@@ -84,7 +84,7 @@ runScanpyNormalizeData <- function(inSCE,

     .updateAssaySCEFromScanpy(inSCE, scanpyObject, normAssayName)

   metadata(inSCE)$scanpy$obj <- scanpyObject

   metadata(inSCE)$scanpy$normValues <- normValue$X

-  inSCE@metadata$scanpy$normAssay <- normAssayName

+  metadata(inSCE)$scanpy$normAssay <- normAssayName

   colData(inSCE)$n_counts <- 

     as.factor(unlist(metadata(inSCE)$scanpy$obj$obs['n_counts']))

@@ -217,11 +217,11 @@ runScanpyFindHVG <- function(inSCE,

     metadata(inSCE)$scanpy$hvg <- scanpyObject

     rowData(inSCE)$scanpy_variableFeatures_seurat_dispersion <-

-      inSCE@metadata$scanpy$hvg["var"][["dispersions"]]

+      metadata(inSCE)$scanpy$hvg["var"][["dispersions"]]

     rowData(inSCE)$scanpy_variableFeatures_seurat_dispersionScaled <-

-      inSCE@metadata$scanpy$hvg["var"][["dispersions_norm"]]    

+      metadata(inSCE)$scanpy$hvg["var"][["dispersions_norm"]]    

     rowData(inSCE)$scanpy_variableFeatures_seurat_mean <-

-      inSCE@metadata$scanpy$hvg["var"][["means"]]  

+      metadata(inSCE)$scanpy$hvg["var"][["means"]]  

     metadata(inSCE)$sctk$runFeatureSelection$seurat <-

       list(

@@ -247,22 +247,22 @@ runScanpyFindHVG <- function(inSCE,

     metadata(inSCE)$scanpy$hvg <- scanpyObject

     if (!altExp) {

       rowData(inSCE)$scanpy_variableFeatures_cr_dispersion <-

-        inSCE@metadata$scanpy$hvg["var"][["dispersions"]]

+        metadata(inSCE)$scanpy$hvg["var"][["dispersions"]]

       rowData(inSCE)$scanpy_variableFeatures_cr_dispersionScaled <-

-        inSCE@metadata$scanpy$hvg["var"][["dispersions_norm"]]    

+        metadata(inSCE)$scanpy$hvg["var"][["dispersions_norm"]]    

       rowData(inSCE)$scanpy_variableFeatures_cr_mean <-

-        inSCE@metadata$scanpy$hvg["var"][["means"]] 

+        metadata(inSCE)$scanpy$hvg["var"][["means"]] 

     }

     else{

       scanpyToSCE <- zellkonverter::AnnData2SCE(adata = scanpyObject)

       altExpRows <- match(rownames(inSCE), rownames(scanpyToSCE))

       rowData(inSCE)$scanpy_variableFeatures_cr_dispersion <-

-        inSCE@metadata$scanpy$hvg["var"][["dispersions"]][altExpRows]

+        metadata(inSCE)$scanpy$hvg["var"][["dispersions"]][altExpRows]

       rowData(inSCE)$scanpy_variableFeatures_cr_dispersionScaled <-

-        inSCE@metadata$scanpy$hvg["var"][["dispersions_norm"]] [altExpRows]   

+        metadata(inSCE)$scanpy$hvg["var"][["dispersions_norm"]] [altExpRows]   

       rowData(inSCE)$scanpy_variableFeatures_cr_mean <-

-        inSCE@metadata$scanpy$hvg["var"][["means"]][altExpRows]

+        metadata(inSCE)$scanpy$hvg["var"][["means"]][altExpRows]

     }

     metadata(inSCE)$sctk$runFeatureSelection$cell_ranger <-

@@ -287,11 +287,11 @@ runScanpyFindHVG <- function(inSCE,

     metadata(inSCE)$scanpy$hvg <- scanpyObject

     rowData(inSCE)$scanpy_variableFeatures_seuratv3_variances <-

-      inSCE@metadata$scanpy$hvg["var"][["variances"]]

+      metadata(inSCE)$scanpy$hvg["var"][["variances"]]

     rowData(inSCE)$scanpy_variableFeatures_seuratv3_variancesScaled <-

-      inSCE@metadata$scanpy$hvg["var"][["variances_norm"]]    

+      metadata(inSCE)$scanpy$hvg["var"][["variances_norm"]]    

     rowData(inSCE)$scanpy_variableFeatures_seuratv3_mean <-

-      inSCE@metadata$scanpy$hvg["var"][["means"]]  

+      metadata(inSCE)$scanpy$hvg["var"][["means"]]  

     metadata(inSCE)$sctk$runFeatureSelection$seurat_v3 <-

       list(

         useAssay = useAssay,

@@ -357,6 +357,8 @@ plotScanpyHVG <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' }

 #' @return Updated \code{SingleCellExperiment} object which now contains the

@@ -426,6 +428,8 @@ runScanpyPCA <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' plotScanpyPCA(sce)

 #' }

@@ -465,6 +469,8 @@ plotScanpyPCA <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' plotScanpyPCAGeneRanking(sce)

 #' }

@@ -490,6 +496,8 @@ plotScanpyPCAGeneRanking <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' plotScanpyPCAVariance(sce)

 #' }

@@ -544,6 +552,8 @@ plotScanpyPCAVariance <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "counts")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

 #' }

@@ -641,6 +651,8 @@ runScanpyFindClusters <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "counts")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

 #' sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

@@ -720,6 +732,16 @@ runScanpyUMAP <- function(inSCE,

 #' tSNE call. Default \code{15}.

