Package 'singleCellTK'

Description

Finds the effect sizes for all genes in the original dataset, regardless of significance.

Usage

calcEffectSizes(countMatrix, condition)

Arguments

Value

A vector of cohen's d effect sizes for each gene.

Examples

data("mouseBrainSubsetSCE")
res <- calcEffectSizes(assay(mouseBrainSubsetSCE, "counts"),
                       condition = colData(mouseBrainSubsetSCE)$level1class)

Description

Combine a list of SingleCellExperiment objects as one SingleCellExperiment object

Usage

combineSCE(sceList, by.r = NULL, by.c = NULL, combined = TRUE)

Arguments

Value

A SingleCellExperiment object which combines all objects in sceList. The colData is merged.

Examples

data(scExample, package = "singleCellTK")
combinedsce <- combineSCE(list(sce,sce), by.r = NULL, by.c = NULL, combined = TRUE)

Description

The computeHeatmap method computes the heatmap visualization for a set of features against a set of dimensionality reduction components. This method uses the heatmap computation algorithm code from Seurat but plots the heatmap using ComplexHeatmap and cowplot libraries.

Usage

computeHeatmap(
  inSCE,
  useAssay,
  dims = 10,
  nfeatures = 30,
  cells = NULL,
  reduction = "pca",
  disp.min = -2.5,
  disp.max = 2.5,
  balanced = TRUE,
  nCol = NULL,
  externalReduction = NULL
)

Arguments

Value

Heatmap plot object.

Description

Computes Z-Score from an input count matrix using the formula ((x-mean(x))/sd(x)) for each gene across all cells. The input count matrix can either be a base matrix, dgCMatrix or a DelayedMatrix. Computations are performed using DelayedMatrixStats package to efficiently compute the Z-Score matrix.

Usage

computeZScore(counts)

Arguments

Value

z-score computed counts matrix (DelayedMatrix)

Examples

data(sce_chcl, package = "scds")
assay(sce_chcl, "countsZScore") <- computeZScore(assay(sce_chcl, "counts"))

Description

Create SingleCellExperiment object from csv or txt input

Usage

constructSCE(data, samplename)

Arguments

Value

A SingleCellExperiment object containing the count matrix.

Description

convertSCEToSeurat Converts sce object to seurat while retaining all assays and metadata

Usage

convertSCEToSeurat(
  inSCE,
  countsAssay = NULL,
  normAssay = NULL,
  scaledAssay = NULL,
  copyColData = FALSE,
  copyReducedDim = FALSE,
  copyDecontX = FALSE,
  pcaReducedDim = NULL,
  icaReducedDim = NULL,
  tsneReducedDim = NULL,
  umapReducedDim = NULL
)

Arguments

Value

Updated seurat object that contains all data from the input sce object

Examples

data(scExample, package = "singleCellTK")
seurat <- convertSCEToSeurat(sce)

Description

convertSeuratToSCE Converts the input seurat object to a sce object

Usage

convertSeuratToSCE(
  seuratObject,
  normAssayName = "seuratNormData",
  scaledAssayName = "seuratScaledData"
)

Arguments

Value

SingleCellExperiment output object

Examples

data(scExample, package = "singleCellTK")
seurat <- convertSCEToSeurat(sce)
sce <- convertSeuratToSCE(seurat)

Description

Adds '-1', '-2', ... '-i' to multiple duplicated rownames, and in place replace the unique rownames, store unique rownames in rowData, or return the unique rownames as character vecetor.

Usage

dedupRowNames(x, as.rowData = FALSE, return.list = FALSE)

Arguments

Value

By default, a matrix or SingleCellExperiment object with rownames deduplicated. When x is a SingleCellExperiment and as.rowData is set to TRUE, will return x with rowData updated. When return.list is set to TRUE, will return a character vector with the deduplicated rownames.

Examples

data("scExample", package = "singleCellTK")
sce <- dedupRowNames(sce)

Description

A wrapper function for isOutlier. Identify outliers from numeric vectors stored in the SingleCellExperiment object.

Usage

detectCellOutlier(
  inSCE,
  slotName,
  itemName,
  sample = NULL,
  nmads = 3,
  type = "both",
  overwrite = TRUE
)

Arguments

Value

A SingleCellExperiment object with ” added to the colData slot. Additionally, the decontaminated counts will be added as an assay called 'decontXCounts'.

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- runDecontX(sce[,sample(ncol(sce),20)])
sce <- detectCellOutlier(sce, slotName = "colData", sample = sce$sample,
 nmads = 4, itemName = "decontX_contamination", type = "both")

Description

Calculate Differential Abundance with FET

Usage

diffAbundanceFET(inSCE, cluster, variable, control, case, analysisName)

Arguments

Details

This function will calculate the cell counting and fraction by dividing all cells to groups specified by the arguments, together with statistical summary by performing Fisher Exact Tests (FET).

Value

The original SingleCellExperiment object with metadata(inSCE) updated with a list diffAbundanceFET, containing a new data.frame for the analysis result, named by analysisName. The data.frame contains columns for number and fraction of cells that belong to different cases, as well as "Odds_Ratio", "PValue" and "FDR".

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- diffAbundanceFET(inSCE = mouseBrainSubsetSCE,
                                                cluster = "tissue",
                                                variable = "level1class",
                                                case = "oligodendrocytes",
                                                control = "microglia",
                                                analysisName = "diffAbundFET")

Description

Three different generation methods are wrapped, including distinctColors, [randomcoloR](SCTK_PerformingQC_Cell_V3.Rmd) and the ggplot default color generation.

Usage

discreteColorPalette(
  n,
  palette = c("random", "ggplot", "celda"),
  seed = 12345,
  ...
)

Arguments

Value

A character vector of n hex color codes.

Examples

discreteColorPalette(n = 3)

Description

Generate a distinct palette for coloring different clusters

Usage

distinctColors(
  n,
  hues = c("red", "cyan", "orange", "blue", "yellow", "purple", "green", "magenta"),
  saturation.range = c(0.7, 1),
  value.range = c(0.7, 1)
)

Arguments

Value

A vector of distinct colors that have been converted to HEX from HSV.

Examples

distinctColors(10)

Description

Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size

Usage

downSampleCells(
  originalData,
  useAssay = "counts",
  minCountDetec = 10,
  minCellsDetec = 3,
  minCellnum = 10,
  maxCellnum = 1000,
  realLabels,
  depthResolution = 10,
  iterations = 10,
  totalReads = 1e+06
)

Arguments

Value

A 3-dimensional array, with dimensions = c(iterations, depthResolution, 3). [,,1] contains the number of detected genes in each simulated dataset, [,,2] contains the number of significantly differentially expressed genes in each simulation, and [,,3] contains the mediansignificant effect size in each simulation. If no genes are significantly differentially expressed, the median effect size defaults to infinity.

Examples

data("mouseBrainSubsetSCE")
subset <- mouseBrainSubsetSCE[seq(100),]
res <- downSampleCells(subset,
                       realLabels = "level1class",
                       iterations=2)

Description

Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size

Usage

downSampleDepth(
  originalData,
  useAssay = "counts",
  minCount = 10,
  minCells = 3,
  maxDepth = 1e+07,
  realLabels,
  depthResolution = 10,
  iterations = 10
)

Arguments

Value

A 3-dimensional array, with dimensions = c(iterations, depthResolution, 3). [,,1] contains the number of detected genes in each simulated dataset, [,,2] contains the number of significantly differentially expressed genes in each simulation, and [,,3] contains the mediansignificant effect size in each simulation. If no genes are significantly differentially expressed, the median effect size defaults to infinity.

