Function reference • singleCellTK

Start the Shiny APP
`singleCellTK()`	Run the single cell analysis app
Importing scRNA-seq Data
`importAlevin()`	Construct SCE object from Salmon-Alevin output
`importAnnData()`	Create a SingleCellExperiment Object from Python AnnData .h5ad files
`importBUStools()`	Construct SCE object from BUStools output
`importCellRanger()` `importCellRangerV2()` `importCellRangerV3()`	Construct SCE object from Cell Ranger output
`importCellRangerV2Sample()`	Construct SCE object from Cell Ranger V2 output for a single sample
`importCellRangerV3Sample()`	Construct SCE object from Cell Ranger V3 output for a single sample
`importDropEst()`	Create a SingleCellExperiment Object from DropEst output
`importExampleData()`	Retrieve example datasets
`importFromFiles()`	Create a SingleCellExperiment object from files
`importGeneSetsFromCollection()`	Imports gene sets from a GeneSetCollection object
`importGeneSetsFromGMT()`	Imports gene sets from a GMT file
`importGeneSetsFromList()`	Imports gene sets from a list
`importGeneSetsFromMSigDB()`	Imports gene sets from MSigDB
`importMitoGeneSet()`	Import mitochondrial gene sets
`importMultipleSources()`	Imports samples from different sources and compiles them into a list of SCE objects
`importOptimus()`	Construct SCE object from Optimus output
`importSEQC()`	Construct SCE object from seqc output
`importSTARsolo()`	Construct SCE object from STARsolo outputs
`readSingleCellMatrix()`	Read single cell expression matrix
Quality Control & Preprocessing
`runCellQC()`	Perform comprehensive single cell QC
`runDropletQC()`	Perform comprehensive droplet QC
`runPerCellQC()`	Wrapper for calculating QC metrics with scater.
Decontamination
`runDecontX()`	Detecting contamination with DecontX.
`runSoupX()`	Detecting and correct contamination with SoupX
Doublet/Empty Droplet Detection
`runBarcodeRankDrops()`	Identify empty droplets using barcodeRanks.
`runEmptyDrops()`	Identify empty droplets using emptyDrops.
`runBcds()`	Find doublets/multiplets using bcds.
`runCxds()`	Find doublets/multiplets using cxds.
`runCxdsBcdsHybrid()`	Find doublets/multiplets using cxds_bcds_hybrid.
`runScDblFinder()`	Detect doublet cells using scDblFinder.
`runDoubletFinder()`	Generates a doublet score for each cell via doubletFinder
`runScrublet()`	Find doublets using `scrublet`.
Normalization
`runNormalization()`	Wrapper function to run any of the integrated normalization/transformation methods in the singleCellTK. The available methods include 'LogNormalize', 'CLR', 'RC' and 'SCTransform' from Seurat, 'logNormCounts and 'CPM' from Scater. Additionally, users can 'scale' using Z.Score, 'transform' using log, log1p and sqrt, add 'pseudocounts' and trim the final matrices between a range of values.
`scaterlogNormCounts()`	scaterlogNormCounts Uses logNormCounts to log normalize input data
`scaterCPM()`	scaterCPM Uses CPM from scater library to compute counts-per-million.
`seuratNormalizeData()`	seuratNormalizeData Wrapper for NormalizeData() function from seurat library Normalizes the sce object according to the input parameters
`seuratScaleData()`	seuratScaleData Scales the input sce object according to the input parameters
`computeZScore()`	Compute Z-Score
`trimCounts()`	Trim Counts
Batch Effect Correction
`runComBatSeq()`	Apply ComBat-Seq batch effect correction method to SingleCellExperiment object
`runBBKNN()`	Apply BBKNN batch effect correction method to SingleCellExperiment object
`runFastMNN()`	Apply a fast version of the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object
`runLimmaBC()`	Apply Limma's batch effect correction method to SingleCellExperiment object
`runMNNCorrect()`	Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object
`runSCANORAMA()`	Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object
`runSCMerge()`	Apply scMerge batch effect correction method to SingleCellExperiment object
`seuratIntegration()`	seuratIntegration A wrapper function to Seurat Batch-Correction/Integration workflow.
`runZINBWaVE()`	Apply ZINBWaVE Batch effect correction method to SingleCellExperiment object
`plotBatchVariance()`	Plot the percent of the variation that is explained by batch and condition in the data
Feature Selection
`runFeatureSelection()`	Wrapper function to run all of the feature selection methods integrated within the singleCellTK package including three methods from Seurat (`vst`, `mean.var.plot` or `dispersion`) and the Scran `modelGeneVar` method.