 #' @param externalReduction Pass DimReduc object if PCA computed through

 #' other libraries. Default \code{NULL}.

+#' @examples

+#' data(scExample, package = "singleCellTK")

+#' \dontrun{

+#' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

+#' sce <- runScanpyPCA(sce, useAssay = "counts")

+#' sce <- runScanpyFindClusters(sce, useAssay = "counts")

+#' sce <- runScanpyTSNE(sce, useReduction = "scanpyPCA")

+#' }

 #' @return Updated sce object with tSNE computations stored

 #' @export

 #' @importFrom SingleCellExperiment reducedDim<-

@@ -782,6 +804,8 @@ runScanpyTSNE <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

 #' sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

@@ -836,6 +860,7 @@ plotScanpyEmbedding <- function(inSCE,

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

 #' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "counts")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts", algorithm = "louvain")

 #' sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -908,6 +933,8 @@ runScanpyFindMarkers <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

 #' sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -921,7 +948,7 @@ plotScanpyMarkerGenes <- function(inSCE,

                                   nCols = 4,

                                   sharey = FALSE){

-  if(is.null(inSCE@metadata[["findMarkerScanpyObject"]])){

+  if(is.null(metadata(inSCE)[["findMarkerScanpyObject"]])){

     stop("marker genes not found. Kindly run runScanpyFindMarkers function first")

   }

   scanpyObject <- metadata(inSCE)[["findMarkerScanpyObject"]]

@@ -945,6 +972,8 @@ plotScanpyMarkerGenes <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

 #' sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -957,7 +986,7 @@ plotScanpyMarkerGenesViolin <- function(inSCE,

                                         features = NULL,

                                         nGenes = 10){

-  if(is.null(inSCE@metadata["findMarkerScanpyObject"])){

+  if(is.null(metadata(inSCE)["findMarkerScanpyObject"])){

     stop("marker genes not found. Kindly run runScanpyFindMarkers function first")

   }

   scanpyObject <- metadata(inSCE)[["findMarkerScanpyObject"]]

@@ -984,6 +1013,8 @@ plotScanpyMarkerGenesViolin <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

 #' sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -998,7 +1029,7 @@ plotScanpyMarkerGenesHeatmap <- function(inSCE,

                                          features = NULL,

                                          log2fcThreshold = NULL){

-  if(is.null(inSCE@metadata["findMarkerScanpyObject"])){

+  if(is.null(metadata(inSCE)["findMarkerScanpyObject"])){

     stop("marker genes not found. Kindly run runScanpyFindMarkers function first")

   }

   scanpyObject <- metadata(inSCE)[["findMarkerScanpyObject"]]

@@ -1058,6 +1089,8 @@ plotScanpyMarkerGenesHeatmap <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

 #' sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -1078,7 +1111,7 @@ plotScanpyMarkerGenesDotPlot <- function(inSCE,

                                          vmax = NULL,

                                          colorBarTitle = "log fold change"){

-  if(is.null(inSCE@metadata["findMarkerScanpyObject"])){

+  if(is.null(metadata(inSCE)["findMarkerScanpyObject"])){

     stop("marker genes not found. Kindly run runScanpyFindMarkers function first")

   }

   scanpyObject <- metadata(inSCE)[["findMarkerScanpyObject"]]

@@ -1151,6 +1184,8 @@ plotScanpyMarkerGenesDotPlot <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

 #' sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -1171,7 +1206,7 @@ plotScanpyMarkerGenesMatrixPlot <- function(inSCE,

                                             vmax = NULL,

                                             colorBarTitle = "log fold change"){

-  if(is.null(inSCE@metadata["findMarkerScanpyObject"])){

+  if(is.null(metadata(inSCE)["findMarkerScanpyObject"])){

     stop("marker genes not found. Kindly run runScanpyFindMarkers function first")

   }

   scanpyObject <- metadata(inSCE)[["findMarkerScanpyObject"]]

@@ -1236,6 +1271,8 @@ plotScanpyMarkerGenesMatrixPlot <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

@@ -1294,6 +1331,8 @@ plotScanpyHeatmap <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

@@ -1341,6 +1380,8 @@ plotScanpyDotPlot <- function(inSCE,

 #' data(scExample, package = "singleCellTK")

 #' \dontrun{

 #' sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+#' sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+#' sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 #' sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 #' sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

 #' sce <- runScanpyFindClusters(sce, useAssay = "counts")

