diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 39ddcff..d6ca66e 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -67,11 +67,10 @@ jobs:
         if: runner.os == 'Linux'
         env:
           RHUB_PLATFORM: linux-x86_64-ubuntu-gcc
-        run: |
-          Rscript -e "remotes::install_github('r-hub/sysreqs')"
-          sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))")
-          sudo -s eval "$sysreqs"
-          sudo apt-get update && sudo apt-get -y install libcurl4-openssl-dev libglpk-dev libharfbuzz-dev libfribidi-dev
+        uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::rcmdcheck
+          pak-version: devel
 
       - name: Install system dependencies (macOS)
         if: runner.os == 'macOS'
diff --git a/DESCRIPTION b/DESCRIPTION
index 7051a4d..2b85760 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: iSEEfier
 Title: Streamlining the creation of initial states for starting an iSEE instance
-Version: 1.1.1
+Version: 1.1.2
 Authors@R:
     c(
     person(given = "Najla", family = "Abassi", role = c("aut", "cre"),
diff --git a/NAMESPACE b/NAMESPACE
index f3bf340..36a6425 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -24,8 +24,6 @@ importFrom(ggplot2,scale_fill_manual)
 importFrom(ggplot2,scale_y_discrete)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_void)
-importFrom(iSEE,RowDataTable)
-importFrom(iSEEu,FeatureSetTable)
 importFrom(iSEEu,createGeneSetCommands)
 importFrom(iSEEu,registerFeatureSetCommands)
 importFrom(igraph,"V<-")
diff --git a/NEWS.md b/NEWS.md
index 7b07135..d8aba93 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,8 @@
 
 * Adding in a new function `iSEEmarker()`, more focused on finding marker genes
 * A new `iSEEinit()` version without the `DynamicMarkerTable` panel
+* Expanding the input of `iSEEinit()` to accept features as `data.frame` in addition to `vector`
+* Adding visualization plots to `iSEEnrich()`
 
 # iSEEfier 1.0.0
 
diff --git a/R/iSEEinit.R b/R/iSEEinit.R
index 3c05e19..5398d9e 100644
--- a/R/iSEEinit.R
+++ b/R/iSEEinit.R
@@ -6,15 +6,19 @@
 #' list in a single view.
 #'
 #' @param sce SingleCellExperiment object
-#' @param features A character vector containing a list of genes
+#' @param features A character vector or a data.frame containing a list of genes.
+#' If `features` is a data.frame, the column containing the gene names must be named "id"
 #' @param reddim_type A string vector containing the dimensionality reduction
 #'   type
 #' @param clusters A character string containing the name of the
 #'   clusters/cell-type/state...(as listed in the colData of the sce)
 #' @param groups A character string of the groups/conditions...(as it appears in
 #'   the colData of the sce)
+#' @param gene_id A character string containing the name of the column name containing
+#'  gene names/ids, when 'features' is a data.frame
 #' @param add_markdown_panel A logical indicating whether or not to include the
 #'   MarkdownBoard panel in the initial configuration
+
 #'
 #' @return A list of "Panel" objects specifying the initial state of iSEE
 #'   instance
@@ -48,14 +52,13 @@ iSEEinit <- function(sce,
                      reddim_type = "TSNE",
                      clusters = colnames(colData(sce))[1],
                      groups = colnames(colData(sce))[1],
+                     gene_id="id",
                      add_markdown_panel = FALSE) {
 
   ## Checks on arguments
   if (!is(sce, "SingleCellExperiment"))
     stop("Please provide a SingleCellExperiment as input!")
 
-  stopifnot(is.character(features), length(features) > 0)
-
   stopifnot(isScalarCharacter(reddim_type))
 
   stopifnot(isScalarCharacter(clusters))
@@ -63,6 +66,8 @@ iSEEinit <- function(sce,
   stopifnot(isScalarCharacter(groups))
 
   stopifnot(isTRUEorFALSE(add_markdown_panel))
+  
+  stopifnot(isScalarCharacter(gene_id))
 