`scranModelGeneVar()`	scranModelGeneVar Generates and stores variability data from scran::modelGeneVar in the input singleCellExperiment object
`seuratFindHVG()`	seuratFindHVG Find highly variable genes and store in the input sce object
`getTopHVG()`	getTopHVG Extracts 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. If `altExp` parameter is a `character` value, this function will return the input `SingleCellExperiment` object with the subset containing only the top variable features stored as an `altExp` slot in returned object. However, if this parameter is set to `NULL`, only the names of the top variable features will be returned as a `character` vector.
`seuratPlotHVG()`	seuratPlotHVG Plot highly variable genes from input sce object (must have highly variable genes computations stored)
Dimensionality Reduction & Embedding
`runDimReduce()`	Generic Wrapper function for running dimensionality reduction
`scaterPCA()`	Perform PCA on a SingleCellExperiment Object A wrapper to runPCA function to compute principal component analysis (PCA) from a given SingleCellExperiment object.
`getUMAP()`	Uniform Manifold Approximation and Projection(UMAP) algorithm for dimension reduction.
`getTSNE()`	Run t-SNE dimensionality reduction method on a SingleCellExperiment Object
`seuratICA()`	seuratICA Computes ICA on the input sce object and stores the calculated independent components within the sce object
`seuratPCA()`	seuratPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object
`seuratRunUMAP()`	seuratRunUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object
`seuratRunTSNE()`	seuratRunTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object
`plotSCEDimReduceColData()`	Dimension reduction plot tool for colData
`plotSCEDimReduceFeatures()`	Dimension reduction plot tool for assay data
Clustering
`runScranSNN()`	Get clustering with SNN graph
`seuratFindClusters()`	seuratFindClusters Computes the clusters from the input sce object and stores them back in sce object
`runKMeans()`	Get clustering with KMeans
Differential Expression
`runDEAnalysis()` `runDESeq2()` `runLimmaDE()` `runANOVA()` `runMAST()` `runWilcox()`	Perform differential expression analysis on SCE object
`plotDEGVolcano()`	Generate volcano plot for DEGs
`plotDEGViolin()`	Generate violin plot to show the expression of top DEGs
`plotDEGRegression()`	Create linear regression plot to show the expression the of top DEGs
`plotDEGHeatmap()`	Heatmap visualization of DEG result
`plotMASTThresholdGenes()`	MAST Identify adaptive thresholds
`diffAbundanceFET()`	Calculate Differential Abundance with FET
Find Marker
`findMarkerDiffExp()`	Find the marker gene set for each cluster With an input SingleCellExperiment object and specifying the clustering labels, this function iteratively call the differential expression analysis on each cluster against all the others.
`findMarkerTopTable()`	Fetch the table of top markers that pass the filtering
`plotMarkerDiffExp()` `plotMarkerDiffExp()`	Plot a heatmap to visualize the result of `findMarkerDiffExp`
Cell Type Labeling
`runSingleR()`	Label cell types with SingleR
Enrichment & Pathway Analysis
`getMSigDBTable()`	Shows MSigDB categories
`enrichRSCE()`	enrichR Given a list of genes this function runs the enrichR() to perform Gene enrichment
`runGSVA()`	Run GSVA analysis on a SingleCellExperiment object
`runVAM()`	Run VAM to score gene sets in single cell data
`getPathwayResultNames()`	List pathway analysis result names
`plotPathway()`	Generate violin plots for pathway analysis results
Seurat Curated Workflow
`seuratComputeHeatmap()`	seuratComputeHeatmap Computes the heatmap plot object from the pca slot in the input sce object
`seuratComputeJackStraw()`	seuratComputeJackStraw Compute jackstraw plot and store the computations in the input sce object
`seuratElbowPlot()`	seuratElbowPlot Computes the plot object for elbow plot from the pca slot in the input sce object
`seuratFindClusters()`	seuratFindClusters Computes the clusters from the input sce object and stores them back in sce object
`seuratFindHVG()`	seuratFindHVG Find highly variable genes and store in the input sce object
`seuratFindMarkers()`	seuratFindMarkers
`seuratGenePlot()`	Compute and plot visualizations for marker genes
`seuratHeatmapPlot()`	seuratHeatmapPlot Modifies the heatmap plot object so it contains specified number of heatmaps in a single plot
`seuratICA()`	seuratICA Computes ICA on the input sce object and stores the calculated independent components within the sce object
`seuratIntegration()`	seuratIntegration A wrapper function to Seurat Batch-Correction/Integration workflow.
`seuratJackStrawPlot()`	seuratJackStrawPlot Computes the plot object for jackstraw plot from the pca slot in the input sce object
`seuratNormalizeData()`	seuratNormalizeData Wrapper for NormalizeData() function from seurat library Normalizes the sce object according to the input parameters
`seuratPCA()`	seuratPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object
`seuratPlotHVG()`	seuratPlotHVG Plot highly variable genes from input sce object (must have highly variable genes computations stored)
`seuratReductionPlot()`	seuratReductionPlot Plots the selected dimensionality reduction method
`seuratReport()`	Computes an HTML report from the Seurat workflow and returns the output SCE object with the computations stored in it.
