diff --git a/DESCRIPTION b/DESCRIPTION
index 3b599605..b8a28dec 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: spatialLIBD
 Title: LIBD Visium spatial transcriptomics human pilot data inspector
-Version: 0.99.6
+Version: 0.99.7
 Date: 2020-02-27
 Authors@R:
     c(
diff --git a/NEWS.md b/NEWS.md
index 70b1d9d5..9b9709f2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,7 @@
+# spatialLIBD 0.99.7
+
+* Run a test that might help with https://github.com/r-lib/pkgdown/issues/1230.
+
 # spatialLIBD 0.99.6
 
 * Add mirrors for the shiny app and change the main location.
diff --git a/R/check_image_path.R b/R/check_image_path.R
index 52b3074c..477d1847 100644
--- a/R/check_image_path.R
+++ b/R/check_image_path.R
@@ -13,13 +13,13 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Get the path to the images
 #' img_path <- system.file('app', 'www', 'data', package = 'spatialLIBD')
 #'
 #' ## Check the object
-#' check_image_path(img_path, ori_sce)
+#' check_image_path(img_path, sce)
 #'
 
 check_image_path <- function(image_path, sce) {
diff --git a/R/check_modeling_results.R b/R/check_modeling_results.R
index ac114819..0eceef44 100644
--- a/R/check_modeling_results.R
+++ b/R/check_modeling_results.R
@@ -12,11 +12,11 @@
 #'
 #' @examples
 #'
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Check the object
-#' xx <- check_modeling_results(ori_modeling_results)
+#' xx <- check_modeling_results(modeling_results)
 #'
 
 check_modeling_results <- function(modeling_results) {
diff --git a/R/check_sce.R b/R/check_sce.R
index b3f444e8..45db614d 100644
--- a/R/check_sce.R
+++ b/R/check_sce.R
@@ -16,10 +16,10 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Check the object
-#' check_sce(ori_sce)
+#' check_sce(sce)
 #'
 
 check_sce <- function(sce,
diff --git a/R/check_sce_layer.R b/R/check_sce_layer.R
index c2f71d05..d3ea293c 100644
--- a/R/check_sce_layer.R
+++ b/R/check_sce_layer.R
@@ -15,10 +15,10 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data('sce_layer')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data('sce_layer')
 #'
 #' ## Check the object
-#' check_sce_layer(ori_sce_layer)
+#' check_sce_layer(sce_layer)
 #'
 
 check_sce_layer <- function(sce_layer, variables = 'layer_guess_reordered_short') {
diff --git a/R/fetch_data.R b/R/fetch_data.R
index 2ab94b11..dbded0b5 100644
--- a/R/fetch_data.R
+++ b/R/fetch_data.R
@@ -42,7 +42,7 @@
 #'
 #' ## Download the SingleCellExperiment object
 #' ## at the layer-level
-#' sce_layer <- fetch_data('sce_layer')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data('sce_layer')
 #'
 #' ## Explore the data
 #' sce_layer
diff --git a/R/gene_set_enrichment.R b/R/gene_set_enrichment.R
index 5f2f2ba3..c9bb82e8 100644
--- a/R/gene_set_enrichment.R
+++ b/R/gene_set_enrichment.R
@@ -41,13 +41,13 @@
 #' )
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Compute the gene set enrichment results
 #' asd_sfari_enrichment <- gene_set_enrichment(
 #'     gene_list = asd_sfari_geneList,
-#'     modeling_results = ori_modeling_results,
+#'     modeling_results = modeling_results,
 #'     model_type = 'enrichment'
 #' )
 #'
diff --git a/R/gene_set_enrichment_plot.R b/R/gene_set_enrichment_plot.R
index 3fbddc95..5aa029f5 100644
--- a/R/gene_set_enrichment_plot.R
+++ b/R/gene_set_enrichment_plot.R
@@ -52,13 +52,13 @@
 #' )
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Compute the gene set enrichment results
 #' asd_sfari_enrichment <- gene_set_enrichment(
 #'     gene_list = asd_sfari_geneList,
-#'     modeling_results = ori_modeling_results,
+#'     modeling_results = modeling_results,
 #'     model_type = 'enrichment'
 #' )
 #'
diff --git a/R/geom_spatial.R b/R/geom_spatial.R
index 55fca38c..bceb44de 100644
--- a/R/geom_spatial.R
+++ b/R/geom_spatial.R
@@ -21,11 +21,11 @@
 #'
 #' @examples
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Select the first sample and extract the data
-#' sample_id <- unique(ori_sce$sample_name)[1]
-#' sce_sub <- ori_sce[, ori_sce$sample_name == sample_id]
+#' sample_id <- unique(sce$sample_name)[1]
+#' sce_sub <- sce[, sce$sample_name == sample_id]
 #' sample_df <- as.data.frame(colData(sce_sub))
 #'
 #' ## Make a plot using geom_spatial
@@ -41,6 +41,9 @@
 #'         y = 0.5
 #'     )
 #'
+#' ## Clean up
+#' rm(sce_sub)
+#'
 