Examples

data("mouseBrainSubsetSCE")
subset <- mouseBrainSubsetSCE[seq(1000),]
res <- downSampleDepth(subset,
                       realLabels = "level1class",
                       iterations=2)

Description

expData Get data item from an input SingleCellExperiment object. The data item can be an assay, altExp (subset) or a reducedDim, which is retrieved based on the name of the data item.

Usage

expData(inSCE, assayName)

Arguments

Value

Specified data item.

Examples

data(scExample, package = "singleCellTK")
mat <- expData(sce, "counts")

Description

expData Get data item from an input SingleCellExperiment object. The data item can be an assay, altExp (subset) or a reducedDim, which is retrieved based on the name of the data item.

Usage

## S4 method for signature 'ANY,character'
expData(inSCE, assayName)

Arguments

Value

Specified data item.

Examples

data(scExample, package = "singleCellTK")
mat <- expData(sce, "counts")

Description

expData Store data items using tags to identify the type of data item stored. To be used as a replacement for assay<- setter function but with additional parameter to set a tag to a data item.

Usage

expData(inSCE, assayName, tag = NULL, altExp = FALSE) <- value

Arguments

Value

A SingleCellExperiment object containing the newly stored data.

Examples

data(scExample, package = "singleCellTK")
mat <- expData(sce, "counts")
expData(sce, "counts", tag = "raw") <- mat

Description

expData Store data items using tags to identify the type of data item stored. To be used as a replacement for assay<- setter function but with additional parameter to set a tag to a data item.

Usage

## S4 replacement method for signature 'ANY,character,CharacterOrNullOrMissing,logical'
expData(inSCE, assayName, tag = NULL, altExp = FALSE) <- value

Arguments

Value

A SingleCellExperiment object containing the newly stored data.

Examples

data(scExample, package = "singleCellTK")
mat <- expData(sce, "counts")
expData(sce, "counts", tag = "raw") <- mat

Description

expDataNames Get names of all the data items in the input SingleCellExperiment object including assays, altExps and reducedDims.

Usage

expDataNames(inSCE)

Arguments

Value

A combined vector of assayNames, altExpNames and reducedDimNames.

Examples

data(scExample, package = "singleCellTK")
expDataNames(sce)

Description

expDataNames Get names of all the data items in the input SingleCellExperiment object including assays, altExps and reducedDims.

Usage

## S4 method for signature 'ANY'
expDataNames(inSCE)

Arguments

Value

A combined vector of assayNames, altExpNames and reducedDimNames.

Examples

data(scExample, package = "singleCellTK")
expDataNames(sce)

Description

expDeleteDataTag Remove tag against an input data from the stored tag information in the metadata of the input object.

Usage

expDeleteDataTag(inSCE, assay)

Arguments

Value

The input SingleCellExperiment object with tag information removed from the metadata slot.

Examples

data(scExample, package = "singleCellTK")
sce <- expSetDataTag(sce, "raw", "counts")
sce <- expDeleteDataTag(sce, "counts")

Description

Export data in SingleCellExperiment object

Usage

exportSCE(
  inSCE,
  samplename = "sample",
  directory = "./",
  type = "Cells",
  format = c("SCE", "AnnData", "FlatFile", "HTAN", "Seurat")
)

Arguments

Value

Generates a file containing data from inSCE, in specified format.

Examples

data(scExample)
## Not run: 
exportSCE(sce, format = "SCE")

## End(Not run)

Export a SingleCellExperiment R object as Python annData object

Description

Writes all assays, colData, rowData, reducedDims, and altExps objects in a SingleCellExperiment to a Python annData object in the .h5ad format All parameters of Anndata.write_h5ad function (https://icb-anndata.readthedocs-hosted.com/en/stable/anndata.AnnData.write_h5ad.html) are available as parameters to this export function and set to defaults. Defaults can be overridden at function call.

Usage

exportSCEtoAnnData(
  sce,
  useAssay = "counts",
  outputDir = "./",
  prefix = "sample",
  overwrite = TRUE,
  compression = c("gzip", "lzf", "None"),
  compressionOpts = NULL,
  forceDense = FALSE
)

Arguments

Value

Generates a Python anndata object containing data from inSCE.

Examples

data(sce_chcl, package = "scds")
## Not run: 
exportSCEtoAnnData(sce=sce_chcl, compression="gzip")

## End(Not run)

Export a SingleCellExperiment object to flat text files

Description

Writes all assays, colData, rowData, reducedDims, and altExps objects in a SingleCellExperiment to text files. The items in the 'metadata' slot remain stored in list and are saved in an RDS file.

Usage

exportSCEtoFlatFile(
  sce,
  outputDir = "./",
  overwrite = TRUE,
  gzipped = TRUE,
  prefix = "SCE"
)

Arguments

Value

Generates text files containing data from inSCE.

Examples

data(sce_chcl, package = "scds")
## Not run: 
exportSCEtoFlatFile(sce_chcl, "sce_chcl")

## End(Not run)

Description

Export data in Seurat object

Usage

exportSCEToSeurat(
  inSCE,
  prefix = "sample",
  outputDir = "./",
  overwrite = TRUE,
  copyColData = TRUE,
  copyReducedDim = TRUE,
  copyDecontX = TRUE
)

Arguments

Value

Generates a Seurat object containing data from inSCE.

Description

expSetDataTag Set tag to an assay or a data item in the input SCE object.

Usage

expSetDataTag(inSCE, assayType, assays)

Arguments

Value

The input SingleCellExperiment object with tag information stored in the metadata slot.

Examples

data(scExample, package = "singleCellTK")
sce <- expSetDataTag(sce, "raw", "counts")

Description

expTaggedData Returns a list of names of data items from the input SingleCellExperiment object based upon the input parameters.

Usage

expTaggedData(
  inSCE,
  tags = NULL,
  redDims = FALSE,
  recommended = NULL,
  showTags = TRUE
)

Arguments

Value

A list of names of data items specified by the other parameters.

Examples

data(scExample, package = "singleCellTK")
sce <- expSetDataTag(sce, "raw", "counts")
tags <- expTaggedData(sce)

Description

This will return indices of features among the rownames or rowData of a data.frame, matrix, or a SummarizedExperiment object including a SingleCellExperiment. Partial matching (i.e. grepping) can be used by setting exactMatch = FALSE.

Usage

featureIndex(
  features,
  inSCE,
  by = "rownames",
  exactMatch = TRUE,
  removeNA = FALSE,
  errorOnNoMatch = TRUE,
  warningOnPartialMatch = TRUE
)

Arguments

Value

A vector of row indices for the matching features in inSCE.

Author(s)

Yusuke Koga, Joshua D. Campbell

See Also

'retrieveFeatureInfo' from package 'scater' and link{regex} for how to use regular expressions when exactMatch = FALSE.

Examples

data(scExample)
ix <- featureIndex(features = c("MT-CYB", "MT-ND2"),
                             inSCE = sce,
                             by = "feature_name")

Description

Generate HTAN manifest file for droplet and cell count data

Usage

generateHTANMeta(
  dropletSCE = NULL,
  cellSCE = NULL,
  samplename,
  htan_biospecimen_id,
  dir,
  dataType = c("Droplet", "Cell", "Both")
)

Arguments

Value

A SingleCellExperiment object which combines all objects in sceList. The colData is merged.

