diff --git a/program/shinyApp/R/enrichment_analysis/ui.R b/program/shinyApp/R/enrichment_analysis/ui.R
index 459cdc05..d41a2dd0 100644
--- a/program/shinyApp/R/enrichment_analysis/ui.R
+++ b/program/shinyApp/R/enrichment_analysis/ui.R
@@ -77,6 +77,7 @@ ea_sidebar <- function(ns){
     id = "sidebar_enrichment_analysis",
     uiOutput(outputId = ns("OrganismChoice_ui")) %>% helper(type = "markdown", content = "EA_Options"),
     uiOutput(outputId = ns("ORA_or_GSE_ui")),
+    uiOutput(outputId = ns("UseBatch_ui")),
     uiOutput(outputId = ns("ValueToAttach_ui")),
     uiOutput(outputId = ns("sample_annotation_types_cmp_GSEA_ui")),
     uiOutput(outputId = ns("Groups2Compare_ref_GSEA_ui")),
diff --git a/program/shinyApp/R/heatmap/server.R b/program/shinyApp/R/heatmap/server.R
index 8a99c1cc..1175a1a2 100644
--- a/program/shinyApp/R/heatmap/server.R
+++ b/program/shinyApp/R/heatmap/server.R
@@ -64,7 +64,16 @@ heatmap_server <- function(id, data, params, updates){
           hide(id = "cluster_rows", anim = T )
         }
       })
-      
+
+      output$UseBatch_ui <- renderUI({
+        req(par_tmp[[session$token]]$BatchColumn != "NULL")
+        selectInput(
+          inputId = ns("UseBatch"),
+          label = "Use batch corrected data?",
+          choices = c("No","Yes"),
+          selected = "No"
+        )
+      })
       
       output$LFC_toHeatmap_ui <-renderUI({
         req(data_input_shiny())
@@ -217,6 +226,7 @@ heatmap_server <- function(id, data, params, updates){
         )
         req(selectedData_processed())
         # update the data
+        useBatch <- ifelse(input$UseBatch == "Yes",T,F)
         data <- update_data(session$token)
         print("Heatmap on selected Data")
         # Value need to be setted in case there is nothing to plot to avoid crash
@@ -234,8 +244,11 @@ heatmap_server <- function(id, data, params, updates){
           input$PreProcessing_Procedure
         )
 
-        ### atm raw data plotted
-        data2Plot <- data$data
+        if(useBatch){
+            data2Plot <- data$data_batch_corrected
+        } else {
+            data2Plot <- data$data
+        }
 
         print(customTitleHeatmap)
         mycolors <- list()
@@ -324,6 +337,7 @@ heatmap_server <- function(id, data, params, updates){
         print(paste0("plot LFC's?",input$LFC_toHeatmap))
         # Dependent to plot raw data or LFC if calculation is needed
         calculate <- 1
+        # TODO: Lea for code snippet?
         # check whether we have to calculate
         # Does not find funtion
         # check <- check_calculations(list(
diff --git a/program/shinyApp/R/heatmap/ui.R b/program/shinyApp/R/heatmap/ui.R
index a0e15723..10e9a4a6 100644
--- a/program/shinyApp/R/heatmap/ui.R
+++ b/program/shinyApp/R/heatmap/ui.R
@@ -4,6 +4,7 @@ heatmap_sidebar<- function(ns){
     #########################################
     # Heatmap
     #########################################
+    uiOutput(outputId = ns("UseBatch_ui")),
     uiOutput(outputId = ns("row_selection_options_ui")) %>% helper(type = "markdown", content = "Heatmap_Options"),
     uiOutput(outputId = ns("LFC_toHeatmap_ui")),
     h5("Further row selection (LFC based)") %>% helper(type = "markdown", content = "Heatmap_FurtherOptions"),
diff --git a/program/shinyApp/R/pca/server.R b/program/shinyApp/R/pca/server.R
index 20a2002c..e8669029 100644
--- a/program/shinyApp/R/pca/server.R
+++ b/program/shinyApp/R/pca/server.R
@@ -21,6 +21,15 @@ pca_Server <- function(id, data, params, row_select){
       ns <- session$ns
 
