTypeBox"),  "Box-plot", value = T),
+        checkboxInput(ns("chBplotTypeDot"),  "Dot-plot", value = F),
+        checkboxInput(ns("chBplotTypeViol"), "Violin-plot", value = F),
+        checkboxInput(ns("chBplotTypeLine"), "Line-plot", value = F),
+        checkboxInput(ns('chBplotInt'), 'Interactive Plot'),
+        actionButton(ns('butPlot'), 'Plot!')
+      ),
+      column(
+        4,
+        uiOutput(ns('uiPlotBoxNotches')),
+        uiOutput(ns('uiPlotBoxOutliers')),
+        uiOutput(ns('uiPlotBoxDodge')),
+        uiOutput(ns('uiPlotBoxAlpha')),
+        uiOutput(ns('uiPlotDotNbins')),
+        uiOutput(ns('uiPlotDotAlpha')),
+        uiOutput(ns('uiPlotViolAlpha')),
+        uiOutput(ns('uiPlotLineAlpha'))
+      ),
+      column(
+        4,
+        selectInput(
+          ns('selPlotBoxLegendPos'),
+          label = 'Legend position',
+          choices = list(
+            "Top" = 'top',
+            "Right" = 'right',
+            "Bottom" = 'bottom'
+          ), 
+          width = "120px",
+          selected = 'top'
+        ),
+        radioButtons(ns("rBAxisLabelsRotate"), "X-axis labels",
+                     c("horizontal" = 0,
+                       "45 deg" = 45,
+                       "90 deg" = 90)),
+        numericInput(
+          ns('inPlotBoxWidth'),
+          'Width [%]',
+          value = PLOTWIDTH,
+          min = 10,
+          width = '100px',
+          step = 10
+        ),
+        numericInput(
+          ns('inPlotBoxHeight'),
+          'Height [px]',
+          value = PLOTBOXHEIGHT,
+          min = 100,
+          width = '100px',
+          step = 50
+        )
+      )
+    ),
+    
+    uiOutput(ns('uiPlotBox')),
+    downPlotUI(ns('downPlotBox'), "Download PDF")
+  )
+}
+
+# SERVER ----
+modDistPlot = function(input, output, session, 
+                      in.data,                       # input data table in long format
+                      in.cols = list(meas.x = COLRT, # column names
+                                     meas.y = COLY,
+                                     group = COLGR,
+                                     id = COLID), 
+                      in.labels = list(x = "",       # plot labels
+                                       y = "", 
+                                       legend = ""),
+                      in.fname) {                      # file name for saving the plot
+
+  ns <- session$ns
+  
+  output$uiPlotBoxNotches = renderUI({
+    cat(file = stderr(), 'boxPlot:uiPlotBoxNotches\n')
+    
+    ns <- session$ns
+    
+    if(input$chBplotTypeBox)
+      checkboxInput(ns('chBplotBoxNotches'), 'Notches in box-plot ', FALSE)
+  })
+  
+  output$uiPlotBoxOutliers = renderUI({
+    cat(file = stderr(), 'boxPlot:uiPlotBoxOutliers\n')
+    
+    ns <- session$ns
+    
+    if(input$chBplotTypeBox)
+      checkboxInput(ns('chBplotBoxOutliers'), 'Outliers in box-plot', FALSE)
+  })
+  
+  output$uiPlotBoxDodge = renderUI({
+    cat(file = stderr(), 'boxPlot:uiPlotBoxDodge\n')
+    
+    ns <- session$ns
+    
+    # Adjust spacing between box-, violin-, dot-plots.
+    # Valid only when plotting multiple groups at a time point.
+    # For line plot, each group is drawn separately per facet, thus no need for dodging..
+    
+    if(!input$chBplotTypeLine)
+      sliderInput(ns('slPlotBoxDodge'), 'Space between groups', min = 0, max = 1, value = .4, step = 0.05)
+  })
+  
+  output$uiPlotBoxAlpha = renderUI({
+    cat(file = stderr(), 'boxPlot:uiPlotBoxAlpha\n')
+    
+    ns <- session$ns
+    
+    if(input$chBplotTypeBox)
+      sliderInput(ns('slPlotBoxAlpha'), 'Box-plot transparency', min = 0, max = 1, value = 1, step = 0.1)
+  })
+  
+  output$uiPlotViolAlpha = renderUI({
+    cat(file = stderr(), 'boxPlot:uiPlotViolAlpha\n')
+    
+    ns <- session$ns
+    
+    if(input$chBplotTypeViol)
+      sliderInput(ns('slPlotViolAlpha'), 'Violin-plot transparency', min = 0, max = 1, value = 1, step = 0.1)
+  })
+  
+  output$uiPlotDotAlpha = renderUI({
+    cat(file = stderr(), 'boxPlot:uiPlotDotAlpha\n')
+    
+    ns <- session$ns
+    
+    if(input$chBplotTypeDot)
+      sliderInput(ns('slPlotDotAlpha'), 'Dot-plot transparency', min = 0, max = 1, value = 1, step = 0.1)
+  })
+  
+  output$uiPlotLineAlpha = renderUI({
+    cat(file = stderr(), 'boxPlot:uiPlotLineAlpha\n')
+    
+    ns <- session$ns
+    
+    if(input$chBplotTypeLine)
+      sliderInput(ns('slPlotLineAlpha'), 'Line-plot transparency', min = 0, max = 1, value = 1, step = 0.1)
+  })
+  
+  output$uiPlotDotNbins = renderUI({
+    cat(file = stderr(), 'boxPlot:uiPlotDotNbins\n')
+    
+    ns <- session$ns
+    
+    if(input$chBplotTypeDot)
+      sliderInput(ns('slPlotDotNbins'), 'Number of bins in dot-plot', min = 2, max = 50, value = 30, step = 1)
