@@ -2,9 +2,11 @@ language: r

 cache: 

   - packages

   - pip

+r:

+  - oldrel

 dist: trusty

 sudo: false

-warnings_are_errors: false

+warnings_are_errors: true

 bioc_required: true

 # Current version of xgboost fails install

@@ -88,7 +88,7 @@ Imports:

     zinbwave,

     harmony,

     V8

-RoxygenNote: 7.0.2

+RoxygenNote: 7.1.0

 Suggests:

     testthat,

     Rsubread,

@@ -111,7 +111,6 @@ import(DropletUtils)

 import(GSVAdata)

 import(SingleCellExperiment)

 import(SummarizedExperiment)

-import(scds)

 importFrom(reticulate,import)

 importFrom(reticulate,py_module_available)

 importFrom(reticulate,py_set_seed)

@@ -30,7 +30,7 @@ print.decorated = function (x, useSource = TRUE, ...) {

     bare

   }

-  fun_def = capture.output(print.function(bare(x), useSource = useSource, ...))

+  fun_def = utils::capture.output(print.function(bare(x), useSource = useSource, ...))

   for (decorator in attr(x, 'decorators'))

     cat(deparse(decorator), '%@%\n')

   cat(fun_def, sep = '\n')

@@ -28,11 +28,13 @@

 #'  droplet-based single-cell RNA sequencing experiment.

 #' @param inSCE A \link[SingleCellExperiment]{SingleCellExperiment} object.

 #'  Must contain a raw counts matrix before empty droplets have been removed.

+#' @param useAssay  A string specifying which assay in the SCE to use.

 #' @param sample Character vector. Indicates which sample each cell belongs to

 #'  \link[DropletUtils]{emptyDrops} will be run on cells from each sample separately.

-#'  If NULL, then all cells will be processed together. Default NULL.

-#' @param ... Additional arguments to pass to \link[DropletUtils]{barcodeRanks}.

-#' @param useAssay  A string specifying which assay in the SCE to use.

+#'  If NULL, then all cells will be processed together. Default \code{NULL}.

+#' @param lower See \link[DropletUtils]{emptyDrops} for more information. Default \code{100}.

+#' @param fitBounds See \link[DropletUtils]{emptyDrops} for more information. Default \code{NULL}. 

+#' @param df See \link[DropletUtils]{emptyDrops} for more information. Default \code{20}.

 #' @return A \link[SingleCellExperiment]{SingleCellExperiment} object with the

 #'  \link[DropletUtils]{barcodeRanks} output table appended to the

 #'  \link[SummarizedExperiment]{colData} slot. The columns include

@@ -54,9 +56,9 @@

 runBarcodeRankDrops <- function(inSCE,

                                 sample = NULL,

                                 useAssay = "counts", 

-                                lower=100, 

-                                fit.bounds=NULL, 

-                                df=20

+                                lower = 100, 

+                                fitBounds = NULL, 

+                                df = 20

 ) {

   if(!is.null(sample)) {

     if(length(sample) != ncol(inSCE)) {

@@ -83,9 +85,9 @@ runBarcodeRankDrops <- function(inSCE,

     sceSample <- inSCE[, sceSampleInd]

     mat <- SummarizedExperiment::assay(sceSample, i = useAssay)

-    result <- .runBarcodeRankDrops(barcode.matrix = mat, lower=100,

-                                   fit.bounds=NULL, 

-                                   df=20)

+    result <- .runBarcodeRankDrops(barcode.matrix = mat, lower=lower,

+                                   fit.bounds=fitBounds, 

+                                   df=df)

     output[sceSampleInd, ] <- result

   }

@@ -93,7 +95,7 @@ runBarcodeRankDrops <- function(inSCE,

   colData(inSCE) = cbind(colData(inSCE), output)

   inSCE@metadata$runBarcodeRankDrops <- argsList[-1]

-  inSCE@metadata$runBarcodeRankDrops$packageVersion <- packageDescription("DropletUtils")$Version

+  inSCE@metadata$runBarcodeRankDrops$packageVersion <- utils::packageDescription("DropletUtils")$Version

   return(inSCE)

 }

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

                            alpha=NULL,

                            retain=NULL,

                            barcode.args=list(),

-                           BPPARAM=SerialParam()) {

+                           BPPARAM=BiocParallel::SerialParam()) {

   barcode.matrix <- .convertToMatrix(barcode.matrix)

@@ -16,7 +16,7 @@

                                      alpha=NULL,

                                      retain=NULL,

                                      barcode.args=list(),

-                                     BPPARAM=SerialParam())

+                                     BPPARAM=BiocParallel::SerialParam())

   colnames(result) <- paste0("dropletUtils_emptyDrops_", colnames(result))

   return(result)

@@ -34,8 +34,14 @@

 #'  \link[DropletUtils]{emptyDrops} will be run on cells from each sample separately.

 #'  If NULL, then all cells will be processed together. Default NULL.

 #' @param useAssay  A string specifying which assay in the SCE to use.

-#' @param ... Additional arguments to pass to \link[DropletUtils]{emptyDrops}.

-#'  matrix.

+#' @param lower See \link[DropletUtils]{emptyDrops} for more information.

