@@ -61,7 +61,7 @@

 #' @export

 seuratNormalizeData <- function(inSCE, useAssay, normAssayName = "seuratNormData", normalizationMethod = "LogNormalize", scaleFactor = 10000, verbose = TRUE) {

   if(missing(useAssay)){

-    useAssay <- assayNames(inSCE)[1]

+    useAssay <- SummarizedExperiment::assayNames(inSCE)[1]

     message("'useAssay' parameter missing. Using the first available assay instead: '", useAssay, "'")

   }

   seuratObject <- Seurat::NormalizeData(convertSCEToSeurat(inSCE, useAssay), normalization.method = normalizationMethod, scale.factor = scaleFactor, verbose = verbose)

@@ -209,6 +209,10 @@ seuratICA <- function(inSCE, useAssay, reducedDimName = "seuratICA", nics = 20)

 #' @param inSCE (sce) object on which to compute and store jackstraw plot

 #' @param useAssay Assay containing scaled counts to use in JackStraw calculation.

 #' @param dims Number of components to test in Jackstraw. If \code{NULL}, then all components are used. Default \code{NULL}.

+#' @param numReplicate Numeric value indicating the number of replicate samplings to perform.

+#'  Default value is \code{100}.

+#' @param propFreq Numeric value indicating the proportion of data to randomly permute for each replicate.

+#'  Default value is \code{0.025}.

 #' @param externalReduction Pass DimReduc object if PCA/ICA computed through other libraries. Default \code{NULL}.

 #' @examples

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

@@ -221,7 +225,7 @@ seuratICA <- function(inSCE, useAssay, reducedDimName = "seuratICA", nics = 20)

 #' }

 #' @return Updated \code{SingleCellExperiment} object with jackstraw computations stored in it

 #' @export

-seuratComputeJackStraw <- function(inSCE, useAssay, dims = NULL, externalReduction = NULL) {

+seuratComputeJackStraw <- function(inSCE, useAssay, dims = NULL, numReplicate = 100, propFreq = 0.025, externalReduction = NULL) {

   seuratObject <- convertSCEToSeurat(inSCE, scaledAssay = useAssay)

   if(!is.null(externalReduction)){

     #convert (_) to (-) as required by Seurat

@@ -250,7 +254,7 @@ seuratComputeJackStraw <- function(inSCE, useAssay, dims = NULL, externalReducti

   if(is.null(dims)) {

     dims <- ncol(seuratObject@reductions[["pca"]])

   }

-  seuratObject <- Seurat::JackStraw(seuratObject, dims = as.double(dims))

+  seuratObject <- Seurat::JackStraw(seuratObject, dims = as.double(dims), num.replicate = numReplicate, prop.freq = propFreq)

   seuratObject <- Seurat::ScoreJackStraw(seuratObject, dims = 1:dims)

   inSCE <- .addSeuratToMetaDataSCE(inSCE, seuratObject)

   return(inSCE)

@@ -260,6 +264,8 @@ seuratComputeJackStraw <- function(inSCE, useAssay, dims = NULL, externalReducti

 #' Computes the plot object for jackstraw plot from the pca slot in the input sce object

 #' @param inSCE (sce) object from which to compute the jackstraw plot (pca should be computed)

 #' @param dims Number of components to plot in Jackstraw. If \code{NULL}, then all components are plotted Default \code{NULL}.

+#' @param xmax X-axis maximum on each QQ plot. Default \code{0.1}.

+#' @param ymax Y-axis maximum on each QQ plot. Default \code{0.3}.

 #' @param externalReduction Pass DimReduc object if PCA/ICA computed through other libraries. Default \code{NULL}.

