diff --git a/DESCRIPTION b/DESCRIPTION
index ade5e74..96c4498 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -19,7 +19,7 @@ Imports: shiny,
     plotly,
     scales,
     shinydashboard,
-    DT,
+    DT (>= 0.3),
     colourpicker,
     RColorBrewer,
     shinyjs,
@@ -42,7 +42,7 @@ Imports: shiny,
     rjson,
     FactoMineR,
     factoextra,
-    heatmaply,
+    heatmaply (>= 0.14.1),
     shinyBS,
     shinythemes,
     shinycssloaders
diff --git a/R/columnSelector.R b/R/columnSelector.R
index 159e89a..528de10 100644
--- a/R/columnSelector.R
+++ b/R/columnSelector.R
@@ -141,7 +141,7 @@ columnSelector <- function(input, output, session, type.columns, type = NULL, co
   })
 
   out.type <- shiny::reactive(input$select.type)
-  out.selectedColumns <- shiny::reactive(ifelse(input$select.column == "None", "", input$select.column))
+  out.selectedColumns <- shiny::reactive(if(shiny::isTruthy(input$select.column) && input$select.column != "None") input$select.column else "")
   out.label <- shiny::reactive({
     if(is.null(input$select.label)) {
       label <- create_label()
diff --git a/R/featureSelector.R b/R/featureSelector.R
index 3f1a6bb..8e773c7 100644
--- a/R/featureSelector.R
+++ b/R/featureSelector.R
@@ -204,7 +204,7 @@ featureSelector <- function(input, output, session, data, features = NULL, featu
   })
 
   # access data table information
-  proxy <- DT::dataTableProxy(session$ns("table"))
+  proxy <- DT::dataTableProxy("table")
 
   # select rows via row_selector
   shiny::observe({
diff --git a/R/function.R b/R/function.R
index 541f3b1..f7d7ed4 100644
--- a/R/function.R
+++ b/R/function.R
@@ -478,14 +478,12 @@ create_heatmap <- function(data, unitlabel='auto', row.label=T, row.custom.label
     #layout
     plot <- heatmaply::heatmaply(plot,
                                  plot_method = "ggplot",
+                                 node_type = "heatmap",
                                  scale_fill_gradient_fun = ggplot2::scale_fill_gradientn(colors = colors, name = unitlabel, limits = winsorize.colors, oob = scales::squish),
                                  heatmap_layers = ggplot2::theme(text = ggplot2::element_text(size = 12 * scale))
     )
 
-    # scale axis ticks
-    ticks <- list(tickfont = list(size = 12 * scale))
-
-    plot <- plotly::layout(plot, autosize = ifelse(width == "auto", TRUE, FALSE), margin = list(l = rowlabel_size, r = legend, b = collabel_size), xaxis = ticks, yaxis2 = ticks)
+    plot <- plotly::layout(plot, autosize = ifelse(width == "auto", TRUE, FALSE), margin = list(l = rowlabel_size, r = legend, b = collabel_size), showlegend = FALSE)
 
     # decide which sizes should be used
     if(width == "auto") {
@@ -516,14 +514,16 @@ create_heatmap <- function(data, unitlabel='auto', row.label=T, row.custom.label
     plot$x$layout$width <- width
     plot$x$layout$height <- height
 
-    #address correct axis
+    # address correct axis
+    # scale axis tickfont
+    ticks <- list(size = 12 * scale)
     if(clustering == "both" || clustering == "column"){
-      plot <- plotly::layout(plot, xaxis = list(showticklabels = column.label),
-                             yaxis2 = list(showticklabels = row.label)
+      plot <- plotly::layout(plot, xaxis = list(showticklabels = column.label, tickfont = ticks),
+                             yaxis2 = list(showticklabels = row.label, tickfont = ticks)
       )
     }else if(clustering == "row" || clustering == "none"){
-      plot <- plotly::layout(plot, xaxis = list(showticklabels = column.label),
-                             yaxis = list(showticklabels = row.label)
+      plot <- plotly::layout(plot, xaxis = list(showticklabels = column.label, tickfont = ticks),
+                             yaxis = list(showticklabels = row.label, tickfont = ticks)
       )
     }
 
