diff --git a/DESCRIPTION b/DESCRIPTION
index 6553c319..ed6b537e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -23,14 +23,14 @@ Imports: plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable,
         MCMCprecision, ggrepel, Rtsne, withr,
         scater (>= 1.14.4), scran, dbscan,
         DelayedArray, stringr, ComplexHeatmap, gridExtra,
-        circlize
+        circlize, dendextend, ggdendro, pROC
 Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr,
         BiocManager, BiocStyle, TENxPBMCData, singleCellTK, M3DExampleData
 LinkingTo: Rcpp, RcppEigen
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 BugReports: https://github.com/campbio/celda/issues
 biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian, ImmunoOncology, DataImport
 NeedsCompilation: yes
diff --git a/R/findMarkersTree.R b/R/findMarkersTree.R
index 164b40f3..e62d0fb8 100644
--- a/R/findMarkersTree.R
+++ b/R/findMarkersTree.R
@@ -32,7 +32,7 @@
 #'  are solely defined by the absence of markers. Default is TRUE.
 #' @param consecutiveOneoff Logical. Whether or not to allow one-off splits at
 #'  consecutive brances. Default is FALSE.
-#' @param autoMetaclusters. Logical. Whether to identify metaclusters prior to
+#' @param autoMetaclusters Logical. Whether to identify metaclusters prior to
 #'  creating the tree based on the distance between clusters in a UMAP 
 #'  dimensionality reduction projection. A metacluster is simply a large
 #'  cluster that includes several clusters within it. Default is TRUE.
@@ -2339,18 +2339,21 @@ subUnderscore <- function(x, n) unlist(lapply(
 #' @param boxColor Character value. Color of rule labels. Default is black.
 #' @examples
 #' # Generate simulated single-cell dataset using celda 
-#' sim_counts <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)
+#' sce <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)
+#' 
+#' # Select top features
+#' sce <- selectFeatures(sce)
 #' 
 #' # Celda clustering into 5 clusters & 10 modules
-#' cm <- celda_CG(sim_counts$counts, K=5, L=10, verbose=FALSE)
+#' sce <- celda_CG(sce, K=5, L=10, verbose=FALSE)
 #' 
 #' # Get features matrix and cluster assignments
-#' factorized <- factorizeMatrix(sim_counts$counts, cm)
-#' features <- factorized$proportions$cell
-#' class <- clusters(cm)$z
+#' factorizedCounts <- factorizeMatrix(sce, type = "counts")
+#' featureMatrix <- factorizedCounts$counts$cell
+#' classes <- as.integer(celdaClusters(sce))
 #' 
 #' # Generate Decision Tree
-#' DecTree <- findMarkersTree(features,class,threshold = 1)
+#' DecTree <- findMarkersTree(featureMatrix, classes)
 #' 
 #' # Plot dendrogram
 #' plotDendro(DecTree)
@@ -2634,23 +2637,25 @@ plotDendro <- function(tree,
 #' @return A heatmap visualizing the counts matrix for the cells and genes at
 #' the specified branch point.
 #' @examples
-#' # Generate simulated single-cell dataset using celda 
-#' sim_counts <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)
+#' sce <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)
+#' 
+#' # Select top features
+#' sce <- selectFeatures(sce)
 #' 
 #' # Celda clustering into 5 clusters & 10 modules
-#' cm <- celda_CG(sim_counts$counts, K=5, L=10, verbose=FALSE)
+#' sce <- celda_CG(sce, K=5, L=10, verbose=FALSE)
 #' 
 #' # Get features matrix and cluster assignments
-#' factorized <- factorizeMatrix(sim_counts$counts, cm)
-#' features <- factorized$proportions$cell
-#' class <- clusters(cm)$z
+#' factorizedCounts <- factorizeMatrix(sce, type = "counts")
+#' featureMatrix <- factorizedCounts$counts$cell
+#' classes <- as.integer(celdaClusters(sce))
 #' 
 #' # Generate Decision Tree
-#' DecTree <- findMarkersTree(features,class,threshold = 1)
+#' DecTree <- findMarkersTree(featureMatrix, classes)
 #' 
 #' # Plot example heatmap
-#' plotMarkerHeatmap(DecTree, sim_counts$counts, branchPoint = "top_level",
-#' featureLabels = paste0("L",clusters(cm)$y))
+#' plotMarkerHeatmap(DecTree, featureMatrix, branchPoint = "top_level", 
+#'   featureLabels = rownames(featureMatrix))
 #' 
 #' @export
 plotMarkerHeatmap <- function(tree, counts, branchPoint, featureLabels,
diff --git a/man/findMarkersTree.Rd b/man/findMarkersTree.Rd
index 303ad7e9..f887f05e 100644
--- a/man/findMarkersTree.Rd
+++ b/man/findMarkersTree.Rd
@@ -60,13 +60,13 @@ are solely defined by the absence of markers. Default is TRUE.}
 \item{consecutiveOneoff}{Logical. Whether or not to allow one-off splits at
 consecutive brances. Default is FALSE.}
 