Description

Generate HTAN manifest file for droplet and cell count data

Usage

generateMeta(
  dropletSCE = NULL,
  cellSCE = NULL,
  samplename,
  dir,
  HTAN = TRUE,
  dataType = c("Droplet", "Cell", "Both")
)

Arguments

Value

A SingleCellExperiment object which combines all objects in sceList. The colData is merged.

Description

Generates a single simulated dataset, bootstrapping from the input counts matrix.

Usage

generateSimulatedData(totalReads, cells, originalData, realLabels)

Arguments

Value

A simulated counts matrix, the first row of which contains the 'true' labels for each virtual cell.

Examples

data("mouseBrainSubsetSCE")
res <- generateSimulatedData(
         totalReads = 1000, cells=10,
         originalData = assay(mouseBrainSubsetSCE, "counts"),
         realLabels = colData(mouseBrainSubsetSCE)[, "level1class"])

Description

Given a list of genes and a SingleCellExperiment object, return the binary or continuous expression of the genes.

Usage

getBiomarker(
  inSCE,
  gene,
  binary = "Binary",
  useAssay = "counts",
  featureLocation = NULL,
  featureDisplay = NULL
)

Arguments

Value

getBiomarker(): A data.frame of expression values

Examples

data("mouseBrainSubsetSCE")
getBiomarker(mouseBrainSubsetSCE, gene="C1qa")

Description

Users have to run runDEAnalysis() first, any of the wrapped functions of this generic function. Users can set further filters on the result. A data.frame object, with variables of Gene, Log2_FC, Pvalue, and FDR, will be returned.

Usage

getDEGTopTable(
  inSCE,
  useResult,
  labelBy = S4Vectors::metadata(inSCE)$featureDisplay,
  onlyPos = FALSE,
  log2fcThreshold = 0.25,
  fdrThreshold = 0.05,
  minGroup1MeanExp = NULL,
  maxGroup2MeanExp = NULL,
  minGroup1ExprPerc = NULL,
  maxGroup2ExprPerc = NULL
)

Arguments

Value

A data.frame object of the top DEGs, with variables of Gene, Log2_FC, Pvalue, and FDR.

Examples

data("sceBatches")
sceBatches <- scaterlogNormCounts(sceBatches, "logcounts")
sce.w <- subsetSCECols(sceBatches, colData = "batch == 'w'")
sce.w <- runWilcox(sce.w, class = "cell_type",
                   classGroup1 = "alpha", classGroup2 = "beta",
                   groupName1 = "w.alpha", groupName2 = "w.beta",
                   analysisName = "w.aVSb")
getDEGTopTable(sce.w, "w.aVSb")

Description

Get/Set diffAbundanceFET result table

Usage

getDiffAbundanceResults(x, analysisName)

## S4 method for signature 'SingleCellExperiment'
getDiffAbundanceResults(x, analysisName)

getDiffAbundanceResults(x, analysisName) <- value

## S4 replacement method for signature 'SingleCellExperiment'
getDiffAbundanceResults(x, analysisName) <- value

Arguments

Value

The differential abundance table for getter method, or update the SCE object with new result for setter method.

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- diffAbundanceFET(inSCE = mouseBrainSubsetSCE,
                                                cluster = "tissue",
                                                variable = "level1class",
                                                case = "oligodendrocytes",
                                                control = "microglia",
                                                analysisName = "diffAbund")
result <- getDiffAbundanceResults(mouseBrainSubsetSCE, "diffAbund")

Description

Get or Set EnrichR Result

Usage

getEnrichRResult(inSCE, analysisName) <- value

getEnrichRResult(inSCE, analysisName)

## S4 method for signature 'SingleCellExperiment'
getEnrichRResult(inSCE, analysisName)

## S4 replacement method for signature 'SingleCellExperiment'
getEnrichRResult(inSCE, analysisName) <- value

Arguments

Value

For getter method, a data.frame of the EnrichR result; For setter method, inSCE with EnrichR results updated.

See Also

runEnrichR

Examples

data("mouseBrainSubsetSCE")
if (Biobase::testBioCConnection()) {
  mouseBrainSubsetSCE <- runEnrichR(mouseBrainSubsetSCE, features = "Cmtm5", 
                                    db = "GO_Cellular_Component_2017",
                                    analysisName = "analysis1")
  result <- getEnrichRResult(mouseBrainSubsetSCE, "analysis1")
}

Description

Fetch the table of top markers that pass the filtering

Usage

getFindMarkerTopTable(
  inSCE,
  log2fcThreshold = 0,
  fdrThreshold = 0.05,
  minClustExprPerc = 0.5,
  maxCtrlExprPerc = 0.5,
  minMeanExpr = 0,
  topN = 1
)

findMarkerTopTable(
  inSCE,
  log2fcThreshold = 1,
  fdrThreshold = 0.05,
  minClustExprPerc = 0.7,
  maxCtrlExprPerc = 0.4,
  minMeanExpr = 1,
  topN = 10
)

Arguments

Details

Users have to run runFindMarker prior to using this function to extract a top marker table.

Value

An organized data.frame object, with the top marker gene information.

See Also

runFindMarker, plotFindMarkerHeatmap

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runFindMarker(mouseBrainSubsetSCE,
                                     useAssay = "logcounts",
                                     cluster = "level1class")
getFindMarkerTopTable(mouseBrainSubsetSCE)

Description

List geneset names from geneSetCollection

Usage

getGenesetNamesFromCollection(inSCE, geneSetCollectionName)

Arguments

Value

A character vector of available genesets from the collection.

Description

Returns a data.frame that shows MSigDB categories and subcategories as well as descriptions for each. The entries in the ID column in this table can be used as input for importGeneSetsFromMSigDB.

Usage

getMSigDBTable()

Value

data.frame, containing MSigDB categories

Author(s)

Joshua D. Campbell

See Also

importGeneSetsFromMSigDB for importing MSigDB gene sets.

Examples

getMSigDBTable()

Description

List pathway analysis result names

Usage

getPathwayResultNames(inSCE, stopIfNone = FALSE, verbose = FALSE)

Arguments

Details

Pathway analysis results will be stored as matrices in reducedDims slot of inSCE. This function lists the result names stored in metadata slot when analysis is performed.

Value

A character vector of valid pathway analysis result names.

Examples

data(scExample)
getPathwayResultNames(sce)

Description

Stores and returns table of QC metrics generated from QC algorithms within the metadata slot of the SingleCellExperiment object.

Usage

getSampleSummaryStatsTable(inSCE, statsName, ...)

setSampleSummaryStatsTable(inSCE, statsName, ...) <- value

## S4 method for signature 'SingleCellExperiment'
getSampleSummaryStatsTable(inSCE, statsName, ...)

## S4 replacement method for signature 'SingleCellExperiment'
setSampleSummaryStatsTable(inSCE, statsName, ...) <- value

Arguments

Value

For getSampleSummaryStatsTable, A matrix/array object. Contains a summary table for QC statistics generated from SingleCellTK. For setSampleSummaryStatsTable<-, A SingleCellExperiment object where the summary table is updated in the metadata slot.

