@@ -315,7 +315,8 @@ importGeneSetsFromCollection <- function(inSCE, geneSetCollection,

 #' @param inSCE Input \linkS4class{SingleCellExperiment} object.

 #' @param categoryIDs Character vector containing the MSigDB gene set ids.

 #' The column \code{ID} in the table returned by \code{getMSigDBTable()} shows

-#' the list of possible gene set IDs that can be obtained.

+#' the list of possible gene set IDs that can be obtained. 

+#' Default is \code{"H"}.

 #' @param species Character. Species available can be found using the function

 #' \code{\link[msigdbr]{msigdbr_show_species}}. Default \code{"Homo sapiens"}.

 #' @param mapping Character. One of "gene_symbol", "human_gene_symbol", or

@@ -2,7 +2,7 @@

 #' and UMAP

 #'

 #' @param inSCE Input SCE object

-#' @param useReduction Reduction to plot

+#' @param useReduction Reduction to plot. Default is \code{"PCA"}.

 #' @param showLegend If legends should be plotted or not

 #' @param xDim Numeric value indicating the dimension to use for X-axis.

 #'  Default is 1 (refers to PC1).

@@ -8,9 +8,9 @@

 #' name to store a logical index of selected features. Default \code{NULL}. See

 #' details.

 #' @param hvgNumber Specify the number of top genes to highlight in red. Default

-#' \code{NULL}. See details.

+#' \code{2000}. See details.

 #' @param labelsCount Specify the number of data points/genes to label. Should

-#' be less than \code{hvgNumber}. Default \code{20}. See details.

+#' be less than \code{hvgNumber}. Default \code{10}. See details.

 #' @param featureDisplay A character string for the \code{rowData} variable name

 #' to indicate what type of feature ID should be displayed. If set by

 #' \code{\link{setSCTKDisplayRow}}, will by default use it. If \code{NULL}, will

@@ -6,7 +6,7 @@

 #' @param inSCE A \linkS4class{SingleCellExperiment} object.

 #' @param useReducedDim A single \code{character}, specifying which

 #' low-dimension representation (\code{\link{reducedDim}})

-#' to perform the clustering algorithm on. Default \code{NULL}.

+#' to perform the clustering algorithm on. Default \code{"PCA"}.

 #' @param useAssay A single \code{character}, specifying which

 #' \code{\link{assay}} to perform the clustering algorithm

 #' on. Default \code{NULL}.

@@ -23,15 +23,15 @@

 #' \code{NULL}.

 #' @param clusterName A single \code{character}, specifying the name to store

 #' the cluster label in \code{\link{colData}}. Default

-#' \code{"scranSNN_cluster"}.

+#' \code{"cluster"}.

 #' @param k An \code{integer}, the number of nearest neighbors used to construct

 #' the graph. Smaller value indicates higher resolution and larger number of

-#' clusters. Default \code{8}.

+#' clusters. Default \code{14}.

 #' @param nComp An \code{integer}. The number of components to use for graph

 #' construction. Default \code{10}. See Detail.

 #' @param weightType A single \code{character}, that specifies the edge weighing

 #' scheme when constructing the Shared Nearest-Neighbor (SNN) graph. Choose from

-#' \code{"rank"}, \code{"number"}, \code{"jaccard"}. Default \code{"rank"}.

+#' \code{"rank"}, \code{"number"}, \code{"jaccard"}. Default \code{"jaccard"}.

 #' @param algorithm A single \code{character}, that specifies the community

 #' detection algorithm to work on the SNN graph. Choose from \code{"leiden"},

 #' \code{"louvain"}, \code{"walktrap"}, \code{"infomap"}, \code{"fastGreedy"},

@@ -205,16 +205,19 @@

 #' compared with all other cells. Default \code{NULL}.

 #' @param class A vector/factor with \code{ncol(inSCE)} elements, or a character

 #' scalar that specifies a column name of \code{colData(inSCE)}. Default

-#' \code{NULL}.