 geom_spatial <-  function(mapping = NULL,
     data = NULL,
diff --git a/R/get_colors.R b/R/get_colors.R
index 5d580d8a..9b81ce51 100644
--- a/R/get_colors.R
+++ b/R/get_colors.R
@@ -19,15 +19,15 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data('sce')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data('sce')
 #'
 #' ## Example layer colors with the corresponding names
-#' get_colors(libd_layer_colors, ori_sce_layer$layer_guess)
-#' get_colors(libd_layer_colors, ori_sce_layer$layer_guess_reordered_short)
+#' get_colors(libd_layer_colors, sce_layer$layer_guess)
+#' get_colors(libd_layer_colors, sce_layer$layer_guess_reordered_short)
 #'
 #' ## Example where colors are assigned automatically
 #' ## based on a pre-defined set of colors
-#' get_colors(clusters = ori_sce_layer$kmeans_k7)
+#' get_colors(clusters = sce_layer$kmeans_k7)
 #'
 #' ## Example where Polychrome::palette36.colors() gets used
 #' get_colors(clusters = letters[seq_len(13)])
diff --git a/R/layer_boxplot.R b/R/layer_boxplot.R
index 21ab4206..e177fbdc 100644
--- a/R/layer_boxplot.R
+++ b/R/layer_boxplot.R
@@ -36,39 +36,39 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
-#' if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data(type = 'sce_layer')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data(type = 'sce_layer')
 #'
 #' ## Top 2 genes from the enrichment model
 #' sig_genes <- sig_genes_extract_all(n = 2,
-#'     modeling_results = ori_modeling_results,
-#'     sce_layer = ori_sce_layer)
+#'     modeling_results = modeling_results,
+#'     sce_layer = sce_layer)
 #'
 #' ## Example default boxplot
-#' layer_boxplot(sig_genes = sig_genes, sce_layer = ori_sce_layer)
+#' layer_boxplot(sig_genes = sig_genes, sce_layer = sce_layer)
 #'
 #' ## Now show the long title version
 #' layer_boxplot(sig_genes = sig_genes,
 #'     short_title = FALSE,
-#'     sce_layer = ori_sce_layer)
+#'     sce_layer = sce_layer)
 #'
 #' layer_boxplot(
 #'     i = which(sig_genes$model_type == 'anova')[1],
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer
+#'     sce_layer = sce_layer
 #' )
 #' layer_boxplot(
 #'     i = which(sig_genes$model_type == 'pairwise')[1],
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer
+#'     sce_layer = sce_layer
 #' )
 #'
 #' ## Viridis colors displayed in the shiny app
 #' library('viridisLite')
 #' layer_boxplot(
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer,
+#'     sce_layer = sce_layer,
 #'     col_low_box = viridis(4)[2],
 #'     col_low_point = viridis(4)[1],
 #'     col_high_box = viridis(4)[3],
@@ -78,7 +78,7 @@
 #' ## Paper colors displayed in the shiny app
 #' layer_boxplot(
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer,
+#'     sce_layer = sce_layer,
 #'     col_low_box = 'palegreen3',
 #'     col_low_point = 'springgreen2',
 #'     col_high_box = 'darkorange2',
@@ -89,7 +89,7 @@
 #' layer_boxplot(
 #'     i = which(sig_genes$model_type == 'pairwise')[1],
 #'     sig_genes = sig_genes,
-#'     sce_layer = ori_sce_layer,
+#'     sce_layer = sce_layer,
 #'     col_bkg_box = 'grey90',
 #'     col_bkg_point = 'grey60',
 #'     col_low_box = 'lightcyan',
diff --git a/R/layer_stat_cor.R b/R/layer_stat_cor.R
index 5b14d336..0222cfa8 100644
--- a/R/layer_stat_cor.R
+++ b/R/layer_stat_cor.R
@@ -24,13 +24,13 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Compute the correlations
 #' cor_stats_layer <- layer_stat_cor(
 #'     tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
-#'     ori_modeling_results,
+#'     modeling_results,
 #'     'enrichment'
 #' )
 #'
diff --git a/R/layer_stat_cor_plot.R b/R/layer_stat_cor_plot.R
index 1bc93036..b300913c 100644
--- a/R/layer_stat_cor_plot.R
+++ b/R/layer_stat_cor_plot.R
@@ -31,13 +31,13 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
 #'
 #' ## Compute the correlations
 #' cor_stats_layer <- layer_stat_cor(
 #'     tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
-#'     ori_modeling_results,
+#'     modeling_results,
 #'     'enrichment'
 #' )
 #'
diff --git a/R/run_app.R b/R/run_app.R
index 08f84061..57ae8b79 100644
--- a/R/run_app.R
+++ b/R/run_app.R
@@ -93,7 +93,7 @@ run_app <- function(sce = fetch_data(type = 'sce'),
     sce <-
         check_sce(sce,
             variables = c(spatial_libd_var, sce_discrete_vars, sce_continuous_vars))
-    sce_layer <-
+    if (!exists('sce_layer')) sce_layer <-
         check_sce_layer(sce_layer, variables = spatial_libd_var)
     modeling_results <- check_modeling_results(modeling_results)
     image_path <- check_image_path(image_path, sce)
diff --git a/R/sce_image_clus.R b/R/sce_image_clus.R
index 039aeba9..a4e223ec 100644
--- a/R/sce_image_clus.R
+++ b/R/sce_image_clus.R
@@ -27,14 +27,14 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Check the colors defined by Lukas M Weber
 #' libd_layer_colors
 #'
 #' ## Use the manual color palette by Lukas M Weber
 #' sce_image_clus(
-#'     sce = ori_sce,
+#'     sce = sce,
 #'     clustervar = 'layer_guess_reordered',
 #'     sampleid = '151673',
 #'     colors = libd_layer_colors,
@@ -43,7 +43,7 @@
 #'
 #' ## Without histology
 #' sce_image_clus(
-#'     sce = ori_sce,
+#'     sce = sce,
 #'     clustervar = 'layer_guess_reordered',
 #'     sampleid = '151673',
 #'     colors = libd_layer_colors,
diff --git a/R/sce_image_clus_p.R b/R/sce_image_clus_p.R
index 273c46bf..e06c629b 100644
--- a/R/sce_image_clus_p.R
+++ b/R/sce_image_clus_p.R
@@ -18,8 +18,8 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
-#' sce_sub <- ori_sce[, ori_sce$sample_name == '151673']
+#' if (!exists('sce')) sce <- fetch_data('sce')
+#' sce_sub <- sce[, sce$sample_name == '151673']
 #'
 #' ## Use the manual color palette by Lukas M Weber
 #' ## Don't plot the histology information
@@ -33,6 +33,9 @@
 #'     spatial = FALSE
 #' )
 #'
+#' ## Clean up
+#' rm(sce_sub)
+#'
 