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- sampleSummaryStats(sce, simple = TRUE, statsName = "qc_table")
getSampleSummaryStatsTable(sce, statsName = "qc_table")

Description

Extract QC parameters from the SingleCellExperiment object

Usage

getSceParams(
  inSCE,
  skip = c("runScrublet", "runDecontX", "runBarcodeRanksMetaOutput", "genesets",
    "runSoupX"),
  ignore = c("algorithms", "estimates", "contamination", "z", "sample", "rank",
    "BPPARAM", "batch", "geneSetCollection", "barcodeArgs"),
  directory = "./",
  samplename = "",
  writeYAML = TRUE
)

Arguments

Value

If writeYAML TRUE, a yaml object will be generated. If FALSE, character object.

Description

Get variable feature names after running runSeuratFindHVG function

Usage

getSeuratVariableFeatures(inSCE)

Arguments

Value

A list of variable feature names.

Description

S4 method for getting and setting SoupX results that cannot be appended to either rowData(inSCE) or colData(inSCE).

S4 method for getting and setting SoupX results that cannot be appended to either rowData(inSCE) or colData(inSCE).

Usage

getSoupX(inSCE, sampleID, background = FALSE) <- value

getSoupX(inSCE, sampleID = NULL, background = FALSE)

## S4 method for signature 'SingleCellExperiment'
getSoupX(inSCE, sampleID = NULL, background = FALSE)

## S4 replacement method for signature 'SingleCellExperiment'
getSoupX(inSCE, sampleID, background = FALSE) <- value

Arguments

Value

For getter method, a list with SoupX results for specified samples. For setter method, inSCE with SoupX results updated.

For getter method, a list with SoupX results for specified samples. For setter method, inSCE with SoupX results updated.

See Also

runSoupX, plotSoupXResults

Examples

## Not run: 
sce <- importExampleData("pbmc3k")
sce <- runSoupX(sce, sample = "sample")
soupXResults <- getSoupX(sce)

## End(Not run)

Description

Extracts or select the top variable genes from an input SingleCellExperiment object. Note that the variability metrics must be computed using the runFeatureSelection method before extracting the feature names of the top variable features. getTopHVG only returns a character vector of the HVG selection, while with setTopHVG, a logical vector of the selection will be saved in the rowData, and optionally, a subset object for the HVGs can be stored in the altExps slot at the same time.

Usage

getTopHVG(
  inSCE,
  method = c("vst", "dispersion", "mean.var.plot", "modelGeneVar", "seurat", "seurat_v3",
    "cell_ranger"),
  hvgNumber = 2000,
  useFeatureSubset = "hvf",
  featureDisplay = metadata(inSCE)$featureDisplay
)

setTopHVG(
  inSCE,
  method = c("vst", "dispersion", "mean.var.plot", "modelGeneVar", "seurat", "seurat_v3",
    "cell_ranger"),
  hvgNumber = 2000,
  featureSubsetName = "hvg2000",
  genes = NULL,
  genesBy = NULL,
  altExp = FALSE
)

Arguments

Value

Author(s)

Irzam Sarfraz, Yichen Wang

See Also

runFeatureSelection, runSeuratFindHVG, runModelGeneVar, plotTopHVG

Examples

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

# Create a "highy variable feature" subset using Seurat's vst method:
sce <- runSeuratFindHVG(sce,  method = "vst", hvgNumber = 2000,
       createFeatureSubset = "hvf")
       
# Get the list of genes for a feature subset:
hvgs <- getTopHVG(sce, useFeatureSubset = "hvf")

# Create a new feature subset on the fly without rerunning the algorithm:
sce <- setTopHVG(sce, method = "vst", hvgNumber = 100,
                featureSubsetName = "hvf100")
hvgs <- getTopHVG(sce, useFeatureSubset = "hvf100")

# Get a list of variable features without creating a new feature subset:
hvgs <- getTopHVG(sce, useFeatureSubset = NULL,
                  method = "vst", hvgNumber = 10)

Description

SCTK allows user to access all TSCAN related results with "getTSCANResults". See details.

Usage

getTSCANResults(x, analysisName = NULL, pathName = NULL)

## S4 method for signature 'SingleCellExperiment'
getTSCANResults(x, analysisName = NULL, pathName = NULL)

getTSCANResults(x, analysisName, pathName = NULL) <- value

## S4 replacement method for signature 'SingleCellExperiment'
getTSCANResults(x, analysisName, pathName = NULL) <- value

listTSCANResults(x)

## S4 method for signature 'SingleCellExperiment'
listTSCANResults(x)

listTSCANTerminalNodes(x)

## S4 method for signature 'SingleCellExperiment'
listTSCANTerminalNodes(x)

Arguments

Details

When analysisName = "Pseudotime", returns the list result from runTSCAN, including the MST structure.

When analysisName = "DEG", returns the list result from runTSCANDEG, including DataFrames containing genes that increase/decrease along each the pseudotime paths. pathName indicates the path index, the available options of which can be listed by listTSCANTerminalNodes.

When analysisName = "ClusterDEAnalysis", returns the list result from runTSCANClusterDEAnalysis. Here pathName needs to match with the useCluster argument when running the algorithm.

Value

Get or set TSCAN results

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runTSCAN(inSCE = mouseBrainSubsetSCE,
                                useReducedDim = "PCA_logcounts")
results <- getTSCANResults(mouseBrainSubsetSCE, "Pseudotime")

Description

Construct SCE object from Salmon-Alevin output

Usage

importAlevin(
  alevinDir = NULL,
  sampleName = "sample",
  delayedArray = FALSE,
  class = c("Matrix", "matrix"),
  rowNamesDedup = TRUE
)

Arguments

Value

A SingleCellExperiment object containing the count matrix, the feature annotations, and the cell annotation (which includes QC metrics stored in 'featureDump.txt').

Description

This function reads in one or more Python AnnData files in the .h5ad format and returns a single SingleCellExperiment object containing all the AnnData samples by concatenating their counts matrices and related information slots.

Usage

importAnnData(
  sampleDirs = NULL,
  sampleNames = NULL,
  delayedArray = FALSE,
  class = c("Matrix", "matrix"),
  rowNamesDedup = TRUE
)

Arguments

Details

importAnnData converts scRNA-seq data in the AnnData format to the SingleCellExperiment object. The .X slot in AnnData is transposed to the features x cells format and becomes the 'counts' matrix in the assay slot. The .vars AnnData slot becomes the SCE rowData and the .obs AnnData slot becomes the SCE colData. Multidimensional data in the .obsm AnnData slot is ported over to the SCE reducedDims slot. Additionally, unstructured data in the .uns AnnData slot is available through the SCE metadata slot. There are 2 currently known minor issues - Anndata python module depends on another python module h5pyto read hd5 format files. If there are errors reading the .h5ad files, such as "ValueError: invalid shape in fixed-type tuple." the user will need to do downgrade h5py by running pip3 install --user h5py==2.9.0 Additionally there might be errors in converting some python objects in the unstructured data slots. There are no known R solutions at present. Refer https://github.com/rstudio/reticulate/issues/209

Value

A SingleCellExperiment object.

Examples

file.path <- system.file("extdata/annData_pbmc_3k", package = "singleCellTK")
## Not run: 
sce <- importAnnData(sampleDirs = file.path,
                     sampleNames = 'pbmc3k_20by20')

## End(Not run)

Description

Read the barcodes, features (genes), and matrix from BUStools output. Import them as one SingleCellExperiment object. Note the cells in the output files for BUStools 0.39.4 are not filtered.