 #' @examples

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

@@ -273,7 +279,7 @@ seuratComputeJackStraw <- function(inSCE, useAssay, dims = NULL, externalReducti

 #' }

 #' @return plot object

 #' @export

-seuratJackStrawPlot <- function(inSCE, dims = NULL, externalReduction = NULL) {

+seuratJackStrawPlot <- function(inSCE, dims = NULL, xmax = 0.1, ymax = 0.3, externalReduction = NULL) {

   seuratObject <- convertSCEToSeurat(inSCE)

   if(!is.null(externalReduction)){

     seuratObject@reductions <- list(pca = externalReduction)

@@ -284,7 +290,7 @@ seuratJackStrawPlot <- function(inSCE, dims = NULL, externalReduction = NULL) {

   if(is.null(dims)) {

     dims <- ncol(seuratObject@reductions[["pca"]])

   }

-  return(Seurat::JackStrawPlot(seuratObject, dims = 1:dims))

+  return(Seurat::JackStrawPlot(seuratObject, dims = 1:dims, xmax = xmax, ymax = ymax))

 }

 #' seuratPlotHVG

@@ -375,6 +381,8 @@ seuratReductionPlot <- function(inSCE, useReduction = c("pca", "ica", "tsne", "u

 #' @param groupSingletons boolean if singletons should be grouped together or not. Default \code{TRUE}.

 #' @param resolution Set the resolution parameter to find larger (value above 1) or smaller (value below 1) number of communities. Default \code{0.8}.

 #' @param externalReduction Pass DimReduc object if PCA/ICA computed through other libraries. Default \code{NULL}.

+#' @param verbose Logical value indicating if informative messages should 

+#'  be displayed. Default is \code{TRUE}.

 #' @examples

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

 #' \dontrun{

@@ -386,8 +394,16 @@ seuratReductionPlot <- function(inSCE, useReduction = c("pca", "ica", "tsne", "u

 #' }

 #' @return Updated sce object which now contains the computed clusters

 #' @export

-seuratFindClusters <- function(inSCE, useAssay, useReduction = c("pca", "ica"), dims = 10, algorithm = c("louvain", "multilevel", "SLM"), groupSingletons = TRUE, resolution = 0.8, externalReduction = NULL) {

-  

+seuratFindClusters <- function(

+  inSCE,

+  useAssay = "seuratScaledData", 

+  useReduction = c("pca", "ica"), 

+  dims = 10, 

+  algorithm = c("louvain", "multilevel", "SLM"), 

+  groupSingletons = TRUE, 

+  resolution = 0.8, 

+  externalReduction = NULL,

+  verbose = TRUE) {

   algorithm <- match.arg(algorithm)

   useReduction <- match.arg(useReduction)

@@ -398,7 +414,7 @@ seuratFindClusters <- function(inSCE, useAssay, useReduction = c("pca", "ica"),

     seuratObject@reductions <- list(pca = externalReduction)

   }

-  seuratObject <- Seurat::FindNeighbors(seuratObject, reduction = useReduction, dims = seq(dims))

+  seuratObject <- Seurat::FindNeighbors(seuratObject, reduction = useReduction, dims = seq(dims), verbose = verbose)

   no_algorithm <- 1

   if (algorithm == "louvain") {

     no_algorithm = 1

@@ -407,7 +423,7 @@ seuratFindClusters <- function(inSCE, useAssay, useReduction = c("pca", "ica"),

   } else if (algorithm == "SLM") {

     no_algorithm = 3

   }

-  seuratObject <- Seurat::FindClusters(seuratObject, algorithm = no_algorithm, group.singletons = groupSingletons, resolution = resolution)

+  seuratObject <- Seurat::FindClusters(seuratObject, algorithm = no_algorithm, group.singletons = groupSingletons, resolution = resolution, verbose = verbose)

   inSCE <- .addSeuratToMetaDataSCE(inSCE, seuratObject)

   colData(inSCE)[[paste0("Seurat","_",algorithm,"_","Resolution",resolution)]] <- [email protected]$seurat_clusters

   S4Vectors::metadata(inSCE)$seurat$clusterName <- paste0("Seurat","_",algorithm,"_","Resolution",resolution)

@@ -436,7 +452,6 @@ seuratRunTSNE <- function(inSCE, useReduction = c("pca", "ica"), reducedDimName

   return(inSCE)

 }

-#RunUMAP(seurat, reduction = "pca", dims = 1:10, min.dist = 0.4, n.neighbors = 40, spread = 20)

 #' seuratRunUMAP

 #' Computes UMAP from the given sce object and stores the UMAP computations back into the sce object

 #' @param inSCE (sce) object on which to compute the UMAP

@@ -446,6 +461,8 @@ seuratRunTSNE <- function(inSCE, useReduction = c("pca", "ica"), reducedDimName

 #' @param minDist Sets the \code{"min.dist"} parameter to the underlying UMAP call. See \link[Seurat]{RunUMAP} for more information. Default \code{0.3}.