       ## UI Section ----
+      output$UseBatch_ui <- renderUI({
+        req(par_tmp[[session$token]]$BatchColumn != "NULL")
+        selectInput(
+          inputId = ns("UseBatch"),
+          label = "Use batch corrected data?",
+          choices = c("No","Yes"),
+          selected = "No"
+        )
+      })
       output$x_axis_selection_ui <- renderUI({radioGroupButtons(
         inputId = ns("x_axis_selection"),
         label = "PC for x-Axis",
@@ -136,9 +145,9 @@ pca_Server <- function(id, data, params, row_select){
         req(input$Do_PCA > pca_reactives$counter)
         pca_reactives$counter <- input$Do_PCA
         check <- check_calculations(list(
-          dummy = "dummy",
           sample_selection_pca = input$sample_selection_pca,
-          SampleAnnotationTypes_pca = input$SampleAnnotationTypes_pca
+          SampleAnnotationTypes_pca = input$SampleAnnotationTypes_pca,
+          batch = ifelse(input$UseBatch == "Yes",T,F)
         ), "PCA")
         if (check == "No Result yet"){
           output$PCA_Info <- renderText("PCA computed.")
@@ -176,22 +185,29 @@ pca_Server <- function(id, data, params, row_select){
         # only calculate PCA, Score and Loadings if the counter is >= 0
         if(pca_reactives$calculate >= 0){
           # update the data
+          useBatch <- ifelse(input$UseBatch == "Yes",T,F)
           data2plot <- update_data(session$token)
           # select the neccesary data
           if(input$data_selection_pca){
             data2plot <- select_data(
               data2plot,
               input$sample_selection_pca,
-              input$SampleAnnotationTypes_pca
+              input$SampleAnnotationTypes_pca,
+              useBatch
             )
           }
+          if(useBatch){
+              data2plot <- data2plot$data_batch_corrected
+          } else {
+              data2plot <- data2plot$data
+          }
           # set the counter to -1 to prevent any further plotting
           pca_reactives$calculate <- -1
           print("Calculate PCA")
           # PCA, for safety measures, wrap in tryCatch
           tryCatch({
             pca <- prcomp(
-              x = as.data.frame(t(as.data.frame(assay(data2plot$data)))),
+              x = as.data.frame(t(as.data.frame(assay(data2plot)))),
               center = T,
               scale. = FALSE
             )
@@ -206,7 +222,7 @@ pca_Server <- function(id, data, params, row_select){
           percentVar <- round(100 * explVar, digits = 1)
 
           # Define data for plotting
-          pcaData <- data.frame(pca$x,colData(data2plot$data))
+          pcaData <- data.frame(pca$x,colData(data2plot))
 