Usage

importBUStools(
  BUStoolsDirs,
  samples,
  matrixFileNames = "genes.mtx",
  featuresFileNames = "genes.genes.txt",
  barcodesFileNames = "genes.barcodes.txt",
  gzipped = "auto",
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  rowNamesDedup = TRUE
)

Arguments

Value

A SingleCellExperiment object containing the count matrix, the gene annotation, and the cell annotation.

Examples

# Example #1
# FASTQ files were downloaded from
# https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0
# /pbmc_1k_v3
# They were concatenated as follows:
# cat pbmc_1k_v3_S1_L001_R1_001.fastq.gz pbmc_1k_v3_S1_L002_R1_001.fastq.gz >
# pbmc_1k_v3_R1.fastq.gz
# cat pbmc_1k_v3_S1_L001_R2_001.fastq.gz pbmc_1k_v3_S1_L002_R2_001.fastq.gz >
# pbmc_1k_v3_R2.fastq.gz
# The following BUStools command generates the gene, cell, and
# matrix files

# bustools correct -w ./3M-february-2018.txt -p output.bus | \
#   bustools sort -T tmp/ -t 4 -p - | \
#   bustools count -o genecount/genes \
#     -g ./transcripts_to_genes.txt \
#     -e matrix.ec \
#     -t transcripts.txt \
#     --genecounts -

# The top 20 genes and the first 20 cells are included in this example.
sce <- importBUStools(
  BUStoolsDirs = system.file("extdata/BUStools_PBMC_1k_v3_20x20/genecount/",
    package = "singleCellTK"),
  samples = "PBMC_1k_v3_20x20")

Description

Read the filtered barcodes, features, and matrices for all samples from (preferably a single run of) Cell Ranger output. Import and combine them as one big SingleCellExperiment object.

Usage

importCellRanger(
  cellRangerDirs = NULL,
  sampleDirs = NULL,
  sampleNames = NULL,
  cellRangerOuts = NULL,
  dataType = c("filtered", "raw"),
  matrixFileNames = "matrix.mtx.gz",
  featuresFileNames = "features.tsv.gz",
  barcodesFileNames = "barcodes.tsv.gz",
  gzipped = "auto",
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  rowNamesDedup = TRUE
)

importCellRangerV2(
  cellRangerDirs = NULL,
  sampleDirs = NULL,
  sampleNames = NULL,
  dataTypeV2 = c("filtered", "raw"),
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  reference = NULL,
  cellRangerOutsV2 = NULL,
  rowNamesDedup = TRUE
)

importCellRangerV3(
  cellRangerDirs = NULL,
  sampleDirs = NULL,
  sampleNames = NULL,
  dataType = c("filtered", "raw"),
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  rowNamesDedup = TRUE
)

Arguments

Details

importCellRangerV2 imports output from Cell Ranger V2. importCellRangerV2Sample imports output from one sample from Cell Ranger V2. importCellRangerV3 imports output from Cell Ranger V3. importCellRangerV3 imports output from one sample from Cell Ranger V3. Some implicit assumptions which match the output structure of Cell Ranger V2 & V3 are made in these 4 functions including cellRangerOuts, matrixFileName, featuresFileName, barcodesFileName, and gzipped. Alternatively, user can call importCellRanger to explicitly specify these arguments.

Value

A SingleCellExperiment object containing the combined count matrix, the feature annotations, and the cell annotation.

Examples

# Example #1
# The following filtered feature, cell, and matrix files were downloaded from
# https://support.10xgenomics.com/single-cell-gene-expression/datasets/
# 3.0.0/hgmm_1k_v3
# The top 10 hg19 & mm10 genes are included in this example.
# Only the first 20 cells are included.
sce <- importCellRanger(
    cellRangerDirs = system.file("extdata/", package = "singleCellTK"),
    sampleDirs = "hgmm_1k_v3_20x20",
    sampleNames = "hgmm1kv3",
    dataType = "filtered")
# The following filtered feature, cell, and matrix files were downloaded from
# https://support.10xgenomics.com/single-cell-gene-expression/datasets/
# 2.1.0/pbmc4k
# Top 20 genes are kept. 20 cell barcodes are extracted.
sce <- importCellRangerV2(
    cellRangerDirs = system.file("extdata/", package = "singleCellTK"),
    sampleDirs = "pbmc_4k_v2_20x20",
    sampleNames = "pbmc4k_20",
    reference = 'GRCh38',
    dataTypeV2 = "filtered")
sce <- importCellRangerV3(
    cellRangerDirs = system.file("extdata/", package = "singleCellTK"),
    sampleDirs = "hgmm_1k_v3_20x20",
    sampleNames = "hgmm1kv3",
    dataType = "filtered")

Description

Read the filtered barcodes, features, and matrices for all samples from Cell Ranger V2 output. Files are assumed to be named "matrix.mtx", "genes.tsv", and "barcodes.tsv".

Usage

importCellRangerV2Sample(
  dataDir = NULL,
  sampleName = NULL,
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  rowNamesDedup = TRUE
)

Arguments

Value

A SingleCellExperiment object containing the count matrix, the feature annotations, and the cell annotation for the sample.

Examples

sce <- importCellRangerV2Sample(
    dataDir = system.file("extdata/pbmc_4k_v2_20x20/outs/",
        "filtered_gene_bc_matrices/GRCh38", package = "singleCellTK"),
    sampleName = "pbmc4k_20")

Description

Read the filtered barcodes, features, and matrices for all samples from Cell Ranger V3 output. Files are assumed to be named "matrix.mtx.gz", "features.tsv.gz", and "barcodes.tsv.gz".

Usage

importCellRangerV3Sample(
  dataDir = "./",
  sampleName = "sample",
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  rowNamesDedup = TRUE
)

Arguments

Value

A SingleCellExperiment object containing the count matrix, the feature annotations, and the cell annotation for the sample.

Examples

sce <- importCellRangerV3Sample(
    dataDir = system.file("extdata/hgmm_1k_v3_20x20/outs/",
        "filtered_feature_bc_matrix", package = "singleCellTK"),
    sampleName = "hgmm1kv3")

Description

imports the RDS file created by DropEst (https://github.com/hms-dbmi/dropEst) and 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.

Usage

importDropEst(
  sampleDirs = NULL,
  dataType = c("filtered", "raw"),
  rdsFileName = "cell.counts",
  sampleNames = NULL,
  delayedArray = FALSE,
  class = c("Matrix", "matrix"),
  rowNamesDedup = TRUE
)

Arguments

Details

importDropEst expects either raw counts matrix stored as "cm_raw" or filtered counts matrix stored as "cm" in the DropEst rds output. ColData is obtained from the DropEst corresponding to "mean_reads_per_umi","aligned_reads_per_cell", "aligned_umis_per_cell","requested_umis_per_cb","requested_reads_per_cb" If using filtered counts matrix, the colData dataframe is 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

Value

A SingleCellExperiment object containing the count matrix, the feature annotations from DropEst as ColData, and any metadata from DropEst

Examples

# Example results were generated as per instructions from the developers of dropEst described in
# https://github.com/hms-dbmi/dropEst/blob/master/examples/EXAMPLES.md
sce <- importDropEst(sampleDirs = system.file("extdata/dropEst_scg71", package = "singleCellTK"),
                     sampleNames = 'scg71')

Description

Retrieves published example datasets stored in SingleCellExperiment using the scRNAseq and TENxPBMCData packages. See 'Details' for a list of available datasets.