@@ -572,7 +572,7 @@ create_heatmap <- function(data, unitlabel='auto', row.label=T, row.custom.label
     row.names(prep.data) <- row.label.strings
     colnames(prep.data) <- column.label.strings
 
-    plot <- ComplexHeatmap::Heatmap(
+    plot <- try(ComplexHeatmap::Heatmap(
       prep.data,
       name = unitlabel,
       col = colors,
@@ -602,7 +602,7 @@ create_heatmap <- function(data, unitlabel='auto', row.label=T, row.custom.label
         labels_gp = grid::gpar(fontsize = 8 * scale),
         grid_height = grid::unit(0.15 * scale, "inches")
       )
-    )
+    ))
 
     #width/ height calculation
     col_names_maxlength_label_width=max(sapply(colnames(prep.data), graphics::strwidth, units="in", font = 12))	#longest column label when plotted in inches
diff --git a/R/global_cor_heatmap.R b/R/global_cor_heatmap.R
index f1c2baa..627e747 100644
--- a/R/global_cor_heatmap.R
+++ b/R/global_cor_heatmap.R
@@ -258,9 +258,24 @@ global_cor_heatmap <- function(input, output, session, data, types, plot.method
     rintrojs::introjs(session, options = list(steps = guide()))
   })
 
+  # show warning if not enough columns selected
+  shiny::observe({
+    shiny::req(columns$selectedColumns())
+
+    if(length(columns$selectedColumns()) < 2) {
+      shiny::showNotification(
+        ui = "Warning! At least two columns needed. Please select more.",
+        id = "less_data_warning",
+        type = "warning"
+      )
+    } else {
+      shiny::removeNotification("less_data_warning")
+    }
+  })
+
   # enable/ disable plot button
   shiny::observe({
-    if(length(columns$selectedColumns()) <= 1) {
+    if(!shiny::isTruthy(columns$selectedColumns()) || length(columns$selectedColumns()) < 2) {
       shinyjs::disable("plot")
     }else {
       shinyjs::enable("plot")
diff --git a/R/heatmap.R b/R/heatmap.R
index 17ac622..02cd532 100644
--- a/R/heatmap.R
+++ b/R/heatmap.R
@@ -158,7 +158,7 @@ heatmap <- function(input, output, session, data, types, plot.method = "static",
   shiny::observe({
     shiny::req(data.r())
 
-    if(length(columns$selectedColumns()) > 0){
+    if(shiny::isTruthy(columns$selectedColumns())){
       if(input$clustering != "none") { # clustering
         if(plot.method == "static" && nrow(data.r()) > static) { # cluster limitation (static)
           shiny::showNotification(
@@ -289,6 +289,7 @@ heatmap <- function(input, output, session, data, types, plot.method = "static",
       plot.method = plot.method,
       winsorize.colors = colorPicker()$winsorize
     )
+
     progress$set(1)
 
     return(plot)
@@ -335,6 +336,12 @@ heatmap <- function(input, output, session, data, types, plot.method = "static",
 
           plot <- plot()$plot
 
+          # handle error
+          if(is(plot, "try-error")) {
+            # TODO add logging
+            stop("An error occured! Please try a different dataset.")
+          }
+
           progress$set(1)
           return(ComplexHeatmap::draw(plot, heatmap_legend_side = "bottom"))
         }
@@ -372,20 +379,45 @@ heatmap <- function(input, output, session, data, types, plot.method = "static",
     all <- list(selection = selection, clustering = clustering, options = options)
   })
 