 sce_image_clus_p <-
     function(sce,
diff --git a/R/sce_image_gene.R b/R/sce_image_gene.R
index 16fcf60b..a07ee8ba 100644
--- a/R/sce_image_gene.R
+++ b/R/sce_image_gene.R
@@ -31,15 +31,15 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Valid `geneid` values are those in
-#' head(rowData(ori_sce)$gene_search)
-#' ## or continuous variables stored in colData(ori_sce)
+#' head(rowData(sce)$gene_search)
+#' ## or continuous variables stored in colData(sce)
 #'
 #' ## Visualize a default gene on the non-viridis scale
 #' sce_image_gene(
-#'     sce = ori_sce,
+#'     sce = sce,
 #'     sampleid = '151507',
 #'     viridis = FALSE
 #' )
@@ -47,7 +47,7 @@
 #' ## Visualize a continuous variable, in this case, the ratio of chrM
 #' ## gene expression compared to the total expression at the spot-level
 #' sce_image_gene(
-#'     sce = ori_sce,
+#'     sce = sce,
 #'     sampleid = '151507',
 #'     geneid = 'expr_chrM_ratio'
 #' )
diff --git a/R/sce_image_gene_p.R b/R/sce_image_gene_p.R
index bceee322..bc94b7aa 100644
--- a/R/sce_image_gene_p.R
+++ b/R/sce_image_gene_p.R
@@ -20,10 +20,10 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Prepare the data for the plotting function
-#' sce_sub <- ori_sce[, ori_sce$sample_name == '151673']
+#' sce_sub <- sce[, sce$sample_name == '151673']
 #' df <- as.data.frame(colData(sce_sub))
 #' df$COUNT <- df$expr_chrM_ratio
 #'
@@ -37,6 +37,9 @@
 #'     spatial = FALSE
 #' )
 #'
+#' ## Clean up
+#' rm(sce_sub)
+#'
 
 sce_image_gene_p <-
     function(sce,
diff --git a/R/sce_image_grid.R b/R/sce_image_grid.R
index b1d60e35..15b864ce 100644
--- a/R/sce_image_grid.R
+++ b/R/sce_image_grid.R
@@ -24,10 +24,10 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Subset to two samples of interest
-#' sce_sub <- ori_sce[, ori_sce$sample_name %in% c('151673', '151674')]
+#' sce_sub <- sce[, sce$sample_name %in% c('151673', '151674')]
 #'
 #' ## Obtain the plot list
 #' p_list <-
@@ -40,6 +40,9 @@
 #'         colors = libd_layer_colors
 #'     )
 #'
+#' ## Clean up
+#' rm(sce_sub)
+#'
 #' ## Visualize the spatial adjacent replicates for position = 0 micro meters
 #' ## for subject 3
 #' cowplot::plot_grid(plotlist = p_list, ncol = 2)
diff --git a/R/sce_image_grid_gene.R b/R/sce_image_grid_gene.R
index 166d9c63..82c1b672 100644
--- a/R/sce_image_grid_gene.R
+++ b/R/sce_image_grid_gene.R
@@ -19,10 +19,10 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+#' if (!exists('sce')) sce <- fetch_data('sce')
 #'
 #' ## Subset to two samples of interest
-#' sce_sub <- ori_sce[, ori_sce$sample_name %in% c('151673', '151674')]
+#' sce_sub <- sce[, sce$sample_name %in% c('151673', '151674')]
 #'
 #' ## Obtain the plot list
 #' p_list <-
@@ -32,9 +32,13 @@
 #'         return_plots = TRUE
 #'     )
 #'
+#' ## Clean up
+#' rm(sce_sub)
+#'
 #' ## Visualize the spatial adjacent replicates for position = 0 micro meters
 #' ## for subject 3
 #' cowplot::plot_grid(plotlist = p_list, ncol = 2)
+#'
 
 sce_image_grid_gene <-
     function(sce,
diff --git a/R/sig_genes_extract.R b/R/sig_genes_extract.R
index a0f5a380..440da786 100644
--- a/R/sig_genes_extract.R
+++ b/R/sig_genes_extract.R
@@ -42,21 +42,21 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
-#' if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data(type = 'sce_layer')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data(type = 'sce_layer')
 #'
 #' ## anova top 10 genes
 #' sig_genes_extract(
-#'     modeling_results = ori_modeling_results,
-#'     sce_layer = ori_sce_layer
+#'     modeling_results = modeling_results,
+#'     sce_layer = sce_layer
 #' )
 #'
 #' ## Extract all genes
 #' sig_genes_extract(
-#'     modeling_results = ori_modeling_results,
-#'     sce_layer = ori_sce_layer,
-#'     n = nrow(ori_sce_layer)
+#'     modeling_results = modeling_results,
+#'     sce_layer = sce_layer,
+#'     n = nrow(sce_layer)
 #' )
 #'
 