Usage

importExampleData(
  dataset,
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  rowNamesDedup = TRUE
)

Arguments

Details

See the list below for the available datasets and their descriptions.

"fluidigm_pollen"

Retrieved with ReprocessedFluidigmData. Returns a dataset of 65 human neural cells from Pollen et al. (2014), each sequenced at high and low coverage (SRA accession SRP041736).

"allen_tasic"

Retrieved with ReprocessedAllenData. Returns a dataset of 379 mouse brain cells from Tasic et al. (2016).

"NestorowaHSCData"

Retrieved with NestorowaHSCData. Returns a dataset of 1920 mouse haematopoietic stem cells from Nestorowa et al. 2015

"pbmc3k"

Retrieved with TENxPBMCData. 2,700 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.

"pbmc4k"

Retrieved with TENxPBMCData. 4,340 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.

"pbmc6k"

Retrieved with TENxPBMCData. 5,419 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.

"pbmc8k"

Retrieved with TENxPBMCData. 8,381 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.

"pbmc33k"

Retrieved with TENxPBMCData. 33,148 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.

"pbmc68k"

Retrieved with TENxPBMCData. 68,579 peripheral blood mononuclear cells (PBMCs) from 10X Genomics.

Value

The specified SingleCellExperiment object.

Author(s)

Joshua D. Campbell, David Jenkins

Examples

sce <- importExampleData("pbmc3k")

Description

Create a SingleCellExperiment object from files

Usage

importFromFiles(
  assayFile,
  annotFile = NULL,
  featureFile = NULL,
  assayName = "counts",
  inputDataFrames = FALSE,
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  annotFileHeader = FALSE,
  annotFileRowName = 1,
  annotFileSep = "\t",
  featureHeader = FALSE,
  featureRowName = 1,
  featureSep = "\t",
  gzipped = "auto",
  rowNamesDedup = TRUE
)

Arguments

Details

Creates a SingleCellExperiment object from a counts file in various formats, and files of cell and feature annotation.

Value

a SingleCellExperiment object

Description

Converts a list of gene sets stored in a GeneSetCollection object and stores it in the metadata of the SingleCellExperiment object. These gene sets can be used in downstream quality control and analysis functions in singleCellTK.

Usage

importGeneSetsFromCollection(
  inSCE,
  geneSetCollection,
  collectionName = "GeneSetCollection",
  by = "rownames",
  noMatchError = TRUE
)

Arguments

Details

The gene identifiers in gene sets in the GeneSetCollection will be mapped to the rownames of inSCE using the by parameter and stored in a GeneSetCollection object from package GSEABase. This object is stored in metadata(inSCE)$sctk$genesets, which can be accessed in downstream analysis functions such as runCellQC.

Value

A SingleCellExperiment object with gene set from collectionName output stored to the metadata slot.

Author(s)

Joshua D. Campbell

See Also

importGeneSetsFromList for importing from lists, importGeneSetsFromGMT for importing from GMT files, and importGeneSetsFromMSigDB for importing MSigDB gene sets.

Examples

data(scExample)
gs1 <- GSEABase::GeneSet(setName = "geneset1",
                         geneIds = rownames(sce)[seq(10)])
gs2 <- GSEABase::GeneSet(setName = "geneset2",
                         geneIds = rownames(sce)[seq(11,20)])
gsc <- GSEABase::GeneSetCollection(list(gs1, gs2))
sce <- importGeneSetsFromCollection(inSCE = sce,
                                    geneSetCollection = gsc,
                                    by = "rownames")

Description

Converts a list of gene sets stored in a GMT file into a GeneSetCollection and stores it in the metadata of the SingleCellExperiment object. These gene sets can be used in downstream quality control and analysis functions in singleCellTK.

Usage

importGeneSetsFromGMT(
  inSCE,
  file,
  collectionName = "GeneSetCollection",
  by = "rownames",
  sep = "\t",
  noMatchError = TRUE
)

Arguments

Details

The gene identifiers in gene sets in the GMT file will be mapped to the rownames of inSCE using the by parameter and stored in a GeneSetCollection object from package GSEABase. This object is stored in metadata(inSCE)$sctk$genesets, which can be accessed in downstream analysis functions such as runCellQC.

Value

A SingleCellExperiment object with gene set from collectionName output stored to the metadata slot.

Author(s)

Joshua D. Campbell

See Also

importGeneSetsFromList for importing from lists, importGeneSetsFromCollection for importing from GeneSetCollection objects, and importGeneSetsFromMSigDB for importing MSigDB gene sets.

Examples

data(scExample)

# GMT file containing gene symbols for a subset of human mitochondrial genes
gmt <- system.file("extdata/mito_subset.gmt", package = "singleCellTK")

# "feature_name" is the second column in the GMT file, so the ids will
# be mapped using this column in the 'rowData' of 'sce'. This
# could also be accomplished by setting by = "feature_name" in the
# function call.
sce <- importGeneSetsFromGMT(inSCE = sce, file = gmt, by = NULL)

Description

Converts a list of gene sets into a GeneSetCollection and stores it in the metadata of the SingleCellExperiment object. These gene sets can be used in downstream quality control and analysis functions in singleCellTK.

Usage

importGeneSetsFromList(
  inSCE,
  geneSetList,
  collectionName = "GeneSetCollection",
  by = "rownames",
  noMatchError = TRUE
)

Arguments

Details

The gene identifiers in gene sets in geneSetList will be mapped to the rownames of inSCE using the by parameter and stored in a GeneSetCollection object from package GSEABase. This object is stored in metadata(inSCE)$sctk$genesets, which can be accessed in downstream analysis functions such as runCellQC.

Value

A SingleCellExperiment object with gene set from collectionName output stored to the metadata slot.

Author(s)

Joshua D. Campbell

See Also

importGeneSetsFromCollection for importing from GeneSetCollection objects, importGeneSetsFromGMT for importing from GMT files, and importGeneSetsFromMSigDB for importing MSigDB gene sets.

Examples

data(scExample)

# Generate gene sets from 'rownames'
gs1 <- rownames(sce)[seq(10)]
gs2 <- rownames(sce)[seq(11,20)]
gs <- list("geneset1" = gs1, "geneset2" = gs2)
sce <- importGeneSetsFromList(inSCE = sce,
                              geneSetList = gs,
                              by = "rownames")

# Generate a gene set for mitochondrial genes using
# Gene Symbols stored in 'rowData'
mito.ix <- grep("^MT-", rowData(sce)$feature_name)
mito <- list(mito = rowData(sce)$feature_name[mito.ix])
sce <- importGeneSetsFromList(inSCE = sce,
                             geneSetList = mito,
                             by = "feature_name")

Description

Gets a list of MSigDB gene sets stores it in the metadata of the SingleCellExperiment object. These gene sets can be used in downstream quality control and analysis functions in singleCellTK.

Usage

importGeneSetsFromMSigDB(
  inSCE,
  categoryIDs = "H",
  species = "Homo sapiens",
  mapping = c("gene_symbol", "human_gene_symbol", "entrez_gene"),
  by = "rownames",
  verbose = TRUE,
  noMatchError = TRUE
)

Arguments

Details

The gene identifiers in gene sets from MSigDB will be retrieved using the msigdbr package. They will be mapped to the IDs in inSCE using the by parameter and stored in a GeneSetCollection object from package GSEABase. This object is stored in metadata(inSCE)$sctk$genesets, which can be accessed in downstream analysis functions such as runCellQC.