           df_out_r <- NULL
           if(input$Show_loadings == "Yes"){
@@ -238,8 +254,8 @@ pca_Server <- function(id, data, params, row_select){
             if(!is.null(input$EntitieAnno_Loadings)){
               req(data_input_shiny())
               df_out_r$chosenAnno <- factor(
-                make.unique(as.character(rowData(data2plot$data)[rownames(df_out_r),input$EntitieAnno_Loadings])),
-                levels = make.unique(as.character(rowData(data2plot$data)[rownames(df_out_r),input$EntitieAnno_Loadings]))
+                make.unique(as.character(rowData(data2plot)[rownames(df_out_r),input$EntitieAnno_Loadings])),
+                levels = make.unique(as.character(rowData(data2plot)[rownames(df_out_r),input$EntitieAnno_Loadings]))
               )
             }
           }
@@ -265,8 +281,8 @@ pca_Server <- function(id, data, params, row_select){
           if(!is.null(input$EntitieAnno_Loadings)){
             req(data_input_shiny())
             LoadingsDF$entitie <- factor(
-              make.unique(as.character(rowData(data2plot$data)[rownames(LoadingsDF),input$EntitieAnno_Loadings])),
-              levels = make.unique(as.character(rowData(data2plot$data)[rownames(LoadingsDF),input$EntitieAnno_Loadings]))
+              make.unique(as.character(rowData(data2plot)[rownames(LoadingsDF),input$EntitieAnno_Loadings])),
+              levels = make.unique(as.character(rowData(data2plot)[rownames(LoadingsDF),input$EntitieAnno_Loadings]))
             )
           }
           # Loadings Matrix plot
@@ -292,8 +308,8 @@ pca_Server <- function(id, data, params, row_select){
           if(!is.null(input$EntitieAnno_Loadings_matrix)){
             req(data_input_shiny())
             df_loadings$chosenAnno <- factor(
-              make.unique(as.character(rowData(data2plot$data)[unique(df_loadings$entity),input$EntitieAnno_Loadings_matrix])),
-              levels = make.unique(as.character(rowData(data2plot$data)[unique(df_loadings$entity),input$EntitieAnno_Loadings_matrix]))
+              make.unique(as.character(rowData(data2plot)[unique(df_loadings$entity),input$EntitieAnno_Loadings_matrix])),
+              levels = make.unique(as.character(rowData(data2plot)[unique(df_loadings$entity),input$EntitieAnno_Loadings_matrix]))
             )
           } else{
             df_loadings$chosenAnno <- df_loadings$entity
@@ -311,8 +327,8 @@ pca_Server <- function(id, data, params, row_select){
           # assign par_temp as empty list
           par_tmp[[session$token]][["PCA"]] <<- list(
             sample_selection_pca = input$sample_selection_pca,
-            SampleAnnotationTypes_pca = input$SampleAnnotationTypes_pca
-
+            SampleAnnotationTypes_pca = input$SampleAnnotationTypes_pca,
+            UseBatch = useBatch
           )
         } else {
           # otherwise read the reactive values
diff --git a/program/shinyApp/R/pca/ui.R b/program/shinyApp/R/pca/ui.R
index f3702a58..66f43c30 100644
--- a/program/shinyApp/R/pca/ui.R
+++ b/program/shinyApp/R/pca/ui.R
@@ -6,6 +6,7 @@ pca_sidebar_panel <- function(ns){
     # PCA
     #########################################
     h4("Explorative Analysis") %>% helper(type = "markdown", content = "PCA_Choices"),
+    uiOutput(outputId = ns("UseBatch_ui")),
     ### data selection
     switchInput(
       inputId = ns("data_selection_pca"),
diff --git a/program/shinyApp/R/pre_processing/ui.R b/program/shinyApp/R/pre_processing/ui.R
index 571ebe23..9c88fa45 100644
--- a/program/shinyApp/R/pre_processing/ui.R
+++ b/program/shinyApp/R/pre_processing/ui.R
@@ -13,8 +13,12 @@ pre_processing_sidebar_panel <- sidebarPanel(
     ),
     selected = "none"
   ) %>% helper(type = "markdown", content = "PreProcessing_Procedures"),
-  uiOutput(outputId = "DESeq_formula_main_ui") %>% helper(type = "markdown", content = "PreProcessing_DESeqMain"),
-  uiOutput(outputId = "DESeq_formula_sub_ui") %>% helper(type = "markdown", content = "PreProcessing_DESeqSub"),
+
+  uiOutput(outputId = "DESeq_formula_main_ui"),
+  uiOutput(outputId = "DESeq_formula_sub_ui"),
+
+  uiOutput(outputId = "batch_effect_ui"),
+
   actionButton(
     inputId = "Do_preprocessing",
     label = "Get Pre-Processing",
diff --git a/program/shinyApp/R/pre_processing/util.R b/program/shinyApp/R/pre_processing/util.R
index 295d8206..7339b737 100644
--- a/program/shinyApp/R/pre_processing/util.R
+++ b/program/shinyApp/R/pre_processing/util.R
@@ -83,7 +83,7 @@ ln_normalisation <- function(data, omic_type, logarithm_procedure){
 
 
 deseq_processing <- function(
-  data, omic_type, formula_main, formula_sub, session_token, advanced_formula = NULL
+  data, omic_type, formula_main, formula_sub, session_token, batch_correct
 ){
   # Center and scale the data
   # prefilter the data
@@ -125,14 +125,17 @@ deseq_processing <- function(
       )
     
     de_seq_result <- DESeq2::DESeq(dds)
-    res_tmp[[session_token]]$DESeq_obj <<- de_seq_result
+    if (batch_correct){
+      res_tmp[[session_token]]$DESeq_obj_batch_corrected <<- de_seq_result
+    } else {
+      res_tmp[[session_token]]$DESeq_obj <<- de_seq_result
+    }
     dds_vst <- vst(
       object = de_seq_result,
       blind = TRUE
-      )
+    )
     assay(data) <- as.data.frame(assay(dds_vst))
     return(data)
   }
-  addWarning <- "<font color=\"#FF0000\"><b>DESeq makes only sense for transcriptomics data - data treated as if 'filterOnly' was selected!</b></font>"
   return(data)
 }
diff --git a/program/shinyApp/R/sample_correlation/server.R b/program/shinyApp/R/sample_correlation/server.R
index a3ee8dc5..2187b068 100644
--- a/program/shinyApp/R/sample_correlation/server.R
+++ b/program/shinyApp/R/sample_correlation/server.R
@@ -14,6 +14,15 @@ sample_correlation_server <- function(id, data, params){
       