diff --git a/R/sig_genes_extract_all.R b/R/sig_genes_extract_all.R
index 46be2a05..b0d673ac 100644
--- a/R/sig_genes_extract_all.R
+++ b/R/sig_genes_extract_all.R
@@ -17,14 +17,14 @@
 #' @examples
 #'
 #' ## Obtain the necessary data
-#' if (!exists('ori_modeling_results'))
-#'     ori_modeling_results <- fetch_data(type = 'modeling_results')
-#' if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data(type = 'sce_layer')
+#' if (!exists('modeling_results'))
+#'     modeling_results <- fetch_data(type = 'modeling_results')
+#' if (!exists('sce_layer')) sce_layer <- fetch_data(type = 'sce_layer')
 #'
 #' ## top 10 genes for all models
 #' sig_genes_extract_all(
-#'     modeling_results = ori_modeling_results,
-#'     sce_layer = ori_sce_layer
+#'     modeling_results = modeling_results,
+#'     sce_layer = sce_layer
 #' )
 #'
 
diff --git a/app.R b/app.R
index 87df93a3..e9eeae3f 100644
--- a/app.R
+++ b/app.R
@@ -14,23 +14,23 @@ options("golem.app.prod" = TRUE)
 
 ## In this case, I'm using my local files instead of downloading them
 ## by telling fetch_data(destdir) where my local files are stored at.
-ori_sce <-
+if (!exists('sce')) sce <-
     fetch_data('sce', here::here('data-raw/spatialLIBD_files'))
-ori_sce_layer <-
+if (!exists('sce_layer')) sce_layer <-
     fetch_data('sce_layer', here::here('data-raw/spatialLIBD_files'))
-ori_modeling_results <-
+if (!exists('modeling_results')) modeling_results <-
     fetch_data('modeling_results',
         here::here('data-raw/spatialLIBD_files'))
 
-ori_sig_genes <-
-    sig_genes_extract_all(n = nrow(ori_sce_layer),
-        ori_modeling_results,
-        sce_layer = ori_sce_layer)
+sig_genes <-
+    sig_genes_extract_all(n = nrow(sce_layer),
+        modeling_results,
+        sce_layer = sce_layer)
 
 options(repos = BiocManager::repositories())
 spatialLIBD::run_app(
-    sce = ori_sce,
-    sce_layer = ori_sce_layer,
-    modeling_results = ori_modeling_results,
-    sig_genes = ori_sig_genes,
+    sce = sce,
+    sce_layer = sce_layer,
+    modeling_results = modeling_results,
+    sig_genes = sig_genes,
 ) # add parameters here (if any)
diff --git a/data-raw/asd_sfari_geneList.R b/data-raw/asd_sfari_geneList.R
index f223701d..969ff122 100644
--- a/data-raw/asd_sfari_geneList.R
+++ b/data-raw/asd_sfari_geneList.R
@@ -53,17 +53,17 @@ testthat::expect_equivalent(asd_sfari_geneList_table, as.matrix(test))
 
 
 ## Test that the input data works for gene_set_enrichment()
-if (!exists('ori_modeling_results'))
-    ori_modeling_results <- fetch_data(type = 'modeling_results')
+if (!exists('modeling_results'))
+    modeling_results <- fetch_data(type = 'modeling_results')
 
 asd_sfari_enrichment <- gene_set_enrichment(
     gene_list = asd_sfari_geneList,
-    modeling_results = ori_modeling_results,
+    modeling_results = modeling_results,
     model_type = 'enrichment'
 )
 test_enrichment <- gene_set_enrichment(
     gene_list = test,
-    modeling_results = ori_modeling_results,
+    modeling_results = modeling_results,
     model_type = 'enrichment'
 )
 
diff --git a/data-raw/logo.R b/data-raw/logo.R
index 365e6052..ba7439ee 100644
--- a/data-raw/logo.R
+++ b/data-raw/logo.R
@@ -1,12 +1,12 @@
 library('spatialLIBD')
 
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+if (!exists('sce')) sce <- fetch_data('sce')
 
 
 pdf(here::here('data-raw', 'logo.pdf'), useDingbats = FALSE)
 sce_image_clus(
-    sce = ori_sce,
+    sce = sce,
     clustervar = 'layer_guess_reordered',
     sampleid = '151673',
     colors = libd_layer_colors,
diff --git a/inst/scripts/make-data_spatialLIBD.R b/inst/scripts/make-data_spatialLIBD.R
index ce7814a3..099611e2 100644
--- a/inst/scripts/make-data_spatialLIBD.R
+++ b/inst/scripts/make-data_spatialLIBD.R
@@ -261,23 +261,23 @@ colnames(colData(sce))[colnames(colData(sce)) == 'Cluster10X'] <-
     'GraphBased'
 
 ## Double check that it all works!
-# ori_sce_layer <-
+# sce_layer <-
 #     fetch_data('sce_layer', here::here('data-raw/spatialLIBD_files'))
-# ori_modeling_results <-
+# modeling_results <-
 #     fetch_data('modeling_results',
 #         here::here('data-raw/spatialLIBD_files'))
 #
-# ori_sig_genes <-
-#     sig_genes_extract_all(n = nrow(ori_sce_layer),
-#         ori_modeling_results,
-#         sce_layer = ori_sce_layer)
+# sig_genes <-
+#     sig_genes_extract_all(n = nrow(sce_layer),
+#         modeling_results,
+#         sce_layer = sce_layer)
 #
 #
 # spatialLIBD::run_app(
 #     sce = sce,
-#     sce_layer = ori_sce_layer,
-#     modeling_results = ori_modeling_results,
-#     sig_genes = ori_sig_genes,
+#     sce_layer = sce_layer,
+#     modeling_results = modeling_results,
+#     sig_genes = sig_genes,
 #     sce_discrete_vars = c('GraphBased',
 #         'Layer',
 #         'Maynard',
diff --git a/man/check_image_path.Rd b/man/check_image_path.Rd
index 284000ea..d9f28a69 100644
--- a/man/check_image_path.Rd
+++ b/man/check_image_path.Rd
@@ -29,13 +29,13 @@ structure. For more details please check the vignette documentation.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+if (!exists('sce')) sce <- fetch_data('sce')
 
