diff --git a/app/server.r b/app/server.r
index b5608d5..302f202 100644
--- a/app/server.r
+++ b/app/server.r
@@ -9,24 +9,31 @@
 #6 maybe start splitting the server into chuncks inside the server folder and source the chuncks (almost DONE!)
 #7 Check for shiny as a R package (it's doable, need to understand how packages are organized first)
 #8 Having some global variables so I won't need to fetch from DeepBlue many times (DONE!)
-#9 Having one legend for subplot and download subplot
+#9 Change all the plots to be in Plotly instead of ggplot
 #10 Advanced option for plotting (choosing which PCAs to plot and filtering option) (DONE!)
 #11 metadata to be interactive upon clicking on list of experiments (DONE!)
 #12 RUV for batch effect
 #13 select expression, for gene expression data
-#14 activate and diactivate tabs and have a workflow for the app
+#14 activate and diactivate tabs and have a workflow for the app (Done!)
 #15 genomic ranges R package
 #16 Have a download button for the adjusted matrix (Done !!)
 #17 Need to have the back button only in the calculate matrix page and the next is added once the calculation is done (Done!!)
+#18 Fix the corrplot download
+#19 Score matrices and tables other than the first, lines don't need to be selected (Done!)
+
 
 # TODOs -------------------------------------------------------------------
-#9 Having one legend for subplot and download subplot
-#7 Check for shiny as a R package (it's doable, need to understand how packages are organized first)
-#12 RUV for batch effect
+#9 Change all the plots to be in Plotly instead of ggplot
+
+#7 Check for shiny as a R package 
+#12 RUV for batch effect (from Markus' scripts to understand how it works)
 #13 select expression, for gene expression data
-#14 activate and diactivate tabs and have a workflow for the app
 #15 genomic ranges R package
 
+#18 Fix the corrplot download
+    #It's working, but for some reason the PDFs are taking long time to show
+    #The plot maybe a bit too big, even thought the pdf file has a small size
+
 
 #New UI with shiny dashboard
 library(foreach)
@@ -93,7 +100,7 @@ function(input, output, session) {
   
   output$summary <- DT::renderDataTable({
     DT::datatable(summary_df(), filter = list(position = 'top', clear = FALSE),
-                  selection = 'none', options = list(
+                  selection = 'none',selection = 'none', options = list(
                     search = list(regex = TRUE, caseInsensitive = TRUE),
                     pageLength = 10)
                   )
@@ -241,25 +248,27 @@ function(input, output, session) {
 # Plotting first matrix ---------------------------------------------------
   
   #plot data
-  plot_pca <- eventReactive(input$plot_btn, {
+  first_pca_plot <- eventReactive(input$plot_btn, {
     #get plot
-
-    plot_pca_labels <- plot_pca_labels(experiments_info_meta = experiments_info_meta(),
-                                       project = input$project,
-                                       filtered_score_matrix = filtered_score_matrix(),
-                                       epigenetic_mark = input$epigenetic_mark,
-                                       color_by = input$color_by,
-                                       first_pc = input$first_pc,
-                                       second_pc = input$second_pc)
     
+    plotly_pca <- plotly_pca(experiments_info_meta = experiments_info_meta(),
+                             filtered_score_matrix = filtered_score_matrix(),
+                             project = input$project,
+                             type_of_score = input$type_of_score,
+                             color_by = input$color_by,
+                             epigenetic_mark = input$epigenetic_mark,
+                             first_pc = input$first_pc,
+                             second_pc = input$second_pc,
+                             show_legend = TRUE)
+
     
     showTab(inputId = "plot_box", target = "Download Plot")
 
     
-    return(plot_pca_labels)
+    return(plotly_pca)
   })
   
-  output$plot <- renderPlotly(plot_pca())
+  output$plot <- renderPlotly(first_pca_plot())
   
   observeEvent(input$plot_matrix_previous_tab,{
     updateTabItems(session, "tabs", "score_matrix_tab")
@@ -281,22 +290,23 @@ function(input, output, session) {
     filename =  function() {
       
       if(input$plot_down_exten == "pdf"){
-        paste0("Tiling Regions"," ", input$epigenetic_mark,".pdf")
+        paste0(input$type_of_score," ", input$epigenetic_mark,".pdf")
         