 #' @param nNeighbors Sets the \code{"n.neighbors"} parameter to the underlying UMAP call. See \link[Seurat]{RunUMAP} for more information. Default \code{30L}.

 #' @param spread Sets the \code{"spread"} parameter to the underlying UMAP call. See \link[Seurat]{RunUMAP} for more information. Default \code{1}.

+#' @param verbose Logical value indicating if informative messages should 

+#'  be displayed. Default is \code{TRUE}.

 #' @examples

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

 #' \dontrun{

@@ -459,7 +476,7 @@ seuratRunTSNE <- function(inSCE, useReduction = c("pca", "ica"), reducedDimName

 #' @return Updated sce object with UMAP computations stored

 #' @export

 #' @importFrom SingleCellExperiment reducedDim<-

-seuratRunUMAP <- function(inSCE, useReduction = c("pca", "ica"), reducedDimName = "seuratUMAP", dims = 10, minDist = 0.3, nNeighbors = 30L, spread = 1) {

+seuratRunUMAP <- function(inSCE, useReduction = c("pca", "ica"), reducedDimName = "seuratUMAP", dims = 10, minDist = 0.3, nNeighbors = 30L, spread = 1, verbose = TRUE) {

   useReduction <- match.arg(useReduction)

   seuratObject <- convertSCEToSeurat(inSCE)

   seuratObject <- Seurat::RunUMAP(seuratObject,

@@ -467,7 +484,8 @@ seuratRunUMAP <- function(inSCE, useReduction = c("pca", "ica"), reducedDimName

                                   dims = 1:dims,

                                   min.dist = minDist,

                                   n.neighbors = nNeighbors,

-                                  spread = spread)

+                                  spread = spread,

+                                  verbose = verbose)

   inSCE <- .addSeuratToMetaDataSCE(inSCE, seuratObject)

   temp <- seuratObject@[email protected]

@@ -494,6 +512,7 @@ seuratRunUMAP <- function(inSCE, useReduction = c("pca", "ica"), reducedDimName

 #' @param inSCE (sce) object from which to compute the elbow plot (pca should be computed)

 #' @param significantPC Number of significant principal components to plot. This is used to alter the color of the points for the corresponding PCs. If \code{NULL}, all points will be the same color. Default \code{NULL}.

 #' @param reduction Reduction to use for elbow plot generation. Either \code{"pca"} or \code{"ica"}. Default \code{"pca"}.

+#' @param ndims Number of components to use. Default \code{20}.

 #' @param externalReduction Pass DimReduc object if PCA/ICA computed through other libraries. Default \code{NULL}.

 #' @param interactive Logical value indicating if the returned object should

 #'  be an interactive plotly object if \code{TRUE} or a ggplot object if

@@ -512,13 +531,14 @@ seuratRunUMAP <- function(inSCE, useReduction = c("pca", "ica"), reducedDimName

 seuratElbowPlot <- function(inSCE, 

                             significantPC = NULL, 

                             reduction = "pca", 

+                            ndims = 20,

                             externalReduction = NULL,

                             interactive = TRUE) {

   seuratObject <- convertSCEToSeurat(inSCE)

   if(!is.null(externalReduction)){

     seuratObject@reductions <- list(pca = externalReduction)

   }

-  plot <- Seurat::ElbowPlot(seuratObject, reduction = reduction)

+  plot <- Seurat::ElbowPlot(seuratObject, reduction = reduction, ndims = ndims)

   if(!is.null(significantPC)){

     plot$data$Significant <- c(rep("Yes", significantPC), rep("No", length(rownames(plot$data)) - significantPC))

     plot <- ggplot2::ggplot(data = plot$data, ggplot2::aes(x = plot$data$dims, y = plot$data$stdev, color = plot$data$Significant)) + ggplot2::geom_point()

@@ -890,12 +910,14 @@ seuratIntegration <- function(inSCE, useAssay = "counts", batch, newAssayName =

 #' @param threshUse Numeric value indicating the logFC threshold value on 

 #'  which on average, at least X-fold difference (log-scale) between the 

 #'  two groups of cells exists. Default is \code{0.25}.