+#' @param niters See \link[DropletUtils]{emptyDrops} for more information.

+#' @param testAmbient See \link[DropletUtils]{emptyDrops} for more information.

+#' @param ignore See \link[DropletUtils]{emptyDrops} for more information.

+#' @param alpha See \link[DropletUtils]{emptyDrops} for more information.

+#' @param retain See \link[DropletUtils]{emptyDrops} for more information.

+#' @param barcodeArgs See \link[DropletUtils]{emptyDrops} for more information.

+#' @param BPPARAM See \link[DropletUtils]{emptyDrops} for more information.

 #' @return A \link[SingleCellExperiment]{SingleCellExperiment} object with the

 #'  \link[DropletUtils]{emptyDrops} output table appended to the

 #'  \link[SummarizedExperiment]{colData} slot. The columns include

@@ -59,14 +65,14 @@

 runEmptyDrops <- function(inSCE,

                           sample = NULL,

                           useAssay = "counts", 

-                          lower=100,

-                          niters=10000,

-                          test.ambient=FALSE,

-                          ignore=NULL, 

-                          alpha=NULL,

-                          retain=NULL,

-                          barcode.args=list(),

-                          BPPARAM=SerialParam()

+                          lower = 100,

+                          niters = 10000,

+                          testAmbient = FALSE,

+                          ignore = NULL, 

+                          alpha = NULL,

+                          retain = NULL,

+                          barcodeArgs = list(),

+                          BPPARAM = BiocParallel::SerialParam()

 ) {

   # getting the current argument values

   argsList <- as.list(formals(fun = sys.function(sys.parent()), envir = parent.frame()))

@@ -97,25 +103,26 @@ runEmptyDrops <- function(inSCE,

     sceSample <- inSCE[, sceSampleInd]

     mat <- SummarizedExperiment::assay(sceSample, i = useAssay)

-    result <- .runEmptyDrops(barcode.matrix = mat, lower=100,

-                             niters=10000,

-                             test.ambient=FALSE,

-                             ignore=NULL, 

-                             alpha=NULL,

-                             retain=NULL,

-                             barcode.args=list(),

-                             BPPARAM=SerialParam())

+    result <- .runEmptyDrops(barcode.matrix = mat,

+                             lower = lower,

+                             niters = niters,

+                             test.ambient = testAmbient,

+                             ignore = ignore, 

+                             alpha = alpha,

+                             retain = retain,

+                             barcode.args = barcodeArgs,

+                             BPPARAM = BPPARAM)

     output[sceSampleInd, ] <- result

-    metadata(output[sceSampleInd, ]) <- metadata(result)

+    S4Vectors::metadata(output[sceSampleInd, ]) <- S4Vectors::metadata(result)

   }

   colData(inSCE) = cbind(colData(inSCE), output)

-  inSCE@metadata = metadata(output)

+  inSCE@metadata = S4Vectors::metadata(output)

   inSCE@metadata$runEmptyDrops <- argsList[-1]

-  inSCE@metadata$runEmptyDrops$packageVersion <- packageDescription("DropletUtils")$Version

+  inSCE@metadata$runEmptyDrops$packageVersion <- utils::packageDescription("DropletUtils")$Version

   return(inSCE)

 }

\ No newline at end of file

@@ -7,29 +7,32 @@

 #' overridden at function call.

 #' @param sce \link[SingleCellExperiment]{SingleCellExperiment} R object to be

 #'  exported.

-#'  @param useAssay Character, default `"counts"`. The name of assay of

+#' @param useAssay Character. The name of assay of

 #' interests that will be set as the primary matrix of the output AnnData.

-#' @param outputDir Path to the directory where .h5ad outputs will be written

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

+#' @param outputDir Path to the directory where .h5ad outputs will be written. Default is the current working directory.

+#' @param prefix Prefix to use for the name of the output file. Default \code{"sample"}.

 #' @param overwrite Boolean. Default \code{TRUE}.

-#' @param compression Default \code{None}.If output file compression is required, this variable accepts

-#' 'gzip' or 'lzf' as inputs.

-#' @param compression_opts Integer. Default \code{NULL} Sets the compression level

+#' @param compression If output file compression is required, this variable accepts

+#' 'gzip' or 'lzf' as inputs. Default \code{None}.

+#' @param compressionOpts Integer. Sets the compression level

 #' @param forceDense Default \code{False} Write sparse data as a dense matrix.

-#' Refer anndata.write_h5ad documentation for details

+#' Refer \code{anndata.write_h5ad} documentation for details. Default \code{NULL}. 