 
   if (!(reddim_type %in% reducedDimNames(sce))) {
@@ -71,6 +76,23 @@ iSEEinit <- function(sce,
          "Please select one of these: ",
          paste(available_reddims, collapse = ", "))
   }
+  
+  if (is.data.frame(features)) {
+    if ((gene_id %in% colnames(features))) {
+      
+      features <- as.character(features[[gene_id]])
+    } else {
+      stop("The column name '", gene_id,"' does not exist in the 'features' data.frame!")
+    }
+  }
+    
+  else if (is.vector(features)) {
+    features <- as.character(features)
+  } else {
+    stop("Unsupported feature type. Must be a character vector, or data.frame!")
+    }
+    
+  stopifnot(is.character(features), NROW(features) > 0)
 
   if (!all(features %in% rownames(sce))) {
     not_available_features <- features[!features %in% rownames(sce)]
@@ -79,7 +101,7 @@ iSEEinit <- function(sce,
             paste(not_available_features, collapse = ", "))
 
     features <- features[features %in% rownames(sce)]
-    if (length(features) == 0)
+    if (NROW(features) == 0)
       stop("No features available!")
   }
 
@@ -104,57 +126,60 @@ iSEEinit <- function(sce,
     message("colData column not found for the `groups` parameter, defaulting to ",
             fallback_groups)
   }
-
-
+  
 
 
   initial <- list()
   
   
-  for (j in features) {
-    initial[[paste0("ReducedDimensionPlot", which(features == j))]] <- new(
+  for (j in 1:NROW(features)) {
+    feature_name <- if (is.data.frame(features)) features[j, ] else features[[j]]
+    initial[[paste0("ReducedDimensionPlot", j)]] <- new(
       "ReducedDimensionPlot",
       Type = reddim_type,
       ColorBy = "Feature name",
-      ColorByFeatureName = j,
-      ColorByFeatureSource = paste0("RowDataTable", which(features == j)),
+      ColorByFeatureName = feature_name,
+      ColorByFeatureSource = paste0("RowDataTable", j),
       ColumnSelectionSource = "ColumnDataPlot1",
       SelectionAlpha = 0.05
     )
-    
-    initial[[paste0("FeatureAssayPlot", which(features == j))]] <- new(
+
+    initial[[paste0("FeatureAssayPlot", j)]] <- new(
       "FeatureAssayPlot",
       XAxis = "Column data",
       XAxisColumnData = clusters,
-      YAxisFeatureName = j,
-      YAxisFeatureSource = paste0("RowDataTable", which(features == j)),
+      YAxisFeatureName = feature_name,
+      YAxisFeatureSource = paste0("RowDataTable", j),
       ColorBy = "Column data",
       ColorByColumnData = clusters
     )
     
-    initial[[paste0("RowDataTable", which(features == j))]] <- new(
+    initial[[paste0("RowDataTable", j)]] <- new(
       "RowDataTable",
-      Selected = j,
-      Search = j
+      Selected = feature_name,
+      Search = feature_name
     )
   }
   
-  if (length(features) > 1) {
-    initial[[paste0("FeatureAssayPlot", length(features) + 1)]] <- new(
+  if (NROW(features) > 1) {
+    feature1 <- if (is.data.frame(features)) features[1, ] else features[[1]]
+    feature2 <- if (is.data.frame(features)) features[2, ] else features[[2]]
+    
+    initial[[paste0("FeatureAssayPlot", NROW(features) + 1)]] <- new(
       "FeatureAssayPlot",
       XAxis = "Feature name",
-      XAxisFeatureName = features[[1]],
-      YAxisFeatureName = features[[2]]
+      XAxisFeatureName = feature1,
+      YAxisFeatureName = feature2
     )
   }
   
   if (add_markdown_panel == TRUE) {
-    initial[[paste0("ReducedDimensionPlot", length(features) + 1)]] <- new(
+    initial[[paste0("ReducedDimensionPlot", NROW(features) + 1)]] <- new(
       "ReducedDimensionPlot",
       Type = reddim_type,
       ColorByColumnData = clusters,
       ColorBy = "Column data",
-      ColumnSelectionSource = paste0("FeatureAssayPlot", length(features) + 1),
+      ColumnSelectionSource = paste0("FeatureAssayPlot", NROW(features) + 1),
       FacetColumnBy = "Column data",
       FacetColumnByColData = groups,
       SelectionAlpha = 0.05
@@ -165,12 +190,12 @@ iSEEinit <- function(sce,
       Content = "# Placeholder\n\nFill me with text!",
       PanelWidth = 4L)
   } else {
-    initial[[paste0("ReducedDimensionPlot", length(features) + 1)]] <- new(
+    initial[[paste0("ReducedDimensionPlot", NROW(features) + 1)]] <- new(
       "ReducedDimensionPlot",
       Type = reddim_type,
       ColorByColumnData = clusters,
       ColorBy = "Column data",
-      ColumnSelectionSource = paste0("FeatureAssayPlot", length(features) + 1),
+      ColumnSelectionSource = paste0("FeatureAssayPlot", NROW(features) + 1),
       FacetColumnBy = "Column data",
       FacetColumnByColData = groups,
       SelectionAlpha = 0.05,
@@ -196,5 +221,6 @@ iSEEinit <- function(sce,
 