       ns <- session$ns
       # UI Section ----
+      output$UseBatch_ui <- renderUI({
+        req(par_tmp[[session$token]]$BatchColumn != "NULL")
+        selectInput(
+          inputId = ns("UseBatch"),
+          label = "Use batch corrected data?",
+          choices = c("No","Yes"),
+          selected = "No"
+        )
+      })
       output$SampleAnnotationChoice_ui <- renderUI({
         req(selectedData_processed())
         selectInput(
@@ -37,19 +46,26 @@ sample_correlation_server <- function(id, data, params){
         
         if(sample_corr_reactive$calculate == 1){
           # update the data if needed
+          useBatch <- ifelse(input$UseBatch == "Yes",T,F)
           data <- update_data(session$token)
+          if(useBatch){
+              data <- data$data_batch_corrected
+          } else {
+              data <- data$data
+          }
           # set the counter to 0 to prevent any further plotting
           sample_corr_reactive$calculate <- 0
           
           # check value of input$Do_SampleCorrelation
-          annotationDF <- colData(data$data)[,input$SampleAnnotationChoice,drop = F]
+          annotationDF <- colData(data)[,input$SampleAnnotationChoice,drop = F]
           check <- check_calculations(
             list(
               corrMethod = input$corrMethod,
               data_info = list(
-                rows = length(rownames(data$data)),
-                cols = length(colnames(data$data)),
-                preprocessing = par_tmp[[session$token]]$PreProcessing_Procedure
+                rows = length(rownames(data)),
+                cols = length(colnames(data)),
+                preprocessing = par_tmp[[session$token]]$PreProcessing_Procedure,
+                batch = useBatch
               )
             ),
             "SampleCorrelation"
@@ -61,7 +77,7 @@ sample_correlation_server <- function(id, data, params){
                 "Correlation Matrix successfully computed."
               )
               cormat <- cor(
-                x = as.matrix(assay(data$data)),
+                x = as.matrix(assay(data)),
                 method = input$corrMethod
               )
             } else if (check == "Result exists"){
@@ -74,7 +90,7 @@ sample_correlation_server <- function(id, data, params){
                 "Correlation Matrix result overwritten with different parameters."
               )
               cormat <- cor(
-                x = as.matrix(assay(data$data)),
+                x = as.matrix(assay(data)),
                 method = input$corrMethod
               )
             }
@@ -106,9 +122,10 @@ sample_correlation_server <- function(id, data, params){
           par_tmp[[session$token]][["SampleCorrelation"]] <<- list(
             corrMethod = input$corrMethod,
             data_info = list(
-              rows = length(rownames(data$data)),
-              cols = length(colnames(data$data)),
-              preprocessing = par_tmp[[session$token]]$PreProcessing_Procedure
+              rows = length(rownames(data)),
+              cols = length(colnames(data)),
+              preprocessing = par_tmp[[session$token]]$PreProcessing_Procedure,
+              batch = useBatch
             )
           )
 