-  #enable/ disable plot button
+  # enable/ disable plot button
+  # show warning if disabled
   shiny::observe({
-    if(length(columns$selectedColumns()) <= 0){ # columns selected
-      shinyjs::disable("plot")
-    } else {
-      # clustering
-      if(input$clustering != "none") {
-        if(plot.method == "static" && nrow(data.r()) > static || plot.method == "interactive" && nrow(data.r()) > interactive) {
-          shinyjs::disable("plot")
-        } else {
+    shinyjs::disable("plot")
+    show_warning <- TRUE
+
+    # are columns selected?
+    if(shiny::isTruthy(columns$selectedColumns())) {
+      row.num <- nrow(shiny::isolate(data.r()))
+      col.num <- length(columns$selectedColumns())
+
+      # minimal heatmap possible (greater 1x1)?
+      if(row.num > 1 || col.num > 1) {
+        # no clustering for single rows or columns
+        if(row.num == 1 && !is.element(input$clustering, c("both", "row"))) {
+          show_warning <- FALSE
+          shinyjs::enable("plot")
+        } else if(col.num == 1 && !is.element(input$clustering, c("both", "column"))) {
+          show_warning <- FALSE
+          shinyjs::enable("plot")
+        } else if(row.num > 1 && col.num > 1) { # no border case heatmaps
+          show_warning <- FALSE
           shinyjs::enable("plot")
         }
+      }
+
+      if(show_warning) {
+        shiny::showNotification(
+          ui = "Warning! Insufficient columns/ rows. Either disable the respective clustering or expand the dataset.",
+          id = "insuf_data",
+          type = "warning"
+        )
       } else {
-        shinyjs::enable("plot")
+        shiny::removeNotification("insuf_data")
+      }
+
+      # maximum heatmap reached?
+      if(plot.method == "static" && row.num > static || plot.method == "interactive" && row.num > interactive) {
+        shinyjs::disable("plot")
       }
     }
   })
diff --git a/R/pca.R b/R/pca.R
index 53de734..a7b76b3 100644
--- a/R/pca.R
+++ b/R/pca.R
@@ -171,21 +171,16 @@ pca <- function(input, output, session, data, types, levels = NULL, entryLabel =
 
   #update dimension inputs
   shiny::observe({
-    col.num <- length(columnSelect$selectedColumns())
-    if(col.num < 3 | nrow(data.r()) < 3 | is.na(input$dimA) | is.na(input$dimB)){
+    col.num <- length(shiny::req(columnSelect$selectedColumns()))
+    if(col.num < 3 || nrow(shiny::isolate(data.r())) < 3 || is.na(input$dimA) || is.na(input$dimB)){
       shinyjs::disable("plot")
 
       # show warning if not enough selected
-      if(col.num > 0) {
-        shiny::showNotification(
-          ui = "Not enough columns selected! At least 3 needed for plotting.",
-          id = "warning",
-          type = "warning"
-        )
-      }else {
-        shiny::removeNotification("warning")
-      }
-
+      shiny::showNotification(
+        ui = "Not enough columns/ rows selected! At least 3 of each needed for plotting.",
+        id = "warning",
+        type = "warning"
+      )
     }else{
       shiny::removeNotification("warning")
       shinyjs::enable("plot")
diff --git a/R/scatterPlot.R b/R/scatterPlot.R
index 40056a1..d2214c8 100644
--- a/R/scatterPlot.R
+++ b/R/scatterPlot.R
@@ -60,6 +60,11 @@ scatterPlotUI <- function(id) {
                          id = ns("zaxis"),
                          title = "Data on z-axis",
                          label = T
+                       ),
+                       shiny::checkboxInput(
+                         inputId = ns("force_cat"),
+                         label = "Force categories",
+                         value = FALSE
                        )
             )
           ),
@@ -117,6 +122,9 @@ scatterPlotUI <- function(id) {
 #'                        column2: corresponding column type
 #'                        column3: label (optional, used instead of id)
 #'                        column4: sub_label (optional, added to id/ label)
+#' @param x.names Character vector of column names(data column names) which will be available for x-axis. Can be reactive.
+#' @param y.names Character vector of column names(data column names) which will be available for y-axis. Can be reactive.
+#' @param z.names Character vector of column names(data column names) which will be available for z-axis. Can be reactive.
 #' @param features data.table of the features to mark (first column = id)
 #' @param markerReac reactive containing inputs of marker module.
 #' @param plot.method Choose to rather render a 'interactive' or 'static' plot. Defaults to 'static'.
@@ -130,7 +138,7 @@ scatterPlotUI <- function(id) {
 #' @details Make sure to have the same columnnames in data and features.
 #'
 #' @export
-scatterPlot <- function(input, output, session, data, types, features = NULL, markerReac = NULL, plot.method = "static", width = "auto", height = "auto", ppi = 72, scale = 1) {
+scatterPlot <- function(input, output, session, data, types, x.names = NULL, y.names = NULL, z.names = NULL, features = NULL, markerReac = NULL, plot.method = "static", width = "auto", height = "auto", ppi = 72, scale = 1) {
   #handle reactive data
   data.r <- shiny::reactive({
     if(shiny::is.reactive(data)){
@@ -174,6 +182,58 @@ scatterPlot <- function(input, output, session, data, types, features = NULL, ma
          scale = scale)
   })
 