+#' \code{"cluster"}.

 #' @param classGroup1 a vector specifying which "levels" given in \code{class}

-#' are of interests. Default \code{NULL}.

+#' are of interests. Default \code{c(1)}.

 #' @param classGroup2 a vector specifying which "levels" given in \code{class}

 #' is the control group against those specified by \code{classGroup1}. If

 #' \code{NULL} when using annotation specification, \code{classGroup1} cells

-#' will be compared with all other cells.

-#' @param analysisName A character scalar naming the DEG analysis. Required

-#' @param groupName1 A character scalar naming the group of interests. Required.

-#' @param groupName2 A character scalar naming the control group. Required.

+#' will be compared with all other cells. Default \code{c(2)}.

+#' @param analysisName A character scalar naming the DEG analysis. 

+#' Default \code{"cluster1_VS_2"}.

+#' @param groupName1 A character scalar naming the group of interests. 

+#' Default \code{"cluster1"}.

+#' @param groupName2 A character scalar naming the control group. 

+#' Default \code{"cluster2"}.

 #' @param covariates A character vector of additional covariates to use when

 #' building the model. All covariates must exist in

 #' \code{names(colData(inSCE))}. Default \code{NULL}.

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

 #' raw counts for \code{"vst"} method, or a normalized assay for other methods.

 #' @param method Specify the method to use for variable gene selection.

 #' Options include \code{"vst"}, \code{"mean.var.plot"} or \code{"dispersion"}

-#' from Seurat and \code{"modelGeneVar"} from Scran.

+#' from Seurat and \code{"modelGeneVar"} from Scran. Default \code{"modelGeneVar"}

 #' @return The input \linkS4class{SingleCellExperiment} object that contains 

 #' the computed statistics in the \code{rowData} slot

 #' @seealso \code{\link{runModelGeneVar}}, \code{\link{runSeuratFindHVG}},

@@ -17,7 +17,7 @@

 #' "mouse", "zeisel"}. See detail. Default \code{"hpca"}.

 #' @param level A string for cell type labeling level. Used only when using

 #' some of the SingleR built-in references. Choose from \code{"main", "fine",

-#' "ont"}. Default \code{"main"}.

+#' "ont"}. Default \code{"fine"}.

 #' @param featureType A string for whether to use gene symbols or Ensembl IDs

 #' when using a SingleR built-in reference. Should be set based on the type of

 #' \code{rownames} of \code{inSCE}. Choose from \code{"symbol", "ensembl"}.

@@ -43,7 +43,7 @@

 #' @param pca Logical. Whether to perform dimension reduction with PCA before

 #' UMAP. Ignored when using \code{useReducedDim}. Default \code{TRUE}.

 #' @param initialDims Number of dimensions from PCA to use as input in UMAP.

-#' Default \code{25}.

+#' Default \code{10}.

 #' @param nNeighbors The size of local neighborhood used for manifold

 #' approximation. Larger values result in more global views of the manifold,

 #' while smaller values result in more local data being preserved. Default

@@ -7,9 +7,9 @@

 #' as \link{importGeneSetsFromList} or \link{importGeneSetsFromMSigDB}

 #' @param inSCE Input \linkS4class{SingleCellExperiment} object.

 #' @param geneSetCollectionName Character. The name of the gene set collection

-#' to use.

+#' to use. Default \code{"H"}.

 #' @param useAssay Character. The name of the assay to use. This assay should

-#' contain log normalized counts.

+#' contain log normalized counts. Default \code{"logcounts"}.

 #' @param resultNamePrefix  Character. Prefix to the name the VAM results which

 #' will be stored in the reducedDim slot of \code{inSCE}. The names of the

 #' output matrices will be \code{resultNamePrefix_Distance} and

@@ -9,7 +9,7 @@

 #' @param useFeatureSubset Subset of feature to use for dimension reduction. A

 #' character string indicating a \code{rowData} variable that stores the logical

 #' vector of HVG selection, or a vector that can subset the rows of

-#' \code{inSCE}. Default \code{NULL}.