diff --git a/program/shinyApp/R/sample_correlation/ui.R b/program/shinyApp/R/sample_correlation/ui.R
index f536cc8a..02b57b49 100644
--- a/program/shinyApp/R/sample_correlation/ui.R
+++ b/program/shinyApp/R/sample_correlation/ui.R
@@ -2,6 +2,7 @@ sampleCorrelation_sidebar_panel <- function(ns){
   sidebarPanel(
     id = "sidebar_sampleCorrelation",
     h4("Sample Correlation") %>% helper(type = "markdown", content = "SampleCorr_Choices"),
+    uiOutput(outputId = ns("UseBatch_ui")),
     selectInput(
       inputId = ns("corrMethod"),
       label = "Choose the correlation method",
diff --git a/program/shinyApp/R/significance_analysis/server.R b/program/shinyApp/R/significance_analysis/server.R
index 8b339808..b5718ecc 100644
--- a/program/shinyApp/R/significance_analysis/server.R
+++ b/program/shinyApp/R/significance_analysis/server.R
@@ -13,7 +13,15 @@ significance_analysis_server <- function(id, data, params){
       )
       ns <- session$ns
       ## Sidebar UI section
-      # UI to choose type of comparison
+      output$UseBatch_ui <- renderUI({
+        req(par_tmp[[session$token]]$BatchColumn != "NULL")
+        selectInput(
+          inputId = ns("UseBatch"),
+          label = "Use batch corrected data?",
+          choices = c("No","Yes"),
+          selected = "No"
+        )
+      })
       output$type_of_comparison_ui <- renderUI({
         req(data_input_shiny())
         if(is.null(sig_ana_reactive$coldata)){
@@ -192,7 +200,13 @@ significance_analysis_server <- function(id, data, params){
         print("Start the Significance Analysis")
         # update the data if needed
         data <- update_data(session$token)
-        sig_ana_reactive$coldata <- colData(data$data)
+        useBatch <- ifelse(input$UseBatch == "Yes",T,F)
+        if(useBatch){
+            data_calculate <- data$data_batch_corrected
+        } else {
+            data_calculate <- data$data
+        }
+        sig_ana_reactive$coldata <- colData(data_calculate)
         # delete old panels
         if(!is.null(sig_ana_reactive$significance_tabs_to_delete)){
           for (i in seq_along(sig_ana_reactive$significance_tabs_to_delete)) {
@@ -204,7 +218,11 @@ significance_analysis_server <- function(id, data, params){
         }
         # if preproccesing method was DESeq2, then use DESeq2 for testing
         if(params$PreProcessing_Procedure == "vst_DESeq"){
-          dds <- data$DESeq_obj
+          if (useBatch){
+            dds <- data$DESeq_obj_batch_corrected
+          } else {
+            dds <- data$DESeq_obj
+          }
           # rewind the comparisons again
           newList <- input$comparisons
           contrasts <- vector("list", length(input$comparisons))
@@ -215,7 +233,11 @@ significance_analysis_server <- function(id, data, params){
           sig_ana_reactive$sig_results <- list()
           for (i in seq_along(contrasts)) {
             if(identical(
-              list(test_method = "Wald", test_correction = PADJUST_METHOD[[input$test_correction]]),
+              list(
+                test_method = "Wald",
+                test_correction = PADJUST_METHOD[[input$test_correction]],
+                batch_corrected = useBatch
+              ),
               par_tmp[[session$token]]$SigAna[[input$sample_annotation_types_cmp]][[input$comparisons[i]]]
             )){
               print("Results exists, skipping calculations.")
@@ -235,7 +257,8 @@ significance_analysis_server <- function(id, data, params){
             res_tmp[[session$token]]$SigAna[[input$sample_annotation_types_cmp]][[input$comparisons[i]]] <<- sig_ana_reactive$sig_results[[input$comparisons[i]]]
             par_tmp[[session$token]]$SigAna[[input$sample_annotation_types_cmp]][[input$comparisons[i]]] <<- list(
               test_method = "Wald",
-              test_correction = PADJUST_METHOD[[input$test_correction]]
+              test_correction = PADJUST_METHOD[[input$test_correction]],
+              batch_corrected = useBatch
             )
           }
         } else {  # all other methods require manual testing
@@ -253,11 +276,11 @@ significance_analysis_server <- function(id, data, params){
           names(contrasts) <- input$comparisons
           # get names of columns we want to choose:
           index_comparisons <- which(
-            colData(data$data)[,input$sample_annotation_types_cmp] %in% contrasts_all
+            colData(data_calculate)[,input$sample_annotation_types_cmp] %in% contrasts_all
           )
-          samples_selected <- colData(data$data)[index_comparisons,]
+          samples_selected <- colData(data_calculate)[index_comparisons,]
           # get the data
-          data_selected <- as.matrix(assay(data$data))[,index_comparisons]
+          data_selected <- as.matrix(assay(data_calculate))[,index_comparisons]
           # significance analysis saved the result in res_tmp.
           # as it is a custom function, wrap in tryCatch
           tryCatch({
@@ -267,7 +290,8 @@ significance_analysis_server <- function(id, data, params){
               contrasts = contrasts,
               method = input$test_method,
               correction = PADJUST_METHOD[[input$test_correction]],
-              contrast_level = input$sample_annotation_types_cmp
+              contrast_level = input$sample_annotation_types_cmp,
+              batch_corrected = useBatch
             )
             sig_ana_reactive$sig_results <- res_tmp[[session$token]]$SigAna[[input$sample_annotation_types_cmp]]
           }, error = function(e){
@@ -336,7 +360,7 @@ significance_analysis_server <- function(id, data, params){
             chosenVizSet <- input$comparisons
           } else {
             chosenVizSet <-  input$comparisons[c(1,2)]
-            sig_ana_reactive$info_text <- "Note: Although you choose 'all' to visualize only first 2 comparisons are shown to avoid unwanted computational overhead, 
+            sig_ana_reactive$info_text <- "Note: Although you choose 'all' to visualize only first 2 comparisons are shown to avoid unwanted computational overhead,
             as you got more than 4 comparisons. Please choose precisely the comparisons for visualisation."
           }
         }else{
@@ -519,7 +543,7 @@ significance_analysis_server <- function(id, data, params){
         fun_LogIt(message = "### SIGNIFICANCE ANALYSIS")
         fun_LogIt(message = paste(
           "- Significance Analysis was performed on",
-          length(data$data),
+          length(data_calculate),
           "entities"
         ))
         # log which tests were performed
diff --git a/program/shinyApp/R/significance_analysis/ui.R b/program/shinyApp/R/significance_analysis/ui.R
index 0e4ee264..19502999 100644
--- a/program/shinyApp/R/significance_analysis/ui.R
+++ b/program/shinyApp/R/significance_analysis/ui.R
@@ -2,6 +2,7 @@ significance_analysis_sidebar_ui<- function(ns){
   sidebarPanel(
     id = "sidebar_significance_analysis",
     h5(" ") %>% helper(type = "markdown", content = "SigAna_Choices"),
+    uiOutput(outputId = ns("UseBatch_ui")),
     uiOutput(outputId = ns("type_of_comparison_ui")),
     uiOutput(outputId = ns("chooseComparisons_ui")),
     # UI to choose test method
diff --git a/program/shinyApp/R/significance_analysis/util.R b/program/shinyApp/R/significance_analysis/util.R
index d515b937..d32b48e9 100644
--- a/program/shinyApp/R/significance_analysis/util.R
+++ b/program/shinyApp/R/significance_analysis/util.R
@@ -736,7 +736,7 @@ create_new_tab_DESeq <- function(title, targetPanel, result, contrast, alpha, ns
 