       }else{
-        paste0("Tiling Regions"," ", input$epigenetic_mark,".html")
+        paste0(input$type_of_score," ", input$epigenetic_mark,".html")
       }
     },
     # content is a function with argument file. content writes the plot to the device
     content = function(file) {
       
       if(input$plot_down_exten == "pdf"){
-        pdf(file) # open the pdf device
-        print(plot_pca())
-        dev.off()  # turn the device off
+        export(first_pca_plot(), file = file)
+        # pdf(file) # open the pdf device
+        # print(plot_pca())
+        # dev.off()  # turn the device off
       }else{
-        plot_pca_plotly <- plotly_build(plot_pca())
-        htmlwidgets::saveWidget(as_widget(plot_pca_plotly), file = file)
+        # plot_pca_plotly <- plotly_build(plot_pca())
+        htmlwidgets::saveWidget(as_widget(first_pca_plot()), file = file)
         
       }
     }
@@ -307,13 +317,16 @@ function(input, output, session) {
   #plotting the matrix after batch effect
   plot_pca_batch <- eventReactive(input$plot_batch, {
     #get plot
-    plot_pca_batch <- plot_pca_labels(experiments_info_meta = experiments_info_meta(),
-                                      project = input$project,
-                                      filtered_score_matrix = batch_adjusted_matrix(),
-                                      epigenetic_mark = input$epigenetic_mark,
-                                      color_by = input$color_by_batch,
-                                      first_pc = input$first_pc_batch,
-                                      second_pc = input$second_pc_batch)
+    
+    plot_pca_batch <- plotly_pca <- plotly_pca(experiments_info_meta = experiments_info_meta(),
+                                               filtered_score_matrix = batch_adjusted_matrix(),
+                                               project = input$project,
+                                               type_of_score = input$type_of_score,
+                                               color_by = input$color_by_batch,
+                                               epigenetic_mark = input$epigenetic_mark,
+                                               first_pc = input$first_pc_batch,
+                                               second_pc = input$second_pc_batch,
+                                               show_legend = TRUE)
 
     
     showTab(inputId =  "batch_plot_box", target = "Download Plot")
@@ -328,7 +341,7 @@ function(input, output, session) {
     filename =  function() {
       
       if(input$plot_batch_down_exten == "pdf"){
-        paste0("Tiling Regions after Batch"," ", input$epigenetic_mark,".pdf")
+        paste0(input$type_of_score," ","after Batch"," ", input$epigenetic_mark,".pdf")
         
       }else{
         paste0("Tiling Regions after Batch"," ", input$epigenetic_mark,".html")
@@ -338,9 +351,11 @@ function(input, output, session) {
     content = function(file) {
       
       if(input$plot_batch_down_exten == "pdf"){
-        pdf(file) # open the pdf device
-        print(plot_pca_batch())
-        dev.off()  # turn the device off
+        export(plot_pca_batch(), file = file)
+        # 
+        # pdf(file) # open the pdf device
+        # print(plot_pca_batch())
+        # dev.off()  # turn the device off
       }else{
         plot_pca_plotly <- plotly_build(plot_pca_batch())
         htmlwidgets::saveWidget(as_widget(plot_pca_plotly), file = file)
@@ -353,26 +368,30 @@ function(input, output, session) {
 # Comparing plots ---------------------------------------------------------
   
   observeEvent(input$compare_plot, {
-    first_plot <-     plot_pca_labels(experiments_info_meta = experiments_info_meta(),
-                                      project = input$project,
-                                      filtered_score_matrix = filtered_score_matrix(),
-                                      epigenetic_mark = input$epigenetic_mark,
-                                      color_by = input$color_by,
-                                      first_pc = input$first_pc,
-                                      second_pc = input$second_pc,
-                                      show_legend = FALSE)
     
-    second_plot <-     plot_pca_labels(experiments_info_meta = experiments_info_meta(),
-                                       project = input$project,
-                                       filtered_score_matrix = batch_adjusted_matrix(),
-                                       epigenetic_mark = input$epigenetic_mark,
-                                       color_by = input$color_by,
-                                       first_pc = input$first_pc,
-                                       second_pc = input$second_pc)
+    p1 <- plotly_pca(experiments_info_meta = experiments_info_meta(),
+                     filtered_score_matrix = filtered_score_matrix(),
+                     project = input$project,
+                     type_of_score = input$type_of_score,
+                     color_by = input$color_by,
+                     epigenetic_mark = input$epigenetic_mark,
+                     first_pc = input$first_pc,
+                     second_pc = input$second_pc,
+                     show_legend = TRUE)
     
+    p2 <- plotly_pca(experiments_info_meta = experiments_info_meta(),
+                     filtered_score_matrix = batch_adjusted_matrix(),
+                     project = input$project,
+                     type_of_score = input$type_of_score,
+                     color_by = input$color_by_batch,
+                     epigenetic_mark = input$epigenetic_mark,
+                     first_pc = input$first_pc_batch,
+                     second_pc = input$second_pc_batch,
+                     show_legend = FALSE)
+
     output$two_plots <- renderPlotly({
-      subplot(plotly_build(first_plot), plotly_build(second_plot),
-              shareX = TRUE, shareY = TRUE) %>% layout(title = "Comparing Plots", showlegend = TRUE)
+      subplot(p1, p2,
+              shareX = TRUE, shareY = TRUE) %>% layout(title = "Comparing Plots")
     })
     