 ## Get the path to the images
 img_path <- system.file('app', 'www', 'data', package = 'spatialLIBD')
 
 ## Check the object
-check_image_path(img_path, ori_sce)
+check_image_path(img_path, sce)
 
 }
 \seealso{
diff --git a/man/check_modeling_results.Rd b/man/check_modeling_results.Rd
index cef34495..0d80535a 100644
--- a/man/check_modeling_results.Rd
+++ b/man/check_modeling_results.Rd
@@ -23,11 +23,11 @@ For more details please check the vignette documentation.
 }
 \examples{
 
-if (!exists('ori_modeling_results'))
-    ori_modeling_results <- fetch_data(type = 'modeling_results')
+if (!exists('modeling_results'))
+    modeling_results <- fetch_data(type = 'modeling_results')
 
 ## Check the object
-xx <- check_modeling_results(ori_modeling_results)
+xx <- check_modeling_results(modeling_results)
 
 }
 \seealso{
diff --git a/man/check_sce.Rd b/man/check_sce.Rd
index 14bd850b..c6db4c09 100644
--- a/man/check_sce.Rd
+++ b/man/check_sce.Rd
@@ -36,10 +36,10 @@ For more details please check the vignette documentation.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+if (!exists('sce')) sce <- fetch_data('sce')
 
 ## Check the object
-check_sce(ori_sce)
+check_sce(sce)
 
 }
 \seealso{
diff --git a/man/check_sce_layer.Rd b/man/check_sce_layer.Rd
index 82f6f754..22101e2e 100644
--- a/man/check_sce_layer.Rd
+++ b/man/check_sce_layer.Rd
@@ -26,10 +26,10 @@ For more details please check the vignette documentation.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data('sce_layer')
+if (!exists('sce_layer')) sce_layer <- fetch_data('sce_layer')
 
 ## Check the object
-check_sce_layer(ori_sce_layer)
+check_sce_layer(sce_layer)
 
 }
 \seealso{
diff --git a/man/fetch_data.Rd b/man/fetch_data.Rd
index fd6cdd12..65d4b05a 100644
--- a/man/fetch_data.Rd
+++ b/man/fetch_data.Rd
@@ -53,7 +53,7 @@ https://github.com/LieberInstitute/spatialLIBD/blob/master/inst/scripts/make-dat
 
 ## Download the SingleCellExperiment object
 ## at the layer-level
-sce_layer <- fetch_data('sce_layer')
+if (!exists('sce_layer')) sce_layer <- fetch_data('sce_layer')
 
 ## Explore the data
 sce_layer
diff --git a/man/gene_set_enrichment.Rd b/man/gene_set_enrichment.Rd
index 0e198513..8a942e21 100644
--- a/man/gene_set_enrichment.Rd
+++ b/man/gene_set_enrichment.Rd
@@ -73,13 +73,13 @@ asd_sfari_geneList <- list(
 )
 
 ## Obtain the necessary data
-if (!exists('ori_modeling_results'))
-    ori_modeling_results <- fetch_data(type = 'modeling_results')
+if (!exists('modeling_results'))
+    modeling_results <- fetch_data(type = 'modeling_results')
 
 ## Compute the gene set enrichment results
 asd_sfari_enrichment <- gene_set_enrichment(
     gene_list = asd_sfari_geneList,
-    modeling_results = ori_modeling_results,
+    modeling_results = modeling_results,
     model_type = 'enrichment'
 )
 
diff --git a/man/gene_set_enrichment_plot.Rd b/man/gene_set_enrichment_plot.Rd
index fd3a1260..783fdf55 100644
--- a/man/gene_set_enrichment_plot.Rd
+++ b/man/gene_set_enrichment_plot.Rd
@@ -75,13 +75,13 @@ asd_sfari_geneList <- list(
 )
 
 ## Obtain the necessary data
-if (!exists('ori_modeling_results'))
-    ori_modeling_results <- fetch_data(type = 'modeling_results')
+if (!exists('modeling_results'))
+    modeling_results <- fetch_data(type = 'modeling_results')
 
 ## Compute the gene set enrichment results
 asd_sfari_enrichment <- gene_set_enrichment(
     gene_list = asd_sfari_geneList,
-    modeling_results = ori_modeling_results,
+    modeling_results = modeling_results,
     model_type = 'enrichment'
 )
 
diff --git a/man/geom_spatial.Rd b/man/geom_spatial.Rd
index 051fd5a5..66b638bf 100644
--- a/man/geom_spatial.Rd
+++ b/man/geom_spatial.Rd
@@ -44,11 +44,11 @@ as in \code{\link[=sce_image_gene_p]{sce_image_gene_p()}}.
 }
 \examples{
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+if (!exists('sce')) sce <- fetch_data('sce')
 