 
   return(initial)
+  
 
 }
diff --git a/R/iSEEnrich.R b/R/iSEEnrich.R
index 4559f97..a1a5d04 100644
--- a/R/iSEEnrich.R
+++ b/R/iSEEnrich.R
@@ -11,14 +11,22 @@
 #'   extract mappings of gene sets to gene IDs.
 #' @param gene_identifier A character string specifying the identifier to use to
 #'   extract gene IDs for the organism package
-#'
+#'  
+#' @param clusters A character string containing the name of the
+#'   clusters/cell-type/state...(as listed in the colData of the sce)
+#'   
+#' @param groups A character string of the groups/conditions...(as it appears in
+#'   the colData of the sce)
+#'   
+#' @param reddim_type A string vector containing the dimensionality reduction
+#'   type
+#'   
+#'   
 #' @return A list of "Panel" objects specifying the initial state of iSEE
 #'   instance
 #' @export iSEEnrich
-#' @importFrom iSEE RowDataTable
 #' @importFrom iSEEu createGeneSetCommands
 #' @importFrom iSEEu registerFeatureSetCommands
-#' @importFrom iSEEu FeatureSetTable
 #' @importFrom BiocBaseUtils isScalarCharacter
 #'
 #'
@@ -29,15 +37,24 @@
 #' GO_collection <- "GO"
 #' Mm_organism <- "org.Mm.eg.db"
 #' gene_id <- "SYMBOL"
+#' clusters <- "stimulus"
+#' groups <- "single cell quality"
+#' reddim_type <- "PCA"
 #' results <- iSEEnrich(sce = sce,
 #'                      collection = GO_collection,
 #'                      organism = Mm_organism,
-#'                      gene_identifier = gene_id)
+#'                      gene_identifier = gene_id,
+#'                      clusters = clusters,
+#'                      groups = groups,
+#'                      reddim_type = reddim_type)
 #' 
 iSEEnrich <- function(sce,
                       collection = c("GO", "KEGG"),
                       organism = "org.Hs.eg.db",
-                      gene_identifier = "ENTREZID") {
+                      gene_identifier = "ENTREZID",
+                      clusters = colnames(colData(sce))[1],
+                      groups = colnames(colData(sce))[1],
+                      reddim_type = "PCA") {
 
   ## Checks on arguments
   if (!is(sce, "SingleCellExperiment"))
@@ -50,7 +67,40 @@ iSEEnrich <- function(sce,
   stopifnot(isScalarCharacter(organism))
 
