diff --git a/R/clustSIGNAL.R b/R/clustSIGNAL.R
index 2d912ed..db18311 100644
--- a/R/clustSIGNAL.R
+++ b/R/clustSIGNAL.R
@@ -28,9 +28,9 @@
 #'
 #' names(colData(spe))
 #' # identify the column names with cell and sample labels
-#' samples = "embryo"
+#' samples = "sample_id"
 #' cells = "uniqueID"
-#' res_list <- clustSIGNAL(spe, samples, cells, dimRed = "PCA", outputs = "c")
+#' res_list <- clustSIGNAL(spe, samples, cells, outputs = "c")
 #'
 #'
 #' @export
diff --git a/R/clustering.R b/R/clustering.R
index af5d408..6295ee8 100644
--- a/R/clustering.R
+++ b/R/clustering.R
@@ -11,15 +11,14 @@
 #' @examples
 #' data(mouseEmbryo2)
 #'
-#' # for non-spatial clustering of normalised counts
+#' spe <- scater::runPCA(spe)
+#'
+#' # reclust = FALSE for non-spatial clustering of normalised counts
+#' # reclust = TRUE for non-spatial clustering of adaptively smoothed counts
 #' spe <- nsClustering(spe, dimRed = "PCA", reclust = FALSE)
 #' head(spe$nsCluster)
 #' head(spe$nsSubcluster)
 #'
-#' # for non-spatial clustering of adaptively smoothed counts
-#' spe <- nsClustering(spe, dimRed = "PCA", reclust = TRUE)
-#' head(spe$reCluster)
-#'
 #' @export
 
 #### Non-spatial clustering
diff --git a/R/entropyMeasure.R b/R/entropyMeasure.R
index 4546e2f..f44ec8d 100644
--- a/R/entropyMeasure.R
+++ b/R/entropyMeasure.R
@@ -13,7 +13,7 @@
 #' @examples
 #' data(mouseEmbryo2)
 #'
-#' # requires list of containing cluster proportions of each region (regXclust),
+#' # requires list containing cluster proportions of each region (regXclust),
 #' # generated using the neighbourDetect() function
 #' spe <- entropyMeasure(spe, cells = "uniqueID", regXclust, threads = 1)
 #' head(spe$entropy)
diff --git a/R/neighborDetect.R b/R/neighborDetect.R
index 4b353dc..cd53e5e 100644
--- a/R/neighborDetect.R
+++ b/R/neighborDetect.R
@@ -18,7 +18,7 @@
 #' @examples
 #' data(mouseEmbryo2)
 #'
-#' out_list <- neighbourDetect(spe, samples = "embryo", NN = 30, cells = "uniqueID", sort = TRUE)
+#' out_list <- neighbourDetect(spe, samples = "sample_id", NN = 30, cells = "uniqueID", sort = TRUE)
 #' names(out_list)
 #'
 #' @export
diff --git a/data/mouseEmbryo2.rda b/data/mouseEmbryo2.rda
index 46bf7ce..26dd553 100644
Binary files a/data/mouseEmbryo2.rda and b/data/mouseEmbryo2.rda differ
diff --git a/man/clustSIGNAL.Rd b/man/clustSIGNAL.Rd
index 3f2299c..afe1ad3 100644
--- a/man/clustSIGNAL.Rd
+++ b/man/clustSIGNAL.Rd
@@ -58,9 +58,9 @@ data(mouseEmbryo2)
 
 names(colData(spe))
 # identify the column names with cell and sample labels
-samples = "embryo"
+samples = "sample_id"
 cells = "uniqueID"
-res_list <- clustSIGNAL(spe, samples, cells, dimRed = "PCA", outputs = "c")
+res_list <- clustSIGNAL(spe, samples, cells, outputs = "c")
 
 
 }
diff --git a/man/entropyMeasure.Rd b/man/entropyMeasure.Rd
index 86ceee9..3331ce5 100644
--- a/man/entropyMeasure.Rd
+++ b/man/entropyMeasure.Rd
@@ -24,7 +24,7 @@ A function to measure the heterogeneity of a cell's neighbourhood in terms of en
 \examples{
 data(mouseEmbryo2)
 
-# requires list of containing cluster proportions of each region (regXclust),
+# requires list containing cluster proportions of each region (regXclust),
 # generated using the neighbourDetect() function
 spe <- entropyMeasure(spe, cells = "uniqueID", regXclust, threads = 1)
 head(spe$entropy)
diff --git a/man/neighbourDetect.Rd b/man/neighbourDetect.Rd
index 31f50a6..a458a39 100644
--- a/man/neighbourDetect.Rd
+++ b/man/neighbourDetect.Rd
@@ -30,7 +30,7 @@ A function to identify the neighbourhood of each cell. If sort = TRUE, the neigh
 \examples{
 data(mouseEmbryo2)
 
-out_list <- neighbourDetect(spe, samples = "embryo", NN = 30, cells = "uniqueID", sort = TRUE)
+out_list <- neighbourDetect(spe, samples = "sample_id", NN = 30, cells = "uniqueID", sort = TRUE)
 names(out_list)
 
 }
diff --git a/man/nsClustering.Rd b/man/nsClustering.Rd
index b9bb0fc..f171feb 100644
--- a/man/nsClustering.Rd
+++ b/man/nsClustering.Rd
@@ -20,13 +20,12 @@ A function containing two steps used at different times in the clustSIGNAL workf
 \examples{
 data(mouseEmbryo2)
 
-# for non-spatial clustering of normalised counts
+spe <- scater::runPCA(spe)
+
+# reclust = FALSE for non-spatial clustering of normalised counts
+# reclust = TRUE for non-spatial clustering of adaptively smoothed counts
 spe <- nsClustering(spe, dimRed = "PCA", reclust = FALSE)
 head(spe$nsCluster)
 head(spe$nsSubcluster)
 
-# for non-spatial clustering of adaptively smoothed counts
-spe <- nsClustering(spe, dimRed = "PCA", reclust = TRUE)
-head(spe$reCluster)
-
 }
diff --git a/vignettes/1_seqFISH_mouseEmbryo.Rmd b/vignettes/1_seqFISH_mouseEmbryo.Rmd
index 2a611be..860885e 100644
--- a/vignettes/1_seqFISH_mouseEmbryo.Rmd
+++ b/vignettes/1_seqFISH_mouseEmbryo.Rmd
@@ -51,7 +51,7 @@ To run clustSIGNAL, we need the column names of sample and cell labels in the co
 
 ```{r}
 set.seed(100)
-samples = "embryo"
+samples = "sample_id"
 cells = "uniqueID"
 res_emb = clustSIGNAL(spe, samples, cells, outputs = "a")
 ```