diff --git a/DESCRIPTION b/DESCRIPTION
index c71d1b30..f1042cb8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -32,7 +32,8 @@ Imports:
     scales,
     SummarizedExperiment,
     GenomicRanges,
-    methods
+    methods,
+    S4Vectors
 Suggests:
     BiocStyle,
     testthat,
@@ -53,7 +54,6 @@ Suggests:
     Seurat,
     KernSmooth,
     Rtsne,
-    S4Vectors,
     ggplot2,
     widyr,
     clusterProfiler,
@@ -82,7 +82,7 @@ Biarch: true
 biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.0
 LazyDataCompression: xz
 URL: https://github.com/stemangiola/tidybulk
 BugReports: https://github.com/stemangiola/tidybulk/issues
diff --git a/NAMESPACE b/NAMESPACE
index e28aaff1..e93b80ba 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -81,6 +81,7 @@ import(readr)
 import(tibble)
 import(tidyr)
 importFrom(GenomicRanges,makeGRangesListFromDataFrame)
+importFrom(S4Vectors,metadata)
 importFrom(SummarizedExperiment,SummarizedExperiment)
 importFrom(SummarizedExperiment,assays)
 importFrom(SummarizedExperiment,colData)
diff --git a/R/dplyr_methods.R b/R/dplyr_methods.R
index 80f8a1ea..f03b8028 100755
--- a/R/dplyr_methods.R
+++ b/R/dplyr_methods.R
@@ -723,7 +723,7 @@ rowwise.tidybulk <- function(data, ...)
 #'
 #' @examples
 #'`%>%` = magrittr::`%>%`
-#' annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% distinct(sample) %>% mutate(source = "AU")
+#' annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% distinct(.sample) %>% mutate(source = "AU")
 #' tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% left_join(annotation)
 #'
 #' @rdname dplyr-methods
@@ -763,7 +763,7 @@ left_join.tidybulk <- function (x, y, by = NULL, copy = FALSE, suffix = c(".x",
 #'
 #' @examples
 #'`%>%` = magrittr::`%>%`
-#' annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% distinct(sample) %>% mutate(source = "AU")
+#' annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% distinct(.sample) %>% mutate(source = "AU")
 #' tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% inner_join(annotation)
 #'
 #' @rdname join-methods
@@ -802,7 +802,7 @@ inner_join.tidybulk <- function (x, y, by = NULL, copy = FALSE, suffix = c(".x",
 #'
 #' @examples
 #'`%>%` = magrittr::`%>%`
-#' annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% distinct(sample) %>% mutate(source = "AU")
+#' annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% distinct(.sample) %>% mutate(source = "AU")
 #' tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% right_join(annotation)
 #'
 #' @rdname join-methods
@@ -843,7 +843,7 @@ right_join.tidybulk <- function (x, y, by = NULL, copy = FALSE, suffix = c(".x",
 #'
 #' @examples
 #'`%>%` = magrittr::`%>%`
-#' annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% distinct(sample) %>% mutate(source = "AU")
+#' annotation = tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% distinct(.sample) %>% mutate(source = "AU")
 #' tidybulk::counts_SE %>% tidybulk() %>% as_tibble() %>% full_join(annotation)
 #'
 #' @rdname join-methods
diff --git a/R/methods.R b/R/methods.R
index 52a4a2fb..94e5ccfc 100755
--- a/R/methods.R
+++ b/R/methods.R
@@ -31,7 +31,7 @@ setOldClass("tidybulk")
 #'
 #' @examples
 #'
-#' my_tt =  tidybulk(tidybulk::se_mini)
+#' tidybulk(tidybulk::se_mini)
 #'
 #'
 #' @docType methods
@@ -1353,9 +1353,7 @@ setMethod("remove_redundancy", "tidybulk", .remove_redundancy)
 #' cm$batch = 0
 #' cm$batch[colnames(cm) %in% c("SRR1740035", "SRR1740043")] = 1
 #'
-#' res =
 #'  cm %>%
-#'  tidybulk(sample, transcript, count) |>
 #'  identify_abundant() |>
 #' 	adjust_abundance(	~ condition + batch	)
 #'
@@ -1675,7 +1673,7 @@ setMethod("aggregate_duplicates", "tidybulk", .aggregate_duplicates)
 #' library(dplyr)
 #'
 #' # Subsetting for time efficiency
-#' tidybulk::se_mini |> tidybulk() |>filter(sample=="SRR1740034") |> deconvolve_cellularity(sample, feature, count, cores = 1)
+#' tidybulk::se_mini |> deconvolve_cellularity(cores = 1)
 #'
 #'
 #' @docType methods
@@ -1815,7 +1813,10 @@ setMethod("deconvolve_cellularity",
 #'
 #' @examples
 #'
-#' tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez(.transcript = feature, .sample = sample)
+#' # This function was designed for data.frame
+#' # Convert from SummarizedExperiment for this example. It is NOT reccomended.
+#'
+#' tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez(.transcript = .feature, .sample = .sample)
 #'
 #' @export
 #'
@@ -2014,7 +2015,10 @@ setMethod("describe_transcript", "tidybulk", .describe_transcript)
 #'
 #' library(dplyr)
 #'
-#' tidybulk::counts_SE |> tidybulk() |> as_tibble() |> ensembl_to_symbol(feature)
+#' # This function was designed for data.frame
+#' # Convert from SummarizedExperiment for this example. It is NOT reccomended.
+#'
+#' tidybulk::counts_SE |> tidybulk() |> as_tibble() |> ensembl_to_symbol(.feature)
 #'
 #'
 #'
@@ -2882,8 +2886,10 @@ setMethod("keep_abundant", "tidybulk", .keep_abundant)
 #' @examples
 #' \dontrun{
 #'
-#' df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez( .transcript = feature, .sample = sample)
-#' df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = sample, .transcript = entrez, .abundance = count)
+#' library(SummarizedExperiment)
+#' se = tidybulk::se_mini
+#' rowData( se)$entrez = rownames(se )
+#' df_entrez = aggregate_duplicates(se,.transcript = entrez )
 #'
 #' library("EGSEA")
 #'
@@ -3075,9 +3081,8 @@ setMethod("test_gene_enrichment",
 #'
 #' @examples
 #'
-#' df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez( .transcript = feature, .sample = sample)
-#' df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = sample, .transcript = entrez, .abundance = count)
-#' df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
+#' #se_mini = aggregate_duplicates(tidybulk::se_mini, .transcript = entrez)
+#' #df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
 #'
 #' \dontrun{
 #' 	test_gene_overrepresentation(
@@ -3245,15 +3250,14 @@ setMethod("test_gene_overrepresentation",
 #'
 #' \dontrun{
 #'
-#' df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez( .transcript = feature, .sample = sample)
-#' df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = sample, .transcript = entrez, .abundance = count)
-#' df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
+#' df_entrez = tidybulk::se_mini
+#' df_entrez = mutate(df_entrez, do_test = .feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
 #' df_entrez  = df_entrez %>% test_differential_abundance(~ condition)
 #'
 #'
 #'	test_gene_rank(
 #'		df_entrez,
-#' 		.sample = sample,
+#' 		.sample = .sample,
 #'		.entrez = entrez,
 #' 		species="Homo sapiens",
 #'    gene_sets =c("C2"),
@@ -3591,7 +3595,7 @@ setMethod("pivot_transcript",
 #'
 #' @examples
 #'
-#' tidybulk::se_mini |> tidybulk() |> fill_missing_abundance( fill_with = 0)
+#' # tidybulk::se_mini |>  fill_missing_abundance( fill_with = 0)
 #'
 #'
 #' @docType methods
@@ -3862,19 +3866,8 @@ setMethod("impute_missing_abundance", "tidybulk", .impute_missing_abundance)
 #' 	)
 #'
 #' 	# Cox regression - multiple
-#' 	library(dplyr)
-#' 	library(tidyr)
 #'
 #'	tidybulk::se_mini |>
-#'	   tidybulk() |>
-#'
-#'		# Add survival data
-#'		nest(data = -sample) |>
-#'		mutate(
-#'			days = c(1, 10, 500, 1000, 2000),
-#'			dead = c(1, 1, 1, 0, 1)
-#'		) %>%
-#'		unnest(data) |>
 #'
 #'		# Test
 #'		test_differential_cellularity(
@@ -4019,15 +4012,6 @@ setMethod("test_differential_cellularity",
 #' library(tidyr)
 #'
 #'	tidybulk::se_mini |>
-#'	   tidybulk() |>
-#'
-#'	# Add survival data
-#'	nest(data = -sample) |>
-#'	mutate(
-#'		days = c(1, 10, 500, 1000, 2000),
-#'		dead = c(1, 1, 1, 0, 1)
-#'	) %>%
-#'	unnest(data) |>
 #'	test_stratification_cellularity(
 #'		survival::Surv(days, dead) ~ .,
 #'		cores = 1
@@ -4138,10 +4122,8 @@ setMethod("test_stratification_cellularity",
 #'
 #' @examples
 #'
-#' # Define tidybulk tibble
-#' df = tidybulk(tidybulk::se_mini)
 #'
-#' get_bibliography(df)
+#' get_bibliography(tidybulk::se_mini)
 #'
 #'
 #'
@@ -4236,9 +4218,8 @@ setMethod("get_bibliography",
 #'
 #' @examples
 #'
-#' library(dplyr)
 #'
-#' tidybulk::se_mini |> tidybulk() |> select(feature, count) |> head() |> as_matrix(rownames=feature)
+#' tibble(.feature = "CD3G", count=1) |> as_matrix(rownames=.feature)
 #'
 #' @export
 as_matrix <- function(tbl,
diff --git a/R/methods_SE.R b/R/methods_SE.R
index dd65d816..a214d3e7 100755
--- a/R/methods_SE.R
+++ b/R/methods_SE.R
@@ -24,41 +24,12 @@
 								~ as.symbol(.x),
 								~ NULL)
 