+  })
+  
+  # Boxplot - display
+  output$outPlotBox = renderPlot({
+    locBut = input$butPlot
+    
+    if (locBut == 0) {
+      cat(file = stderr(), 'boxPlot:Go button not pressed\n')
+      return(NULL)
+    }
+    
+    plotBox()
+    
+  })
+  
+  
+  output$outPlotBoxInt = renderPlotly({
+    locBut = input$butPlot
+    
+    if (locBut == 0) {
+      cat(file = stderr(), 'boxPlot:Go button not pressed\n')
+      return(NULL)
+    }
+    
+    # This is required to avoid 
+    # "Warning: Error in <Anonymous>: cannot open file 'Rplots.pdf'"
+    # When running on a server. Based on:
+    # https://github.com/ropensci/plotly/issues/494
+    if (names(dev.cur()) != "null device") dev.off()
+    pdf(NULL)
+    
+    return( ggplotly(plotBox())  %>% layout(boxmode = 'group', width = '100%', height = '100%'))
+    
+  })
+  
+  
+  output$uiPlotBox <- renderUI({
+    ns <- session$ns
+    
+    if (input$chBplotInt)
+      plotlyOutput(ns("outPlotBoxInt"), 
+                   width = paste0(input$inPlotBoxWidth, '%'),
+                   height = paste0(input$inPlotBoxHeight, 'px'))
+    else
+      plotOutput(ns('outPlotBox'),
+                 width = paste0(input$inPlotBoxWidth, '%'),
+                 height = paste0(input$inPlotBoxHeight, 'px'))
+  })
+  
+  # Boxplot - download pdf
+  callModule(downPlot, "downPlotBox", in.fname, plotBox, TRUE)
+  
+  # Function instead of reactive as per:
+  # http://stackoverflow.com/questions/26764481/downloading-png-from-shiny-r
+  # This function is used to plot and to downoad a pdf
+  
+  plotBox <- function() {
+    cat(file = stderr(), 'plotBox\n')
+    
+    loc.dt = in.data()
+    
+    cat(file = stderr(), "plotBox: on to plot\n\n")
+    if (is.null(loc.dt)) {
+      cat(file = stderr(), 'plotBox: dt is NULL\n')
+      return(NULL)
+    }
+    
+    cat(file = stderr(), 'plotBox:dt not NULL\n')
+    
+    if(!input$chBplotTypeLine) {
+      # Dodging series only for box-, dot-, and violin-plots 
+      loc.par.dodge <- position_dodge(width = input$slPlotBoxDodge)
+      
+      # Color fill for all oplots except line, in which groups are plotted per facet
+      p.out = ggplot(loc.dt, aes_string(x = sprintf("factor(%s)", in.cols$meas.x), 
+                                        y = in.cols$meas.y,
+                                        fill = in.cols$group)) 
+      
+    }
+    else {
+      loc.par.dodge = position_dodge(width = 1)
+      p.out = ggplot(loc.dt, aes_string(x = sprintf("factor(%s)", in.cols$meas.x), 
+                                        y = in.cols$meas.y)) 
+      
+    }
+    
+    
+    if(input$chBplotTypeDot)
+      p.out = p.out + geom_dotplot(color = NA,
+                                   binaxis = "y", 
+                                   stackdir = "center", 
+                                   position = loc.par.dodge, 
+                                   binwidth = abs(max(loc.dt[[ in.cols$meas.y ]], 
+                                                      na.rm = T) - 
+                                                    min(loc.dt[[ in.cols$meas.y ]], 
+                                                        na.rm = T)) / (input$slPlotDotNbins - 1), 
+                                   method = 'histodot',
+                                   alpha = input$slPlotDotAlpha)
+    
+    if(input$chBplotTypeViol)
+      p.out = p.out + 
+      geom_violin(position = loc.par.dodge,
+                  width = 0.2,
+                  alpha = input$slPlotViolAlpha)
+    
+    if(input$chBplotTypeLine)
+      p.out = p.out + 
+        geom_path(aes_string(group = in.cols$id),
+                  alpha = input$slPlotLineAlpha) +
+        facet_wrap(as.formula(paste("~", in.cols$group)))
+    
+    if (input$chBplotTypeBox)
+      p.out = p.out + geom_boxplot(
+        position = loc.par.dodge,
+        notch = input$chBplotBoxNotches, 
+        alpha = input$slPlotBoxAlpha,
+        outlier.colour = if (input$chBplotBoxOutliers)
+          'red'
+        else
+          NA
+      ) 
+    
+    p.out = p.out +
+      scale_fill_discrete(name = in.labels$legend) +
+      xlab(in.labels$x) +
+      ylab(in.labels$y) +
+      LOCggplotTheme(in.font.base = PLOTFONTBASE, 
+                     in.font.axis.text = PLOTFONTAXISTEXT, 
+                     in.font.axis.title = PLOTFONTAXISTITLE, 
+                     in.font.strip = PLOTFONTFACETSTRIP, 
+                     in.font.legend = PLOTFONTLEGEND) + 
+      theme(legend.position = input$selPlotBoxLegendPos,
+            axis.text.x = LOCrotatedAxisElementText(as.numeric(input$rBAxisLabelsRotate), 
+                                                    size = PLOTFONTAXISTEXT))
+    
+    
+    return(p.out)
+  }
+  
+}
\ No newline at end of file