+  # available types for corresponding axis
+  types_x <- shiny::reactive({
+    if(shiny::is.reactive(types)) {
+      t <- types()
+    } else {
+      t <- types
+    }
+
+    if(shiny::is.reactive(x.names)) {
+      x <- x.names()
+    } else {
+      x <- x.names
+    }
+    if(is.null(x)) return(t)
+
+    t[key %in% x]
+  })
+
+  types_y <- shiny::reactive({
+    if(shiny::is.reactive(types)) {
+      t <- types()
+    } else {
+      t <- types
+    }
+
+    if(shiny::is.reactive(y.names)) {
+      y <- y.names()
+    } else {
+      y <- y.names
+    }
+    if(is.null(y)) return(t)
+
+    t[key %in% y]
+  })
+
+  types_z <- shiny::reactive({
+    if(shiny::is.reactive(types)) {
+      t <- types()
+    } else {
+      t <- types
+    }
+
+    if(shiny::is.reactive(z.names)) {
+      z <- z.names()
+    } else {
+      z <- z.names
+    }
+    if(is.null(z)) return(t)
+
+    t[key %in% z]
+  })
+
   #Fetch the reactive guide for this module
   guide <- scatterPlotGuide(session, !is.null(markerReac))
   shiny::observeEvent(input$guide, {
@@ -192,9 +252,10 @@ scatterPlot <- function(input, output, session, data, types, features = NULL, ma
     shinyjs::reset("line")
     shinyjs::reset("pointsize")
     shinyjs::reset("labelsize")
-    xaxis <<- shiny::callModule(columnSelector, "xaxis", type.columns = types, columnTypeLabel = "Column type to choose from", labelLabel = "Axis label", multiple = FALSE, suffix = shiny::reactive(transform_x$method()))
-    yaxis <<- shiny::callModule(columnSelector, "yaxis", type.columns = types, columnTypeLabel = "Column type to choose from", labelLabel = "Axis label", multiple = FALSE, suffix = shiny::reactive(transform_y$method()))
-    zaxis <<- shiny::callModule(columnSelector, "zaxis", type.columns = types, columnTypeLabel = "Column type to choose from", multiple = FALSE, none = TRUE)
+    shinyjs::reset("force_cat")
+    xaxis <<- shiny::callModule(columnSelector, "xaxis", type.columns = types_x, columnTypeLabel = "Column type to choose from", labelLabel = "Axis label", multiple = FALSE, suffix = shiny::reactive(transform_x$method()))
+    yaxis <<- shiny::callModule(columnSelector, "yaxis", type.columns = types_y, columnTypeLabel = "Column type to choose from", labelLabel = "Axis label", multiple = FALSE, suffix = shiny::reactive(transform_y$method()))
+    zaxis <<- shiny::callModule(columnSelector, "zaxis", type.columns = types_z, columnTypeLabel = "Column type to choose from", multiple = FALSE, none = TRUE)
     colorPicker <<- shiny::callModule(colorPicker2, "color", distribution = list("sequential", "diverging"), winsorize = winsorize)
     transform_x <<- shiny::callModule(transformation, "transform_x", data = data_x)
     transform_y <<- shiny::callModule(transformation, "transform_y", data = data_y)
@@ -211,9 +272,9 @@ scatterPlot <- function(input, output, session, data, types, features = NULL, ma
     }
   })
 