 ## Select the first sample and extract the data
-sample_id <- unique(ori_sce$sample_name)[1]
-sce_sub <- ori_sce[, ori_sce$sample_name == sample_id]
+sample_id <- unique(sce$sample_name)[1]
+sce_sub <- sce[, sce$sample_name == sample_id]
 sample_df <- as.data.frame(colData(sce_sub))
 
 ## Make a plot using geom_spatial
@@ -64,6 +64,9 @@ ggplot2::ggplot(sample_df,
         y = 0.5
     )
 
+## Clean up
+rm(sce_sub)
+
 }
 \author{
 10x Genomics
diff --git a/man/get_colors.Rd b/man/get_colors.Rd
index db57cc12..5909f180 100644
--- a/man/get_colors.Rd
+++ b/man/get_colors.Rd
@@ -27,15 +27,15 @@ names. It can be used to automatically assign colors.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data('sce')
+if (!exists('sce_layer')) sce_layer <- fetch_data('sce')
 
 ## Example layer colors with the corresponding names
-get_colors(libd_layer_colors, ori_sce_layer$layer_guess)
-get_colors(libd_layer_colors, ori_sce_layer$layer_guess_reordered_short)
+get_colors(libd_layer_colors, sce_layer$layer_guess)
+get_colors(libd_layer_colors, sce_layer$layer_guess_reordered_short)
 
 ## Example where colors are assigned automatically
 ## based on a pre-defined set of colors
-get_colors(clusters = ori_sce_layer$kmeans_k7)
+get_colors(clusters = sce_layer$kmeans_k7)
 
 ## Example where Polychrome::palette36.colors() gets used
 get_colors(clusters = letters[seq_len(13)])
diff --git a/man/layer_boxplot.Rd b/man/layer_boxplot.Rd
index e9575709..cd557df9 100644
--- a/man/layer_boxplot.Rd
+++ b/man/layer_boxplot.Rd
@@ -66,39 +66,39 @@ of a given gene and display the modeling results for the given gene.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_modeling_results'))
-    ori_modeling_results <- fetch_data(type = 'modeling_results')
-if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data(type = 'sce_layer')
+if (!exists('modeling_results'))
+    modeling_results <- fetch_data(type = 'modeling_results')
+if (!exists('sce_layer')) sce_layer <- fetch_data(type = 'sce_layer')
 
 ## Top 2 genes from the enrichment model
 sig_genes <- sig_genes_extract_all(n = 2,
-    modeling_results = ori_modeling_results,
-    sce_layer = ori_sce_layer)
+    modeling_results = modeling_results,
+    sce_layer = sce_layer)
 
 ## Example default boxplot
-layer_boxplot(sig_genes = sig_genes, sce_layer = ori_sce_layer)
+layer_boxplot(sig_genes = sig_genes, sce_layer = sce_layer)
 
 ## Now show the long title version
 layer_boxplot(sig_genes = sig_genes,
     short_title = FALSE,
-    sce_layer = ori_sce_layer)
+    sce_layer = sce_layer)
 
 layer_boxplot(
     i = which(sig_genes$model_type == 'anova')[1],
     sig_genes = sig_genes,
-    sce_layer = ori_sce_layer
+    sce_layer = sce_layer
 )
 layer_boxplot(
     i = which(sig_genes$model_type == 'pairwise')[1],
     sig_genes = sig_genes,
-    sce_layer = ori_sce_layer
+    sce_layer = sce_layer
 )
 
 ## Viridis colors displayed in the shiny app
 library('viridisLite')
 layer_boxplot(
     sig_genes = sig_genes,
-    sce_layer = ori_sce_layer,
+    sce_layer = sce_layer,
     col_low_box = viridis(4)[2],
     col_low_point = viridis(4)[1],
     col_high_box = viridis(4)[3],
@@ -108,7 +108,7 @@ layer_boxplot(
 ## Paper colors displayed in the shiny app
 layer_boxplot(
     sig_genes = sig_genes,
-    sce_layer = ori_sce_layer,
+    sce_layer = sce_layer,
     col_low_box = 'palegreen3',
     col_low_point = 'springgreen2',
     col_high_box = 'darkorange2',
@@ -119,7 +119,7 @@ layer_boxplot(
 layer_boxplot(
     i = which(sig_genes$model_type == 'pairwise')[1],
     sig_genes = sig_genes,
-    sce_layer = ori_sce_layer,
+    sce_layer = sce_layer,
     col_bkg_box = 'grey90',
     col_bkg_point = 'grey60',
     col_low_box = 'lightcyan',
diff --git a/man/layer_stat_cor.Rd b/man/layer_stat_cor.Rd
index f8409a3f..88281b9c 100644
--- a/man/layer_stat_cor.Rd
+++ b/man/layer_stat_cor.Rd
@@ -56,13 +56,13 @@ for a full analysis from which this family of functions is derived from.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_modeling_results'))
-    ori_modeling_results <- fetch_data(type = 'modeling_results')
+if (!exists('modeling_results'))
+    modeling_results <- fetch_data(type = 'modeling_results')
 
 ## Compute the correlations
 cor_stats_layer <- layer_stat_cor(
     tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
-    ori_modeling_results,
+    modeling_results,
     'enrichment'
 )
 
diff --git a/man/layer_stat_cor_plot.Rd b/man/layer_stat_cor_plot.Rd
index 51a79652..36cdc336 100644
--- a/man/layer_stat_cor_plot.Rd
+++ b/man/layer_stat_cor_plot.Rd
@@ -45,13 +45,13 @@ for a full analysis from which this family of functions is derived from.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_modeling_results'))
-    ori_modeling_results <- fetch_data(type = 'modeling_results')
+if (!exists('modeling_results'))
+    modeling_results <- fetch_data(type = 'modeling_results')
 