-	sample_info <-
-		colData(.data) %>%
+	.as_tibble_optimised(.data) %>%
 
-		# If reserved column names are present add .x
-		change_reserved_column_names() %>%
-
-		# Convert to tibble
-		tibble::as_tibble(rownames="sample")
-
-
-	range_info <-
-		 get_special_datasets(.data) %>%
-			reduce(left_join, by="coordinate")
-
-	gene_info <-
-		rowData(.data) %>%
-
-		# If reserved column names are present add .x
-		change_reserved_column_names() %>%
-
-		# Convert to tibble
-		tibble::as_tibble(rownames="feature")
-
-	count_info <- get_count_datasets(.data)
-
-	# Return
-	count_info %>%
-	left_join(sample_info, by="sample") %>%
-	left_join(gene_info, by="feature") %>%
-	when(nrow(range_info) > 0 ~ (.) %>% left_join(range_info) %>% suppressMessages(), ~ (.)) %>%
-
-	mutate_if(is.character, as.factor) %>%
+	# mutate_if(is.character, as.factor) %>%
 	tidybulk(
-		sample,
-		feature,
+		!!as.symbol(sample__$name),
+		!!as.symbol(feature__$name),
 		!!as.symbol(SummarizedExperiment::assays(.data)[1] %>%  names	),
 		!!norm_col # scaled counts if any
 	)
@@ -787,23 +758,22 @@ setMethod("adjust_abundance",
 
   collapse_function = function(x){ x %>% unique() %>% paste(collapse = "___")	}
 
-  feature_column_name = ".feature"
 
   # Row data
   new_row_data =
     .data %>%
     rowData() %>%
-    as_tibble(rownames = feature_column_name) %>%
+    as_tibble(rownames = feature__$name) %>%
     group_by(!!as.symbol(quo_name(.transcript))) %>%
     summarise(
       across(everything(), ~ .x %>% collapse_function()),
       merged.transcripts = n()
     ) %>%
-    arrange(!!as.symbol(feature_column_name)) %>%
+    arrange(!!as.symbol(feature__$name)) %>%
     as.data.frame()
 
-  rownames(new_row_data) = new_row_data[,feature_column_name]
-  new_row_data = new_row_data %>% select(-feature_column_name)
+  rownames(new_row_data) = new_row_data[,feature__$name]
+  new_row_data = new_row_data %>% select(-feature__$name)
 
   # Counts
   new_count_data =
@@ -824,7 +794,7 @@ setMethod("adjust_abundance",
     )
 
   # GRanges
-  columns_to_collapse = .data %>% rowData() %>% colnames() %>% setdiff(quo_name(.transcript)) %>% c(feature_column_name)
+  columns_to_collapse = .data %>% rowData() %>% colnames() %>% setdiff(quo_name(.transcript)) %>% c(feature__$name)
 
   rr = rowRanges(.data)
 
@@ -834,27 +804,27 @@ setMethod("adjust_abundance",
       as_tibble() %>%
       # Add names
       when(
-        is(rr, "CompressedGRangesList") ~ mutate(., !!as.symbol(feature_column_name) := group_name),
-        ~ mutate(., !!as.symbol(feature_column_name) := rr@ranges@NAME)
+        is(rr, "CompressedGRangesList") ~ mutate(., !!as.symbol(feature__$name) := group_name),
+        ~ mutate(., !!as.symbol(feature__$name) := rr@ranges@NAME)
       ) %>%
       left_join(
         rowData(.data) %>%
           as.data.frame() %>%
           select(!!as.symbol(quo_name(.transcript))) %>%
-          as_tibble(rownames =feature_column_name),
-            by = feature_column_name
+          as_tibble(rownames =feature__$name),
+            by = feature__$name
       ) %>%
       group_by(!!as.symbol(quo_name(.transcript))) %>%
       mutate(
         across(columns_to_collapse, ~ .x %>% collapse_function()),
         merged.transcripts = n()
       ) %>%
-      arrange(!!as.symbol(feature_column_name)) %>%
+      arrange(!!as.symbol(feature__$name)) %>%
 
       select(-one_of("group_name", "group")) %>%
       suppressWarnings() %>%
 
-      makeGRangesListFromDataFrame( split.field = feature_column_name,
+      makeGRangesListFromDataFrame( split.field = feature__$name,
                                     keep.extra.columns = TRUE) %>%
 
       .[match(rownames(new_count_data[[1]]), names(.))]
@@ -1894,7 +1864,7 @@ setMethod("test_gene_rank",
 		) %>%
 
 		# Convert to tibble
-		tibble::as_tibble(rownames="sample")
+		tibble::as_tibble(rownames=sample__$name)
 
 
 
@@ -1934,7 +1904,7 @@ setMethod("pivot_sample",
 
 	range_info <-
 		get_special_datasets(.data) %>%
-		reduce(left_join, by="feature")
+		reduce(left_join, by=feature__$name)
 
 	gene_info <-
 		rowData(.data) %>%
@@ -1946,11 +1916,11 @@ setMethod("pivot_sample",
 		) %>%
 
 		# Convert to tibble
-		tibble::as_tibble(rownames="feature")
+		tibble::as_tibble(rownames=feature__$name)
 
 	gene_info %>%
 		when(
-			nrow(range_info) > 0 ~ (.) %>% left_join(range_info, by="feature"),
+			nrow(range_info) > 0 ~ (.) %>% left_join(range_info, by=feature__$name),
 			~ (.)
 		)
 }
diff --git a/R/tidySummarizedExperiment.R b/R/tidySummarizedExperiment.R
index f7ab582e..443b394e 100644
--- a/R/tidySummarizedExperiment.R
+++ b/R/tidySummarizedExperiment.R
@@ -1,16 +1,12 @@
-change_reserved_column_names = function(.data){
-
-	.data %>%
-
-		setNames(
-			colnames(.) %>%
-				str_replace("^feature$", "feature.x") %>%
-				str_replace("^sample$", "sample.x") %>%
-				str_replace("^coordinate$", "coordinate.x")
-		)
+eliminate_GRanges_metadata_columns_also_present_in_Rowdata = function(.my_data, se){
+  .my_data %>%
+    select(-one_of(colnames(rowData(se)))) %>%
 
+    # In case there is not metadata column
+    suppressWarnings()
 }
 
+
 #' @importFrom dplyr select
 #' @importFrom tidyselect one_of
 #' @importFrom tibble as_tibble
@@ -18,71 +14,60 @@ change_reserved_column_names = function(.data){
 #' @importFrom SummarizedExperiment rowRanges
 #' @importFrom tibble rowid_to_column
 #'
-#' @keywords internal
 #' @noRd
-get_special_datasets <- function(SummarizedExperiment_object) {
-
-	SummarizedExperiment_object %>%
-		rowRanges() %>%
-		when(
-			# If no ranges
-			as.data.frame(.) %>%
-				nrow() %>%
-				equals(0) ~ tibble(),
-
-			# If it is a range list (multiple rows per feature)
-			class(.) %>% equals("CompressedGRangesList") ~
-				tibble::as_tibble(.) %>%
-				eliminate_GRanges_metadata_columns_also_present_in_Rowdata(SummarizedExperiment_object) %>%
-				nest(coordinate = -group_name) %>%
-				rename(feature = group_name),
-
-			# If standard GRanges (one feature per line)
-			~ {
-				transcript_column =
-					rowRanges(SummarizedExperiment_object) %>%
-					as.data.frame() %>%
-					lapply(function(x) rownames(SummarizedExperiment_object)[1] %in% x) %>%
-					unlist() %>%
-					which() %>%
-					names()
-
-
-				# Just rename
-				(.) %>%
-
-					# If transcript_column exists all good
-					when(
-						!is.null(transcript_column) ~  tibble::as_tibble(.) %>%
-							eliminate_GRanges_metadata_columns_also_present_in_Rowdata(SummarizedExperiment_object) %>%
-							rename(feature := !!transcript_column) ,
-
-						# If transcript_column is NULL add numeric column
-						~ tibble::as_tibble(.) %>%
-							eliminate_GRanges_metadata_columns_also_present_in_Rowdata(SummarizedExperiment_object) %>%
-							rowid_to_column(var = "feature") %>%
-							mutate(feature = as.character(feature))
-					) %>%
-
-					# Always nest
-					nest(coordinate = -feature)
-
-			}
-		) %>%
-		list()
+get_special_datasets <- function(se) {
+
+  rr =  se %>%
+    rowRanges()
+
+  rr %>%
+    when(
+
+      # If no ranges
+      as.data.frame(.) %>%
+        nrow() %>%
+        equals(0) ~ tibble(),
+
+      # If it is a range list (multiple rows per feature)
+      is(., "CompressedGRangesList") ~ {
+
+        # If GRanges does not have row names
+        if(is.null(rr@partitioning@NAMES)) rr@partitioning@NAMES = as.character(1:nrow(se))
+
+        tibble::as_tibble(rr) %>%
+          eliminate_GRanges_metadata_columns_also_present_in_Rowdata(se) %>%
+          nest(GRangesList = -group_name) %>%
+          rename(!!f_(se)$symbol := group_name)
+
+      },
+
+      # If standard GRanges (one feature per line)
+      ~ {
+
+        # If GRanges does not have row names
+        if(is.null(rr@ranges@NAMES)) rr@ranges@NAMES = as.character(1:nrow(se))
+
+        tibble::as_tibble(rr) %>%
+          eliminate_GRanges_metadata_columns_also_present_in_Rowdata(se) %>%
+          mutate(!!f_(se)$symbol := rr@ranges@NAMES)
+      }
+
+    ) %>%
+    list()
 
 }
 
-change_reserved_column_names = function(.data){
+#' @importFrom stringr str_replace
+change_reserved_column_names = function(col_data, .data ){
 
