diff --git a/NAMESPACE b/NAMESPACE
index 324cfe6..1aafe74 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -125,7 +125,9 @@ importFrom(Seurat,ScaleData)
 importFrom(Seurat,VariableFeatures)
 importFrom(Seurat,as.Seurat)
 importFrom(Seurat,as.SingleCellExperiment)
+importFrom(SingleCellExperiment,"altExp<-")
 importFrom(SingleCellExperiment,SingleCellExperiment)
+importFrom(SingleCellExperiment,altExp)
 importFrom(SingleR,SingleR)
 importFrom(SummarizedExperiment,"assay<-")
 importFrom(SummarizedExperiment,"colData<-")
diff --git a/R/functions.R b/R/functions.R
index 9354a4e..49ef35d 100644
--- a/R/functions.R
+++ b/R/functions.R
@@ -696,7 +696,8 @@ non_batch_variation_removal <- function(input_read_RNA_assay,
                                         alive_identification_tbl, 
                                         cell_cycle_score_tbl,
                                         assay = NULL,
-                                        factors_to_regress = NULL){
+                                        factors_to_regress = NULL,
+                                        external_path){
   #Fix GChecks 
   empty_droplet = NULL 
   .cell <- NULL 
@@ -705,7 +706,7 @@ non_batch_variation_removal <- function(input_read_RNA_assay,
   G2M.Score = NULL 
   
   # Your code for non_batch_variation_removal function here
-  
+  class_input = input_read_RNA_assay |> class()
   
   # Get assay
   if(is.null(assay)) assay = input_read_RNA_assay@assays |> names() |> extract2(1)
@@ -722,7 +723,7 @@ non_batch_variation_removal <- function(input_read_RNA_assay,
         new.assay.name = assay)
   }
   
-  counts =
+  input_read_RNA_assay =
     input_read_RNA_assay |>
     left_join(empty_droplets_tbl, by = ".cell") |>
     filter(!empty_droplet) |>
@@ -734,7 +735,7 @@ non_batch_variation_removal <- function(input_read_RNA_assay,
     ) 
   
   if(!is.null(cell_cycle_score_tbl)) 
-    counts = counts |>
+    input_read_RNA_assay = input_read_RNA_assay |>
     
     left_join(
       cell_cycle_score_tbl |>
@@ -750,40 +751,57 @@ non_batch_variation_removal <- function(input_read_RNA_assay,
   # VariableFeatures(counts) = variable_features
   
   # Normalise RNA
-  normalized_rna <- Seurat::SCTransform(
-    counts, 
+  normalized_rna <- 
+    Seurat::SCTransform(
+      input_read_RNA_assay, 
     assay=assay,
     return.only.var.genes=FALSE,
     residual.features = NULL,
     vars.to.regress = factors_to_regress,
     vst.flavor = "v2",
-    scale_factor=2186
-  )
-  
-  my_assays = "SCT"
+    scale_factor=2186,  
+    conserve.memory=T, 
+    min_cells=0,
+  )  |> 
+    GetAssayData(assay="SCT")
   
-  if (inherits(input_read_RNA_assay, "SingleCellExperiment")) {
-    normalized_rna <- normalized_rna |> as.SingleCellExperiment(assay = assay)
-  } else if (inherits(input_read_RNA_assay, "Seurat")) {
-    normalized_rna
-  }
 
-  
-  # Normalise antibodies
-  if ( "ADT" %in% names(normalized_rna@assays)) {
-    normalized_data <- normalized_rna %>%
-      NormalizeData(normalization.method = 'CLR', margin = 2, assay="ADT") %>%
-      select(-subsets_Ribo_percent, -subsets_Mito_percent, -G2M.Score)
+  if (class_input == "SingleCellExperiment") {
+    dir.create(external_path, showWarnings = FALSE, recursive = TRUE)
+    
+    
+    # Write the slice to the output HDF5 file
+    normalized_rna |> 
+      HDF5Array::writeHDF5Array(
+      filepath = glue("{external_path}/{digest(normalized_rna)}"),
+      name = "SCT",
+      as.sparse = TRUE
+    ) 
     
-    my_assays = my_assays |> c("CLR")
+  } else if (class_input ==  "Seurat") {
+    
+    normalized_rna 
     
-  } else { 
-    normalized_data <- normalized_rna %>%
-      # Drop alive columns
-      select(-subsets_Ribo_percent, -subsets_Mito_percent, -G2M.Score)
   }
+
+  
+  # # Normalise antibodies
+  # if ( "ADT" %in% names(normalized_rna@assays)) {
+  #   normalized_data <- normalized_rna %>%
+  #     NormalizeData(normalization.method = 'CLR', margin = 2, assay="ADT") %>%
+  #     select(-subsets_Ribo_percent, -subsets_Mito_percent, -G2M.Score)
+  #   
+  #   my_assays = my_assays |> c("CLR")
+  #   
+  # } else { 
+  #   normalized_data <- normalized_rna %>%
+  #     # Drop alive columns
+  #     select(-subsets_Ribo_percent, -subsets_Mito_percent, -G2M.Score)
+  # }
   