-  xaxis <- shiny::callModule(columnSelector, "xaxis", type.columns = types, columnTypeLabel = "Column type to choose from", labelLabel = "Axis label", multiple = FALSE, suffix = shiny::reactive(transform_x$method()))
-  yaxis <- shiny::callModule(columnSelector, "yaxis", type.columns = types, columnTypeLabel = "Column type to choose from", labelLabel = "Axis label", multiple = FALSE, suffix = shiny::reactive(transform_y$method()))
-  zaxis <- shiny::callModule(columnSelector, "zaxis", type.columns = types, columnTypeLabel = "Column type to choose from", labelLabel = "Color label", multiple = FALSE, none = TRUE)
+  xaxis <- shiny::callModule(columnSelector, "xaxis", type.columns = types_x, columnTypeLabel = "Column type to choose from", labelLabel = "Axis label", multiple = FALSE, suffix = shiny::reactive(transform_x$method()))
+  yaxis <- shiny::callModule(columnSelector, "yaxis", type.columns = types_y, columnTypeLabel = "Column type to choose from", labelLabel = "Axis label", multiple = FALSE, suffix = shiny::reactive(transform_y$method()))
+  zaxis <- shiny::callModule(columnSelector, "zaxis", type.columns = types_z, columnTypeLabel = "Column type to choose from", labelLabel = "Color label", multiple = FALSE, none = TRUE)
 
   colorPicker <- shiny::callModule(colorPicker2, "color", distribution = list("sequential", "diverging"), winsorize = winsorize)
   transform_x <- shiny::callModule(transformation, "transform_x", data = data_x)
@@ -232,6 +293,45 @@ scatterPlot <- function(input, output, session, data, types, features = NULL, ma
     })
   }
 
+  # show warning if there would more than 10 categories
+  shiny::observe({
+    shiny::req(!is.null(zaxis$selectedColumn()))
+
+    # something selected?
+    if(zaxis$selectedColumn() != "") {
+      # categories used?
+      if(input$force_cat || !is.numeric(data.r()[[zaxis$selectedColumn()]])) {
+        cat_num <- length(unique(data.r()[[zaxis$selectedColumn()]]))
+
+        if(cat_num > 10) {
+          shiny::showNotification(
+            id = session$ns("cat-limit"),
+            paste("Warning! There are", cat_num, "different categories selected. This can result in unexpected behavior. Recommended are 10 or less categories."),
+            duration = NULL,
+            type = "warning"
+          )
+
+          shinyjs::runjs(paste0("$(document.getElementById('", paste0("shiny-notification-", session$ns("cat-limit")), "')).addClass('notification-position-center');"))
+        } else {
+          shiny::removeNotification(session$ns("cat-limit"))
+        }
+      } else {
+        shiny::removeNotification(session$ns("cat-limit"))
+      }
+    } else {
+      shiny::removeNotification(session$ns("cat-limit"))
+    }
+  })
+
+  # disable plot if mandatory x- or y-axis missing
+  shiny::observe({
+    if(!isTruthy(xaxis$selectedColumn()) || !isTruthy(yaxis$selectedColumn())) {
+      shinyjs::disable("plot")
+    } else {
+      shinyjs::enable("plot")
+    }
+  })
+
   transformed_data <- shiny::reactive({
     #reassemble after transformation
     if(zaxis$selectedColumn() != ""){
@@ -284,7 +384,7 @@ scatterPlot <- function(input, output, session, data, types, features = NULL, ma
             duration = NULL,
             type = "warning"
           )
-          shinyjs::addClass(selector = paste0("#shiny-notification-", session$ns("label-limit")), class = "notification-position-center")
+          shinyjs::runjs(paste0("$(document.getElementById('", paste0("shiny-notification-", session$ns("label-limit")), "')).addClass('notification-position-center');"))
         }
       }
 
