@@ -182,7 +182,12 @@ export(runQuickUMAP)

 export(runSCANORAMA)

 export(runSCMerge)

 export(runScDblFinder)

+export(runScanpyFindHVG)

 export(runScanpyNormalizeData)

+export(runScanpyPCA)

+export(runScanpyScaleData)

+export(runScanpyTSNE)

+export(runScanpyUMAP)

 export(runScranSNN)

 export(runScrublet)

 export(runSeuratFindClusters)

@@ -9328,10 +9328,11 @@ shinyServer(function(input, output, session) {

     {

       req(vals$counts)

       message(paste0(date(), " ... Finding High Variable Genes"))

-        # vals$counts <- runSeuratFindHVG(inSCE = vals$counts,

-        #                                 useAssay = metadata(vals$counts)$sctk$seuratUseAssay,

-        #                                 method = input$hvg_method,

-        #                                 hvgNumber = as.numeric(input$hvg_no_features))

+        vals$counts <- runScanpyFindHVG(inSCE = vals$counts,

+                                        #useAssay = metadata(vals$counts)$sctk$seuratUseAssay,

+                                        useAssay = "counts",

+                                        method = "cell_ranger")

+                                        #hvgNumber = as.numeric(input$hvg_no_features))

       # vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, varFeatures = FALSE)

       # message(paste0(date(), " ... Plotting HVG"))

@@ -9367,12 +9368,12 @@ shinyServer(function(input, output, session) {

       # # incompatible with Seurat's "_"-to-"-" change. But in `runSeuratPCA/ICA`,

       # # we automatically detect seurat HVG from the object when `useFeatureSubset

       # # = NULL`, so no need to specify this now.

-      # vals$counts <- runSeuratPCA(inSCE = vals$counts,

-      #                             useAssay = "seuratNormData",

-      #                             reducedDimName = "seuratPCA",

-      #                             #useFeatureSubset = getSeuratVariableFeatures(vals$counts),

-      #                             nPCs = input$pca_no_components,

-      #                             seed = input$seed_PCA)

+      vals$counts <- runScanpyPCA(inSCE = vals$counts,

+                                  algorithm = "auto")

+                                  #useAssay = "seuratNormData",

+                                  #reducedDimName = "seuratPCA",

+                                  #nPCs = input$pca_no_components,

+                                  #seed = input$seed_PCA)

       # 

       # vals$counts@metadata$seurat$count_pc <- dim(convertSCEToSeurat(vals$counts)[["pca"]])[2]

       # vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE)

@@ -25,11 +25,6 @@ shinyPanelScanpy <- fluidPage(

         )

     ),

     bsCollapse(id = "ScanpyUI", open = "scanpy QC & Filtering", # ScanPy

-            bsCollapsePanel("QC & Filtering",

-                fluidRow(

-                        ),

-                            style = "primary"

-                            ),

             bsCollapsePanel("Normalize Data",

                                                                    fluidRow(

                                                                      column(4,

@@ -7,7 +7,8 @@

 \usage{

 getTopHVG(

   inSCE,

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

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

+    "seurat_v3", "cell_ranger"),

   hvgNumber = 2000,

   useFeatureSubset = NULL,

   featureDisplay = metadata(inSCE)$featureDisplay

new file mode 100644

@@ -0,0 +1,41 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/scanpyFunctions.R

+\name{runScanpyFindHVG}

+\alias{runScanpyFindHVG}

+\title{runScanpyFindHVG

+Find highly variable genes and store in the input sce object}

+\usage{

+runScanpyFindHVG(

+  inSCE,

+  useAssay = "counts",

+  method = c("seurat", "cell_ranger", "seurat_v3"),

+  hvgNumber = 2000

+)

+}

+\arguments{

+\item{inSCE}{(sce) object to compute highly variable genes from and to store

+back to it}

+

+\item{useAssay}{Specify the name of the assay to use for computation

+of variable genes. It is recommended to use a raw counts assay with the}

+

+\item{method}{selected method to use for computation of highly variable

+genes. One of \code{'seurat'}, \code{'cell_ranger'}, or \code{'seurat_v3'}.