+#' \code{inSCE}. Default \code{"hvg2000"}.

 #' @param scale Logical scalar, whether to standardize the expression values.

 #' Default \code{TRUE}.

 #' @param reducedDimName Name to use for the reduced output assay. Default

@@ -7,12 +7,12 @@

 \usage{

 getFindMarkerTopTable(

   inSCE,

-  log2fcThreshold = 1,

+  log2fcThreshold = 0,

   fdrThreshold = 0.05,

-  minClustExprPerc = 0.7,

-  maxCtrlExprPerc = 0.4,

-  minMeanExpr = 1,

-  topN = 10

+  minClustExprPerc = 0.5,

+  maxCtrlExprPerc = 0.5,

+  minMeanExpr = 0,

+  topN = 1

 )

 findMarkerTopTable(

@@ -10,16 +10,15 @@ getTopHVG(

   method = c("vst", "dispersion", "mean.var.plot", "modelGeneVar", "seurat", "seurat_v3",

     "cell_ranger"),

   hvgNumber = 2000,

-  useFeatureSubset = NULL,

+  useFeatureSubset = "hvg2000",

   featureDisplay = metadata(inSCE)$featureDisplay

 )

 setTopHVG(

   inSCE,

-  method = c("vst", "dispersion", "mean.var.plot", "modelGeneVar", "seurat", "seurat_v3",

-    "cell_ranger"),

+  method = "modelGeneVar",

   hvgNumber = 2000,

-  featureSubsetName = NULL,

+  featureSubsetName = "hvg2000",

   genes = NULL,

   genesBy = NULL,

   altExp = FALSE

@@ -6,7 +6,7 @@

 \usage{

 importGeneSetsFromMSigDB(

   inSCE,

-  categoryIDs,

+  categoryIDs = "H",

   species = "Homo sapiens",

   mapping = c("gene_symbol", "human_gene_symbol", "entrez_gene"),

   by = "rownames",

@@ -7,7 +7,7 @@ and UMAP}

 \usage{

 plotDimRed(

   inSCE,

-  useReduction,

+  useReduction = "PCA",

   showLegend = FALSE,

   xDim = 1,

   yDim = 2,

@@ -32,9 +32,6 @@ to "ridge", "violin", "feature", "dot" and "heatmap".}

 \item{groupVariable}{Specify the column name from the colData slot that

 should be used as grouping variable.}

-\item{reducedDimName}{saved dimension reduction name in the

-SingleCellExperiment object. Default \code{seuratUMAP}.}

-

 \item{splitBy}{Specify the column name from the colData slot that should be

 used to split samples.

  Default is \code{NULL}.}

@@ -6,10 +6,10 @@

 \usage{

 plotTopHVG(

   inSCE,

-  method = c("vst", "mean.var.plot", "dispersion", "modelGeneVar"),

-  hvgNumber = NULL,

+  method = "modelGeneVar",

+  hvgNumber = 2000,

   useFeatureSubset = NULL,

-  labelsCount = 20,

+  labelsCount = 10,

   featureDisplay = metadata(inSCE)$featureDisplay,

   labelSize = 2,

   dotSize = 2,

@@ -9,7 +9,7 @@

 \alias{runWilcox}

 \title{Perform differential expression analysis on SCE object}

 \usage{

-runDEAnalysis(method = c("wilcox", "MAST", "DESeq2", "Limma", "ANOVA"), ...)