@@ -354,7 +454,8 @@ scatterPlot <- function(input, output, session, data, types, features = NULL, ma
       width = size()$width,
       height = size()$height,
       ppi = size()$ppi,
-      scale = size()$scale
+      scale = size()$scale,
+      categorized = if(input$force_cat || ncol(result.data()$processed.data) >= 4 && !is.numeric(result.data()$processed.data[[4]])) TRUE else FALSE
     )
 
     progress$set(1)
@@ -472,7 +573,8 @@ scatterPlotGuide <- function(session, marker = FALSE) {
       Use upper/ lower limit to customize the axis limits.",
     "guide_zaxis" = "<h4>Data selection: z-axis</h4>
       Select a column type for visualization, then select an individual column of the chosen type. The data from the selected column will be mapped onto a color scale. <br />
-      You can also set a customized label for the color bar. If left empty, the column name will be used as default.",
+      You can also set a customized label for the color bar. If left empty, the column name will be used as default.
+      If there is a non numeric column selected scatterplot will attempt to do a categorized coloring approach, whereas numeric data will result in a gradient. Use 'force categories' to achieve categorization with numeric values.",
     "guide_color" = "<h4>Color palettes</h4>
       Based on the data distribution, select either a sequential or diverging color palette.<br/>
       Choose the range of the color legend by defining it's upper and lower limits with 'Winsorize to upper/lower'. Be aware that out of bounds values will be mapped to their nearest color.<br/>
diff --git a/exec/scatterPlot_example.R b/exec/scatterPlot_example.R
index 2728010..9f36aca 100644
--- a/exec/scatterPlot_example.R
+++ b/exec/scatterPlot_example.R
@@ -8,6 +8,7 @@ source("../R/transformation.R")
 source("../R/scatterPlot.R")
 source("../R/marker.R")
 source("../R/limit.R")
+source("../R/label.R")
 
 ####Test Data
 data <- data.table::data.table(id = rownames(mtcars), mtcars)
@@ -33,7 +34,7 @@ ui <- dashboardPage(header = dashboardHeader(),
 server <- function(input, output) {
   marker <- callModule(marker, "marker", data)
   # highlight all manual cars
-  plot <- callModule(scatterPlot, "id", data = data, types = metadata[level != "annotation"], features = data[am == 1], markerReac = marker, plot.method = "interactive", width = reactive(input$width), height = reactive(input$height), scale = reactive(input$scale))
+  plot <- callModule(scatterPlot, "id", data = data, types = metadata, x.names = metadata[level != "annotation"][["key"]], y.names = metadata[level != "annotation"][["key"]], features = data[am == 1], markerReac = marker, plot.method = "interactive", width = reactive(input$width), height = reactive(input$height), scale = reactive(input$scale))
 
   observe({
     print(plot())
diff --git a/man/scatterPlot.Rd b/man/scatterPlot.Rd
index 53dc5c2..7d7489b 100644
--- a/man/scatterPlot.Rd
+++ b/man/scatterPlot.Rd
@@ -4,9 +4,10 @@
 \alias{scatterPlot}
 \title{scatterPlot module server logic}
 \usage{
-scatterPlot(input, output, session, data, types, features = NULL,
-  markerReac = NULL, plot.method = "static", width = "auto",
-  height = "auto", ppi = 72, scale = 1)
+scatterPlot(input, output, session, data, types, x.names = NULL,
+  y.names = NULL, z.names = NULL, features = NULL, markerReac = NULL,
+  plot.method = "static", width = "auto", height = "auto", ppi = 72,
+  scale = 1)
 }
 \arguments{
 \item{input}{Shiny's input object}
@@ -23,6 +24,12 @@ column2: corresponding column type
 column3: label (optional, used instead of id)
 column4: sub_label (optional, added to id/ label)}
 
+\item{x.names}{Character vector of column names(data column names) which will be available for x-axis. Can be reactive.}
+
+\item{y.names}{Character vector of column names(data column names) which will be available for y-axis. Can be reactive.}
+
+\item{z.names}{Character vector of column names(data column names) which will be available for z-axis. Can be reactive.}
+
 \item{features}{data.table of the features to mark (first column = id)}
 
 \item{markerReac}{reactive containing inputs of marker module.}