-	.data %>%
+  col_data %>%
 
-		setNames(
-			colnames(.) %>%
-				str_replace("^feature$", "feature.x") %>%
-				str_replace("^sample$", "sample.x") %>%
-				str_replace("^coordinate$", "coordinate.x")
-		)
+    setNames(
+      colnames(.) %>%
+        sapply(function(x) if(x==f_(.data)$name) sprintf("%s.x", f_(.data)$name) else x) %>%
+        sapply(function(x) if(x==s_(.data)$name) sprintf("%s.x", s_(.data)$name) else x) %>%
+        str_replace("^coordinate$", "coordinate.x")
+    )
 
 }
 
@@ -93,24 +78,201 @@ change_reserved_column_names = function(.data){
 #' @importFrom purrr reduce
 #' @importFrom SummarizedExperiment assays
 #'
-#' @keywords internal
 #' @noRd
-get_count_datasets <- function(SummarizedExperiment_object) {
-	map2(
-		assays(SummarizedExperiment_object) %>% as.list(),
-		names(assays(SummarizedExperiment_object)),
-		~ .x %>%
-			tibble::as_tibble(rownames = "feature", .name_repair = "minimal") %>%
-
-			# If the matrix does not have sample names, fix column names
-			when(colnames(.x) %>% is.null() ~ setNames(., c(
-				"feature",  seq_len(ncol(.x))
-			)),
-			~ (.)
-			) %>%
-
-			gather(sample, count,-feature) %>%
-			rename(!!.y := count)
-	) %>%
-		reduce(left_join, by = c("feature", "sample"))
+get_count_datasets <- function(se) {
+  map2(
+    assays(se) %>% as.list(),
+    names(assays(se)),
+    ~ {
+
+      # If the counts are in a sparse matrix convert to a matrix
+      # This might happen because the user loaded tidySummarizedExperiment and is
+      # print a SingleCellExperiment
+      if(is(.x, "dgCMatrix")) {
+        .x = as.matrix(.x)
+      }
+
+      .x %>%
+        # matrix() %>%
+        # as.data.frame() %>%
+        tibble::as_tibble(rownames = f_(se)$name, .name_repair = "minimal") %>%
+
+        # If the matrix does not have sample names, fix column names
+        when(colnames(.x) %>% is.null() ~ setNames(., c(
+          f_(se)$name,  seq_len(ncol(.x))
+        )),
+        ~ (.)
+        ) %>%
+
+        gather(!!s_(se)$symbol, !!.y,-!!f_(se)$symbol)
+
+      #%>%
+      #  rename(!!.y := count)
+    }) %>%
+    when(
+      length(.)>0 ~ bind_cols(.,  .name_repair = c("minimal")) %>% .[!duplicated(colnames(.))], # reduce(., left_join, by = c(f_(se)$name, s_(se)$name)),
+      ~ expand.grid(
+        rownames(se), colnames(se)
+      ) %>%
+        setNames(c(f_(se)$name, s_(se)$name)) %>%
+        tibble::as_tibble()
+    ) %>%
+
+    # Add dummy sample or feature if we have empty assay.
+    # This is needed for a correct isualisation of the tibble form
+    when(
+      f_(se)$name %in% colnames(.) %>% not ~ mutate(., !!f_(se)$symbol := as.character(NA)),
+      s_(se)$name %in% colnames(.) %>% not ~ mutate(., !!s_(se)$symbol := as.character(NA)),
+      ~ (.)
+    )
+}
+
+subset_tibble_output = function(.data, count_info, sample_info, gene_info, range_info, .subset){
+  # This function outputs a tibble after subsetting the columns
+  .subset = enquo(.subset)
+
+  # Build template of the output
+  output_colnames =
+    slice(count_info, 0) %>%
+    left_join(slice(sample_info, 0), by=s_(.data)$name) %>%
+    left_join(slice(gene_info, 0), by = f_(.data)$name) %>%
+    when(nrow(range_info) > 0 ~ (.) %>% left_join(range_info, by=f_(.data)$name), ~ (.)) %>%
+    select(!!.subset) %>%
+    colnames()
+
+
+  # Sample table
+  sample_info =
+    sample_info %>%
+    when(
+      colnames(.) %>% intersect(output_colnames) %>% length() %>% equals(0) ~ NULL,
+      select(., one_of(s_(.data)$name, output_colnames)) %>%
+        suppressWarnings()
+    )
+
+  # Ranges table
+  range_info =
+    range_info %>%
+    when(
+      colnames(.) %>% intersect(output_colnames) %>% length() %>% equals(0) ~ NULL,
+      select(., one_of(f_(.data)$name, output_colnames)) %>%
+        suppressWarnings()
+    )
+
+  # Ranges table
+  gene_info =
+    gene_info %>%
+    when(
+      colnames(.) %>% intersect(output_colnames) %>% length() %>% equals(0) ~ NULL,
+      select(., one_of(f_(.data)$name, output_colnames)) %>%
+        suppressWarnings()
+    )
+
+  # Ranges table
+  count_info =
+    count_info %>%
+    when(
+      colnames(.) %>% intersect(output_colnames) %>% length() %>% equals(0) ~ NULL,
+      select(., one_of(f_(.data)$name, s_(.data)$name, output_colnames)) %>%
+        suppressWarnings()
+    )
+
+
+  if(
+    !is.null(count_info) &
+    (
+      !is.null(sample_info) & !is.null(gene_info) |
+
+      # Make exception for weirs cases (e.g. c(sample, counts))
+      (colnames(count_info) %>% outersect(c(f_(.data)$name, s_(.data)$name)) %>% length() %>% gt(0))
+    )
+  ) {
+    output_df =
+      count_info %>%
+      when(!is.null(sample_info) ~ (.) %>% left_join(sample_info, by=s_(.data)$name), ~ (.)) %>%
+      when(!is.null(gene_info) ~ (.) %>% left_join(gene_info, by=f_(.data)$name), ~ (.)) %>%
+      when(!is.null(range_info) ~ (.) %>% left_join(range_info, by=f_(.data)$name), ~ (.))
+  }
+  else if(!is.null(sample_info) ){
+    output_df = sample_info
+  }
+  else if(!is.null(gene_info)){
+    output_df = gene_info %>%
+
+      # If present join GRanges
+      when(!is.null(range_info) ~ (.) %>% left_join(range_info, by=f_(.data)$name), ~ (.))
+  }
+
+  output_df %>%
+
+    # Cleanup
+    select(one_of(output_colnames)) %>%
+    suppressWarnings()
+
+}
+
+
+.as_tibble_optimised = function(x, skip_GRanges = FALSE, .subset = NULL,
+                                .name_repair=c("check_unique", "unique", "universal", "minimal"),
+                                rownames=pkgconfig::get_config("tibble::rownames", NULL)){
+
+  .subset = enquo(.subset)
+
+  sample_info <-
+    colData(x)  %>%
+
+    # If reserved column names are present add .x
+    change_reserved_column_names(x) %>%
+
+    # Convert to tibble
+    tibble::as_tibble(rownames=s_(x)$name) %>%
+    setNames(c(s_(x)$name, colnames(colData(x))))
+
+  range_info <-
+    skip_GRanges %>%
+    when(
+      (.) ~ tibble() %>% list,
+      ~  get_special_datasets(x)
+    ) %>%
+    reduce(left_join, by="coordinate")
+
+  gene_info <-
+    rowData(x)  %>%
+
+    # If reserved column names are present add .x
+    change_reserved_column_names(x)%>%
+
+    # Convert to tibble
+    tibble::as_tibble(rownames=f_(x)$name) %>%
+    setNames(c(f_(x)$name, colnames(rowData(x))))
+
+
+  count_info <- get_count_datasets(x)
+
+  # Return
+  if(quo_is_null(.subset))
+
+    # If I want to return all columns
+    count_info %>%
+    inner_join(sample_info, by=s_(x)$name) %>%
+    inner_join(gene_info, by=f_(x)$name) %>%
+    when(nrow(range_info) > 0 ~ (.) %>% left_join(range_info) %>% suppressMessages(), ~ (.))
+
+  # This function outputs a tibble after subsetting the columns
+  else subset_tibble_output(x, count_info, sample_info, gene_info, range_info, !!.subset)
+
+
+}
+
+#' @importFrom S4Vectors metadata
+f_ =  function(x){
+  # Check if old deprecated columns are used
+  if("feature__" %in% names(metadata(x))) feature__ = metadata(x)$feature__
+  return(feature__)
+}
+
+#' @importFrom S4Vectors metadata
+s_ = function(x){
+  if("sample__" %in% names(metadata(x))) sample__ = metadata(x)$sample__
+  return(sample__)
 }
diff --git a/R/tidyr_methods.R b/R/tidyr_methods.R
index c9693c4e..dc103381 100755
--- a/R/tidyr_methods.R
+++ b/R/tidyr_methods.R
@@ -42,8 +42,8 @@
 #' @examples
 #'
 #' library(dplyr)
-#' 
-#' tidybulk::se_mini %>% tidybulk() %>% nest( data = -feature) %>%
+#'
+#' tidybulk::se_mini %>% tidybulk() %>% nest( data = -.feature) %>%
 #' unnest(data)
 #'
 #' @rdname nest-methods
@@ -57,16 +57,16 @@ unnest.nested_tidybulk <- function (data, cols, ..., keep_empty=FALSE, ptype=NUL
 {
 
 	cols <- enquo(cols)
-	
-	
+
+
 	data %>%
 		drop_class(c("nested_tidybulk", "tt")) %>%
-		tidyr::unnest(!!cols, ..., keep_empty = keep_empty, ptype = ptype, 
+		tidyr::unnest(!!cols, ..., keep_empty = keep_empty, ptype = ptype,
 								names_sep = names_sep, names_repair = names_repair) %>%
 