   })
diff --git a/app/ui.r b/app/ui.r
index ca6cce9..65adad8 100644
--- a/app/ui.r
+++ b/app/ui.r
@@ -16,7 +16,6 @@ dashboardPage(title = "Batch Effect Analysis and Visualization",
             # tags$li(class = "dropdown", textOutput("logged_user"), style = "padding-top: 15px; padding-bottom: 15px; color: #fff;"),
             tags$li(class = "dropdown", actionLink("login", textOutput("logintext")))
             )
-    
   ),
 
 # Sidebar items -----------------------------------------------------------
@@ -193,6 +192,8 @@ dashboardPage(title = "Batch Effect Analysis and Visualization",
                               
                               ),
                      tabPanel("Downlad Plot",
+                              numericInput("pdf_width", label = "Width", value = 10),
+                              numericInput("pdf_height", label = "Height", value = 10),
                               downloadButton(outputId = "corr_plot_down", label = "Download Corr. plot")
                               
                               )
diff --git a/functions/plot_pca_labels.r b/functions/plot_pca_labels.r
index d2d8297..2c8c255 100644
--- a/functions/plot_pca_labels.r
+++ b/functions/plot_pca_labels.r
@@ -1,6 +1,8 @@
 #Plotting the score matrix with the relevant metadata with it
 