Value

A SingleCellExperiment object with gene set from collectionName output stored to the metadata slot.

Author(s)

Joshua D. Campbell

See Also

importGeneSetsFromList for importing from lists, importGeneSetsFromGMT for importing from GMT files, and GeneSetCollection objects.

Examples

data(scExample)
sce <- importGeneSetsFromMSigDB(inSCE = sce,
                                categoryIDs = "H",
                                species = "Homo sapiens",
                                mapping = "gene_symbol",
                                by = "feature_name")

Description

Imports mitochondrial gene sets and stores it in the metadata of the SingleCellExperiment object. These gene sets can be used in downstream quality control and analysis functions in singleCellTK.

Usage

importMitoGeneSet(
  inSCE,
  reference = "human",
  id = "ensembl",
  by = "rownames",
  collectionName = "mito",
  noMatchError = TRUE
)

Arguments

Details

The gene identifiers of mitochondrial genes will be loaded with "data(AllMito)". Currently, it supports human and mouse references. Also, it supports entrez ID, gene symbol, ensemble ID and ensemble transcript ID. They will be mapped to the IDs in inSCE using the by parameter and stored in a GeneSetCollection object from package GSEABase. This object is stored in metadata(inSCE)$sctk$genesets, which can be accessed in downstream analysis functions such as runCellQC.

Value

A SingleCellExperiment object with gene set from collectionName output stored to the metadata slot.

Author(s)

Rui Hong

See Also

importGeneSetsFromList for importing from lists, importGeneSetsFromGMT for importing from GMT files, and GeneSetCollection objects.

Examples

data(scExample)
sce <- importMitoGeneSet(inSCE = sce,
                         reference = "human",
                         id = "ensembl",
                         collectionName = "human_mito",
                         by = "rownames")

Description

Imports samples from different sources and compiles them into a list of SCE objects

Usage

importMultipleSources(allImportEntries, delayedArray = FALSE)

Arguments

Value

A list of SingleCellExperiment object containing the droplet or cell data or both,depending on the dataType that users provided.

Description

Read the barcodes, features (genes), and matrices from Optimus outputs. Import them as one SingleCellExperiment object.

Usage

importOptimus(
  OptimusDirs,
  samples,
  matrixLocation = "call-MergeCountFiles/sparse_counts.npz",
  colIndexLocation = "call-MergeCountFiles/sparse_counts_col_index.npy",
  rowIndexLocation = "call-MergeCountFiles/sparse_counts_row_index.npy",
  cellMetricsLocation = "call-MergeCellMetrics/merged-cell-metrics.csv.gz",
  geneMetricsLocation = "call-MergeGeneMetrics/merged-gene-metrics.csv.gz",
  emptyDropsLocation = "call-RunEmptyDrops/empty_drops_result.csv",
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  rowNamesDedup = TRUE
)

Arguments

Value

A SingleCellExperiment object containing the count matrix, the gene annotation, and the cell annotation.

Examples

file.path <- system.file("extdata/Optimus_20x1000",
  package = "singleCellTK")
## Not run: 
sce <- importOptimus(OptimusDirs = file.path,
  samples = "Optimus_20x1000")

## End(Not run)

Description

Read the filtered barcodes, features, and matrices for all samples from (preferably a single run of) seqc output. Import and combine them as one big SingleCellExperiment object.

Usage

importSEQC(
  seqcDirs = NULL,
  samples = NULL,
  prefix = NULL,
  gzipped = FALSE,
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  cbNotFirstCol = TRUE,
  feNotFirstCol = TRUE,
  combinedSample = TRUE,
  rowNamesDedup = TRUE
)

Arguments

Details

importSEQC imports output from seqc. The default sparse_counts_barcode.csv or sparse_counts_genes.csv from seqc output contains two columns. The first column is row index and the second column is cell-barcode or gene symbol. importSEQC will remove first column. Alternatively, user can call cbNotFirstCol or feNotFirstCol as FALSE to keep the first column of these files. When combinedSample is TRUE, importSEQC will combined count matrix with genes detected in at least one sample.

Value

A SingleCellExperiment object containing the combined count matrix, the feature annotations, and the cell annotation.

Examples

# Example #1
# The following filtered feature, cell, and matrix files were downloaded from
# https://support.10xgenomics.com/single-cell-gene-expression/datasets/
# 3.0.0/pbmc_1k_v3
# The top 50 hg38 genes are included in this example.
# Only the top 50 cells are included.
sce <- importSEQC(
    seqcDirs = system.file("extdata/pbmc_1k_50x50", package = "singleCellTK"),
    samples = "pbmc_1k_50x50",
    prefix = "pbmc_1k",
    combinedSample = FALSE)

Description

Read the barcodes, features (genes), and matrices from STARsolo outputs. Import them as one SingleCellExperiment object.

Usage

importSTARsolo(
  STARsoloDirs,
  samples,
  STARsoloOuts = c("Gene", "GeneFull"),
  matrixFileNames = "matrix.mtx",
  featuresFileNames = "features.tsv",
  barcodesFileNames = "barcodes.tsv",
  gzipped = "auto",
  class = c("Matrix", "matrix"),
  delayedArray = FALSE,
  rowNamesDedup = TRUE
)

Arguments

Value

A SingleCellExperiment object containing the count matrix, the gene annotation, and the cell annotation.

Examples

# Example #1
# FASTQ files were downloaded from
# https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0
# /pbmc_1k_v3
# They were concatenated as follows:
# cat pbmc_1k_v3_S1_L001_R1_001.fastq.gz pbmc_1k_v3_S1_L002_R1_001.fastq.gz >
# pbmc_1k_v3_R1.fastq.gz
# cat pbmc_1k_v3_S1_L001_R2_001.fastq.gz pbmc_1k_v3_S1_L002_R2_001.fastq.gz >
# pbmc_1k_v3_R2.fastq.gz
# The following STARsolo command generates the filtered feature, cell, and
# matrix files
# STAR \
#   --genomeDir ./index \
#   --readFilesIn ./pbmc_1k_v3_R2.fastq.gz \
#                 ./pbmc_1k_v3_R1.fastq.gz \
#   --readFilesCommand zcat \
#   --outSAMtype BAM Unsorted \
#   --outBAMcompression -1 \
#   --soloType CB_UMI_Simple \
#   --soloCBwhitelist ./737K-august-2016.txt \
#   --soloUMIlen 12

# The top 20 genes and the first 20 cells are included in this example.
sce <- importSTARsolo(
  STARsoloDirs = system.file("extdata/STARsolo_PBMC_1k_v3_20x20",
    package = "singleCellTK"),
  samples = "PBMC_1k_v3_20x20")

Description

Returns significance data from a snapshot.

Usage

iterateSimulations(
  originalData,
  useAssay = "counts",
  realLabels,
  totalReads,
  cells,
  iterations
)

Arguments

Value

A matrix of significance information from a snapshot

Examples

data("mouseBrainSubsetSCE")
res <- iterateSimulations(mouseBrainSubsetSCE, realLabels = "level1class",
                          totalReads = 1000, cells = 10, iterations = 2)

Description

Returns a character vector of the tables within the metadata slot of the SingleCellExperiment object.

Usage

listSampleSummaryStatsTables(inSCE, ...)

## S4 method for signature 'SingleCellExperiment'
listSampleSummaryStatsTables(inSCE, ...)

Arguments

Value

A character vector. Contains a list of summary tables within the SingleCellExperiment object.