 		# Attach attributes
 		reattach_internals(data) %>%
-		
+
 		# Add class
 		add_class("tt") %>%
 		add_class("tidybulk")
@@ -84,7 +84,7 @@ unnest.nested_tidybulk <- function (data, cols, ..., keep_empty=FALSE, ptype=NUL
 #'
 #' @examples
 #'
-#' tidybulk::se_mini %>% tidybulk() %>% nest( data = -feature)
+#' tidybulk::se_mini %>% tidybulk() %>% nest( data = -.feature)
 #'
 #' @rdname nest-methods
 #' @name nest
@@ -97,26 +97,26 @@ nest.tidybulk <- function (.data, ..., .names_sep = NULL)
 {
 	cols <- enquos(...)
 	col_name_data  = names(cols)
-	
+
 	.data %>%
-		
+
 		# This is needed otherwise nest goes into loop and fails
 		drop_class(c("tidybulk", "tt")) %>%
 		tidyr::nest(...) %>%
-		
+
 		# Add classes afterwards
 		mutate(!!as.symbol(col_name_data) := map(
-			!!as.symbol(col_name_data), 
-			~ .x %>% 
+			!!as.symbol(col_name_data),
+			~ .x %>%
 				add_class("tt") %>%
 				add_class("tidybulk")
 		)) %>%
-		
+
 		# Attach attributes
 		reattach_internals(.data) %>%
-		
+
 		# Add class
 		add_class("tt") %>%
 		add_class("nested_tidybulk")
-	
-}
\ No newline at end of file
+
+}
diff --git a/R/utilities.R b/R/utilities.R
index e6476b26..6a2bcab9 100755
--- a/R/utilities.R
+++ b/R/utilities.R
@@ -1460,36 +1460,6 @@ rotation = function(m, d) {
 	) %>% as_matrix) %*% m)
 }
 
-#'
-#' @keywords internal
-#' @noRd
-#'
-#' @importFrom dplyr select
-#' @importFrom tibble as_tibble
-#' @importFrom tibble tibble
-get_special_datasets <- function(SummarizedExperiment_object) {
-	if (
-		"RangedSummarizedExperiment" %in% .class2(SummarizedExperiment_object) &
-
-		rowRanges(SummarizedExperiment_object) %>%
-		as.data.frame() %>%
-		nrow() %>%
-		gt(0)
-	) {
-		rowRanges(SummarizedExperiment_object) %>%
-			as.data.frame() %>%
-
-			# Take off rowData columns as there is a recursive anomaly within gene ranges
-			suppressWarnings(
-				select(-one_of(colnames(rowData(SummarizedExperiment_object))))
-			) %>%
-			tibble::as_tibble(rownames="feature") %>%
-			list()
-	} else {
-		tibble() %>% list()
-	}
-}
-
 combineByRow <- function(m, fun = NULL) {
   # Shown here
   #https://stackoverflow.com/questions/8139301/aggregate-rows-in-a-large-matrix-by-rowname
@@ -1595,3 +1565,10 @@ fill_NA_matrix_with_factor_colwise = function(.data, factor){
     .[rn, cn]
 
 }
+
+get_special_column_name_symbol = function(name){
+  list(name = name, symbol = as.symbol(name))
+}
+
+feature__ =  get_special_column_name_symbol(".feature")
+sample__ = get_special_column_name_symbol(".sample")
diff --git a/data/se_mini.rda b/data/se_mini.rda
index 518d41dc..678bc2c5 100755
Binary files a/data/se_mini.rda and b/data/se_mini.rda differ
diff --git a/man/adjust_abundance-methods.Rd b/man/adjust_abundance-methods.Rd
index 9c970b64..1310c087 100644
--- a/man/adjust_abundance-methods.Rd
+++ b/man/adjust_abundance-methods.Rd
@@ -126,9 +126,7 @@ cm = tidybulk::se_mini
 cm$batch = 0
 cm$batch[colnames(cm) \%in\% c("SRR1740035", "SRR1740043")] = 1
 
-res =
  cm \%>\%
- tidybulk(sample, transcript, count) |>
  identify_abundant() |>
 	adjust_abundance(	~ condition + batch	)
 
diff --git a/man/as_matrix.Rd b/man/as_matrix.Rd
index 31c9b089..f03e4296 100644
--- a/man/as_matrix.Rd
+++ b/man/as_matrix.Rd
@@ -21,8 +21,7 @@ Get matrix from tibble
 }
 \examples{
 
-library(dplyr)
 
-tidybulk::se_mini |> tidybulk() |> select(feature, count) |> head() |> as_matrix(rownames=feature)
+tibble(.feature = "CD3G", count=1) |> as_matrix(rownames=.feature)
 
 }
diff --git a/man/deconvolve_cellularity-methods.Rd b/man/deconvolve_cellularity-methods.Rd
index 5c885ed1..a393e5ac 100644
--- a/man/deconvolve_cellularity-methods.Rd
+++ b/man/deconvolve_cellularity-methods.Rd
@@ -131,7 +131,7 @@ CIBERSORT(Y = data, X = reference, ...)
 library(dplyr)
 
 # Subsetting for time efficiency
-tidybulk::se_mini |> tidybulk() |>filter(sample=="SRR1740034") |> deconvolve_cellularity(sample, feature, count, cores = 1)
+tidybulk::se_mini |> deconvolve_cellularity(cores = 1)
 
 
 }
diff --git a/man/dplyr-methods.Rd b/man/dplyr-methods.Rd
index 20999c40..d5e32a52 100644
--- a/man/dplyr-methods.Rd
+++ b/man/dplyr-methods.Rd
@@ -24,7 +24,7 @@ Left join datasets
 }
 \examples{
 `\%>\%` = magrittr::`\%>\%`
-annotation = tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% distinct(sample) \%>\% mutate(source = "AU")
+annotation = tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% distinct(.sample) \%>\% mutate(source = "AU")
 tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% left_join(annotation)
 
 }
diff --git a/man/ensembl_to_symbol-methods.Rd b/man/ensembl_to_symbol-methods.Rd
index 8cc48e50..73365d83 100644
--- a/man/ensembl_to_symbol-methods.Rd
+++ b/man/ensembl_to_symbol-methods.Rd
@@ -44,7 +44,10 @@ This is useful since different resources use ensembl IDs while others use gene s
 
 library(dplyr)
 
-tidybulk::counts_SE |> tidybulk() |> as_tibble() |> ensembl_to_symbol(feature)
+# This function was designed for data.frame
+# Convert from SummarizedExperiment for this example. It is NOT reccomended.
+
+tidybulk::counts_SE |> tidybulk() |> as_tibble() |> ensembl_to_symbol(.feature)
 
 
 
diff --git a/man/fill_missing_abundance-methods.Rd b/man/fill_missing_abundance-methods.Rd
index eff5fa8e..429c64da 100644
--- a/man/fill_missing_abundance-methods.Rd
+++ b/man/fill_missing_abundance-methods.Rd
@@ -70,7 +70,7 @@ This function fills the abundance of missing sample-transcript pair using the me
 }
 \examples{
 
-tidybulk::se_mini |> tidybulk() |> fill_missing_abundance( fill_with = 0)
+# tidybulk::se_mini |>  fill_missing_abundance( fill_with = 0)
 
 
 }
diff --git a/man/get_bibliography-methods.Rd b/man/get_bibliography-methods.Rd
index 6aed72a6..759e8a0e 100644
--- a/man/get_bibliography-methods.Rd
+++ b/man/get_bibliography-methods.Rd
@@ -45,10 +45,8 @@ This methods returns the bibliography list of your workflow from the internals o
 }
 \examples{
 
-# Define tidybulk tibble
-df = tidybulk(tidybulk::se_mini)
 
-get_bibliography(df)
+get_bibliography(tidybulk::se_mini)
 
 
 
diff --git a/man/join-methods.Rd b/man/join-methods.Rd
index 6febe09c..0abea038 100644
--- a/man/join-methods.Rd
+++ b/man/join-methods.Rd
@@ -34,15 +34,15 @@ Full join datasets
 }
 \examples{
 `\%>\%` = magrittr::`\%>\%`
-annotation = tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% distinct(sample) \%>\% mutate(source = "AU")
+annotation = tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% distinct(.sample) \%>\% mutate(source = "AU")
 tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% inner_join(annotation)
 
 `\%>\%` = magrittr::`\%>\%`
-annotation = tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% distinct(sample) \%>\% mutate(source = "AU")
+annotation = tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% distinct(.sample) \%>\% mutate(source = "AU")
 tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% right_join(annotation)
 
 `\%>\%` = magrittr::`\%>\%`
-annotation = tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% distinct(sample) \%>\% mutate(source = "AU")
+annotation = tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% distinct(.sample) \%>\% mutate(source = "AU")
 tidybulk::counts_SE \%>\% tidybulk() \%>\% as_tibble() \%>\% full_join(annotation)
 
 }
diff --git a/man/nest-methods.Rd b/man/nest-methods.Rd
index b81e5897..8e0e16cb 100644
--- a/man/nest-methods.Rd
+++ b/man/nest-methods.Rd
@@ -54,10 +54,10 @@ nest
 
 library(dplyr)
 
-tidybulk::se_mini \%>\% tidybulk() \%>\% nest( data = -feature) \%>\%
+tidybulk::se_mini \%>\% tidybulk() \%>\% nest( data = -.feature) \%>\%
 unnest(data)
 
 
-tidybulk::se_mini \%>\% tidybulk() \%>\% nest( data = -feature)
+tidybulk::se_mini \%>\% tidybulk() \%>\% nest( data = -.feature)
 
 }
diff --git a/man/symbol_to_entrez.Rd b/man/symbol_to_entrez.Rd
index 7a2304f7..f02ca98d 100644
--- a/man/symbol_to_entrez.Rd
+++ b/man/symbol_to_entrez.Rd
@@ -21,6 +21,9 @@ Get ENTREZ id from gene SYMBOL
 }
 \examples{
 
-tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez(.transcript = feature, .sample = sample)
+# This function was designed for data.frame
+# Convert from SummarizedExperiment for this example. It is NOT reccomended.
+
+tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez(.transcript = .feature, .sample = .sample)
 