-\item{seed}{Numeric. Seed used to enable reproducible UMAP results
-for identifying metaclusters. Default is 12345.}
-
-\item{autoMetaclusters.}{Logical. Whether to identify metaclusters prior to
+\item{autoMetaclusters}{Logical. Whether to identify metaclusters prior to
 creating the tree based on the distance between clusters in a UMAP 
 dimensionality reduction projection. A metacluster is simply a large
 cluster that includes several clusters within it. Default is TRUE.}
+
+\item{seed}{Numeric. Seed used to enable reproducible UMAP results
+for identifying metaclusters. Default is 12345.}
 }
 \value{
 A named list with six elements:
diff --git a/man/plotDendro.Rd b/man/plotDendro.Rd
index e1e43ad8..4b242888 100644
--- a/man/plotDendro.Rd
+++ b/man/plotDendro.Rd
@@ -41,18 +41,21 @@ Generates a dendrogram of the rules and performance
 }
 \examples{
 # Generate simulated single-cell dataset using celda 
-sim_counts <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)
+sce <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)
+
+# Select top features
+sce <- selectFeatures(sce)
 
 # Celda clustering into 5 clusters & 10 modules
-cm <- celda_CG(sim_counts$counts, K=5, L=10, verbose=FALSE)
+sce <- celda_CG(sce, K=5, L=10, verbose=FALSE)
 
 # Get features matrix and cluster assignments
-factorized <- factorizeMatrix(sim_counts$counts, cm)
-features <- factorized$proportions$cell
-class <- clusters(cm)$z
+factorizedCounts <- factorizeMatrix(sce, type = "counts")
+featureMatrix <- factorizedCounts$counts$cell
+classes <- as.integer(celdaClusters(sce))
 
 # Generate Decision Tree
-DecTree <- findMarkersTree(features,class,threshold = 1)
+DecTree <- findMarkersTree(featureMatrix, classes)
 
 # Plot dendrogram
 plotDendro(DecTree)
diff --git a/man/plotMarkerHeatmap.Rd b/man/plotMarkerHeatmap.Rd
index 5263cdd5..28520c05 100644
--- a/man/plotMarkerHeatmap.Rd
+++ b/man/plotMarkerHeatmap.Rd
@@ -41,22 +41,24 @@ the specified branch point.
 Creates heatmap for a specified branch point in a marker tree.
 }
 \examples{
-# Generate simulated single-cell dataset using celda 
-sim_counts <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)
+sce <- celda::simulateCells("celda_CG", K = 4, L = 10, G = 100)
+
+# Select top features
+sce <- selectFeatures(sce)
 
 # Celda clustering into 5 clusters & 10 modules
-cm <- celda_CG(sim_counts$counts, K=5, L=10, verbose=FALSE)
+sce <- celda_CG(sce, K=5, L=10, verbose=FALSE)
 
 # Get features matrix and cluster assignments
-factorized <- factorizeMatrix(sim_counts$counts, cm)
-features <- factorized$proportions$cell
-class <- clusters(cm)$z
+factorizedCounts <- factorizeMatrix(sce, type = "counts")
+featureMatrix <- factorizedCounts$counts$cell
+classes <- as.integer(celdaClusters(sce))
 
 # Generate Decision Tree
-DecTree <- findMarkersTree(features,class,threshold = 1)
+DecTree <- findMarkersTree(featureMatrix, classes)
 
 # Plot example heatmap
-plotMarkerHeatmap(DecTree, sim_counts$counts, branchPoint = "top_level",
-featureLabels = paste0("L",clusters(cm)$y))
+plotMarkerHeatmap(DecTree, featureMatrix, branchPoint = "top_level", 
+  featureLabels = rownames(featureMatrix))
 
 }