+#' @param verbose Logical value indicating if informative messages should 

+#'  be displayed. Default is \code{TRUE}.

 #' @return A \code{SingleCellExperiment} object that contains marker genes populated in a data.frame stored inside metadata slot.

 #' @export

 seuratFindMarkers <- function(

   inSCE, cells1 = NULL, cells2 = NULL, group1 = NULL, group2 = NULL, 

   allGroup = NULL, conserved = FALSE, test = "wilcox", onlyPos = FALSE,

-  minPCT = 0.1, threshUse = 0.25){

+  minPCT = 0.1, threshUse = 0.25, verbose = TRUE){

   seuratObject <- convertSCEToSeurat(inSCE)

   markerGenes <- NULL

   if(is.null(allGroup)

@@ -953,7 +975,8 @@ seuratFindMarkers <- function(

       test.use = test, 

       only.pos = onlyPos,

       logfc.threshold = threshUse,

-      min.pct = minPCT)

+      min.pct = minPCT,

+      verbose = verbose)

     gene.id <- markerGenes$gene

     markerGenes <- cbind(gene.id, markerGenes)

     markerGenes$gene <- NULL

@@ -973,7 +996,8 @@ seuratFindMarkers <- function(

       test.use = test, 

       only.pos = onlyPos,

       logfc.threshold = threshUse,

-      min.pct = minPCT)

+      min.pct = minPCT,

+      verbose = verbose)

     gene.id <- markerGenes$gene

     markerGenes <- cbind(gene.id, markerGenes)

     markerGenes$gene <- NULL

@@ -1173,8 +1197,8 @@ seuratGenePlot <- function(inSCE,

 #' @return A list of variable feature names.

 #' @export

 seuratVariableFeatures <- function(inSCE){

-  if(!is.null(metadata(inSCE)$seurat$obj)){

-    return(Seurat::VariableFeatures(metadata(inSCE)$seurat$obj))

+  if(!is.null(S4Vectors::metadata(inSCE)$seurat$obj)){

+    return(Seurat::VariableFeatures(S4Vectors::metadata(inSCE)$seurat$obj))

   }

   else{

     return(NULL)

@@ -5,7 +5,14 @@

 \title{seuratComputeJackStraw

 Compute jackstraw plot and store the computations in the input sce object}

 \usage{

-seuratComputeJackStraw(inSCE, useAssay, dims = NULL, externalReduction = NULL)

+seuratComputeJackStraw(

+  inSCE,

+  useAssay,

+  dims = NULL,

+  numReplicate = 100,

+  propFreq = 0.025,

+  externalReduction = NULL

+)

 }

 \arguments{

 \item{inSCE}{(sce) object on which to compute and store jackstraw plot}

@@ -14,6 +21,12 @@ seuratComputeJackStraw(inSCE, useAssay, dims = NULL, externalReduction = NULL)

 \item{dims}{Number of components to test in Jackstraw. If \code{NULL}, then all components are used. Default \code{NULL}.}

+\item{numReplicate}{Numeric value indicating the number of replicate samplings to perform.

+Default value is \code{100}.}

+

+\item{propFreq}{Numeric value indicating the proportion of data to randomly permute for each replicate.