 }
diff --git a/man/test_differential_cellularity-methods.Rd b/man/test_differential_cellularity-methods.Rd
index 9c55e048..8be30ec7 100644
--- a/man/test_differential_cellularity-methods.Rd
+++ b/man/test_differential_cellularity-methods.Rd
@@ -153,19 +153,8 @@ deconvolve_cellularity(
 	)
 
 	# Cox regression - multiple
-	library(dplyr)
-	library(tidyr)
 
 tidybulk::se_mini |>
-   tidybulk() |>
-
-	# Add survival data
-	nest(data = -sample) |>
-	mutate(
-		days = c(1, 10, 500, 1000, 2000),
-		dead = c(1, 1, 1, 0, 1)
-	) \%>\%
-	unnest(data) |>
 
 	# Test
 	test_differential_cellularity(
diff --git a/man/test_gene_enrichment-methods.Rd b/man/test_gene_enrichment-methods.Rd
index b91308fa..d4126abd 100644
--- a/man/test_gene_enrichment-methods.Rd
+++ b/man/test_gene_enrichment-methods.Rd
@@ -178,8 +178,10 @@ dge %>%
 \examples{
 \dontrun{
 
-df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez( .transcript = feature, .sample = sample)
-df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = sample, .transcript = entrez, .abundance = count)
+library(SummarizedExperiment)
+se = tidybulk::se_mini
+rowData( se)$entrez = rownames(se )
+df_entrez = aggregate_duplicates(se,.transcript = entrez )
 
 library("EGSEA")
 
diff --git a/man/test_gene_overrepresentation-methods.Rd b/man/test_gene_overrepresentation-methods.Rd
index a946dc46..1886d484 100644
--- a/man/test_gene_overrepresentation-methods.Rd
+++ b/man/test_gene_overrepresentation-methods.Rd
@@ -122,9 +122,8 @@ Undelying method:
 }
 \examples{
 
-df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez( .transcript = feature, .sample = sample)
-df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = sample, .transcript = entrez, .abundance = count)
-df_entrez = mutate(df_entrez, do_test = feature \%in\% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
+#se_mini = aggregate_duplicates(tidybulk::se_mini, .transcript = entrez)
+#df_entrez = mutate(df_entrez, do_test = feature \%in\% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
 
 \dontrun{
 	test_gene_overrepresentation(
diff --git a/man/test_gene_rank-methods.Rd b/man/test_gene_rank-methods.Rd
index 2603d497..bd76b5ce 100644
--- a/man/test_gene_rank-methods.Rd
+++ b/man/test_gene_rank-methods.Rd
@@ -134,15 +134,14 @@ mutate(fit =
 
 \dontrun{
 
-df_entrez = tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez( .transcript = feature, .sample = sample)
-df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = sample, .transcript = entrez, .abundance = count)
-df_entrez = mutate(df_entrez, do_test = feature \%in\% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
+df_entrez = tidybulk::se_mini
+df_entrez = mutate(df_entrez, do_test = .feature \%in\% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
 df_entrez  = df_entrez \%>\% test_differential_abundance(~ condition)
 
 
 test_gene_rank(
 	df_entrez,
-		.sample = sample,
+		.sample = .sample,
 	.entrez = entrez,
 		species="Homo sapiens",
    gene_sets =c("C2"),
diff --git a/man/test_stratification_cellularity-methods.Rd b/man/test_stratification_cellularity-methods.Rd
index 629b8d6e..bfad1fd1 100644
--- a/man/test_stratification_cellularity-methods.Rd
+++ b/man/test_stratification_cellularity-methods.Rd
@@ -130,15 +130,6 @@ library(dplyr)
 library(tidyr)
 
 tidybulk::se_mini |>
-   tidybulk() |>
-
-# Add survival data
-nest(data = -sample) |>
-mutate(
-	days = c(1, 10, 500, 1000, 2000),
-	dead = c(1, 1, 1, 0, 1)
-) \%>\%
-unnest(data) |>
 test_stratification_cellularity(
 	survival::Surv(days, dead) ~ .,
 	cores = 1
diff --git a/man/tidybulk-methods.Rd b/man/tidybulk-methods.Rd
index 1ef8cbce..0ef4e838 100644
--- a/man/tidybulk-methods.Rd
+++ b/man/tidybulk-methods.Rd
@@ -54,7 +54,7 @@ arguments are stored as metadata. They can be extracted as attr(<object>, "inter
 }
 \examples{
 
-my_tt =  tidybulk(tidybulk::se_mini)
+tidybulk(tidybulk::se_mini)
 
 
 }
diff --git a/tests/testthat/test-bulk_methods.R b/tests/testthat/test-bulk_methods.R
index ed44cd17..b0fdd2b2 100755
--- a/tests/testthat/test-bulk_methods.R
+++ b/tests/testthat/test-bulk_methods.R
@@ -3,7 +3,7 @@ context('Bulk methods')
 data("se_mini")
 data("breast_tcga_mini_SE")
 
-input_df = se_mini %>% tidybulk() %>% as_tibble() %>% setNames(c("b","a",  "c", "Cell type", "time" , "condition"))
+input_df = se_mini %>% tidybulk() %>% as_tibble() %>% setNames(c("b","a",  "c", "Cell type", "time" , "condition", "days",  "dead", "entrez"))
 
 input_df_breast =   breast_tcga_mini_SE %>% tidybulk() %>% as_tibble() %>% setNames(c( "b","a", "c", "c norm", "call"))
 
@@ -128,7 +128,7 @@ test_that("Adding scaled counts - no object",{
 
 	expect_equal(
 		ncol(res),
-		10
+		13
 	)
 
 })
@@ -379,7 +379,7 @@ test_that("Add differential trancript abundance - no object",{
 
 	expect_equal(
 		ncol(res),
-		12
+		15
 	)
 
 	expect_equal(	class(attr(res, "internals")$edgeR)[1], 	"DGEGLM"  )
@@ -824,7 +824,9 @@ test_that("test prefix",{
 test_that("Get entrez from symbol - no object",{
 
 	res =
-		symbol_to_entrez(input_df, .transcript = b, .sample = a)
+	  input_df %>%
+	  select(-entrez) %>%
+	  symbol_to_entrez(.transcript = b, .sample = a)
 
 	expect_equal(
 		res$entrez[1:4],
@@ -928,7 +930,7 @@ test_that("Get adjusted counts - no object",{
 
 	expect_equal(
 		ncol(res),
-		7
+		9
 	)
 
 })
@@ -957,7 +959,7 @@ test_that("Add adjusted counts - no object",{
 
 	expect_equal(
 		ncol(res),
-		9
+		12
 	)
 
 })
@@ -1008,7 +1010,7 @@ test_that("Get cluster lables based on Kmeans - no object",{
 
 	expect_equal(
 		ncol(res),
-		5
+		7
 	)
 	expect_equal(
 		nrow(res),
@@ -1037,7 +1039,7 @@ test_that("Add cluster lables based on Kmeans - no object",{
 
 	expect_equal(
 		ncol(res),
-		7
+		10
 	)
 
 })
@@ -1179,7 +1181,7 @@ test_that("Get reduced dimensions MDS - no object",{
 
 	expect_equal(
 		ncol(res),
-		6
+		8
 	)
 	expect_equal(
 		nrow(res),
@@ -1208,7 +1210,7 @@ test_that("Add reduced dimensions MDS - no object",{
 
 	expect_equal(
 		ncol(res),
-		9
+		12
 	)
 
 	expect_equal(	class(attr(res, "internals")$MDS[[1]])[1], 	"MDS"  )
@@ -1271,7 +1273,7 @@ test_that("Get reduced dimensions PCA - no object",{
 
 	expect_equal(
 		ncol(res),
-		6
+		8
 	)
 
 	expect_equal(	class(attr(res, "internals")$PCA), 	"prcomp"  )
@@ -1298,7 +1300,7 @@ test_that("Add reduced dimensions PCA - no object",{
 
 	expect_equal(
 		ncol(res),
-		9
+		12
 	)
 
 	expect_equal(	class(attr(res, "internals")$PCA), 	"prcomp"  )
@@ -1447,7 +1449,7 @@ test_that("Get rotated dimensions - no object",{
 
 	expect_equal(
 		ncol(res),
-		8
+		10
 	)
 	expect_equal(
 		nrow(res),
@@ -1486,7 +1488,7 @@ test_that("Add rotated dimensions - no object",{
 
 	expect_equal(
 		ncol(res),
-		11
+		14
 	)
 
 })
@@ -1508,7 +1510,7 @@ test_that("Aggregate duplicated transcript - no object",{
 
 	expect_equal(
 		ncol(res),
-		7
+		10
 	)
 
 })
@@ -1531,7 +1533,7 @@ test_that("Drop redundant correlated - no object",{
 
 	expect_equal(
 		ncol(res),
-		6
+		9
 	)
 
 })
@@ -1595,7 +1597,7 @@ test_that("Add description to symbol",{
 
 	expect_equal(
 		ncol(res),
-		7
+		10
 	)
 
 	res =
@@ -1606,7 +1608,7 @@ test_that("Add description to symbol",{
 
 	expect_equal(
 		ncol(res),
-		7
+		10
 	)
 
 })
@@ -1816,7 +1818,7 @@ test_that("filter abundant - no object",{
 
 	expect_equal(
 		ncol(res1),
-		7
+		10
 	)
 
 	res2 =
@@ -1831,7 +1833,7 @@ test_that("filter abundant - no object",{
 
 	expect_equal(
 		ncol(res2),
-		7
+		10
 	)
 
 	expect_gt(
@@ -1849,7 +1851,7 @@ test_that("filter abundant - no object",{
 
 	expect_equal(
 		ncol(res),
-		7
+		10
 	)
 