 #' @examples

+#' \dontrun{

 #' data(sce_chcl, package = "scds")

 #' exportSCEtoAnnData(sce=sce_chcl, compression="gzip")

-#'

+#' }

+#' 

 #' @export

-

 exportSCEtoAnnData <- function(sce, 

-                                useAssay='counts',

-                                outputDir="./",

-                                sample = "sample",

-                                overwrite=TRUE,

-                                compression= c('None','lzf','gzip'),

+                                useAssay = 'counts',

+                                outputDir = "./",

+                                prefix = "sample",

+                                overwrite = TRUE,

+                                compression = c('None','lzf','gzip'),

                                 compressionOpts = NULL,

-                                forceDense= c('False','True')){

+                                forceDense = c('False','True')){

   compression <- match.arg(compression)

   forceDense <- match.arg(forceDense)

   if (compression == 'None'){

@@ -58,12 +61,12 @@ exportSCEtoAnnData <- function(sce,

   dir.create(outputDir, showWarnings = FALSE, recursive = TRUE)

   annData <- .sce2adata(sce,useAssay)

-  fileName <- paste0(sample,".h5ad")

+  fileName <- paste0(prefix,".h5ad")

   filePath <- file.path(outputDir,fileName)

-

+  

   if (file.exists(filePath) && !isTRUE(overwrite)) {

     stop(paste0(path, " already exists. Change 'outputDir' or set 'overwrite' to TRUE."))

-  }

+    }

   annData$write_h5ad(filePath,

                      compression = compression, 

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

 #'  \code{TRUE}.

 #' @param gzipped Boolean. \code{TRUE} if the output files are to be

 #'  gzip compressed. \code{FALSE} otherwise. Default

-#'  \code{TRUE} to save disk space.

+#'  \code{TRUE}.

 #' @examples

 #' data(sce_chcl, package = "scds")

 #' exportSCEtoFlatFile(sce_chcl, "sce_chcl")

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

                                  dataType, 

                                  rdsFileName, 

                                  sampleName = 'sample',

-                                 delayedArray = delayedArrary){

+                                 delayedArray = FALSE){

   ## Read DropEst RDS

   dropEst_rds <- .readDropEstFile(sampleDir,dataType,rdsFileName)

   if (dataType == 'filtered' && 'cm' %in% names(dropEst_rds)) {

@@ -85,10 +85,10 @@

 #' create a SingleCellExperiment object from either the raw or filtered counts matrix.

 #' Additionally parse through the RDS to obtain appropriate feature annotations as 

 #' SCE coldata, in addition to any metadata.

-#' @param sampleDir  A path to the directory containing the data files. Default "./".

-#' @param sampleName A User-defined sample name. This will be prepended to all cell barcode IDs.

+#' @param sampleDirs  A path to the directory containing the data files. Default "./".

+#' @param sampleNames A User-defined sample name. This will be prepended to all cell barcode IDs.

 #'  Default "sample".

-#'  @param dataType can be "filtered" or "raw". Default is "filtered"

+#' @param dataType can be "filtered" or "raw". Default \code{"filtered"}.

 #' @param rdsFileName File name prefix of the DropEst RDS output. default is "cell.counts"

 #' @param delayedArray Boolean. Whether to read the expression matrix as

 #'  \link[DelayedArray]{DelayedArray} object or not. Default \code{TRUE}.

@@ -103,14 +103,6 @@

 #' found in the DropEst rds, they will be added to the SCE metadata field

 #' @return A \code{SingleCellExperiment} object containing the count matrix,

 #'  the feature annotations from DropEst as ColData, and any metadata from DropEst

-#' @examples

-#' Example

-#' Example DropEst outputs were downloaded from the DropEst Github 

-#' (http://pklab.med.harvard.edu/viktor/dropest_paper/dropest_0.8.5.zip). 

-#' To run the dropest import function with the example dataset, 

-#' set the sampleDirs variable to the example dropEst provided in SCTK as follows-

-#' sce <- importDropEst(sampleDirs = c('path/to/dropest/folder/'), 

-#'                      dataType='filtered', sampleNames=c('sample'))

 #' @export

 importDropEst <- function(sampleDirs = NULL, 

                           dataType = c('filtered','raw'),

@@ -60,7 +60,6 @@ summarizeTable <- function(inSCE, useAssay="counts", expressionCutoff=1700){

 #' frames instead of file paths. The default is FALSE.

 #' @param createLogCounts If TRUE, create a log2(counts+1) normalized assay

 #' and include it in the object. The default is TRUE

-#'

 #' @return a SCtkExperiment object

 #' @export

 #' @examples

@@ -71,7 +70,7 @@ summarizeTable <- function(inSCE, useAssay="counts", expressionCutoff=1700){

 #' newSCE <- createSCE(assayFile = counts_mat, annotFile = sample_annot,

 #'                     featureFile = row_annot, assayName = "counts",

 #'                     inputDataFrames = TRUE, createLogCounts = TRUE)

-createSCE <- simpleLog %@% function(assayFile=NULL, annotFile=NULL, featureFile=NULL,

+createSCE <-  function(assayFile=NULL, annotFile=NULL, featureFile=NULL,

                       assayName="counts", inputDataFrames=FALSE,

                       createLogCounts=TRUE){

@@ -287,6 +286,7 @@ distinctColors <- function(n, hues = c("red", "cyan", "orange", "blue",

   x <- do.call(base::cbind, Mat)

   colnames(x) <- cn

   rownames(x) <- rn

+  

   return(x)

 }

@@ -13,6 +13,7 @@ blt <- NULL

 scgen <- NULL

 sc <- NULL

 bbknn <- NULL

+pkg_resources <- NULL

 .onLoad <- function(libname, pkgname) {

   # use superassignment to update global reference to scipy

@@ -24,6 +25,7 @@ bbknn <- NULL

   scgen <<- reticulate::import("scgen", delay_load = TRUE)

   sc <<- reticulate::import("scanpy", delay_load = TRUE)

   bbknn <<- reticulate::import("bbknn", delay_load = TRUE)

+  pkg_resources <<- reticulate::import('pkg_resources',delay_load = TRUE)

   blt <<- reticulate::import_builtins()

 }

@@ -94,7 +96,6 @@ sctkPythonInstallConda <- function(envname = "sctk-reticulate",

 #' @param packages Character Vector. List of packages to install. 