 ## Compute the correlations
 cor_stats_layer <- layer_stat_cor(
     tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
-    ori_modeling_results,
+    modeling_results,
     'enrichment'
 )
 
diff --git a/man/sce_image_clus.Rd b/man/sce_image_clus.Rd
index 02b11308..4bfb14b8 100644
--- a/man/sce_image_clus.Rd
+++ b/man/sce_image_clus.Rd
@@ -53,14 +53,14 @@ data and title for \code{\link[=sce_image_clus_p]{sce_image_clus_p()}}.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+if (!exists('sce')) sce <- fetch_data('sce')
 
 ## Check the colors defined by Lukas M Weber
 libd_layer_colors
 
 ## Use the manual color palette by Lukas M Weber
 sce_image_clus(
-    sce = ori_sce,
+    sce = sce,
     clustervar = 'layer_guess_reordered',
     sampleid = '151673',
     colors = libd_layer_colors,
@@ -69,7 +69,7 @@ sce_image_clus(
 
 ## Without histology
 sce_image_clus(
-    sce = ori_sce,
+    sce = sce,
     clustervar = 'layer_guess_reordered',
     sampleid = '151673',
     colors = libd_layer_colors,
diff --git a/man/sce_image_clus_p.Rd b/man/sce_image_clus_p.Rd
index 5bbd6002..0ff14ed9 100644
--- a/man/sce_image_clus_p.Rd
+++ b/man/sce_image_clus_p.Rd
@@ -44,8 +44,8 @@ gene-level (or any continuous variable) use \code{\link[=sce_image_gene_p]{sce_i
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
-sce_sub <- ori_sce[, ori_sce$sample_name == '151673']
+if (!exists('sce')) sce <- fetch_data('sce')
+sce_sub <- sce[, sce$sample_name == '151673']
 
 ## Use the manual color palette by Lukas M Weber
 ## Don't plot the histology information
@@ -59,6 +59,9 @@ sce_image_clus_p(
     spatial = FALSE
 )
 
+## Clean up
+rm(sce_sub)
+
 }
 \seealso{
 Other Spatial cluster visualization functions: 
diff --git a/man/sce_image_gene.Rd b/man/sce_image_gene.Rd
index b4c3f876..57457dc5 100644
--- a/man/sce_image_gene.Rd
+++ b/man/sce_image_gene.Rd
@@ -66,15 +66,15 @@ data and title for \code{\link[=sce_image_gene_p]{sce_image_gene_p()}}. It also
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+if (!exists('sce')) sce <- fetch_data('sce')
 
 ## Valid `geneid` values are those in
-head(rowData(ori_sce)$gene_search)
-## or continuous variables stored in colData(ori_sce)
+head(rowData(sce)$gene_search)
+## or continuous variables stored in colData(sce)
 
 ## Visualize a default gene on the non-viridis scale
 sce_image_gene(
-    sce = ori_sce,
+    sce = sce,
     sampleid = '151507',
     viridis = FALSE
 )
@@ -82,7 +82,7 @@ sce_image_gene(
 ## Visualize a continuous variable, in this case, the ratio of chrM
 ## gene expression compared to the total expression at the spot-level
 sce_image_gene(
-    sce = ori_sce,
+    sce = sce,
     sampleid = '151507',
     geneid = 'expr_chrM_ratio'
 )
diff --git a/man/sce_image_gene_p.Rd b/man/sce_image_gene_p.Rd
index d31d1799..29079be9 100644
--- a/man/sce_image_gene_p.Rd
+++ b/man/sce_image_gene_p.Rd
@@ -46,10 +46,10 @@ To visualize clusters (or any discrete variable) use \code{\link[=sce_image_clus
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+if (!exists('sce')) sce <- fetch_data('sce')
 
 ## Prepare the data for the plotting function
-sce_sub <- ori_sce[, ori_sce$sample_name == '151673']
+sce_sub <- sce[, sce$sample_name == '151673']
 df <- as.data.frame(colData(sce_sub))
 df$COUNT <- df$expr_chrM_ratio
 
@@ -63,6 +63,9 @@ sce_image_gene_p(
     spatial = FALSE
 )
 
+## Clean up
+rm(sce_sub)
+
 }
 \seealso{
 Other Spatial gene visualization functions: 
diff --git a/man/sce_image_grid.Rd b/man/sce_image_grid.Rd
index 7f1fba67..3d9b6c01 100644
--- a/man/sce_image_grid.Rd
+++ b/man/sce_image_grid.Rd
@@ -61,10 +61,10 @@ objects.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+if (!exists('sce')) sce <- fetch_data('sce')
 
 ## Subset to two samples of interest
-sce_sub <- ori_sce[, ori_sce$sample_name \%in\% c('151673', '151674')]
+sce_sub <- sce[, sce$sample_name \%in\% c('151673', '151674')]
 
 ## Obtain the plot list
 p_list <-
@@ -77,6 +77,9 @@ p_list <-
         colors = libd_layer_colors
     )
 
+## Clean up
+rm(sce_sub)
+
 ## Visualize the spatial adjacent replicates for position = 0 micro meters
 ## for subject 3
 cowplot::plot_grid(plotlist = p_list, ncol = 2)
diff --git a/man/sce_image_grid_gene.Rd b/man/sce_image_grid_gene.Rd
index 5a2491cd..a0bc45c2 100644
--- a/man/sce_image_grid_gene.Rd
+++ b/man/sce_image_grid_gene.Rd
@@ -71,10 +71,10 @@ objects.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_sce')) ori_sce <- fetch_data('sce')
+if (!exists('sce')) sce <- fetch_data('sce')
 