 })
@@ -1867,7 +1869,7 @@ test_that("filter abundant with design - no object",{
 
 	expect_equal(
 		ncol(res),
-		7
+		10
 	)
 
 
@@ -1882,13 +1884,13 @@ test_that("nest - no object",{
 
 test_that("pivot",{
 
-	expect_equal(	ncol(pivot_sample(tidybulk(input_df, a, b, c))),	4	)
+	expect_equal(	ncol(pivot_sample(tidybulk(input_df, a, b, c))),	6	)
 
-	expect_equal(	ncol(pivot_sample(input_df, a)),	4	)
+	expect_equal(	ncol(pivot_sample(input_df, a)),	6	)
 
-	expect_equal(	ncol(pivot_transcript(tidybulk(input_df, a, b, c))),	1	)
+	expect_equal(	ncol(pivot_transcript(tidybulk(input_df, a, b, c))),	2	)
 
-	expect_equal(	ncol(pivot_transcript(input_df, b)),	1	)
+	expect_equal(	ncol(pivot_transcript(input_df, b)),	2	)
 
 })
 
@@ -1913,14 +1915,14 @@ test_that("pivot",{
 
 test_that("gene over representation",{
 
-	df_entrez =  se_mini %>% tidybulk() %>% as_tibble() %>% symbol_to_entrez(.transcript = feature, .sample = sample)
-	df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = sample, .transcript = entrez, .abundance = count)
-	df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
+	df_entrez =  se_mini %>% tidybulk() %>% as_tibble()
+	df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = .sample, .transcript = entrez, .abundance = count)
+	df_entrez = mutate(df_entrez, do_test = .feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
 
 	res =
 		test_gene_overrepresentation(
 			df_entrez,
-			.sample = sample,
+			.sample = .sample,
 			.entrez = entrez,
 			.do_test = do_test,
 			species="Homo sapiens"
diff --git a/tests/testthat/test-bulk_methods_SummarizedExperiment.R b/tests/testthat/test-bulk_methods_SummarizedExperiment.R
index d10faafc..4b4c212c 100755
--- a/tests/testthat/test-bulk_methods_SummarizedExperiment.R
+++ b/tests/testthat/test-bulk_methods_SummarizedExperiment.R
@@ -3,65 +3,65 @@ context('Bulk methods SummarizedExperiment')
 data("se_mini")
 data("breast_tcga_mini_SE")
 
-input_df =  setNames(se_mini %>% tidybulk() %>% as_tibble(), c( "b","a",  "c", "Cell type",  "time" , "condition"))
+input_df =  setNames(se_mini %>% tidybulk() %>% as_tibble(), c( "b","a",  "c", "Cell type",  "time" , "condition", "days",  "dead", "entrez"))
 
 input_df_breast = setNames( breast_tcga_mini_SE %>% tidybulk() %>% as_tibble(), c( "b", "a","c", "c norm", "call" ))
 
 test_that("tidybulk SummarizedExperiment conversion",{
 
-	res = tidybulk(tidybulk::se)
+  res = tidybulk(tidybulk::se)
 
-	expect_equal(	class(res)[1],	"tidybulk"	)
+  expect_equal(	class(res)[1],	"tidybulk"	)
 
-	expect_equal(	nrow(res),	800	)
+  expect_equal(	nrow(res),	800	)
 
-	expect_equal(	ncol(res),	21	)
+  expect_equal(	ncol(res),	13	)
 
-	res = res %>% tidybulk:::tidybulk_to_SummarizedExperiment()
+  res = res %>% tidybulk:::tidybulk_to_SummarizedExperiment()
 
-	expect_equal(	class(res)[1],	"SummarizedExperiment"	)
+  expect_equal(	class(res)[1],	"SummarizedExperiment"	)
 
-	expect_equal(	nrow(res),	100	)
+  expect_equal(	nrow(res),	100	)
 
-	expect_equal(	ncol(res),	8	)
+  expect_equal(	ncol(res),	8	)
 
 })
 
 test_that("tidybulk SummarizedExperiment normalisation manual",{
 
-	res = tidybulk(tidybulk:::tidybulk_to_SummarizedExperiment(scale_abundance(tidybulk(se) %>% identify_abundant())))
+  res = tidybulk(tidybulk:::tidybulk_to_SummarizedExperiment(scale_abundance(tidybulk(se) %>% identify_abundant())))
 
-	res2 = tidybulk(se) %>% identify_abundant() %>% scale_abundance()
+  res2 = tidybulk(se) %>% identify_abundant() %>% scale_abundance()
 
-	res %>% distinct(sample, multiplier) %>% pull(multiplier)
-	res2 %>% distinct(sample, multiplier) %>% pull(multiplier)
+  res %>% distinct(.sample, multiplier) %>% pull(multiplier)
+  res2 %>% distinct(.sample, multiplier) %>% pull(multiplier)
 
 
-	expect_equal(
-		res %>% distinct(sample, multiplier) %>% pull(multiplier),
-		res2 %>% distinct(sample, multiplier) %>% pull(multiplier) %>% as.numeric(),
-		tolerance=1e-3
-	)
+  expect_equal(
+    res %>% distinct(.sample, multiplier) %>% pull(multiplier),
+    res2 %>% distinct(.sample, multiplier) %>% pull(multiplier) %>% as.numeric(),
+    tolerance=1e-3
+  )
 
-	expect_equal(	nrow(res),	800	)
+  expect_equal(	nrow(res),	800	)
 
-	expect_equal(	ncol(res),	25	)
+  expect_equal(	ncol(res),	17	)
 
 
-	res = rlang::quo_name(attr(res, "internals")$tt_columns[[4]])
+  res = rlang::quo_name(attr(res, "internals")$tt_columns[[4]])
 
-	expect_equal( res,	"counts_scaled"	)
+  expect_equal( res,	"counts_scaled"	)
 
 })
 
 test_that("tidybulk SummarizedExperiment normalisation",{
 
-	res = se %>% identify_abundant() %>% scale_abundance()
+  res = se %>% identify_abundant() %>% scale_abundance()
 
-	expect_equal(
-		names(SummarizedExperiment::assays(res)),
-		c("counts" ,"counts_scaled")
-	)
+  expect_equal(
+    names(SummarizedExperiment::assays(res)),
+    c("counts" ,"counts_scaled")
+  )
 
 })
 
@@ -85,80 +85,80 @@ test_that("tidybulk SummarizedExperiment normalisation subset",{
 
 test_that("tidybulk SummarizedExperiment clustering",{
 
-	res = cluster_elements(se, method="kmeans", centers = 2)
+  res = cluster_elements(se, method="kmeans", centers = 2)
 
-	expect_equal(
-		tail(names(SummarizedExperiment::colData(res)), 1),
-		"cluster_kmeans"
-	)
+  expect_equal(
+    tail(names(SummarizedExperiment::colData(res)), 1),
+    "cluster_kmeans"
+  )
 
-	expect_equal(
-		levels(SummarizedExperiment::colData(res)$cluster_kmeans),
-		c("1", "2")
-	)
+  expect_equal(
+    levels(SummarizedExperiment::colData(res)$cluster_kmeans),
+    c("1", "2")
+  )
 
 })
 
 test_that("tidybulk SummarizedExperiment clustering",{
 
-	res = se %>% identify_abundant() %>% reduce_dimensions(method="PCA")
+  res = se %>% identify_abundant() %>% reduce_dimensions(method="PCA")
 
-	expect_equal(
-		tail(names(SummarizedExperiment::colData(res)), 1),
-		"PC2"
-	)
+  expect_equal(
+    tail(names(SummarizedExperiment::colData(res)), 1),
+    "PC2"
+  )
 
 })
 
 test_that("Get rotated dimensions - SummarizedExperiment",{
 
-	res.pca =
-		reduce_dimensions(se %>% identify_abundant(),		method="PCA"	)
+  res.pca =
+    reduce_dimensions(se %>% identify_abundant(),		method="PCA"	)
 
-	res =
-		rotate_dimensions(
-			res.pca,
-			dimension_1_column = PC1,
-			dimension_2_column = PC2,
-			rotation_degrees = 45
-		)
+  res =
+    rotate_dimensions(
+      res.pca,
+      dimension_1_column = PC1,
+      dimension_2_column = PC2,
+      rotation_degrees = 45
+    )
 
-	expect_equal(
-		tail(names(SummarizedExperiment::colData(res)), 1),
-		"PC2_rotated_45"
-	)
+  expect_equal(
+    tail(names(SummarizedExperiment::colData(res)), 1),
+    "PC2_rotated_45"
+  )
 
 })
 
 test_that("Drop redundant correlated - SummarizedExperiment",{
 
-	res =
-		remove_redundancy(
-			se,
-			method = "correlation", correlation_threshold = 0.99	)
+  res =
+    remove_redundancy(
+      se,
+      method = "correlation", correlation_threshold = 0.99	)
 
-	expect_equal(
-		nrow(res),
-		100
-	)
+  expect_equal(
+    nrow(res),
+    100
+  )
 
 })
 
 test_that("Get adjusted counts - SummarizedExperiment",{
 
-	cm = se_mini
-	cm$batch = 0
-	cm$batch[colnames(cm) %in% c("SRR1740035", "SRR1740043")] = 1
+  cm = se_mini
+  cm$batch = 0
+  cm$batch[colnames(cm) %in% c("SRR1740035", "SRR1740043")] = 1
 
-	res =
-		adjust_abundance(
-			cm %>% identify_abundant(),
-			~ condition + batch
-		)
+  res =
+    adjust_abundance(
+      cm %>% identify_abundant(),
+      ~ condition + batch
+    )
 
-	expect_equal(nrow(res),	527	)
+  expect_equal(nrow(res),	527	)
 
-	expect_equal(	names(SummarizedExperiment::assays(res)),	c("count" ,"count_adjusted")	)
+  expect_equal(	names(SummarizedExperiment::assays(res)),	c("count" ,"count_adjusted")	)
 