-plot_pca_labels <- function(experiments_info_meta,filtered_score_matrix,project,
+plot_pca_labels <- function(experiments_info_meta,
+                            filtered_score_matrix,
+                            project,
                             color_by = "biosource_name",
                             first_pc="1", second_pc="2",
                             epigenetic_mark = "Not Selected",
diff --git a/functions/plotly_pca.r b/functions/plotly_pca.r
new file mode 100644
index 0000000..65d2d21
--- /dev/null
+++ b/functions/plotly_pca.r
@@ -0,0 +1,53 @@
+plotly_pca <- function(experiments_info_meta,filtered_score_matrix, project,type_of_score,
+                       color_by = "biosource_name",
+                       epigenetic_mark = "No Epigenetic mark selected",
+                       first_pc="1",
+                       second_pc="2",
+                       show_legend = T){
+  
+  #calculating PCA
+  pca <- prcomp(filtered_score_matrix, center = TRUE, scale. = TRUE)
+  
+  #preparing the plot data by taking the PCAs and adding metadata
+  plot.data <- as.data.frame(pca$rotation) %>%
+    tibble::rownames_to_column(var = "experiment") %>%
+    dplyr::left_join(experiments_info_meta, by=c("experiment"))
+  
+  # #Getting colour pallet
+  # colourCount <- 9
+  # getPalette <- colorRampPalette(brewer.pal(colourCount, "Set1"))
+
+  if(project == "DEEP"){
+    label <- "DEEP_SAMPLE_ID"
+    hover = ~paste("Sample ID: ", DEEP_SAMPLE_ID,
+                   '</br>Biosource Name: ', biosource_name)
+  }else{
+    label <- "experiment"
+    hover = ~paste("Sample ID: ", experiment,
+                   '</br>Biosource Name: ', biosource_name)
+  }
+  
+  x_lab <- paste0(paste0("PC", first_pc," ", "("),
+                  round(pca$sdev[as.integer(first_pc)]^2/sum(pca$sdev^2), 2) * 100, "%)")
+  
+  y_lab <- paste0(paste0("PC", second_pc," ", "("),
+                  round(pca$sdev[as.integer(second_pc)]^2/sum(pca$sdev^2), 2) * 100, "%)")
+  
+  browser()
+  p <-
+    plot.data %>%
+    arrange(plot.data[,color_by]) %>%
+    plot_ly(x = as.formula(paste0("~","PC", first_pc)),
+            text = hover,
+            color = as.formula(paste0("~",color_by)),
+            legendgroup = as.formula(paste0("~",color_by)),
+            colors = brewer.pal(9, "Set1"),
+            marker = list(size = 17.5)) %>%
+    add_markers(y = as.formula(paste0("~","PC", second_pc)), showlegend = show_legend) %>%
+      layout(title = paste("2 PCs plot", type_of_score, epigenetic_mark),
+             yaxis = list(title = y_lab, zeroline = FALSE),
+             xaxis = list(title = x_lab, zeroline = FALSE))
+  
+  return(p)
+  
+}
\ No newline at end of file
diff --git a/functions/supervised_sva_batch_effect.r b/functions/supervised_sva_batch_effect.r
new file mode 100644
index 0000000..2ada953
--- /dev/null
+++ b/functions/supervised_sva_batch_effect.r
@@ -0,0 +1,68 @@
+supervised_sva_batch_effect <- function(filtered_score_matrix,
+                                        adjustment_var,
+                                        interest_var){
+  
+  
+  # filtered_matrix <- filtered_score_matrix$data
+  metadata <- attr(filtered_score_matrix, "meta")
+  
+  #validation of the inptus
+  #The variable selected should have more than 1 level
+  if(adjustment_var == ""){
+    adjustment_var = NULL
+  }else{
+    for (adj_var in adjustment_var){
+      
+      validate(
+        need(!anyNA(metadata[,adj_var]), message = paste(adj_var, "has NAs and cannot be used to make the model"))
+      )
+      validate(
+        need(nlevels(metadata[,adj_var]) > 1, message = paste(adj_var,"has less than 2 level",
+                                                              "check levels using the pie chart"))
+      )
+    }
+  }
+  
+  if(interest_var == ""){
+    validate(
+      need(FALSE, message = "You need to choose a variable of interest for the full model in SVA")
+    )
+  }else{
+    for (inter_var in interest_var){
+      
+      validate(
+        need(!anyNA(metadata[,inter_var]), message = paste(inter_var, "has NAs and cannot be used for the model"))
+      )
+      validate(
+        need(nlevels(metadata[,inter_var]) > 1, message = paste(inter_var,"has less than 2 level",
+                                                                "check levels using the pie chart"))
+      )
+    }
+  }
+  
+  if(is.null(adjustment_var)){
+    #No interest variable, mod0 is the intercept, full mod is the interest_var
+    mod0 <- model.matrix(~1, data = metadata)
+    mod <- model.matrix(as.formula(paste0("~", paste(interest_var, collapse = "+"))),
+                        data = metadata)
+    
+  }else{
+    mod0 <- model.matrix(as.formula(paste0("~", paste(adjustment_var, collapse = "+"))),
+                         data = metadata)
+    
+    mod <- model.matrix(as.formula(paste0("~", paste(
+      paste(interest_var, collapse = " + "),"+", paste(adjustment_var, collapse = " + ")
+    )
+    )),data = metadata)
+  }
+  n.sv <- num.sv(filtered_score_matrix, mod, method = "leek")
+  showNotification(paste("The number of latent factors estimated is", n.sv), duration = 3)
+  
+  sva_object <- sva(filtered_score_matrix, mod, mod0, n.sv = n.sv)
+  
+  batch_adjusted_matrix <- sva_object$sv
+  
+  attr(batch_adjusted_matrix, "meta") <- metadata
+  
+  return(batch_adjusted_matrix)
+}
\ No newline at end of file
diff --git a/functions/sva_batch_effect.r b/functions/sva_batch_effect.r
index 33b4f5e..fe3d4a3 100644
--- a/functions/sva_batch_effect.r
+++ b/functions/sva_batch_effect.r
@@ -31,7 +31,7 @@ sva_batch_effect <- function(filtered_score_matrix,
     for (inter_var in interest_var){
       
       validate(
-        need(!anyNA(metadata[,inter_var]), message = paste(inter_var, "has NAs and cannot be used to make the model"))
+        need(!anyNA(metadata[,inter_var]), message = paste(inter_var, "has NAs and cannot be used for the model"))
       )
       validate(
         need(nlevels(metadata[,inter_var]) > 1, message = paste(inter_var,"has less than 2 level",
@@ -41,6 +41,7 @@ sva_batch_effect <- function(filtered_score_matrix,
   }
   
   if(is.null(adjustment_var)){
+    #No interest variable, mod0 is the intercept, full mod is the interest_var
     mod0 <- model.matrix(~1, data = metadata)
     mod <- model.matrix(as.formula(paste0("~", paste(interest_var, collapse = "+"))),
                         data = metadata)
diff --git a/server/correlation_plot.r b/server/correlation_plot.r
index 969565e..bc89241 100644
--- a/server/correlation_plot.r
+++ b/server/correlation_plot.r
@@ -28,7 +28,7 @@ output$corr_plot_down <- downloadHandler(
   # content is a function with argument file. content writes the plot to the device
   content = function(file) {
     
-    pdf(file) # open the pdf device
+    pdf(file, width = input$pdf_width, height = input$pdf_height) # open the pdf device
     
     # correlation_plot()
     plot_correlation(filtered_score_matrix = filtered_score_matrix(),