 ## Subset to two samples of interest
-sce_sub <- ori_sce[, ori_sce$sample_name \%in\% c('151673', '151674')]
+sce_sub <- sce[, sce$sample_name \%in\% c('151673', '151674')]
 
 ## Obtain the plot list
 p_list <-
@@ -84,9 +84,13 @@ p_list <-
         return_plots = TRUE
     )
 
+## Clean up
+rm(sce_sub)
+
 ## Visualize the spatial adjacent replicates for position = 0 micro meters
 ## for subject 3
 cowplot::plot_grid(plotlist = p_list, ncol = 2)
+
 }
 \seealso{
 Other Spatial gene visualization functions: 
diff --git a/man/sig_genes_extract.Rd b/man/sig_genes_extract.Rd
index 599d1ee8..b0f5dfe8 100644
--- a/man/sig_genes_extract.Rd
+++ b/man/sig_genes_extract.Rd
@@ -56,21 +56,21 @@ informative titles.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_modeling_results'))
-    ori_modeling_results <- fetch_data(type = 'modeling_results')
-if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data(type = 'sce_layer')
+if (!exists('modeling_results'))
+    modeling_results <- fetch_data(type = 'modeling_results')
+if (!exists('sce_layer')) sce_layer <- fetch_data(type = 'sce_layer')
 
 ## anova top 10 genes
 sig_genes_extract(
-    modeling_results = ori_modeling_results,
-    sce_layer = ori_sce_layer
+    modeling_results = modeling_results,
+    sce_layer = sce_layer
 )
 
 ## Extract all genes
 sig_genes_extract(
-    modeling_results = ori_modeling_results,
-    sce_layer = ori_sce_layer,
-    n = nrow(ori_sce_layer)
+    modeling_results = modeling_results,
+    sce_layer = sce_layer,
+    n = nrow(sce_layer)
 )
 
 }
diff --git a/man/sig_genes_extract_all.Rd b/man/sig_genes_extract_all.Rd
index 4b49c9e0..812a21b7 100644
--- a/man/sig_genes_extract_all.Rd
+++ b/man/sig_genes_extract_all.Rd
@@ -39,14 +39,14 @@ functions such as \code{\link[=layer_boxplot]{layer_boxplot()}}.
 \examples{
 
 ## Obtain the necessary data
-if (!exists('ori_modeling_results'))
-    ori_modeling_results <- fetch_data(type = 'modeling_results')
-if (!exists('ori_sce_layer')) ori_sce_layer <- fetch_data(type = 'sce_layer')
+if (!exists('modeling_results'))
+    modeling_results <- fetch_data(type = 'modeling_results')
+if (!exists('sce_layer')) sce_layer <- fetch_data(type = 'sce_layer')
 
 ## top 10 genes for all models
 sig_genes_extract_all(
-    modeling_results = ori_modeling_results,
-    sce_layer = ori_sce_layer
+    modeling_results = modeling_results,
+    sce_layer = sce_layer
 )
 
 }
diff --git a/man/spatialLIBD-package.Rd b/man/spatialLIBD-package.Rd
index b263c917..6e9fd388 100644
--- a/man/spatialLIBD-package.Rd
+++ b/man/spatialLIBD-package.Rd
@@ -25,6 +25,7 @@ Useful links:
 
 Other contributors:
 \itemize{
+  \item Kristen R. Maynard \email{Kristen.Maynard@libd.org} (\href{https://orcid.org/0000-0003-0031-8468}{ORCID}) [contributor]
   \item Andrew E. Jaffe \email{andrew.jaffe@libd.org} (\href{https://orcid.org/0000-0001-6886-1454}{ORCID}) [contributor]
 }
 
diff --git a/vignettes/spatialLIBD.Rmd b/vignettes/spatialLIBD.Rmd
index 80fb0779..86533ea5 100644
--- a/vignettes/spatialLIBD.Rmd
+++ b/vignettes/spatialLIBD.Rmd
@@ -188,7 +188,7 @@ ehub <- ExperimentHub::ExperimentHub()
 ## Maybe subsetting the sce object will help
 ## with the memory issues at 
 ## https://travis-ci.com/LieberInstitute/spatialLIBD/builds/150177257?utm_medium=notification&utm_source=slack
-sce <- fetch_data(type = 'sce', eh = ehub)
+if (!exists('sce')) sce <- fetch_data(type = 'sce', eh = ehub)
 pryr::object_size(sce)
 # 2.08 
 # GB
@@ -220,7 +220,7 @@ system(paste('ls -lh', here::here('data-raw', 'sce_sub_for_vignette.Rdata')))
 
 ```{r 'download_fake', eval = FALSE}
 ## Query ExperimentHub and download the data
-sce <- fetch_data(type = 'sce', eh = ehub)
+if (!exists('sce')) sce <- fetch_data(type = 'sce', eh = ehub)
 ```
 
 
@@ -237,12 +237,12 @@ vignette_data <- function(destdir = tempdir()) {
     load(file_path, verbose = FALSE)
     return(sce_sub)
 }
-sce <- vignette_data()
+if (!exists('sce')) sce <- vignette_data()
 ```
 
 
 ```{r 'download_rest'}
-sce_layer <- fetch_data(type = 'sce_layer', eh = ehub)
+if (!exists('sce_layer')) sce_layer <- fetch_data(type = 'sce_layer', eh = ehub)
 modeling_results <- fetch_data('modeling_results', eh = ehub)
 ```