 
 })
@@ -172,287 +172,287 @@ test_that("Aggregate duplicated transcript - SummarizedExperiment",{
 
   res =
     se %>%
-		aggregate_duplicates(	.transcript = bla )
+    aggregate_duplicates(	.transcript = bla )
 
-	expect_equal(	dim(res),	c( 99,   8  )	)
+  expect_equal(	dim(res),	c( 99,   8  )	)
 
 
 })
 
 test_that("Add cell type proportions - SummarizedExperiment",{
 
-	res =		deconvolve_cellularity(se_mini, cores=1	)
+  res =		deconvolve_cellularity(se_mini, cores=1	)
 
-	expect_equal(
-		as.numeric(as.data.frame(res@colData[1, 4:7])),
-		c( 0.6196622 ,0.2525598, 0.0000000, 0.0000000),
-		tolerance=1e-3
-	)
+  expect_equal(
+    as.numeric(as.data.frame(res@colData[1, 6:9])),
+    c(  0.619662   ,     0.25256  ,          0    ,       0),
+    tolerance=1e-3
+  )
 
 })
 
 test_that("differential trancript abundance - SummarizedExperiment",{
 
-	res =		test_differential_abundance(
-		se_mini %>%
-			identify_abundant(factor_of_interest = condition),
-		~ condition
-	)
-
-	w = match(  c("CLEC7A" , "FAM198B", "FCN1"  ,  "HK3"   ), rownames(res) )
-
-	# Quasi likelihood
-	res_tibble =		test_differential_abundance(
-		input_df %>% identify_abundant(a, b, c, factor_of_interest = condition),
-		~ condition	,
-		a, b, c
-	)
-
-	expect_equal(
-		res@elementMetadata[w,]$logFC,
-		c(-11.58385, -13.53406, -12.58204, -12.19271),
-		tolerance=1e-4
-	)
-
-	expect_equal(
-		res@elementMetadata[w,]$logFC,
-		res_tibble %>%
-			pivot_transcript(b) %>%
-			filter(b %in% rownames(res)[w]) %>%
-			dplyr::arrange(b) %>%
-			dplyr::pull(logFC),
-		tolerance=1e-4
-	)
-
-	# Likelihood ratio
-	res2 =		test_differential_abundance(
-		se_mini %>%
-			identify_abundant(factor_of_interest = condition),
-		~ condition, method = "edgeR_likelihood_ratio"	)
-
-	res2_tibble =		test_differential_abundance(
-		input_df %>% identify_abundant(a, b, c, factor_of_interest = condition),
-		~ condition	,
-		a, b, c, method = "edgeR_likelihood_ratio"
-	)
-
-	expect_equal(
-		res2@elementMetadata[w,]$logFC,
-		c(-11.57989, -13.53476, -12.57969, -12.19303),
-		tolerance=1e-4
-	)
-
-	expect_equal(
-		res2@elementMetadata[w,]$logFC,
-		res2_tibble %>%
-			pivot_transcript(b) %>%
-			filter(b %in% rownames(res)[w]) %>%
-			dplyr::arrange(b) %>%
-			dplyr::pull(logFC),
-		tolerance=1e-4
-	)
-
-	# Treat
-	se_mini %>%
-		identify_abundant(a, b, c, factor_of_interest = condition) %>%
-		test_differential_abundance(
-			~ condition,
-			.sample = a,
-			.transcript = b,
-			.abundance = c,
-			scaling_method = "TMM",
-			method = "edgeR_likelihood_ratio",
-			test_above_log2_fold_change = 1,
-			action="only"
-		) %>%
-		`@` (elementMetadata) %>%
-		as_tibble() %>%
-		filter(FDR<0.05) %>%
-		nrow %>%
-		expect_equal(169)
+  res =		test_differential_abundance(
+    se_mini %>%
+      identify_abundant(factor_of_interest = condition),
+    ~ condition
+  )
+
+  w = match(  c("CLEC7A" , "FAM198B", "FCN1"  ,  "HK3"   ), rownames(res) )
+
+  # Quasi likelihood
+  res_tibble =		test_differential_abundance(
+    input_df %>% identify_abundant(a, b, c, factor_of_interest = condition),
+    ~ condition	,
+    a, b, c
+  )
+
+  expect_equal(
+    res@elementMetadata[w,]$logFC,
+    c(-11.58385, -13.53406, -12.58204, -12.19271),
+    tolerance=1e-4
+  )
+
+  expect_equal(
+    res@elementMetadata[w,]$logFC,
+    res_tibble %>%
+      pivot_transcript(b) %>%
+      filter(b %in% rownames(res)[w]) %>%
+      dplyr::arrange(b) %>%
+      dplyr::pull(logFC),
+    tolerance=1e-4
+  )
+
+  # Likelihood ratio
+  res2 =		test_differential_abundance(
+    se_mini %>%
+      identify_abundant(factor_of_interest = condition),
+    ~ condition, method = "edgeR_likelihood_ratio"	)
+
+  res2_tibble =		test_differential_abundance(
+    input_df %>% identify_abundant(a, b, c, factor_of_interest = condition),
+    ~ condition	,
+    a, b, c, method = "edgeR_likelihood_ratio"
+  )
+
+  expect_equal(
+    res2@elementMetadata[w,]$logFC,
+    c(-11.57989, -13.53476, -12.57969, -12.19303),
+    tolerance=1e-4
+  )
+
+  expect_equal(
+    res2@elementMetadata[w,]$logFC,
+    res2_tibble %>%
+      pivot_transcript(b) %>%
+      filter(b %in% rownames(res)[w]) %>%
+      dplyr::arrange(b) %>%
+      dplyr::pull(logFC),
+    tolerance=1e-4
+  )
+
+  # Treat
+  se_mini %>%
+    identify_abundant(a, b, c, factor_of_interest = condition) %>%
+    test_differential_abundance(
+      ~ condition,
+      .sample = a,
+      .transcript = b,
+      .abundance = c,
+      scaling_method = "TMM",
+      method = "edgeR_likelihood_ratio",
+      test_above_log2_fold_change = 1,
+      action="only"
+    ) %>%
+    `@` (elementMetadata) %>%
+    as_tibble() %>%
+    filter(FDR<0.05) %>%
+    nrow %>%
+    expect_equal(169)
 
 })
 
 
 test_that("Voom with treat method",{
 
-	se_mini %>%
-        identify_abundant(a, b, c, factor_of_interest = condition) %>%
-		test_differential_abundance(
-			~ condition,
-			.sample = a,
-			.transcript = b,
-			.abundance = c,
-			method = "limma_voom",
-			test_above_log2_fold_change = 1,
-			action="only"
-		) %>%
-    	`@` (elementMetadata) %>%
-		as_tibble() %>%
-	    filter(adj.P.Val<0.05) %>%
-		nrow %>%
-		expect_equal(97)
-
-    # with multiple contrasts
-    res <-
-    	se_mini %>%
-        identify_abundant(a, b, c, factor_of_interest = Cell.type) %>%
-		test_differential_abundance(
-			~ 0 + Cell.type,
-			.sample = a,
-			.transcript = b,
-			.abundance = c,
-			.contrasts = c("Cell.typeb_cell-Cell.typemonocyte", "Cell.typeb_cell-Cell.typet_cell"),
-			method = "limma_voom",
-			test_above_log2_fold_change = 1,
-			action="only"
-		) %>%
-    	`@` (elementMetadata) %>%
-		as_tibble()
-
-    	res %>%
-    	filter(adj.P.Val___Cell.typeb_cell.Cell.typemonocyte < 0.05) %>%
-		nrow %>%
-		expect_equal(293)
-
-    	res %>%
-    	filter(adj.P.Val___Cell.typeb_cell.Cell.typet_cell < 0.05) %>%
-		nrow %>%
-		expect_equal(246)
+  se_mini %>%
+    identify_abundant(a, b, c, factor_of_interest = condition) %>%
+    test_differential_abundance(
+      ~ condition,
+      .sample = a,
+      .transcript = b,
+      .abundance = c,
+      method = "limma_voom",
+      test_above_log2_fold_change = 1,
+      action="only"
+    ) %>%
+    `@` (elementMetadata) %>%
+    as_tibble() %>%
+    filter(adj.P.Val<0.05) %>%
+    nrow %>%
+    expect_equal(97)
+
+  # with multiple contrasts
+  res <-
+    se_mini %>%
+    identify_abundant(a, b, c, factor_of_interest = Cell.type) %>%
+    test_differential_abundance(
+      ~ 0 + Cell.type,
+      .sample = a,
+      .transcript = b,
+      .abundance = c,
+      .contrasts = c("Cell.typeb_cell-Cell.typemonocyte", "Cell.typeb_cell-Cell.typet_cell"),
+      method = "limma_voom",
+      test_above_log2_fold_change = 1,
+      action="only"
+    ) %>%
+    `@` (elementMetadata) %>%
+    as_tibble()
+
+  res %>%
+    filter(adj.P.Val___Cell.typeb_cell.Cell.typemonocyte < 0.05) %>%
+    nrow %>%
+    expect_equal(293)
+
+  res %>%
+    filter(adj.P.Val___Cell.typeb_cell.Cell.typet_cell < 0.05) %>%
+    nrow %>%
+    expect_equal(246)
 
 })
 
 
 test_that("filter abundant - SummarizedExperiment",{
 
-	res =		keep_abundant(		se	)
+  res =		keep_abundant(		se	)
 
-	expect_equal(		nrow(res),		23	)
+  expect_equal(		nrow(res),		23	)
 
 })
 
 test_that("filter variable - no object",{
 
-	res =		keep_variable(se, top = 5		)
+  res =		keep_variable(se, top = 5		)
 
-	expect_equal(	nrow(res),5	)
+  expect_equal(	nrow(res),5	)
 
-	res =
-		keep_variable(
-			se_mini,
-			top = 5
-		)
+  res =
+    keep_variable(
+      se_mini,
+      top = 5
+    )
 