+Default \code{"seurat"}.}

+

+\item{hvgNumber}{numeric value of how many genes to select as highly

+variable. Default \code{2000}}

+}

+\value{

+Updated \code{SingleCellExperiment} object with highly variable genes

+computation stored

+\code{\link{getTopHVG}}, \code{\link{plotTopHVG}}

+}

+\description{

+runScanpyFindHVG

+Find highly variable genes and store in the input sce object

+}

+\examples{

+data(scExample, package = "singleCellTK")

+sce <- runScanpyFindHVG(sce)

+}

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

 \alias{runScanpyNormalizeData}

 \title{runScanpyNormalizeData

 Wrapper for NormalizeData() function from scanpy library

-Normalizes the sce object according to the input parameters provided.}

+Normalizes the sce object according to the input parameters}

 \usage{

 runScanpyNormalizeData(

   inSCE,

@@ -30,7 +30,7 @@ Normalized \code{SingleCellExperiment} object

 \description{

 runScanpyNormalizeData

 Wrapper for NormalizeData() function from scanpy library

-Normalizes the sce object according to the input parameters provided.

+Normalizes the sce object according to the input parameters

 }

 \examples{

 data(scExample, package = "singleCellTK")

new file mode 100644

@@ -0,0 +1,52 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/scanpyFunctions.R

+\name{runScanpyPCA}

+\alias{runScanpyPCA}

+\title{runScanpyPCA

+Computes PCA on the input sce object and stores the calculated principal

+components within the sce object}

+\usage{

+runScanpyPCA(

+  inSCE,

+  useAssay = "scanpyScaledData",

+  reducedDimName = "scanpyPCA",

+  nPCs = 50L,

+  algorithm = c("arpack", "randomized", "auto", "lobpcg"),

+  use_highly_variable = TRUE

+)

+}

+\arguments{

+\item{inSCE}{(sce) object on which to compute PCA}

+

+\item{useAssay}{Assay containing scaled counts to use in PCA. Default

+\code{"scanpyScaledData"}.}

+

+\item{reducedDimName}{Name of new reducedDims object containing Scanpy PCA.

+Default \code{scanpyPCA}.}

+

+\item{nPCs}{numeric value of how many components to compute. Default

+\code{20}.}

+

+\item{algorithm}{selected method to use for computation of pca. One of \code{'arpack'}, \code{'randomized'}, \code{'auto'} or \code{'lobpcg'}.

+Default \code{"arpack"}.}

+

+\item{use_highly_variable}{boolean value of whether to use highly variable genes only. By default uses them if they have been determined beforehand.}

+}

+\value{

+Updated \code{SingleCellExperiment} object which now contains the

+computed principal components

+}

+\description{

+runScanpyPCA

+Computes PCA on the input sce object and stores the calculated principal

+components within the sce object

+}

+\examples{

+data(scExample, package = "singleCellTK")

+\dontrun{

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

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

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

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

+}

+}

new file mode 100644

@@ -0,0 +1,36 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/scanpyFunctions.R

+\name{runScanpyScaleData}

+\alias{runScanpyScaleData}

+\title{runScanpyScaleData

+Scales the input sce object according to the input parameters}

+\usage{

+runScanpyScaleData(

+  inSCE,

+  useAssay = "scanpyNormData",

+  scaledAssayName = "scanpyScaledData"

+)

+}

+\arguments{

+\item{inSCE}{(sce) object to scale}

+

+\item{useAssay}{Assay containing normalized counts to scale.}

+

+\item{scaledAssayName}{Name of new assay containing scaled data. Default

+\code{scanpyScaledData}.}

+}

+\value{

+Scaled \code{SingleCellExperiment} object

+}

+\description{

+runScanpyScaleData

+Scales the input sce object according to the input parameters

+}

+\examples{

+data(scExample, package = "singleCellTK")

+\dontrun{

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

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

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

+}

+}

new file mode 100644

@@ -0,0 +1,43 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/scanpyFunctions.R

+\name{runScanpyTSNE}

+\alias{runScanpyTSNE}

+\title{runScanpyTSNE

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

+into the sce object}

+\usage{

+runScanpyTSNE(

+  inSCE,

+  useReduction = "scanpyPCA",

+  reducedDimName = "scanpyTSNE",

+  dims = 10L,

+  perplexity = 15L,

+  externalReduction = NULL

+)

+}

+\arguments{

+\item{inSCE}{(sce) object on which to compute the tSNE}

+

+\item{useReduction}{selected reduction algorithm to use for computing tSNE.

+Default \code{"pca"}.}

+

+\item{reducedDimName}{Name of new reducedDims object containing Scanpy tSNE

+Default \code{scanpyTSNE}.}

+

+\item{dims}{Number of reduction components to use for tSNE computation.