+Default value is \code{0.025}.}

+

 \item{externalReduction}{Pass DimReduc object if PCA/ICA computed through other libraries. Default \code{NULL}.}

 }

 \value{

@@ -9,6 +9,7 @@ seuratElbowPlot(

   inSCE,

   significantPC = NULL,

   reduction = "pca",

+  ndims = 20,

   externalReduction = NULL,

   interactive = TRUE

 )

@@ -20,6 +21,8 @@ seuratElbowPlot(

 \item{reduction}{Reduction to use for elbow plot generation. Either \code{"pca"} or \code{"ica"}. Default \code{"pca"}.}

+\item{ndims}{Number of components to use. Default \code{20}.}

+

 \item{externalReduction}{Pass DimReduc object if PCA/ICA computed through other libraries. Default \code{NULL}.}

 \item{interactive}{Logical value indicating if the returned object should

@@ -7,13 +7,14 @@ Computes the clusters from the input sce object and stores them back in sce obje

 \usage{

 seuratFindClusters(

   inSCE,

-  useAssay,

+  useAssay = "seuratScaledData",

   useReduction = c("pca", "ica"),

   dims = 10,

   algorithm = c("louvain", "multilevel", "SLM"),

   groupSingletons = TRUE,

   resolution = 0.8,

-  externalReduction = NULL

+  externalReduction = NULL,

+  verbose = TRUE

 )

 }

 \arguments{

@@ -32,6 +33,9 @@ seuratFindClusters(

 \item{resolution}{Set the resolution parameter to find larger (value above 1) or smaller (value below 1) number of communities. Default \code{0.8}.}

 \item{externalReduction}{Pass DimReduc object if PCA/ICA computed through other libraries. Default \code{NULL}.}

+

+\item{verbose}{Logical value indicating if informative messages should 

+be displayed. Default is \code{TRUE}.}

 }

 \value{

 Updated sce object which now contains the computed clusters

@@ -15,7 +15,8 @@ seuratFindMarkers(

   test = "wilcox",

   onlyPos = FALSE,

   minPCT = 0.1,

-  threshUse = 0.25

+  threshUse = 0.25,

+  verbose = TRUE

 )

 }

 \arguments{

@@ -45,6 +46,9 @@ cells in which genes are detected. Default is \code{0.1}.}

 \item{threshUse}{Numeric value indicating the logFC threshold value on 

 which on average, at least X-fold difference (log-scale) between the 

 two groups of cells exists. Default is \code{0.25}.}

+

+\item{verbose}{Logical value indicating if informative messages should 

+be displayed. Default is \code{TRUE}.}

 }

 \value{

 A \code{SingleCellExperiment} object that contains marker genes populated in a data.frame stored inside metadata slot.

@@ -5,13 +5,23 @@

 \title{seuratJackStrawPlot

 Computes the plot object for jackstraw plot from the pca slot in the input sce object}

 \usage{

-seuratJackStrawPlot(inSCE, dims = NULL, externalReduction = NULL)

+seuratJackStrawPlot(

+  inSCE,

+  dims = NULL,

+  xmax = 0.1,

+  ymax = 0.3,

+  externalReduction = NULL

+)

 }

 \arguments{

 \item{inSCE}{(sce) object from which to compute the jackstraw plot (pca should be computed)}

 \item{dims}{Number of components to plot in Jackstraw. If \code{NULL}, then all components are plotted Default \code{NULL}.}

+\item{xmax}{X-axis maximum on each QQ plot. Default \code{0.1}.}

+

+\item{ymax}{Y-axis maximum on each QQ plot. Default \code{0.3}.}

+

 \item{externalReduction}{Pass DimReduc object if PCA/ICA computed through other libraries. Default \code{NULL}.}

 }

 \value{

@@ -12,7 +12,8 @@ seuratRunUMAP(

   dims = 10,

   minDist = 0.3,

   nNeighbors = 30L,

-  spread = 1

+  spread = 1,

+  verbose = TRUE

 )

 }

 \arguments{

@@ -29,6 +30,9 @@ seuratRunUMAP(

 \item{nNeighbors}{Sets the \code{"n.neighbors"} parameter to the underlying UMAP call. See \link[Seurat]{RunUMAP} for more information. Default \code{30L}.}

 \item{spread}{Sets the \code{"spread"} parameter to the underlying UMAP call. See \link[Seurat]{RunUMAP} for more information. Default \code{1}.}

+

+\item{verbose}{Logical value indicating if informative messages should 

+be displayed. Default is \code{TRUE}.}

 }

 \value{

 Updated sce object with UMAP computations stored