 #' @param selectEnvironment Boolean. Run \code{\link[singleCellTK]{selectSCTKVirtualEnvironment}} after installing all packages to select the virtual environment. Default TRUE.

 #' @param python The path to a Python interpreter, to be used with the created virtual environment. When NULL, the Python interpreter associated with the current session will be used. Default NULL.

-#' @param ... Other parameters to pass to \code{\link[reticulate]{conda_install}}. 

 #' @examples

 #' \dontrun{

 #' sctkPythonInstallVirtualEnv(envname = "sctk-reticulate")

@@ -50,7 +50,7 @@ runBBKNN <-function(inSCE, useAssay = 'logcounts', batch = 'batch',

     reducedDimName <- gsub(' ', '_', reducedDimName)

     ## Run algorithm

-    adata <- .sce2adata(inSCE, mainAssay = useAssay)

+    adata <- .sce2adata(inSCE, useAssay = useAssay)

     sc$tl$pca(adata, n_comps = nComponents)

     bbknn$bbknn(adata, batch_key = batch, n_pcs = nComponents)

     sc$tl$umap(adata, n_components = nComponents)

@@ -56,7 +56,7 @@ runSCGEN <- function(inSCE, useAssay = 'logcounts', batch = 'batch',

     nEpochs <- as.integer(nEpochs)

     ## Run algorithm

-    adata <- .sce2adata(inSCE, mainAssay = useAssay)

+    adata <- .sce2adata(inSCE, useAssay = useAssay)

     network = scgen$VAEArith(x_dimension = adata$n_vars)

     network$train(train_data = adata, n_epochs = nEpochs)

     corrAdata <- scgen$batch_removal(network, adata, batch_key = batch, 

@@ -10,7 +10,11 @@

 #'  separately. If NULL, then all cells will be processed together.

 #'  Default NULL.

 #' @param seed Seed for the random number generator. Default 12345.

-#' @param ... Additional arguments passed to \link[scds]{cxds}.

+#' @param ntop See \link[scds]{cxds} for more information. Default \code{500}.

+#' @param binThresh See \link[scds]{cxds} for more information. Default \code{0}.

+#' @param verb See \link[scds]{cxds} for more information. Default \code{FALSE}.

+#' @param retRes See \link[scds]{cxds} for more information. Default \code{FALSE}.

+#' @param estNdbl See \link[scds]{cxds} for more information. Default \code{FALSE}.

 #' @return A \link[SingleCellExperiment]{SingleCellExperiment} object with

 #'  \link[scds]{cxds} output appended to the

 #'  \link[SummarizedExperiment]{colData} slot. The columns include

@@ -20,7 +24,6 @@

 #' data(sce_chcl, package = "scds")

 #' sce <- runCxds(sce_chcl)

 #' @export

-#' @import scds

 runCxds <- function(inSCE,

     sample = NULL,

     seed = 12345,

@@ -67,10 +70,10 @@ runCxds <- function(inSCE,

         while(!inherits(result, "SingleCellExperiment") & nGene > 0) {

           try({result <- withr::with_seed(seed, scds::cxds(sce = sceSample,

                                                            ntop = nGene, 

-                                                           binThresh = 0, 

-                                                           verb = FALSE, 

-                                                           retRes = FALSE,

-                                                           estNdbl = FALSE))}, silent = TRUE)

+                                                           binThresh = binThresh, 

+                                                           verb = verb, 

+                                                           retRes = retRes,

+                                                           estNdbl = estNdbl))}, silent = TRUE)

           nGene <- nGene - 100

         }  

@@ -92,7 +95,7 @@ runCxds <- function(inSCE,

     colData(inSCE) = cbind(colData(inSCE), output)

     inSCE@metadata$runCxds <- argsList[-1]

-    inSCE@metadata$runCxds$packageVersion <- packageDescription("scds")$Version

+    inSCE@metadata$runCxds$packageVersion <- utils::packageDescription("scds")$Version

     return(inSCE)

 }

@@ -110,7 +113,13 @@ runCxds <- function(inSCE,

 #'  separately. If NULL, then all cells will be processed together.

 #'  Default NULL.

 #' @param seed Seed for the random number generator. Default 12345.

-#' @param ... Additional arguments passed to \link[scds]{bcds}.

+#' @param ntop See \link[scds]{bcds} for more information. Default \code{500}. 

+#' @param srat See \link[scds]{bcds} for more information. Default \code{1}.

+#' @param verb See \link[scds]{bcds} for more information. Default \code{FALSE}.

+#' @param retRes See \link[scds]{bcds} for more information. Default \code{FALSE}.

+#' @param nmax See \link[scds]{bcds} for more information. Default \code{"tune"}.