   stopifnot(isScalarCharacter(gene_identifier))
-
+  
+  stopifnot(isScalarCharacter(clusters))
+  
+  stopifnot(isScalarCharacter(groups))
+  
+  if (!(clusters %in% colnames(colData(sce)))) {
+    if (ncol(colData(sce)) > 0) {
+      fallback_clusters <- colnames(colData(sce))[1]
+    } else {
+      stop("No colData provided for the `clusters`!")
+    }
+    
+    message("colData column not found for the `clusters` parameter, defaulting to ",
+            fallback_clusters)
+  }
+  
+  if (!(groups %in% colnames(colData(sce)))) {
+    if (ncol(colData(sce)) > 0) {
+      fallback_groups <- colnames(colData(sce))[1]
+    } else {
+      stop("No colData provided for the `groups`!")
+    }
+    
+    message("colData column not found for the `groups` parameter, defaulting to ",
+            fallback_groups)
+  }
+  
+  if (!(reddim_type %in% reducedDimNames(sce))) {
+    available_reddims <- reducedDimNames(sce)
+    stop("The selected reduced dimensionality embedding is not available!\n",
+         "Please select one of these: ",
+         paste(available_reddims, collapse = ", "))
+  }
+  
   if (!requireNamespace(organism, quietly = TRUE))
     stop("Please check the value of the provided orgDb package ",
          "(the package needs to be installed)...",
@@ -64,11 +114,46 @@ iSEEnrich <- function(sce,
                                 organism = organism,
                                 identifier = gene_identifier)
   sce1 <- registerFeatureSetCommands(sce, cmds)
-
-  initial[["FeatureSetTable1"]] <- FeatureSetTable()
-
-  initial[["RowDataTable1"]] <- RowDataTable(
-    RowSelectionSource = "FeatureSetTable1")
+  
+  initial[["FeatureSetTable1"]] <- new(
+    "FeatureSetTable", Collection = collection)
+  
+  initial[["RowDataTable1"]] <- new(
+    "RowDataTable", RowSelectionSource = "FeatureSetTable1")
+  
+  initial[["FeatureAssayPlot1"]] <- new(
+    "FeatureAssayPlot",
+    XAxis = "Column data",
+    XAxisColumnData = groups,
+    YAxisFeatureSource = "RowDataTable1",
+    ColorBy = "Column data",
+    ColorByColumnData = groups,
+    ColumnSelectionSource = "ColumnDataPlot1")
+  
+  initial[["ColumnDataPlot1"]] <- new(
+    "ColumnDataPlot",
+    XAxis = "Column data",
+    YAxis = clusters,
+    XAxisColumnData = groups,
+    ColorByColumnData = clusters,
+    ColorBy = "Column data"
+    
+  )
+  
+  initial[["ReducedDimensionPlot1"]] <- new(
+    "ReducedDimensionPlot",
+    Type = reddim_type,
+    FacetColumnByColData = groups,
+    FacetColumnBy = "Column data",
+    ColorBy = "Feature name",
+    ColorByFeatureSource = "RowDataTable1",
+    ColumnSelectionSource = "ColumnDataPlot1"
+  )
+  
+  initial[["MarkdownBoard1"]] <- new(
+    "MarkdownBoard",
+    Content = "# Placeholder\n\nFill me with genes, or anything!")
+  
 
   return(
     list(
diff --git a/man/iSEEinit.Rd b/man/iSEEinit.Rd
index e2bce4a..6b9cf53 100644
--- a/man/iSEEinit.Rd
+++ b/man/iSEEinit.Rd
@@ -11,13 +11,15 @@ iSEEinit(
   reddim_type = "TSNE",
   clusters = colnames(colData(sce))[1],
   groups = colnames(colData(sce))[1],
+  gene_id = "id",
   add_markdown_panel = FALSE
 )
 }
 \arguments{
 \item{sce}{SingleCellExperiment object}
 
-\item{features}{A character vector containing a list of genes}
+\item{features}{A character vector or a data.frame containing a list of genes.
+If \code{features} is a data.frame, the column containing the gene names must be named "id"}
 
 \item{reddim_type}{A string vector containing the dimensionality reduction
 type}
@@ -28,6 +30,9 @@ clusters/cell-type/state...(as listed in the colData of the sce)}
 \item{groups}{A character string of the groups/conditions...(as it appears in
 the colData of the sce)}
 
+\item{gene_id}{A character string containing the name of the column name containing
+gene names/ids, when 'features' is a data.frame}
+
 \item{add_markdown_panel}{A logical indicating whether or not to include the
 MarkdownBoard panel in the initial configuration}
 }
diff --git a/man/iSEEnrich.Rd b/man/iSEEnrich.Rd
index af24797..7a49091 100644
--- a/man/iSEEnrich.Rd
+++ b/man/iSEEnrich.Rd
@@ -8,7 +8,10 @@ iSEEnrich(
   sce,
   collection = c("GO", "KEGG"),
   organism = "org.Hs.eg.db",
-  gene_identifier = "ENTREZID"
+  gene_identifier = "ENTREZID",
+  clusters = colnames(colData(sce))[1],
+  groups = colnames(colData(sce))[1],
+  reddim_type = "PCA"
 )
 }
 \arguments{
@@ -22,6 +25,15 @@ extract mappings of gene sets to gene IDs.}
 