modules/tabAUC.R

@@ -7,25 +7,26 @@
 # Calculates area under curve (AUC) for every single time course provided in the input
 
 # UI ----
-modAUCplotUI =  function(id, label = "Plot Area Under Curves") {
+tabAUCplotUI =  function(id, label = "Plot Area Under Curves") {
   ns <- NS(id)
   
   tagList(
     h4(
-      "Calculate area under curve and plot per group"
+      "Area under curve (AUC)"
     ),
+    actionLink(ns("alAUC"), "Learn more"),
     br(),
     
     uiOutput(ns('uiSlTimeTrim')),
     modStatsUI(ns('dispStats')),
     br(),
-    modBoxPlotUI(ns('boxPlot')
+    modAUCplotUI(ns('aucPlot')
     )
   )
 }
 
 # SERVER ----
-modAUCplot = function(input, output, session, in.data, in.fname) {
+tabAUCplot = function(input, output, session, in.data, in.fname) {
   
   ns <- session$ns
   
@@ -83,14 +84,20 @@ modAUCplot = function(input, output, session, in.data, in.fname) {
              in.bycols = COLGR,
              in.fname = 'data4boxplotAUC.csv')
   
-  callModule(modBoxPlot, 'boxPlot', 
+  callModule(modAUCplot, 'aucPlot', 
              in.data = AUCcells, 
              in.cols = list(meas.x = COLGR,
                             meas.y = 'AUC',
                             group = COLGR,
                             id = COLID),
+             in.labels = list(x = "Groups", y = "", legend = ""),
              in.fname = in.fname)
   