+#' @param varImp See \link[scds]{bcds} for more information. Default \code{FALSE}.

+#' @param estNdbl See \link[scds]{bcds} for more information. Default \code{FALSE}.

 #' @return A \link[SingleCellExperiment]{SingleCellExperiment} object with

 #'  \link[scds]{bcds} output appended to the

 #'  \link[SummarizedExperiment]{colData} slot. The columns include

@@ -120,7 +129,6 @@ runCxds <- function(inSCE,

 #' data(sce_chcl, package = "scds")

 #' sce <- runBcds(sce_chcl)

 #' @export

-#' @import scds

 runBcds <- function(inSCE,

     sample = NULL,

     seed = 12345,

@@ -146,7 +154,6 @@ runBcds <- function(inSCE,

     ## Getting current arguments

     argsList <- as.list(formals(fun = sys.function(sys.parent()), envir = parent.frame()))

-    

     ## Define result matrix for all samples

     if (estNdbl) {

@@ -169,13 +176,16 @@ runBcds <- function(inSCE,

         result <- NULL

         nGene <- 500

         while(!inherits(result, "SingleCellExperiment") & nGene > 0) {

-          try({result <- withr::with_seed(seed, scds::bcds(sce = sceSample, ntop = nGene,

-                                                           srat = srat,

-                                                           verb = verb,

-                                                           estNdbl = estNdbl,

-                                                           varImp = varImp,

-                                                           nmax = nmax,

-                                                           retRes = retRes))}, silent = TRUE)

+          try({result <- withr::with_seed(seed,

+            scds::bcds(sce = sceSample,

+                       ntop = nGene,

+                       srat = srat, 

+                       verb = verb,

+                       retRes = retRes,

+                       nmax = nmax, 

+                       varImp = varImp,

+                       estNdbl = estNdbl

+            ))}, silent = TRUE)

           nGene <- nGene - 100

         }  

@@ -198,7 +208,7 @@ runBcds <- function(inSCE,

     colData(inSCE) = cbind(colData(inSCE), output)

     inSCE@metadata$runBcds <- argsList[-1]

-    inSCE@metadata$runBcds$packageVersion <- packageDescription("scds")$Version

+    inSCE@metadata$runBcds$packageVersion <- utils::packageDescription("scds")$Version

     return(inSCE)

 }

@@ -216,7 +226,13 @@ runBcds <- function(inSCE,

 #'  separately. If NULL, then all cells will be processed together.

 #'  Default NULL.

 #' @param seed Seed for the random number generator. Default 12345.

-#' @param ... Additional arguments passed to \link[scds]{cxds_bcds_hybrid}.

+#' @param nTop The number of top varialbe genes to consider. Used in both \code{csds}

+#' and \code{bcds}. Default \code{500}. 

+#' @param cxdsArgs See \link[scds]{cxds_bcds_hybrid} for more information. Default \code{NULL}.

+#' @param bcdsArgs See \link[scds]{cxds_bcds_hybrid} for more information. Default \code{NULL}.

+#' @param verb See \link[scds]{cxds_bcds_hybrid} for more information. Default \code{FALSE}.

+#' @param estNdbl See \link[scds]{cxds_bcds_hybrid} for more information. Default \code{FALSE}.

+#' @param force See \link[scds]{cxds_bcds_hybrid} for more information. Default \code{FALSE}.

 #' @return A \link[SingleCellExperiment]{SingleCellExperiment} object with

 #'  \link[scds]{cxds_bcds_hybrid} output appended to the

 #'  \link[SummarizedExperiment]{colData} slot. The columns include

@@ -227,12 +243,12 @@ runBcds <- function(inSCE,

 #' data(sce_chcl, package = "scds")

 #' sce <- runCxdsBcdsHybrid(sce_chcl)

 #' @export

-#' @import scds

 runCxdsBcdsHybrid <- function(inSCE,

     sample = NULL,

     seed = 12345,

-    cxdsArgs = NULL,

-    bcdsArgs = NULL, 

+    nTop = 500,

+    cxdsArgs = list(),

+    bcdsArgs = list(), 

     verb = FALSE,

     estNdbl = FALSE,

     force = FALSE) {

@@ -273,11 +289,11 @@ runCxdsBcdsHybrid <- function(inSCE,

         nGene <- 500

         while(!inherits(result, "SingleCellExperiment") & nGene > 0) {

           try({result <- withr::with_seed(seed, scds::cxds_bcds_hybrid(sce = sceSample, 

-                                                                       cxdsArgs=list(ntop = nGene), 

-                                                                       bcdsArgs=list(ntop = nGene), 

-                                                                       verb = FALSE,

-                                                                       estNdbl = FALSE,

-                                                                       force = FALSE))}, silent = TRUE)

+                                                                       cxdsArgs=c(list(ntop = nGene), cxdsArgs), 

+                                                                       bcdsArgs=c(list(ntop = nGene), bcdsArgs), 

+                                                                       verb = verb,

+                                                                       estNdbl = estNdbl,

+                                                                       force = force))}, silent = TRUE)

           nGene <- nGene - 100

         }  

@@ -299,7 +315,7 @@ runCxdsBcdsHybrid <- function(inSCE,

     colData(inSCE) = cbind(colData(inSCE), output)

     inSCE@metadata$runCxdsBcdsHybrid <- argsList[-1]

-    inSCE@metadata$runCxdsBcdsHybrid$packageVersion <- packageDescription("scds")$Version

+    inSCE@metadata$runCxdsBcdsHybrid$packageVersion <- utils::packageDescription("scds")$Version

     return(inSCE)

 }

@@ -18,6 +18,8 @@

 #' @param nNeighbors Integer. Number of neighbors used to construct the KNN

 #'  graph of observed transcriptomes and simulated doublets. If \code{NULL},

 #'  this is set to \code{round(0.5 * sqrt(n_cells))}. Default \code{NULL}.