+Default \code{10}.}

+

+\item{perplexity}{Adjust the perplexity tuneable parameter for the underlying

+tSNE call. Default \code{15}.}

+

+\item{externalReduction}{Pass DimReduc object if PCA computed through

+other libraries. Default \code{NULL}.}

+}

+\value{

+Updated sce object with tSNE computations stored

+}

+\description{

+runScanpyTSNE

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

+into the sce object

+}

new file mode 100644

@@ -0,0 +1,67 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/scanpyFunctions.R

+\name{runScanpyUMAP}

+\alias{runScanpyUMAP}

+\title{runScanpyUMAP

+Computes UMAP from the given sce object and stores the UMAP computations back

+into the sce object}

+\usage{

+runScanpyUMAP(

+  inSCE,

+  useReduction = "scanpyPCA",

+  reducedDimName = "scanpyUMAP",

+  dims = 10L,

+  minDist = 0.5,

+  nNeighbors = 15L,

+  spread = 1,

+  alpha = 1,

+  gamma = 1,

+  externalReduction = NULL

+)

+}

+\arguments{

+\item{inSCE}{(sce) object on which to compute the UMAP}

+

+\item{useReduction}{Reduction to use for computing UMAP. Default is \code{"pca"}.}

+

+\item{reducedDimName}{Name of new reducedDims object containing Scanpy UMAP

+Default \code{scanpyUMAP}.}

+

+\item{dims}{Numerical value of how many reduction components to use for UMAP

+computation. Default \code{10}.}

+

+\item{minDist}{Sets the \code{"min_dist"} parameter to the underlying UMAP

+call. Default \code{0.5}.}

+

+\item{nNeighbors}{Sets the \code{"n_neighbors"} parameter to the underlying

+UMAP call. Default \code{15L}.}

+

+\item{spread}{Sets the \code{"spread"} parameter to the underlying UMAP call.

+Default \code{1}.

+#' @param alpha Sets the \code{"alpha"} parameter to the underlying UMAP call.

+Default \code{1}.

+#' @param gamma Sets the \code{"gamma"} parameter to the underlying UMAP call.

+Default \code{1}.}

+

+\item{externalReduction}{Pass DimReduce object if PCA computed through

+other libraries. Default \code{NULL}.}

+}

+\value{

+Updated sce object with UMAP computations stored

+}

+\description{

+runScanpyUMAP

+Computes UMAP from the given sce object and stores the UMAP computations back

+into the sce object

+}

+\examples{

+data(scExample, package = "singleCellTK")

+\dontrun{

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

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

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

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

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

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

+}

+}

@@ -1,11 +1,23 @@

 % Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/seuratFunctions.R

+% Please edit documentation in R/scanpyFunctions.R, R/seuratFunctions.R

 \name{runSeuratFindClusters}

 \alias{runSeuratFindClusters}

-\title{runSeuratFindClusters

+\title{runScanpyFindClusters

 Computes the clusters from the input sce object and stores them back in sce

 object}

 \usage{

+runSeuratFindClusters(

+  inSCE,

+  useAssay = "seuratScaledData",

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

+  dims = 10,

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

+  groupSingletons = TRUE,

+  resolution = 0.8,

+  externalReduction = NULL,

+  verbose = TRUE

+)

+

 runSeuratFindClusters(

   inSCE,

   useAssay = "seuratScaledData",

@@ -46,11 +58,38 @@ other libraries. Default \code{NULL}.}

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

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

+

+\item{nNeighbors}{The size of local neighborhood (in terms of number of 

+neighboring data points) 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 \code{15L}.}

+

+\item{colDataName}{colName to store the result in annData object}

+

+\item{niterations}{How many iterations of the Leiden clustering algorithm to 

+perform. Positive values above 2 define the total number of iterations to 

+perform, -1 has the algorithm run until it reaches its optimal clustering.}

+

+\item{flavor}{Choose between to packages for computing the clustering.}

+

+\item{use_weights}{Boolean. Use weights from knn graph.}

+

+\item{cor_method}{correlation method to use. Options are ‘pearson’, 

+‘kendall’, and ‘spearman’. Default 'pearson'.}

+

+\item{inplace}{If True, adds dendrogram information to annData object, 

+else this function returns the information.}

 }

 \value{

+Updated sce object which now contains the computed clusters

+

 Updated sce object which now contains the computed clusters

 }

 \description{

+runScanpyFindClusters

+Computes the clusters from the input sce object and stores them back in sce

+object

+

 runSeuratFindClusters

 Computes the clusters from the input sce object and stores them back in sce

 object

@@ -58,6 +97,14 @@ object

 \examples{

 data(scExample, package = "singleCellTK")

 \dontrun{

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

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

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

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

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

+}

+data(scExample, package = "singleCellTK")

+\dontrun{

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

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

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