+  addPopover(session, 
+             id = ns("alAUC"), 
+             title = "AUC",
+             content = "Calculate area under curve (AUC) for every time series using trapezoidal rule",
+             trigger = "click")
   
 }
 

modules/tabClHier.R

@@ -149,9 +149,9 @@ clustHierUI <- function(id, label = "Hierarchical CLustering") {
                  )
                ),
                
-               downPlotUI(ns('downPlotHier'), "Download PNG"),
                actionButton(ns('butPlotHierHeatMap'), 'Plot!'),
-               withSpinner(plotOutput(ns('outPlotHier')))
+               withSpinner(plotOutput(ns('outPlotHier'))),
+               downPlotUI(ns('downPlotHier'), "Download PNG")
       ),
       
       tabPanel('Averages',

modules/tabClHierSpar.R

@@ -156,17 +156,13 @@ clustHierSparUI <- function(id, label = "Sparse Hierarchical CLustering") {
                ),
                
                br(),
-               
-               p('Note: columns in the heatmap labeled according to their \"importance\":'),
-               tags$ol(
-                 tags$li("Black - not taken into account"),
-                 tags$li("Blue with \"*\" - low importance (weight factor in (0, 0.1]"),
-                 tags$li("Green with \"**\" - medium importance (weight factor in (0.1, 0.5]"),
-                 tags$li("Red with \"***\" - high importance (weight factor in (0.5, 1.0]")),
-                 
                downPlotUI(ns('downPlotHierSparHM'), "Download PNG"),
                actionButton(ns('butPlotHierSparHeatMap'), 'Plot!'),
+               br(),
+               "Columns in the heatmap labeled according to their ",
+               actionLink(ns("alImportance"), "importance"),
                withSpinner(plotOutput(ns('outPlotHierSpar')))
+
       ),
       
       tabPanel('Averages',
@@ -179,7 +175,7 @@ clustHierSparUI <- function(id, label = "Sparse Hierarchical CLustering") {
       
       tabPanel('PSD',
                br(),
-               modPSDPlotUI(ns('modPlotHierPsd'))),
+               modPSDPlotUI(ns('modPlotHierSparPsd'))),
       
       tabPanel('Cluster distribution',
                br(),
@@ -189,7 +185,12 @@ clustHierSparUI <- function(id, label = "Sparse Hierarchical CLustering") {
 }
 
 # SERVER ----
-clustHierSpar <- function(input, output, session, in.data4clust, in.data4trajPlot, in.data4stimPlot) {
+clustHierSpar <- function(input, output, session, 
+                          in.data4clust, 
+                          in.data4trajPlot, 
+                          in.data4stimPlot) {
+  
+  ns = session$ns
   
   # UI for advanced options
   output$uiPlotHierSparNperms = renderUI({
@@ -384,7 +385,9 @@ clustHierSpar <- function(input, output, session, in.data4clust, in.data4trajPlo
     #cat('rownames: ', rownames(in.data4clust()), '\n')
     
     # get cellIDs with cluster assignments based on dendrogram cut
-    loc.dt.cl = getDataClSpar(userFitDendHierSpar(), input$inPlotHierSparNclust, getDataTrackObjLabUni_afterTrim())
+    loc.dt.cl = getDataClSpar(userFitDendHierSpar(), 
+                              input$inPlotHierSparNclust, 
+                              getDataTrackObjLabUni_afterTrim())
     
     ####
     ## PROBLEM!!!
@@ -426,7 +429,10 @@ clustHierSpar <- function(input, output, session, in.data4clust, in.data4trajPlo
     },
     
     content = function(file) {
-      write.csv(x = getDataClSpar(userFitDendHierSpar(), input$inPlotHierSparNclust, getDataTrackObjLabUni_afterTrim()), file = file, row.names = FALSE)
+      write.csv(x = getDataClSpar(userFitDendHierSpar(), 
+                                  input$inPlotHierSparNclust, 
+                                  getDataTrackObjLabUni_afterTrim()), 
+                file = file, row.names = FALSE)
     }
   )
   