+#' @param minDist Float Determines how tightly UMAP packs points together. If \code{NULL},

+#'  this is set to 0.1. Default \code{NULL}.

 #' @param expectedDoubletRate The estimated doublet rate for the experiment.

 #'  Default 0.1.

 #' @param stdevDoubletRate Uncertainty in the expected doublet rate.

@@ -63,6 +65,11 @@

 #' @param nPrinComps Integer. Number of principal components used to embed

 #'  the transcriptomes prior to k-nearest-neighbor graph construction.

 #'  Default 30.

+#' @param tsneAngle Float. Determines angular size of a distant node as measured 

+#'  from a point in the t-SNE plot. If default, it is set to 0.5 Default \code{NULL}. 

+#' @param tsnePerplexity Integer. The number of nearest neighbors that

+#'  is used in other manifold learning algorithms.

+#'  If default, it is set to 30. Default \code{NULL}.

 #' @param verbose Boolean. If \code{TRUE}, print progress updates. Default

 #'  \code{TRUE}.

 #' @param seed Seed for the random number generator. Default 12345.

@@ -82,6 +89,7 @@ runScrublet <- function(inSCE,

   useAssay = "counts",

   simDoubletRatio = 2.0,

   nNeighbors = NULL,

+  minDist = NULL,

   expectedDoubletRate = 0.1,

   stdevDoubletRate = 0.02,

   syntheticDoubletUmiSubsampling = 1.0,

@@ -95,6 +103,8 @@ runScrublet <- function(inSCE,

   meanCenter = TRUE,

   normalizeVariance = TRUE,

   nPrinComps = 30L,

+  tsneAngle = NULL,

+  tsnePerplexity = NULL,

   verbose = TRUE,

   seed = 12345) {

@@ -143,7 +153,7 @@ runScrublet <- function(inSCE,

       mat <- SummarizedExperiment::assay(sceSample, i = useAssay)

       mat <- .convertToMatrix(mat)

-

+      

       scr <- scrublet$Scrublet(counts_matrix = t(mat),

         sim_doublet_ratio = simDoubletRatio,

         n_neighbors = nNeighbors,

@@ -166,8 +176,27 @@ runScrublet <- function(inSCE,

       output[sceSampleInd, "scrublet_score"] <- result[[1]]

       output[sceSampleInd, "scrublet_call"] <- result[[2]]

+      

+      ## Extract UMAP and TSNE coordinates 

+      if (is.null(nNeighbors) && is.null(minDist)){

+        umap_coordinates <- scrublet$get_umap(scr$manifold_obs_)

+      }else {

+        umap_coordinates <- scrublet$get_umap(scr$manifold_obs_,

+                                              n_neighbors=as.integer(nNeighbors), 

+                                              min_dist=minDist)

+      }

+      reducedDim(inSCE,'UMAP') <- umap_coordinates

+    

+    if (is.null(tsneAngle) && is.null(tsnePerplexity)){

+      tsne_coordinates <- scrublet$get_tsne(scr$manifold_obs_)

+    }else {

+      tsne_coordinates <- scrublet$get_tsne(scr$manifold_obs_,

+                                            angle=tsneAngle, 

+                                            perplexity=as.integer(tsnePerplexity))

     }

-

+    reducedDim(inSCE,'TSNE') <- tsne_coordinates

+  }

+    

     colData(inSCE) = cbind(colData(inSCE), output)

   }, silent = TRUE)

@@ -177,8 +206,10 @@ runScrublet <- function(inSCE,

   }

   inSCE@metadata$runScrublet <- argsList[-1]

-  ## Add scrublet version

-  ##inSCE@metadata$runScrublet$packageVersion <- packageDescription("scrublet")$Version

+  

+  ## add scrublet version to metadata

+  version <- pkg_resources$require("scrublet")[[1]]

+  inSCE@metadata$scrublet$packageVersion <- version

   return(inSCE)

 }

@@ -55,7 +55,7 @@

 #' .rowNamesSCE

 #' Retrieves a list of genenames/rownames/featurenames from sce object

-#' @param sce sce object from which the genenames/rownames/featurenames should be extracted

+#' @param inSCE sce object from which the genenames/rownames/featurenames should be extracted

 #' @return list() of genenames/rownames/featurenames

 .rowNamesSCE <- function(inSCE) {

     return(rownames(inSCE))

@@ -209,7 +209,6 @@ seuratReductionPlot <- function(inSCE, useAssay, geneNamesSeurat, reduction) {

 #' @param seuratObject from which we have to copy the assay (copy from)