 \item{gene_identifier}{A character string specifying the identifier to use to
 extract gene IDs for the organism package}
+
+\item{clusters}{A character string containing the name of the
+clusters/cell-type/state...(as listed in the colData of the sce)}
+
+\item{groups}{A character string of the groups/conditions...(as it appears in
+the colData of the sce)}
+
+\item{reddim_type}{A string vector containing the dimensionality reduction
+type}
 }
 \value{
 A list of "Panel" objects specifying the initial state of iSEE
@@ -39,9 +51,15 @@ sce <- scater::runPCA(sce)
 GO_collection <- "GO"
 Mm_organism <- "org.Mm.eg.db"
 gene_id <- "SYMBOL"
+clusters <- "stimulus"
+groups <- "single cell quality"
+reddim_type <- "PCA"
 results <- iSEEnrich(sce = sce,
                      collection = GO_collection,
                      organism = Mm_organism,
-                     gene_identifier = gene_id)
+                     gene_identifier = gene_id,
+                     clusters = clusters,
+                     groups = groups,
+                     reddim_type = reddim_type)
 
 }
diff --git a/tests/testthat/test-iSEEinit.R b/tests/testthat/test-iSEEinit.R
index 6e46c5c..29e3bc4 100644
--- a/tests/testthat/test-iSEEinit.R
+++ b/tests/testthat/test-iSEEinit.R
@@ -13,6 +13,14 @@ test_that("test iSEEinit",{
                                  groups = "Secondary.Type",
                                  add_markdown_panel = TRUE)
   expect_true(is.list(initial_with_board))
+  
+  initial_with_df <- iSEEinit(sce = sce_allen,
+                                 features = as.data.frame(c("Il2rb",
+                                              "Klre1"),nm = "features"),
+                                 clusters = "Primary.Type",
+                                 groups = "Secondary.Type",
+                                 gene_id = "features")
+  expect_true(is.list(initial_with_df))
 
   ## This is to trigger the argument checks
   expect_error({
@@ -23,12 +31,14 @@ test_that("test iSEEinit",{
              groups = "Secondary.Type")
   }, "Please provide a SingleCellExperiment as input!")
 
-  expect_error({
-    iSEEinit(sce = sce_allen,
-             reddim_type = "PCA",
-             features = TRUE,
-             clusters = "Primary.Type",
-             groups = "Secondary.Type")
+  expect_message({
+    expect_error({
+      iSEEinit(sce = sce_allen,
+               reddim_type = "PCA",
+               features = TRUE,
+               clusters = "Primary.Type",
+               groups = "Secondary.Type")
+    })
   })
 
   expect_error({
@@ -131,13 +141,16 @@ test_that("test iSEEinit",{
                            clusters = "Primary.Type",
                            groups = "Secondary.Type")
   })
-  expect_error({
-    init_pippo_pluto <- iSEEinit(sce = sce_allen,
-                                 reddim_type = "PCA",
-                                 features = c("Pluto",
-                                              "Pippo"),
-                                 clusters = "Primary.Type",
-                                 groups = "Secondary.Type")
+
+  expect_message({
+    expect_error({
+      init_pippo_pluto <- iSEEinit(sce = sce_allen,
+                                   reddim_type = "PCA",
+                                   features = c("Pluto",
+                                                "Pippo"),
+                                   clusters = "Primary.Type",
+                                   groups = "Secondary.Type")
+    })
   })
 
   sce_nocd <- sce_allen
@@ -174,5 +187,24 @@ test_that("test iSEEinit",{
                         clusters = "Primary.Type",
                         groups = "anything_else")
   })
+  
+  expect_error({
+    initial <- iSEEinit(sce = sce_allen,
+                        features = c("Il2rb",
+                                     "Klre1"),
+                        clusters = "Primary.Type",
+                        groups = "Secondary_Type",
+                        gene_id = TRUE)
+  })
+  
+  expect_error({
+    initial <- iSEEinit(sce = sce_allen,
+                        features = as.data.frame(c("Il2rb",
+                                                   "Klre1"),nm = "features"),
+                        clusters = "Primary.Type",
+                        groups = "Secondary_Type",
+                        gene_id = "gene_name")
+  })
 