@@ -617,5 +623,22 @@ clustHierSpar <- function(input, output, session, in.data4clust, in.data4trajPlo
     
     plotHierSpar()
   }, height = getPlotHierSparHeatMapHeight)
-  
-}
\ No newline at end of file
+
+  addPopover(session, 
+             ns("alImportance"),
+             title = "Variable importance",
+             content = paste0("<p>Weight factors (WF) calculated during clustering ",
+                              "reflect the importance of time points in the clustering. ",
+                              "The following labels are used to indicate the importance:",
+                              "<li>Black - time point not taken into account</li>",
+                              "<li><p, style=\"color:DodgerBlue;\">* - low, WF∈(0, 0.1]</p></li>",
+                              "<li><p, style=\"color:MediumSeaGreen;\">** - medium, WF∈(0.1, 0.5]</p></li>",
+                              "<li><p, style=\"color:Tomato;\">*** - high, WF∈(0.5, 1.0]</p></li>",
+                              "</p><p>Witten and Tibshirani (2010): ",
+                              "<i>A framework for feature selection in clustering</i>; ",
+                              "Journal of the American Statistical Association 105(490): 713-726.</p>"),
+             trigger = "click")
+
+}
+
+

modules/tabBoxPlot.R → modules/tabDist.R

@@ -6,7 +6,7 @@
 #
 
 # UI ----
-tabBoxPlotUI =  function(id, label = "Snapshots at time points") {
+tabDistPlotUI =  function(id, label = "Snapshots at time points") {
   ns <- NS(id)
   
   tagList(
@@ -26,12 +26,12 @@ tabBoxPlotUI =  function(id, label = "Snapshots at time points") {
     modStatsUI(ns('dispStats')),
     br(),
    
-    modBoxPlotUI(ns('boxPlot')) 
+    modDistPlotUI(ns('distPlot')) 
   )
 }
 
 # SERVER ----
-tabBoxPlot = function(input, output, session, in.data, in.fname) {
+tabDistPlot = function(input, output, session, in.data, in.fname) {
   
   callModule(modStats, 'dispStats',
              in.data = data4boxPlot,
@@ -39,12 +39,13 @@ tabBoxPlot = function(input, output, session, in.data, in.fname) {
              in.bycols = c(COLRT, COLGR),
              in.fname = 'data4boxplotTP.csv')
   
-  callModule(modBoxPlot, 'boxPlot', 
+  callModule(modDistPlot, 'distPlot', 
              in.data = data4boxPlot, 
              in.cols = list(meas.x = COLRT,
                             meas.y = COLY,
                             group = COLGR,
-                            id = 'id'),
+                            id = COLID),
+             in.labels = list(x = "Time points", y = "", legend = "Groups:"),
              in.fname = in.fname)
   
   # return all unique time points (real time)
@@ -108,7 +109,7 @@ tabBoxPlot = function(input, output, session, in.data, in.fname) {
     if (!is.null(loc.v)) {
       selectInput(
         ns('inSelTpts'),
-        'Select one or more t-points:',
+        'Select one or more time points:',
         loc.v,
         width = '100%',
         selected = loc.v[[1]],

modules/tabScatter.R

@@ -22,6 +22,7 @@ tabScatterPlotUI <- function(id, label = "Comparing t-points") {
     h4(
       "Scatter plot between two time points"
     ),
+    actionLink(ns("alScatter"), "Learn more"),
     br(),
     