 
 significance_analysis <- function(
-  df, samples, contrasts, method, correction, contrast_level
+  df, samples, contrasts, method, correction, contrast_level, batch_corrected = FALSE
 ){
   # perform significance analysis
   # df: dataframe or matrix with the data
@@ -795,7 +795,7 @@ significance_analysis <- function(
   for(contrast in contrasts){
     # skip if already there
     if(identical(
-      list(test_method = method, test_correction = correction),
+      list(test_method = method, test_correction = correction, batch_corrected=batch_corrected),
       par_tmp[[session$token]]$SigAna[[contrast_level]][[names(contrasts)[comp_name]]]
     )){
       print("Results exists, skipping calculations.")
@@ -839,7 +839,8 @@ significance_analysis <- function(
     res_tmp[[session$token]]$SigAna[[contrast_level]][[names(contrasts)[comp_name]]] <<- res
     par_tmp[[session$token]]$SigAna[[contrast_level]][[names(contrasts)[comp_name]]]  <<- list(
       test_method = method,
-      test_correction = correction
+      test_correction = correction,
+      batch_corrected = batch_corrected
     )
     comp_name <- comp_name + 1
   }
diff --git a/program/shinyApp/R/single_gene_visualisation/server.R b/program/shinyApp/R/single_gene_visualisation/server.R
index 7561316d..14b31e8c 100644
--- a/program/shinyApp/R/single_gene_visualisation/server.R
+++ b/program/shinyApp/R/single_gene_visualisation/server.R
@@ -14,7 +14,7 @@ single_gene_visualisation_server <- function(id, data){
           selectInput(
             inputId = ns("type_of_data_gene"),
             label = "Choose Data to use (in case of DESeq- vst normalized counts are used)",
-            choices = c("raw","preprocessed"),
+            choices = c("raw","preprocessed", "batch_corrected_preprocessed"),
             multiple = F ,
             selected = "preprocessed"
           )
@@ -120,7 +120,6 @@ single_gene_visualisation_server <- function(id, data){
             GeneData$anno <- colData(data$data)[,input$accross_condition]
             GeneDataFlag <- T
           } else {
-            print("different Gene")
             GeneDataFlag <- F
           }
           