-  normalized_data[[my_assays]] 
+  
+  
+
   
 }
 
@@ -807,10 +825,12 @@ non_batch_variation_removal <- function(input_read_RNA_assay,
 #' @importFrom dplyr filter
 #' @importFrom dplyr select
 #' @import SeuratObject
-#' @importFrom SummarizedExperiment left_join
+#' @importFrom dplyr left_join
 #' @import tidySingleCellExperiment 
 #' @import tidyseurat
 #' @importFrom magrittr not
+#' @importFrom SingleCellExperiment altExp
+#' @importFrom SingleCellExperiment altExp<-
 #' @export
 preprocessing_output <- function(input_read_RNA_assay,
                                  empty_droplets_tbl,
@@ -840,11 +860,13 @@ preprocessing_output <- function(input_read_RNA_assay,
     if(input_read_RNA_assay |> is("Seurat"))
       input_read_RNA_assay[["SCT"]] = non_batch_variation_removal_S
     else if(input_read_RNA_assay |> is("SingleCellExperiment")){
-      message("HPCell says: in order to attach SCT assay to the SingleCellExperiment, the non overlapping features (lowly abundant in the majority of cells) have been dropped")
+      message("HPCell says: in order to attach SCT assay to the SingleCellExperiment, SCT was added to external experiments slot")
+      
+      #input_read_RNA_assay = input_read_RNA_assay[rownames(non_batch_variation_removal_S), ]
       
-      input_read_RNA_assay = input_read_RNA_assay[rownames(non_batch_variation_removal_S), ]
+      # altExp(input_read_RNA_assay) = SingleCellExperiment(assay  = list(SCT = non_batch_variation_removal_S))
       
-      assay(input_read_RNA_assay, "SCT") <- GetAssayData(non_batch_variation_removal_S)
+      assay(input_read_RNA_assay, "SCT") <- non_batch_variation_removal_S
       
     }
   }
diff --git a/R/modules_grammar_hpc.R b/R/modules_grammar_hpc.R
index 8298256..3c7aa15 100644
--- a/R/modules_grammar_hpc.R
+++ b/R/modules_grammar_hpc.R
@@ -589,7 +589,7 @@ normalise_abundance_seurat_SCT.HPCell = function(input_hpc, factors_to_regress =
   # Optionally, you can evaluate the arguments if they are expressions
   args_list <- lapply(args_list, eval, envir = parent.frame())
   
-  args_list$factory = function(tiers, target_input, target_output){
+  args_list$factory = function(tiers, target_input, target_output, external_path){
     list(
       tar_target_raw("factors_to_regress", readRDS("factors_to_regress.rds") |> quote(), deployment = "main"),
       
@@ -601,9 +601,10 @@ normalise_abundance_seurat_SCT.HPCell = function(input_hpc, factors_to_regress =
             empty_droplets_tbl,
             alive_identification_tbl,
             cell_cycle_score_tbl,
-            factors_to_regress = factors_to_regress
+            factors_to_regress = factors_to_regress,
+            external_path = e
           ) |> 
-          substitute(env = list(i=as.symbol(target_input))),
+          substitute(env = list(i=as.symbol(target_input), e = external_path)),
         tiers, 
         other_arguments_to_tier = c(target_input, "empty_droplets_tbl", "alive_identification_tbl", "cell_cycle_score_tbl"), other_arguments_to_map = c(target_input, "empty_droplets_tbl", "alive_identification_tbl", "cell_cycle_score_tbl")
         
@@ -633,6 +634,7 @@ normalise_abundance_seurat_SCT.HPCell = function(input_hpc, factors_to_regress =
       tiers = input_hpc$initialisation$tier |> get_positions() ,
       target_input = target_input,
       target_output = target_output,
+      external_path = glue("{input_hpc$initialisation$store}/external"),
       script = glue("{input_hpc$initialisation$store}.R")
     )
   }
@@ -1031,7 +1033,7 @@ evaluate_hpc.HPCell = function(input_hpc) {
   if(input_hpc$last_call |> is.null() |> not())
     return(
       tar_meta(store = glue("{input_hpc$initialisation$store}")) |> 
-        filter(name |> str_detect("preprocessing_output_S_?.*$")) |> 
+        filter(name |> str_detect("preprocessing_output_S_?.*$"), type=="pattern") |> 
         pull(name) |> 
         map(tar_read_raw) |> 
         unlist() |> 
@@ -1041,7 +1043,7 @@ evaluate_hpc.HPCell = function(input_hpc) {
   else
     return(
       tar_meta(store = glue("{input_hpc$initialisation$store}")) |> 
-        filter(name |> str_detect("preprocessing_output_S_?.*$")) 
+        filter(name |> str_detect("preprocessing_output_S_?.*$"), type=="pattern") 
     )
 }
 
diff --git a/man/non_batch_variation_removal.Rd b/man/non_batch_variation_removal.Rd
index 4e2383b..c747c3b 100644
--- a/man/non_batch_variation_removal.Rd
+++ b/man/non_batch_variation_removal.Rd
@@ -10,7 +10,8 @@ non_batch_variation_removal(
   alive_identification_tbl,
   cell_cycle_score_tbl,
   assay = NULL,
-  factors_to_regress = NULL
+  factors_to_regress = NULL,
+  external_path
 )
 }
 \arguments{
diff --git a/tests/testthat/test_single_functions.R b/tests/testthat/test_single_functions.R
index f7d8d68..4b2c309 100644
--- a/tests/testthat/test_single_functions.R
+++ b/tests/testthat/test_single_functions.R
@@ -489,7 +489,7 @@ library(crew.cluster)
     data_container_type = "sce_hdf5",
     # tier = c("tier_1", "tier_2"),
     # 
-   #  debug_step = "pseudobulk_table_dispersion_gene",
+    # debug_step = "non_batch_variation_removal_S_1",
 
 
     # Default resourced