     fluidRow(
@@ -29,33 +30,37 @@ tabScatterPlotUI <- function(id, label = "Comparing t-points") {
         4,
         uiOutput(ns('uiSelTptX')),
         uiOutput(ns('uiSelTptY')),
-        checkboxInput(ns('chBfoldChange'), 'Difference between two time points on Y-axis'),
-        bsTooltip(ns('chBfoldChange'), help.text.short[15], placement = "right", trigger = "hover", options = NULL),
+        bsAlert("alert2differentTpts"),
+        radioButtons(ns('rBfoldChange'), 'Y-axis', 
+                     choices = c("Y" = "y", "Y-X" = "diff"), 
+                     width = "100px", inline = T),
+        bsTooltip(ns('rBfoldChange'), help.text.short[15], placement = "right", trigger = "hover", options = NULL),
         checkboxInput(ns('chBregression'), 'Linear regression with 95% CI'),
         bsTooltip(ns('chBregression'), help.text.short[16], placement = "right", trigger = "hover", options = NULL)
       ),
       column(
         4, 
-        numericInput(ns('inNeighTpts'), 'Time points left & right:', value = 0, step = 1, min = 0),
-        bsTooltip(ns('inNeighTpts'), help.text.short[17], placement = "right", trigger = "hover", options = NULL),
-        radioButtons(ns('rBstats'), 'Operation:', list('Mean' = 1, 'Min' = 2, 'Max' = 3)),
-        bsTooltip(ns('inNeighTpts'), help.text.short[18], placement = "right", trigger = "hover", options = NULL)
+        numericInput(ns('inNeighTpts'), 'Smoothing', value = 0, step = 1, min = 0, width = "150px"),
+        bsTooltip(ns('inNeighTpts'), help.text.short[17], placement = "right", trigger = "hover", options = NULL)
       ),
       column(
         4,
         numericInput(
           ns('inPlotHeight'),
-          'Display plot height [px]',
+          'Height [px]',
           value = PLOTSCATTERHEIGHT,
           min = 100,
-          step = 100
+          step = 100,
+          width = "100px"
         ),
         numericInput(
           ns('inPlotNcolFacet'),
-          '#Columns',
+          '#columns',
           value = PLOTNFACETDEFAULT,
           min = 1,
-          step = 1
+          step = 1,
+          width = "100px"
+          
         )
       )
     ),
@@ -73,6 +78,8 @@ tabScatterPlotUI <- function(id, label = "Comparing t-points") {
 # SERVER ----
 tabScatterPlot <- function(input, output, session, in.data, in.fname) {
   
+  ns <- session$ns
+  
 # return all unique time points (real time)
 # This will be used to display in UI for box-plot
 # These timepoints are from the original dt and aren't affected by trimming of x-axis
@@ -95,9 +102,9 @@ output$uiSelTptX = renderUI({
   if (!is.null(loc.v)) {
     selectInput(
       ns('inSelTptX'),
-      'Time point for X-axis:',
+      'Time point for X-axis',
       loc.v,
-      width = '100%',
+      width = '200px',
       selected = 0,
       multiple = FALSE
     )
@@ -113,10 +120,10 @@ output$uiSelTptY = renderUI({
   if (!is.null(loc.v)) {
     selectInput(
       ns('inSelTptY'),
-      'Time point for Y-axis:',
+      'Time point for Y-axis',
       loc.v,
-      width = '100%',
-      selected = 0,
+      width = '200px',
+      selected = 1,
       multiple = FALSE
     )
   }
@@ -129,47 +136,62 @@ data4scatterPlot <- reactive({
   if(is.null(loc.dt.in))
     return(NULL)
   
-  loc.tpts.x = input$inSelTptX
-  loc.tpts.y = input$inSelTptY
+  # obtain selected time points from UI
+  loc.tpts.x = as.integer(input$inSelTptX)
+  loc.tpts.y = as.integer(input$inSelTptY)
   
-  # if neigbbouring points selected
+  if (loc.tpts.x == loc.tpts.y) {
+    createAlert(session, "alert2differentTpts", "exampleAlert", title = "",
+                content = "Select two different time points.", append = FALSE)
+    return(NULL)
+    
+  } else {
+    closeAlert(session, "exampleAlert")    
+  }
+  
+  # if neigbbouring points selected, obtain time points for which the aggregation will be calculated
   if (input$inNeighTpts > 0) {
+    # get all time points in the dataset
     loc.dt.in.tpts = unique(loc.dt.in[[COLRT]])
     
+    # get indices of time points around selected time points
     loc.tpts.x.id = seq(which(loc.dt.in.tpts == loc.tpts.x) - input$inNeighTpts, which(loc.dt.in.tpts == loc.tpts.x) + input$inNeighTpts, 1)
     loc.tpts.y.id = seq(which(loc.dt.in.tpts == loc.tpts.y) - input$inNeighTpts, which(loc.dt.in.tpts == loc.tpts.y) + input$inNeighTpts, 1)