+runDEAnalysis(inSCE, method = "wilcox", ...)

 runDESeq2(

   inSCE,

@@ -115,12 +115,12 @@ runWilcox(

   useReducedDim = NULL,

   index1 = NULL,

   index2 = NULL,

-  class = NULL,

-  classGroup1 = NULL,

-  classGroup2 = NULL,

-  analysisName,

-  groupName1,

-  groupName2,

+  class = "cluster",

+  classGroup1 = c(1),

+  classGroup2 = c(2),

+  analysisName = "cluster1_VS_2",

+  groupName1 = "cluster1",

+  groupName2 = "cluster2",

   covariates = NULL,

   onlyPos = FALSE,

   log2fcThreshold = NULL,

@@ -134,6 +134,8 @@ runWilcox(

 )

 }

 \arguments{

+\item{inSCE}{\linkS4class{SingleCellExperiment} inherited object.}

+

 \item{method}{Character. Specify which method to use when using

 \code{runDEAnalysis()}. Choose from \code{"wilcox"}, \code{"MAST"},

 \code{"DESeq2"}, \code{"Limma"}, \code{"ANOVA"}. Default \code{"wilcox"}.}

@@ -141,8 +143,6 @@ runWilcox(

 \item{...}{Arguments to pass to specific methods when using the generic

 \code{runDEAnalysis()}.}

-\item{inSCE}{\linkS4class{SingleCellExperiment} inherited object.}

-

 \item{useAssay}{character. A string specifying which assay to use for the

 DE regression. Ignored when \code{useReducedDim} is specified. Default

 \code{"counts"} for DESeq2, \code{"logcounts"} for other methods.}

@@ -4,11 +4,7 @@

 \alias{runFeatureSelection}

 \title{Run Variable Feature Detection Methods}

 \usage{

-runFeatureSelection(

-  inSCE,

-  useAssay,

-  method = c("vst", "mean.var.plot", "dispersion", "modelGeneVar", "cell_ranger")

-)

+runFeatureSelection(inSCE, useAssay, method = "modelGeneVar")

 }

 \arguments{

 \item{inSCE}{Input \linkS4class{SingleCellExperiment} object.}

@@ -9,7 +9,7 @@ runFindMarker(

   inSCE,

   useAssay = "logcounts",

   useReducedDim = NULL,

-  method = c("wilcox", "MAST", "DESeq2", "Limma", "ANOVA"),

+  method = "wilcox",

   cluster = "cluster",

   covariates = NULL,

   log2fcThreshold = NULL,

@@ -6,15 +6,15 @@

 \usage{

 runScranSNN(

   inSCE,

-  useReducedDim = NULL,

+  useReducedDim = "PCA",

   useAssay = NULL,

   useAltExp = NULL,

   altExpAssay = "counts",

   altExpRedDim = NULL,

-  clusterName = "scranSNN_cluster",

-  k = 8,

+  clusterName = "cluster",

+  k = 14,

   nComp = 10,

-  weightType = c("rank", "number", "jaccard"),

+  weightType = "jaccard",

   algorithm = c("louvain", "leiden", "walktrap", "infomap", "fastGreedy", "labelProp",

     "leadingEigen"),

   BPPARAM = BiocParallel::SerialParam(),

@@ -10,7 +10,7 @@ runSingleR(

   useSCERef = NULL,

   labelColName = NULL,

   useBltinRef = c("hpca", "bpe", "mp", "dice", "immgen", "mouse", "zeisel"),

-  level = c("main", "fine", "ont"),

+  level = "fine",

   featureType = c("symbol", "ensembl"),

   labelByCluster = NULL

 )

@@ -6,8 +6,8 @@

 \usage{

 runVAM(

   inSCE,

-  geneSetCollectionName,

-  useAssay,

+  geneSetCollectionName = "H",

+  useAssay = "logcounts",

   resultNamePrefix = NULL,

   center = FALSE,

   gamma = TRUE

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

 scaterPCA(

   inSCE,

   useAssay = "logcounts",

-  useFeatureSubset = NULL,

+  useFeatureSubset = "hvg2000",

   scale = TRUE,

   reducedDimName = "PCA",

   nComponents = 50,