-	expect_equal(	nrow(res),5	)
+  expect_equal(	nrow(res),5	)
 
-	expect_equivalent(
-		sort(rownames(res)),
-		c("FCN1",  "IGHD",  "IGHM",  "IGKC",  "TCL1A")
-	)
+  expect_equivalent(
+    sort(rownames(res)),
+    c("FCN1",  "IGHD",  "IGHM",  "IGKC",  "TCL1A")
+  )
 
 })
 
 test_that("impute missing",{
 
-	res =
-		input_df %>%
-		dplyr::slice(-1) %>%
-		tidybulk:::tidybulk_to_SummarizedExperiment(a, b, c) %>%
-		impute_missing_abundance(	~ condition	)
+  res =
+    input_df %>%
+    dplyr::slice(-1) %>%
+    tidybulk:::tidybulk_to_SummarizedExperiment(a, b, c) %>%
+    impute_missing_abundance(	~ condition	)
 
-	expect_equal(	SummarizedExperiment::assays(res) %>% as.list() %>% .[[1]] %>% .["TNFRSF4", "SRR1740034"],	6	)
+  expect_equal(	SummarizedExperiment::assays(res) %>% as.list() %>% .[[1]] %>% .["TNFRSF4", "SRR1740034"],	6	)
 
 
-	expect_equal(	nrow(res)*ncol(res),	nrow(input_df)	)
+  expect_equal(	nrow(res)*ncol(res),	nrow(input_df)	)
 
 })
 
 test_that("differential composition",{
 
-	# Cibersort
-	se_mini %>%
-		test_differential_cellularity(. ~ condition	, cores = 1	) %>%
-		pull(`estimate_(Intercept)`) %>%
-		.[[1]] %>%
-		as.integer %>%
-		expect_equal(	-2, 	tollerance =1e-3)
-
-	# llsr
-	se_mini %>%
-	test_differential_cellularity(
-		. ~ condition,
-		method="llsr"
-	) %>%
-		pull(`estimate_(Intercept)`) %>%
-		.[[1]] %>%
-		as.integer %>%
-		expect_equal(	-2, 	tollerance =1e-3)
-
-	# Survival analyses
-	input_df %>%
-		dplyr::select(a, b, c) %>%
-		nest(data = -a) %>%
-		mutate(
-			days = c(1, 10, 500, 1000, 2000),
-			dead = c(1, 1, 1, 0, 1)
-		) %>%
-		unnest(data) %>%
-		tidybulk:::tidybulk_to_SummarizedExperiment(a, b, c) %>%
-		test_differential_cellularity(
-			survival::Surv(days, dead) ~ .,
-			cores = 1
-		) %>%
-		pull(estimate) %>%
-		.[[1]] %>%
-	  expect_equal(26.2662279, tolerance = 30)
-		# round() %in% c(
-		# 	26,  # 97 is the github action MacOS that has different value
-		# 	26, # 112 is the github action UBUNTU that has different value
-		# 	26 # 93 is the github action Windows that has different value
-		# ) %>%
-		# expect_true()
+  # Cibersort
+  se_mini %>%
+    test_differential_cellularity(. ~ condition	, cores = 1	) %>%
+    pull(`estimate_(Intercept)`) %>%
+    .[[1]] %>%
+    as.integer %>%
+    expect_equal(	-2, 	tollerance =1e-3)
+
+  # llsr
+  se_mini %>%
+    test_differential_cellularity(
+      . ~ condition,
+      method="llsr"
+    ) %>%
+    pull(`estimate_(Intercept)`) %>%
+    .[[1]] %>%
+    as.integer %>%
+    expect_equal(	-2, 	tollerance =1e-3)
+
+  # Survival analyses
+  input_df %>%
+    dplyr::select(a, b, c) %>%
+    nest(data = -a) %>%
+    mutate(
+      days = c(1, 10, 500, 1000, 2000),
+      dead = c(1, 1, 1, 0, 1)
+    ) %>%
+    unnest(data) %>%
+    tidybulk:::tidybulk_to_SummarizedExperiment(a, b, c) %>%
+    test_differential_cellularity(
+      survival::Surv(days, dead) ~ .,
+      cores = 1
+    ) %>%
+    pull(estimate) %>%
+    .[[1]] %>%
+    expect_equal(26.2662279, tolerance = 30)
+  # round() %in% c(
+  # 	26,  # 97 is the github action MacOS that has different value
+  # 	26, # 112 is the github action UBUNTU that has different value
+  # 	26 # 93 is the github action Windows that has different value
+  # ) %>%
+  # expect_true()
 
 })
 
 test_that("test_stratification_cellularity",{
 
-	# Cibersort
-	input_df %>%
-		select(a, b, c) %>%
-		nest(data = -a) %>%
-		mutate(
-			days = c(1, 10, 500, 1000, 2000),
-			dead = c(1, 1, 1, 0, 1)
-		) %>%
-		unnest(data) %>%
-		tidybulk:::tidybulk_to_SummarizedExperiment(a, b, c) %>%
-		test_stratification_cellularity(
-			survival::Surv(days, dead) ~ .,
-			cores = 1
-		) %>%
-		pull(.low_cellularity_expected) %>%
-		.[[1]] %>%
-		expect_equal(3.35, tolerance  =1e-1)
-
-	# llsr
-	input_df %>%
-		select(a, b, c) %>%
-		nest(data = -a) %>%
-		mutate(
-			days = c(1, 10, 500, 1000, 2000),
-			dead = c(1, 1, 1, 0, 1)
-		) %>%
-		unnest(data) %>%
-		test_stratification_cellularity(
-			survival::Surv(days, dead) ~ .,
-			.sample = a,
-			.transcript = b,
-			.abundance = c,
-			method = "llsr"
-		) %>%
-		pull(.low_cellularity_expected) %>%
-		.[[1]] %>%
-		expect_equal(3.35, tolerance  =1e-1)
+  # Cibersort
+  input_df %>%
+    select(a, b, c) %>%
+    nest(data = -a) %>%
+    mutate(
+      days = c(1, 10, 500, 1000, 2000),
+      dead = c(1, 1, 1, 0, 1)
+    ) %>%
+    unnest(data) %>%
+    tidybulk:::tidybulk_to_SummarizedExperiment(a, b, c) %>%
+    test_stratification_cellularity(
+      survival::Surv(days, dead) ~ .,
+      cores = 1
+    ) %>%
+    pull(.low_cellularity_expected) %>%
+    .[[1]] %>%
+    expect_equal(3.35, tolerance  =1e-1)
+
+  # llsr
+  input_df %>%
+    select(a, b, c) %>%
+    nest(data = -a) %>%
+    mutate(
+      days = c(1, 10, 500, 1000, 2000),
+      dead = c(1, 1, 1, 0, 1)
+    ) %>%
+    unnest(data) %>%
+    test_stratification_cellularity(
+      survival::Surv(days, dead) ~ .,
+      .sample = a,
+      .transcript = b,
+      .abundance = c,
+      method = "llsr"
+    ) %>%
+    pull(.low_cellularity_expected) %>%
+    .[[1]] %>%
+    expect_equal(3.35, tolerance  =1e-1)
 })
 
 
@@ -498,28 +498,27 @@ test_that("test_stratification_cellularity",{
 
 test_that("pivot",{
 
-	expect_equal(	ncol(pivot_sample(se_mini)	), 4)
+  expect_equal(	ncol(pivot_sample(se_mini)	), 6)
 
-	expect_equal(	ncol(pivot_transcript(se_mini)	), 1)
+  expect_equal(	ncol(pivot_transcript(se_mini)	), 2)
 
 })
 
 test_that("gene over representation",{
 
-	df_entrez = symbol_to_entrez(input_df, .transcript = b, .sample = a)
-	df_entrez = aggregate_duplicates(df_entrez, aggregation_function = sum, .sample = a, .transcript = entrez, .abundance = c)
-	df_entrez = mutate(df_entrez, do_test = b %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
+  df_entrez = aggregate_duplicates(input_df, aggregation_function = sum, .sample = a, .transcript = entrez, .abundance = c)
+  df_entrez = mutate(df_entrez, do_test = b %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
 
-	res =
-		df_entrez %>%
-		tidybulk:::tidybulk_to_SummarizedExperiment(a, b, c) %>%
-		test_gene_overrepresentation(
-			.entrez = entrez,
-			.do_test = do_test,
-			species="Homo sapiens"
-		)
+  res =
+    df_entrez %>%
+    tidybulk:::tidybulk_to_SummarizedExperiment(a, b, c) %>%
+    test_gene_overrepresentation(
+      .entrez = entrez,
+      .do_test = do_test,
+      species="Homo sapiens"
+    )
 
-	expect_equal(	ncol(res),	10	)
+  expect_equal(	ncol(res),	10	)
 
 
 
@@ -531,22 +530,22 @@ test_that("Only reduced dimensions MDS - no object",{
 
 
 
-	res =
-	  breast_tcga_mini_SE %>%
-		reduce_dimensions(method = "MDS")
+  res =
+    breast_tcga_mini_SE %>%
+    reduce_dimensions(method = "MDS")
 
-	expect_equal(
-		res$`Dim1`[1:4],
-		c(-0.2723808836, -0.1105770207, -0.3034092668, -0.0064569358),
-		tolerance=10
-	)
+  expect_equal(
+    res$`Dim1`[1:4],
+    c(-0.2723808836, -0.1105770207, -0.3034092668, -0.0064569358),
+    tolerance=10
+  )
 
-	expect_equal(
-		ncol(colData(res)),
-		3
-	)
+  expect_equal(
+    ncol(colData(res)),
+    3
+  )
 
-	expect_equal(	class(attr(res, "internals")$MDS[[1]])[1], 	"MDS"  )
+  expect_equal(	class(attr(res, "internals")$MDS[[1]])[1], 	"MDS"  )
 
 })