 })
+
diff --git a/tests/testthat/test-iSEEnrich.R b/tests/testthat/test-iSEEnrich.R
index ebff11c..0cbeadb 100644
--- a/tests/testthat/test-iSEEnrich.R
+++ b/tests/testthat/test-iSEEnrich.R
@@ -3,11 +3,18 @@ test_that("test iSEEnrich", {
   GO_collection <- "GO"
   Mm_organism <- "org.Mm.eg.db"
   gene_id <- "SYMBOL"
+  clusters <- "Primary.Type"
+  groups <- "Secondary.Type"
+  reddim_type <- "PCA"
 
   results <- iSEEnrich(sce = sce_allen,
                        collection = GO_collection,
                        organism = Mm_organism,
-                       gene_identifier = gene_id)
+                       gene_identifier = gene_id,
+                       clusters = clusters,
+                       groups = groups,
+                       reddim_type = reddim_type)
+  
   expect_true(is.list(results))
   expect_true(is.list(results$initial))
   expect_true(is(results$sce, "SingleCellExperiment"))
@@ -17,46 +24,130 @@ test_that("test iSEEnrich", {
     iSEEnrich(sce = "Pippo",
               collection = GO_collection,
               organism = Mm_organism,
-              gene_identifier = gene_id)
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = groups,
+              reddim_type = reddim_type)
   })
 
   expect_error({
     iSEEnrich(sce = sce_allen,
               collection = "MSIGDB",
               organism = Mm_organism,
-              gene_identifier = gene_id)
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = groups,
+              reddim_type = reddim_type)
   })
 
   expect_error({
     iSEEnrich(sce = sce_allen,
               collection = c("MSIGDB", "GO"),
               organism = Mm_organism,
-              gene_identifier = gene_id)
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = groups,
+              reddim_type = reddim_type)
   })
 
   expect_error({
     iSEEnrich(sce = sce_allen,
               collection = GO_collection,
               organism = TRUE,
-              gene_identifier = gene_id)
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = groups,
+              reddim_type = reddim_type)
   })
 
   expect_error({
     iSEEnrich(sce = sce_allen,
               collection = GO_collection,
               organism = c("org.Mm.eg.db", "mouse"),
-              gene_identifier = gene_id)
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = groups,
+              reddim_type = reddim_type)
   })
 
   expect_error({
     iSEEnrich(sce = sce_allen,
               collection = GO_collection,
               organism = c("mouse"),
-              gene_identifier = gene_id)
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = groups,
+              reddim_type = reddim_type)
   })
-
-
-
-
-
+  
+  expect_error({
+    iSEEnrich(sce = sce_allen,
+              collection = GO_collection,
+              organism = Mm_organism,
+              gene_identifier = gene_id,
+              clusters = TRUE,
+              groups = groups,
+              reddim_type = reddim_type)
+  })
+  
+  expect_error({
+    iSEEnrich(sce = sce_allen,
+              collection = GO_collection,
+              organism = Mm_organism,
+              gene_identifier = gene_id,
+              clusters = c("zina","aziza"),
+              groups = groups,
+              reddim_type = reddim_type)
+  })
+  
+  expect_error({
+    iSEEnrich(sce = sce_allen,
+              collection = GO_collection,
+              organism = Mm_organism,
+              gene_identifier = gene_id,
+              clusters = TRUE,
+              groups = c("Primary.Type", "Secondary.Type"),
+              reddim_type = reddim_type)
+  })
+  
+  expect_error({
+    iSEEnrich(sce = sce_allen,
+              collection = GO_collection,
+              organism = Mm_organism,
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = c("Primary.Type", "Secondary.Type"),
+              reddim_type = reddim_type)
+  })
+  
+  expect_error({
+    iSEEnrich(sce = sce_allen,
+              collection = GO_collection,
+              organism = Mm_organism,
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = groups,
+              reddim_type = TRUE)
+  })
+  
+  expect_error({
+    iSEEnrich(sce = sce_allen,
+              collection = GO_collection,
+              organism = Mm_organism,
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = groups,
+              reddim_type = "UMAP")
+  })
+  
+  expect_error({
+    iSEEnrich(sce = sce_allen,
+              collection = GO_collection,
+              organism = Mm_organism,
+              gene_identifier = gene_id,
+              clusters = clusters,
+              groups = groups,
+              reddim_type = "pca")
+  })
+  
 })
diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R
index e88cd9e..857bca2 100644
--- a/tests/testthat/test-utils.R
+++ b/tests/testthat/test-utils.R
@@ -28,13 +28,18 @@ test_that("iSEEfier utils work", {
   expect_true(is(g_tiles_2, "igraph"))
 
   init_chopped <- init_1[c(3,6)]
-  g_tiles_noedges <- view_initial_network(init_chopped, plot_format = "none")
+  expect_message({
+    g_tiles_noedges <- view_initial_network(init_chopped, plot_format = "none")
+  }, "Returning the graph object")
+
   expect_true(length(igraph::E(g_tiles_noedges)) == 0)
 