@@ -134,7 +133,16 @@ single_gene_visualisation_server <- function(id, data){
             # select to selection of processed data
             annoToSelect <- unique(c(colData(data$data)[,input$accross_condition]))
             GeneData <- subset(GeneData, anno %in% annoToSelect)
-            #print(data$data_original)
+            GeneDataFlag <- T
+          } else {
+            GeneDataFlag <- F
+          }
+        } else if(input$type_of_data_gene == "batch_corrected_preprocessed"){
+          if(input$Select_Gene %in% rowData(data$data_batch_corrected)[,input$Select_GeneAnno]){
+            #get IDX to data
+            idx_selected <- which(input$Select_Gene == rowData(data$data_batch_corrected)[,input$Select_GeneAnno])
+            GeneData <- as.data.frame(t(as.data.frame(assay(data$data_batch_corrected))[idx_selected,,drop=F]))
+            GeneData$anno <- colData(data$data_batch_corrected)[,input$accross_condition]
             GeneDataFlag <- T
           } else {
             GeneDataFlag <- F
diff --git a/program/shinyApp/R/util.R b/program/shinyApp/R/util.R
index 64c38bad..29ac58a4 100644
--- a/program/shinyApp/R/util.R
+++ b/program/shinyApp/R/util.R
@@ -1,15 +1,24 @@
 ### general utility functions will be defined here
 
 # tryCatch modal dialog
-error_modal <- function(e){
+error_modal <- function(e, additional_text = NULL){
+  if (is.null(e$message)){
+    e$message <- "An unknown error occured"
+  }
+  if (is.null(additional_text)){
+    additional_text <- "Please check your data set and annotation and try again.<br><br>"
+  }
+  additional_text <- paste0(
+    additional_text,
+    "<br><br>Otherwise, please contact the cOmicsArtist Lea and Paul via cOmicsArtist@outlook.de",
+    "or open an issue on <a href='https://github.com/LeaSeep/OmicShiny'>github</a> ",
+    "describing your problem."
+  )
   showModal(modalDialog(
     title = HTML("<font color='red'>An unknown Error occured</font>"),
     HTML(paste0(
       "<font color='red'>Error: ",e$message,"</font><br><br>",
-      "Please check your data set and annotation and try again.<br><br>",
-      "Otherwise, please contact the cOmicsArtist Lea and Paul via cOmicsArtist@outlook.de",
-      "or open an issue on <a href='https://github.com/LeaSeep/OmicShiny'>github</a>",
-      "describing your problem."
+      additional_text
     )),
     footer = modalButton("Close")
   ))
@@ -24,20 +33,25 @@ update_data <- function(session_id){
 }
 
 
-select_data <- function(data, selected_samples, sample_type){
+select_data <- function(data, selected_samples, sample_type, useBatch = F){
   # select data for e.g. pca's or alike
+  if(useBatch){
+    data_entry <- "data_batch_corrected"
+  } else {
+    data_entry <- "data"
+  }
   samples_selected <- c()
   if(any(selected_samples == "all")){
-    samples_selected <- colnames(assay(data$data))
+    samples_selected <- colnames(assay(data[[data_entry]]))
   }else{
     samples_selected <- c(
       samples_selected,
-      rownames(colData(data$data))[which(
-        colData(data$data)[,sample_type] %in% selected_samples
+      rownames(colData(data[[data_entry]]))[which(
+        colData(data[[data_entry]])[,sample_type] %in% selected_samples
         )]
       )
   }
-  data$data <- data$data[,samples_selected]
+  data[[data_entry]] <- data[[data_entry]][,samples_selected]
   return(data)
 }
 
diff --git a/program/shinyApp/server.R b/program/shinyApp/server.R
index efed60b9..da76006d 100644
--- a/program/shinyApp/server.R
+++ b/program/shinyApp/server.R
@@ -785,6 +785,20 @@ server <- function(input,output,session){
 # Set Selected Data as Head to allow reiteration of pre-processing
 
