Added examples to exported functions.

R/adaptiveSmoothing.R

@@ -11,8 +11,12 @@
 #' @return SpatialExperiment object including smoothed gene expression values as another assay.
 #'
 #' @examples
+#' data(mouseEmbryo2)
 #'
-#' # to do
+#' # requires matrix containing NN nearest neighbour cell labels (nnCells),
+#' # generated using the neighbourDetect() function
+#' spe <- adaptiveSmoothing(spe, nnCells, NN = 30, kernel = "G", spread = 0.05)
+#' spe
 #'
 #' @export
 

R/clustSIGNAL.R

@@ -24,8 +24,13 @@
 #' 3. spe_final: a SpatialExperiment object with initial 'putative cell type' groups, entropy values, smoothed gene expression, post-smoothing clusters, and silhouette widths included.
 #'
 #' @examples
-#' spe = load(mouseEmbryo2)
-#' clustSIGNAL <- function (spe, spe$embryo, spe$uniqueIDs)
+#' data(mouseEmbryo2)
+#'
+#' names(colData(spe))
+#' # identify the column names with cell and sample labels
+#' samples = "embryo"
+#' cells = "uniqueID"
+#' res_list <- clustSIGNAL(spe, samples, cells, dimRed = "PCA", outputs = "c")
 #'
 #'
 #' @export

R/clustering.R

@@ -9,8 +9,16 @@
 #' @return SpatialExperiment object containing 'putative cell type' group allotted to each cell (reclust = FALSE) or clusters generated from smoothed data (reclust = TRUE).
 #'
 #' @examples
+#' data(mouseEmbryo2)
 #'
-#' # to do
+#' # for non-spatial clustering of normalised 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
 

R/entropyMeasure.R

@@ -11,7 +11,12 @@
 #' @return SpatialExperiment object including entropy values for each cell neighbourhood.
 #'
 #' @examples
-#' # to do
+#' data(mouseEmbryo2)
+#'
+#' # requires list of containing cluster proportions of each region (regXclust),
+#' # generated using the neighbourDetect() function
+#' spe <- entropyMeasure(spe, cells = "uniqueID", regXclust, threads = 1)
+#' head(spe$entropy)
 #'
 #' @export
 

R/neighborDetect.R

@@ -16,8 +16,10 @@
 #' 2. regXclust, a list of vectors for each cell's neighbourhood composition indicated by the proportion of 'putative cell type' groups it contains.
 #
 #' @examples
+#' data(mouseEmbryo2)
 #'
-#' # to do
+#' out_list <- neighbourDetect(spe, samples = "embryo", NN = 30, cells = "uniqueID", sort = TRUE)
+#' names(out_list)
 #'
 #' @export