-  expect_message(
-    init_combined <- glue_initials(init_1, init_2, remove_duplicate_panels = TRUE),
-    "Dropping"
-  )
+  expect_message({
+    expect_message({
+      init_combined <- glue_initials(init_1, init_2, remove_duplicate_panels = TRUE)
+      }, "Dropping"
+    )}, "Merging together")
+
   expect_true(length(init_combined) == 20)
 
   p_tiles_combined <- view_initial_tiles(init_combined)
@@ -49,14 +54,19 @@ test_that("iSEEfier utils work", {
   init_mod <- init_1
   names(init_mod)[1] <- "ImaginaryPanel1"
   class(init_mod[[1]]) <- "ImaginaryPanel"
-  expect_error(
-    glue_initials(init_mod, init_2),
-    "Some elements included in the provided input are not recognized as iSEE panels!"
-  )
+  expect_message({
+    expect_error(
+      glue_initials(init_mod, init_2),
+      "Some elements included in the provided input are not recognized as iSEE panels!"
+    )}, "Combining sets of 10, 11 different panels")
+
   ## This one works by enabling the specific panel
-  glued_custom <- glue_initials(init_mod, init_2,
-                                custom_panels_allowed = "ImaginaryPanel",
-                                remove_duplicate_panels = FALSE)
+  expect_message({
+    glued_custom <- glue_initials(init_mod, init_2,
+                                  custom_panels_allowed = "ImaginaryPanel",
+                                  remove_duplicate_panels = FALSE)},
+    "Returning an `initial` configuration including 21 different panels. Enjoy!")
+
   expect_true(length(glued_custom) == 21)
 
 })
diff --git a/vignettes/iSEEfier_userguide.Rmd b/vignettes/iSEEfier_userguide.Rmd
index dd8b5ea..844d561 100644
--- a/vignettes/iSEEfier_userguide.Rmd
+++ b/vignettes/iSEEfier_userguide.Rmd
@@ -111,7 +111,8 @@ sce <- runUMAP(sce)
 
 Now our `sce` is ready, we can move on to the next argument.
 
-2.  `features` : which is a list or a vector of genes/features of interest.
+2.  `features` : which is a vector or a dataframe containing the genes/features
+of interest.
 Let's say we would like to visualize the expression of some genes that were
 identified as marker genes for different cell population.
 
@@ -153,6 +154,8 @@ initial1 <- iSEEinit(sce = sce,
                     add_markdown_panel = TRUE)
 ```
 
+In case our `features` parameter was a data.frame, we could assign the name of the column containing the features to the `gene_id` parameter.
+
 Now we are one step away from visualizing our list of genes of interest. All
 that's left to do is to run `iSEE` with the initial state created with
 `iSEEinit()`
@@ -181,11 +184,18 @@ will need 4 elements to explore feature sets of interest:
 -   `collection`: A character vector specifying the gene set collections of interest (it is possible to use GO or KEGG terms)
 -   `gene_identifier`: A character string specifying the identifier to use to extract gene IDs for the organism package. This can be **"ENS"** for ENSEMBL ids, **"SYMBOL"** for gene names...
 -   `organism`: A character string of the `org.*.eg.db` package to use to extract mappings of gene sets to gene IDs.
+-   `reddim_type`: A string vector containing the dimensionality reduction type
+-   `clusters`: A character string containing the name of the clusters/cell-type/state...(as listed in the colData of the sce)
+-   `groups`: A character string of the groups/conditions...(as it appears in the colData of the sce)
+
 
 ```{r set-param}
 GO_collection <- "GO"
 Mm_organism <- "org.Mm.eg.db"
 gene_id <- "SYMBOL"
+cluster <- "label"
+group <- "strain"
+reddim_type <- "PCA"
 ```
 
 Now let's create this initial setup for `iSEE` using `iSEEnrich()`
@@ -194,7 +204,10 @@ Now let's create this initial setup for `iSEE` using `iSEEnrich()`
 results <- iSEEnrich(sce = sce,
                      collection = GO_collection,
                      organism = Mm_organism,
-                     gene_identifier = gene_id)
+                     gene_identifier = gene_id,
+                     clusters = cluster,
+                     groups = group,
+                     reddim_type = reddim_type)
 ```
 
 `iSEEnrich` will specifically return a list with the updated `sce` object and