@@ -50,6 +50,8 @@ plotScanpyDotPlot

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

@@ -35,6 +35,8 @@ plotScanpyEmbedding

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

 sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

@@ -44,6 +44,8 @@ plotScanpyHeatmap

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

@@ -35,6 +35,8 @@ plotScanpyMarkerGenes

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

 sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -69,6 +69,8 @@ plotScanpyMarkerGenesDotPlot

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

 sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -40,6 +40,8 @@ plotScanpyMarkerGenesHeatmap

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

 sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -69,6 +69,8 @@ plotScanpyMarkerGenesMatrixPlot

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

 sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -27,6 +27,8 @@ plotScanpyMarkerGenesViolin

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

 sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -34,6 +34,8 @@ plotScanpyPCA

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 plotScanpyPCA(sce)

 }

@@ -25,6 +25,8 @@ plotScanpyPCAGeneRanking

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 plotScanpyPCAGeneRanking(sce)

 }

@@ -23,6 +23,8 @@ plotScanpyPCAVariance

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 plotScanpyPCAVariance(sce)

 }

@@ -29,6 +29,8 @@ plotScanpyViolin

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

@@ -78,6 +78,8 @@ object

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "counts")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

 }

@@ -51,6 +51,7 @@ data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

 sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "counts")

 sce <- runScanpyFindClusters(sce, useAssay = "counts", algorithm = "louvain")

 sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )

@@ -47,6 +47,8 @@ components within the sce object

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "scanpyNormData")

 }

 }

@@ -45,3 +45,14 @@ runScanpyTSNE

 Computes tSNE from the given sce object and stores the tSNE computations back

 into the sce object

 }

+\examples{

+data(scExample, package = "singleCellTK")

+\dontrun{

+sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

+sce <- runScanpyPCA(sce, useAssay = "counts")

+sce <- runScanpyFindClusters(sce, useAssay = "counts")

+sce <- runScanpyTSNE(sce, useReduction = "scanpyPCA")

+}

+}

@@ -65,6 +65,8 @@ into the sce object

 data(scExample, package = "singleCellTK")

 \dontrun{

 sce <- runScanpyNormalizeData(sce, useAssay = "counts")

+sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")

+sce <- runScanpyFindHVG(sce, useAssay = "scanpyScaledData", method = "seurat")

 sce <- runScanpyPCA(sce, useAssay = "counts")

 sce <- runScanpyFindClusters(sce, useAssay = "counts")

 sce <- runScanpyUMAP(sce, useReduction = "scanpyPCA")

@@ -183,12 +183,12 @@ sce <- runScanpyTSNE(inSCE = sce, useReduction = "scanpyPCA", reducedDimName = "

 ```

 ```{r, warning=FALSE, message=FALSE}

-# sce <- runScanpyUMAP(inSCE = sce, useReduction = "scanpyPCA", reducedDimName = "scanpyUMAP")

+sce <- runScanpyUMAP(inSCE = sce, useReduction = "scanpyPCA", reducedDimName = "scanpyUMAP")

 ```

 ```{r, warning=FALSE}

 plotScanpyEmbedding(sce, reducedDimName = "scanpyTSNE")

-# plotScanpyEmbedding(sce, reducedDimName = "scanpyUMAP")

+plotScanpyEmbedding(sce, reducedDimName = "scanpyUMAP")

 ```

 **6. Clustering** <br>

@@ -201,7 +201,7 @@ sce <- runScanpyFindClusters(inSCE = sce, useAssay = "scanpyNormData", useReduct

 ```{r "scanpy_cluster_plots", warning=FALSE, message=FALSE}

 plotScanpyEmbedding(sce, reducedDimName = "scanpyPCA", color = 'Scanpy_leiden_0.8')

 plotScanpyEmbedding(sce, reducedDimName = "scanpyTSNE", color = 'Scanpy_leiden_0.8')

-# plotScanpyEmbedding(sce, reducedDimName = "scanpyUMAP", color = 'Scanpy_leiden_0.8')

+plotScanpyEmbedding(sce, reducedDimName = "scanpyUMAP", color = 'Scanpy_leiden_0.8')

 ```

 **7. Find Markers** <br>

@@ -218,8 +218,8 @@ The marker genes identified can be visualized through one of the available plots

 plotScanpyMarkerGenes(sce, groups = '0')

 plotScanpyMarkerGenesViolin(sce, groups = '0')

 plotScanpyMarkerGenesHeatmap(sce, groupBy = 'Scanpy_leiden_0.8', nGenes = 10)

-# plotScanpyMarkerGenesDotPlot(sce, groupBy = 'Scanpy_leiden_0.8', nGenes = 10)

-# plotScanpyMarkerGenesMatrixPlot(sce, groupBy = 'Scanpy_leiden_0.8', nGenes = 10)

+plotScanpyMarkerGenesDotPlot(sce, groupBy = 'Scanpy_leiden_0.8', nGenes = 10)

+plotScanpyMarkerGenesMatrixPlot(sce, groupBy = 'Scanpy_leiden_0.8', nGenes = 10)

 ```

 ````{=html}