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- sampleSummaryStats(sce, simple = TRUE, statsName = "qc_table")
listSampleSummaryStatsTables(sce)

Description

Merges colData of the singleCellExperiment objects obtained from the same dataset which contain differing colData. (i.e. raw data and filtered data)

Usage

mergeSCEColData(inSCE1, inSCE2, id1 = "column_name", id2 = "column_name")

Arguments

Value

SingleCellExperiment object containing combined colData from both singleCellExperiment for samples in inSCE1.

Examples

sce1 <- importCellRanger(
    cellRangerDirs = system.file("extdata/", package = "singleCellTK"),
    sampleDirs = "hgmm_1k_v3_20x20",
    sampleNames = "hgmm1kv3",
    dataType = "filtered")
data(scExample)
sce2 <- sce
sce <- mergeSCEColData(inSCE1 = sce1, inSCE2 = sce2, id1 = "column_name", id2 = "column_name")

Description

A list of gene set that contains mitochondrial genes of multiple reference (hg38, hg19, mm10 and mm9). It contains multiple types of gene identifier: gene symbol, entrez ID, ensemble ID and ensemble transcript ID. It's used for the function 'importMitoGeneSet'.

Usage

data("MitoGenes")

Format

A list

Value

List of mitochondrial genes of multiple reference

Examples

data("MitoGenes")

Description

A subset of 30 cells from a single cell RNA-Seq experiment from Zeisel, et al. Science 2015. The data was produced from cells from the mouse somatosensory cortex (S1) and hippocampus (CA1). 15 of the cells were identified as oligodendrocytes and 15 of the cell were identified as microglia.

Usage

data("mouseBrainSubsetSCE")

Format

SingleCellExperiment

Value

A subset of 30 cells from a single cell RNA-Seq experiment

Source

DOI: 10.1126/science.aaa1934

Examples

data("mouseBrainSubsetSCE")

Description

A table of gene set categories that can be download from MSigDB. The categories and descriptions can be found here: https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp. The IDs in the first column can be used to retrieve the gene sets for these categories using the importGeneSetsFromMSigDB function.

Usage

data("msigdb_table")

Format

A data.frame.

Value

A table of gene set categories

Examples

data("msigdb_table")

Description

A wrapper function which visualizes outputs from the runBarcodeRankDrops function stored in the metadata slot of the SingleCellExperiment object.

Usage

plotBarcodeRankDropsResults(
  inSCE,
  sample = NULL,
  defaultTheme = TRUE,
  dotSize = 0.5,
  titleSize = 18,
  axisSize = 15,
  axisLabelSize = 18,
  legendSize = 15
)

Arguments

Value

list of .ggplot objects

Examples

data(scExample, package = "singleCellTK")
sce <- runBarcodeRankDrops(inSCE = sce)
plotBarcodeRankDropsResults(inSCE = sce)

Description

A plotting function which visualizes outputs from the runBarcodeRankDrops function stored in the colData slot of the SingleCellExperiment object via scatterplot.

Usage

plotBarcodeRankScatter(
  inSCE,
  sample = NULL,
  defaultTheme = TRUE,
  dotSize = 0.1,
  title = NULL,
  titleSize = 18,
  xlab = NULL,
  ylab = NULL,
  axisSize = 12,
  axisLabelSize = 15,
  legendSize = 10,
  combinePlot = "none",
  sampleRelHeights = 1,
  sampleRelWidths = 1
)

Arguments

Value

a ggplot object of the scatter plot.

See Also

plotBarcodeRankDropsResults, runBarcodeRankDrops

Examples

data(scExample, package = "singleCellTK")
sce <- runBarcodeRankDrops(inSCE = sce)
plotBarcodeRankScatter(inSCE = sce)

Description

Plot comparison of batch corrected result against original assay

Usage

plotBatchCorrCompare(
  inSCE,
  corrMat,
  batch = NULL,
  condition = NULL,
  origAssay = NULL,
  origLogged = NULL,
  method = NULL,
  matType = NULL
)

Arguments

Details

Four plots will be combined. Two of them are violin/box-plots for percent variance explained by the batch variation, and optionally the covariate, for original and corrected. The other two are UMAPs of the original assay and the correction result matrix. If SCTK batch correction methods are performed in advance, this function will automatically detect necessary input. Otherwise, users can also customize the input. Future improvement might include solution to reduce redundant UMAP calculation.

Value

An object of class "gtable", combining four ggplots.

Author(s)

Yichen Wang

Examples

data("sceBatches")
logcounts(sceBatches) <- log1p(counts(sceBatches))
sceBatches <- runLimmaBC(sceBatches)
plotBatchCorrCompare(sceBatches, "LIMMA", condition = "cell_type")

Description

Visualize the percent variation in the data that is explained by batch and condition, individually, and that explained by combining both annotations. Plotting only the variation explained by batch is supported but not recommended, because this can be confounded by potential condition.

Usage

plotBatchVariance(
  inSCE,
  useAssay = NULL,
  useReddim = NULL,
  useAltExp = NULL,
  batch = "batch",
  condition = NULL,
  title = NULL
)

Arguments

Details

When condition and batch both are causing some variation, if the difference between full variation and condition variation is close to batch variation, this might imply that batches are causing some effect; if the difference is much less than batch variation, then the batches are likely to be confounded by the conditions.

Value

A ggplot object of a boxplot of variation explained by batch, condition, and batch+condition.

Examples

data('sceBatches', package = 'singleCellTK')
plotBatchVariance(sceBatches,
                  useAssay="counts",
                  batch="batch",
                  condition = "cell_type")

Description

A wrapper function which visualizes outputs from the runBcds function stored in the colData slot of the SingleCellExperiment object via various plots.

Usage

plotBcdsResults(
  inSCE,
  sample = NULL,
  shape = NULL,
  groupBy = NULL,
  combinePlot = "all",
  violin = TRUE,
  boxplot = FALSE,
  dots = TRUE,
  reducedDimName = "UMAP",
  xlab = NULL,
  ylab = NULL,
  dim1 = NULL,
  dim2 = NULL,
  bin = NULL,
  binLabel = NULL,
  defaultTheme = TRUE,
  dotSize = 0.5,
  summary = "median",
  summaryTextSize = 3,
  transparency = 1,
  baseSize = 15,
  titleSize = NULL,
  axisLabelSize = NULL,
  axisSize = NULL,
  legendSize = NULL,
  legendTitleSize = NULL,
  relHeights = 1,
  relWidths = c(1, 1, 1),
  plotNCols = NULL,
  plotNRows = NULL,
  labelSamples = TRUE,
  samplePerColumn = TRUE,
  sampleRelHeights = 1,
  sampleRelWidths = 1
)

Arguments

Value

list of .ggplot objects

See Also

runBcds

Examples

data(scExample, package="singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- runQuickUMAP(sce)
sce <- runBcds(sce)
plotBcdsResults(inSCE=sce, reducedDimName="UMAP")

Description

Plot a bubble plot with the color of the plot being the mean expression and the size of the dot being the percent of cells in the cluster expressing the gene.

Usage

plotBubble(
  inSCE,
  useAssay = "logcounts",
  featureNames,
  displayName = NULL,
  groupNames = "cluster",
  title = "",
  xlab = NULL,
  ylab = NULL,
  colorLow = "white",
  colorHigh = "blue",
  scale = FALSE
)

Arguments