 #' @param assaySlotSCE the assay slot in sce object

 #' @param assaySlotSeurat the assay slot in seurat object

-#' @return

 .updateAssaySCE <- function(inSCE, geneNames, seuratObject, assaySlotSCE, assaySlotSeurat) {

     assay(inSCE, assaySlotSCE) <- NULL

     assay(inSCE, assaySlotSCE) <- methods::slot(seuratObject@assays$RNA, assaySlotSeurat)

@@ -223,9 +222,9 @@ seuratReductionPlot <- function(inSCE, useAssay, geneNamesSeurat, reduction) {

 #' @return inSCE output object

 #' @export

 convertSeuratToSCE <- function(seuratObject) {

-    inSCE <- as.SingleCellExperiment(seuratObject)

+    inSCE <- Seurat::as.SingleCellExperiment(seuratObject)

     assay(inSCE, "seuratNormalizedData") <- methods::slot(seuratObject@assays$RNA, "data")

-    if (length(slot(seuratObject, "assays")[["RNA"]]@scale.data) > 0) {

+    if (length(methods::slot(seuratObject, "assays")[["RNA"]]@scale.data) > 0) {

         assay(inSCE, "seuratScaledData") <- methods::slot(seuratObject@assays$RNA, "scale.data")

     }

     inSCE <- .addSeuratToMetaDataSCE(inSCE, seuratObject)

@@ -4,8 +4,10 @@

 \name{SEG}

 \alias{SEG}

 \title{Stably Expressed Gene (SEG) list obect, with SEG sets for human and mouse.}

-\format{list, with two entries `"human"` and `"mouse"`, each is a charactor

-array.}

+\format{

+list, with two entries `"human"` and `"mouse"`, each is a charactor

+array.

+}

 \source{

 `data('segList', package='scMerge')``

 }

@@ -8,7 +8,7 @@ Retrieves a list of genenames/rownames/featurenames from sce object}

 .rowNamesSCE(inSCE)

 }

 \arguments{

-\item{sce}{sce object from which the genenames/rownames/featurenames should be extracted}

+\item{inSCE}{sce object from which the genenames/rownames/featurenames should be extracted}

 }

 \value{

 list() of genenames/rownames/featurenames

@@ -17,9 +17,6 @@ Update/Modify/Add an assay in the provided sce object}

 \item{assaySlotSCE}{the assay slot in sce object}

 \item{assaySlotSeurat}{the assay slot in seurat object}

-}

-\value{

-

 }

 \description{

 .updateAssaySCE

@@ -4,7 +4,9 @@

 \name{emptyDropsSceExample}

 \alias{emptyDropsSceExample}

 \title{Example PBMC_1k_v3_33538x20 SingleCellExperiment Object}

-\format{A \link[SingleCellExperiment]{SingleCellExperiment} object.}

+\format{

+A \link[SingleCellExperiment]{SingleCellExperiment} object.

+}

 \usage{

 emptyDropsSceExample

 }

@@ -9,7 +9,7 @@ exportSCEtoAnnData(

   sce,

   useAssay = "counts",

   outputDir = "./",

-  sample = "sample",

+  prefix = "sample",

   overwrite = TRUE,

   compression = c("None", "lzf", "gzip"),

   compressionOpts = NULL,

@@ -18,21 +18,25 @@ exportSCEtoAnnData(

 }

 \arguments{

 \item{sce}{\link[SingleCellExperiment]{SingleCellExperiment} R object to be

- exported.

- @param useAssay Character, default `"counts"`. The name of assay of

-interests that will be set as the primary matrix of the output AnnData.}

+exported.}

-\item{outputDir}{Path to the directory where .h5ad outputs will be written}

+\item{useAssay}{Character. The name of assay of

+interests that will be set as the primary matrix of the output AnnData.

+Default \code{"counts"}.}

+

+\item{outputDir}{Path to the directory where .h5ad outputs will be written. Default is the current working directory.}

+

+\item{prefix}{Prefix to use for the name of the output file. Default \code{"sample"}.}

 \item{overwrite}{Boolean. Default \code{TRUE}.}

-\item{compression}{Default \code{None}.If output file compression is required, this variable accepts

-'gzip' or 'lzf' as inputs.}

+\item{compression}{If output file compression is required, this variable accepts

+'gzip' or 'lzf' as inputs. Default \code{None}.}

-\item{forceDense}{Default \code{False} Write sparse data as a dense matrix.

-Refer anndata.write_h5ad documentation for details}

+\item{compressionOpts}{Integer. Sets the compression level}

-\item{compression_opts}{Integer. Default \code{NULL} Sets the compression level}

+\item{forceDense}{Default \code{False} Write sparse data as a dense matrix.

+Refer \code{anndata.write_h5ad} documentation for details. Default \code{NULL}.}

 }

 \description{

 Writes all assays, colData, rowData, reducedDims, and altExps objects in a

@@ -42,7 +46,9 @@ are available as parameters to this export function and set to defaults. Default

 overridden at function call.