 ## UI section ----
+  # Update the batch effect UI based on the available columns
+  output$batch_effect_ui <- renderUI({
+    req(data_input_shiny())
+    column_names <- colnames(colData(res_tmp[[session$token]]$data_original))
+    if (input$PreProcessing_Procedure == "vst_DESeq") {
+      column_names <- column_names[!column_names %in% c(input$DESeq_formula_main, input$DESeq_formula_sub)]
+    }
+    selectInput(
+      inputId = "BatchEffect_Column",
+      label = "Select Batch Effect Column",
+      choices = c("NULL", column_names),
+      selected = "NULL"
+    )
+  })
   output$DESeq_formula_main_ui <- renderUI({
     req(data_input_shiny())
     req(input$PreProcessing_Procedure == "vst_DESeq")
@@ -797,7 +811,7 @@ server <- function(input,output,session){
       choices = c(colnames(colData(tmp_data_selected))),
       multiple = F,
       selected = "condition"
-    )
+    ) %>% helper(type = "markdown", content = "PreProcessing_DESeqMain")
   })
   output$DESeq_formula_sub_ui <- renderUI({
     req(data_input_shiny())
@@ -811,7 +825,7 @@ server <- function(input,output,session){
       choices = c(colnames(colData(tmp_data_selected))),
       multiple = T,
       selected = "condition"
-    )
+    ) %>% helper(type = "markdown", content = "PreProcessing_DESeqSub")
   })
 
 ## Do preprocessing ----  
@@ -830,6 +844,49 @@ server <- function(input,output,session){
     # explicitly set rownames to avoid any errors.
     # new object Created for res_tmp[[session$token]]
     res_tmp[[session$token]]$data <<- res_tmp[[session$token]]$data[rownames(res_tmp[[session$token]]$data),]
+    par_tmp[[session$token]]['BatchColumn'] <<- input$BatchEffect_Column
+
+    # Batch correction before preprocessing
+    if (input$BatchEffect_Column != "NULL" & input$PreProcessing_Procedure != "vst_DESeq") {
+      tryCatch({
+        res_tmp[[session$token]]$data_batch_corrected <<- prefiltering(
+          res_tmp[[session$token]]$data,
+          par_tmp[[session$token]]$omic_type
+        )
+        assay(res_tmp[[session$token]]$data_batch_corrected) <<- sva::ComBat(
+          dat = assay(res_tmp[[session$token]]$data_batch_corrected),
+          batch = as.factor(colData(res_tmp[[session$token]]$data_batch_corrected)[,input$BatchEffect_Column])
+        )
+      }, error = function(e){
+        error_modal(
+          e, additional_text = "Batch correction failed. Make sure the batch effect column is correct!"
+        )
+        req(FALSE)
+      })
+    } else if (input$BatchEffect_Column != "NULL" & input$PreProcessing_Procedure == "vst_DESeq"){
+      tryCatch({
+        res_tmp[[session$token]]$data_batch_corrected <<- deseq_processing(
+            data = tmp_data_selected,
+            omic_type = par_tmp[[session$token]]$omic_type,
+            formula_main = input$DESeq_formula_main,
+            formula_sub = c(input$DESeq_formula_sub, input$BatchEffect_Column),
+            session_token = session$token,
+            batch_correct = T
+          )
+      }, error = function(e){
+        error_modal(
+          e, additional_text = paste0(
+            "Batch correction using DESeq failed. Most likely due to linear dependencies ",
+            "in the design matrix (one or more factors informing about another one).",
+            "Make sure the batch effect column is correct and ",
+            "that the design matrix is not singular!"
+            )
+        )
+        req(FALSE)
+      })
+    } else {
+      res_tmp[[session$token]]$data_batch_corrected <<- NULL
+    }
 
     # preprocessing
     print(paste0("Do chosen Preprocessing:",input$PreProcessing_Procedure))
@@ -841,7 +898,7 @@ server <- function(input,output,session){
             formula_main = input$DESeq_formula_main,
             formula_sub = input$DESeq_formula_sub,
             session_token = session$token,
-            advanced_formula = ifelse(input$DESeq_show_advanced, input$DESeq_formula_advanced, "")
+            batch_correct = F
           )
       } else {
         res_tmp[[session$token]]$data <<- preprocessing(
@@ -849,6 +906,13 @@ server <- function(input,output,session){
           omic_type = par_tmp[[session$token]]$omic_type,
           procedure = input$PreProcessing_Procedure
         )
+        if (!is.null(res_tmp[[session$token]]$data_batch_corrected)) {
+          res_tmp[[session$token]]$data_batch_corrected <<- preprocessing(
+            data = res_tmp[[session$token]]$data_batch_corrected,
+            omic_type = par_tmp[[session$token]]$omic_type,
+            procedure = input$PreProcessing_Procedure
+          )
+        }
       }
     }, error = function(e){
       error_modal(e)