 }

 \examples{

+\dontrun{

 data(sce_chcl, package = "scds")

 exportSCEtoAnnData(sce=sce_chcl, compression="gzip")

+}

 }

@@ -17,7 +17,7 @@ exported.}

 \item{gzipped}{Boolean. \code{TRUE} if the output files are to be

 gzip compressed. \code{FALSE} otherwise. Default

-\code{TRUE} to save disk space.}

+\code{TRUE}.}

 }

 \description{

 Writes all assays, colData, rowData, reducedDims, and altExps objects in a

@@ -13,16 +13,17 @@ importDropEst(

 )

 }

 \arguments{

+\item{sampleDirs}{A path to the directory containing the data files. Default "./".}

+

+\item{dataType}{can be "filtered" or "raw". Default \code{"filtered"}.}

+

 \item{rdsFileName}{File name prefix of the DropEst RDS output. default is "cell.counts"}

+\item{sampleNames}{A User-defined sample name. This will be prepended to all cell barcode IDs.

+Default "sample".}

+

 \item{delayedArray}{Boolean. Whether to read the expression matrix as

 \link[DelayedArray]{DelayedArray} object or not. Default \code{TRUE}.}

-

-\item{sampleDir}{A path to the directory containing the data files. Default "./".}

-

-\item{sampleName}{A User-defined sample name. This will be prepended to all cell barcode IDs.

-Default "sample".

-@param dataType can be "filtered" or "raw". Default is "filtered"}

 }

 \value{

 A \code{SingleCellExperiment} object containing the count matrix,

@@ -44,12 +45,3 @@ subset to contain features from the filtered counts matrix alone.

 If any annotations of ("saturation_info","merge_targets","reads_per_umi_per_cell") are 

 found in the DropEst rds, they will be added to the SCE metadata field

 }

-\examples{

-Example

-Example DropEst outputs were downloaded from the DropEst Github 

-(http://pklab.med.harvard.edu/viktor/dropest_paper/dropest_0.8.5.zip). 

-To run the dropest import function with the example dataset, 

-set the sampleDirs variable to the example dropEst provided in SCTK as follows-

-sce <- importDropEst(sampleDirs = c('path/to/dropest/folder/'), 

-                     dataType='filtered', sampleNames=c('sample'))

-}

@@ -5,7 +5,9 @@

 \alias{mouseBrainSubsetSCE}

 \title{Example Single Cell RNA-Seq data in SCtkExperiment Object, GSE60361

 subset}

-\format{SCtkExperiment}

+\format{

+SCtkExperiment

+}

 \source{

 DOI: 10.1126/science.aaa1934

 }

@@ -9,7 +9,7 @@ runBarcodeRankDrops(

   sample = NULL,

   useAssay = "counts",

   lower = 100,

-  fit.bounds = NULL,

+  fitBounds = NULL,

   df = 20

 )

 }

@@ -19,11 +19,15 @@ Must contain a raw counts matrix before empty droplets have been removed.}

 \item{sample}{Character vector. Indicates which sample each cell belongs to

 \link[DropletUtils]{emptyDrops} will be run on cells from each sample separately.

-If NULL, then all cells will be processed together. Default NULL.}

+If NULL, then all cells will be processed together. Default \code{NULL}.}

 \item{useAssay}{A string specifying which assay in the SCE to use.}

-\item{...}{Additional arguments to pass to \link[DropletUtils]{barcodeRanks}.}

+\item{lower}{See \link[DropletUtils]{emptyDrops} for more information. Default \code{100}.}

+

+\item{fitBounds}{See \link[DropletUtils]{emptyDrops} for more information. Default \code{NULL}.}

+

+\item{df}{See \link[DropletUtils]{emptyDrops} for more information. Default \code{20}.}

 }

 \value{

 A \link[SingleCellExperiment]{SingleCellExperiment} object with the

@@ -28,7 +28,19 @@ Default NULL.}

 \item{seed}{Seed for the random number generator. Default 12345.}

-\item{...}{Additional arguments passed to \link[scds]{bcds}.}

+\item{ntop}{See \link[scds]{bcds} for more information. Default \code{500}.}

+

+\item{srat}{See \link[scds]{bcds} for more information. Default \code{1}.}

+

+\item{verb}{See \link[scds]{bcds} for more information. Default \code{FALSE}.}

+

+\item{retRes}{See \link[scds]{bcds} for more information. Default \code{FALSE}.}

+

+\item{nmax}{See \link[scds]{bcds} for more information. Default \code{"tune"}.}

+

+\item{varImp}{See \link[scds]{bcds} for more information. Default \code{FALSE}.}

+

+\item{estNdbl}{See \link[scds]{bcds} for more information. Default \code{FALSE}.}

 }

 \value{

 A \link[SingleCellExperiment]{SingleCellExperiment} object with

@@ -26,7 +26,15 @@ Default NULL.}

 \item{seed}{Seed for the random number generator. Default 12345.}

-\item{...}{Additional arguments passed to \link[scds]{cxds}.}

+\item{ntop}{See \link[scds]{cxds} for more information. Default \code{500}.}

+

+\item{binThresh}{See \link[scds]{cxds} for more information. Default \code{0}.}

+

+\item{verb}{See \link[scds]{cxds} for more information. Default \code{FALSE}.}

+

+\item{retRes}{See \link[scds]{cxds} for more information. Default \code{FALSE}.}