`seuratRunTSNE()`	seuratRunTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object
`seuratRunUMAP()`	seuratRunUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object
`seuratScaleData()`	seuratScaleData Scales the input sce object according to the input parameters
`seuratSCTransform()`	seuratSCTransform Runs the SCTransform function to transform/normalize the input data
`seuratVariableFeatures()`	Get variable feature names after running seuratFindHVG function
`computeHeatmap()`	computeHeatmap 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.
Visualization
`plotBarcodeRankDropsResults()`	Plots for runEmptyDrops outputs.
`plotBarcodeRankScatter()`	Plots for runBarcodeRankDrops outputs.
`plotBatchCorrCompare()`	Plot comparison of batch corrected result against original assay
`plotBatchVariance()`	Plot the percent of the variation that is explained by batch and condition in the data
`plotBcdsResults()`	Plots for runBcds outputs.
`plotClusterAbundance()`	Plot the differential Abundance
`plotCxdsResults()`	Plots for runCxds outputs.
`plotDecontXResults()`	Plots for runDecontX outputs.
`plotDEGHeatmap()`	Heatmap visualization of DEG result
`plotDEGRegression()`	Create linear regression plot to show the expression the of top DEGs
`plotDEGViolin()`	Generate violin plot to show the expression of top DEGs
`plotDEGVolcano()`	Generate volcano plot for DEGs
`plotDimRed()`	Plot dimensionality reduction from computed metrics including PCA, ICA, tSNE and UMAP
`plotDoubletFinderResults()`	Plots for runDoubletFinder outputs.
`plotEmptyDropsResults()`	Plots for runEmptyDrops outputs.
`plotEmptyDropsScatter()`	Plots for runEmptyDrops outputs.
`plotMarkerDiffExp()` `plotMarkerDiffExp()`	Plot a heatmap to visualize the result of `findMarkerDiffExp`
`plotMASTThresholdGenes()`	MAST Identify adaptive thresholds
`plotPathway()`	Generate violin plots for pathway analysis results
`plotPCA()`	Plot PCA run data from its components.
`plotRunPerCellQCResults()`	Plots for runPerCellQC outputs.
`plotScDblFinderResults()`	Plots for runScDblFinder outputs.
`plotScdsHybridResults()`	Plots for runCxdsBcdsHybrid outputs.
`plotSCEBarAssayData()`	Bar plot of assay data.
`plotSCEBarColData()`	Bar plot of colData.
`plotSCEBatchFeatureMean()`	Plot mean feature value in each batch of a SingleCellExperiment object
`plotSCEDensity()`	Density plot of any data stored in the SingleCellExperiment object.
`plotSCEDensityAssayData()`	Density plot of assay data.
`plotSCEDensityColData()`	Density plot of colData.
`plotSCEDimReduceColData()`	Dimension reduction plot tool for colData
`plotSCEDimReduceFeatures()`	Dimension reduction plot tool for assay data
`plotSCEHeatmap()` `plotSCEDimReduceHeatmap()`	Plot heatmap of using data stored in SingleCellExperiment Object
`plotSCEScatter()`	Dimension reduction plot tool for all types of data
`plotSCEViolin()`	Violin plot of any data stored in the SingleCellExperiment object.
`plotSCEViolinAssayData()`	Violin plot of assay data.
`plotSCEViolinColData()`	Violin plot of colData.
`plotScrubletResults()`	Plots for runScrublet outputs.
`plotSoupXResults()`	Plot SoupX Result
`plotTopHVG()`	Plot highly variable genes
`plotTSNE()`	Plot t-SNE plot on dimensionality reduction data run from t-SNE method.
`plotUMAP()`	Plot UMAP results either on already run results or run first and then plot.
Report Generation
`reportCellQC()`	Get runCellQC .html report
`reportClusterAbundance()`	Get plotClusterAbundance .html report
`reportDiffAbundanceFET()`	Get diffAbundanceFET .html report
`reportDiffExp()`	Get runDEAnalysis .html report
`reportDropletQC()`	Get runDropletQC .html report
`reportFindMarker()`	Get findMarkerDiffExp .html report
`reportQCTool()`	Get .html report of the output of the selected QC algorithm
Exporting Results
`exportSCE()`	Export data in SingleCellExperiment object
`exportSCEtoAnnData()`	Export a SingleCellExperiment R object as Python annData object
`exportSCEtoFlatFile()`	Export a SingleCellExperiment object to flat text files
`exportSCEToSeurat()`	Export data in Seurat object
Datasets
`mouseBrainSubsetSCE`	Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE60361 subset
`sceBatches`	Example Single Cell RNA-Seq data in SingleCellExperiment object, with different batches annotated
`MitoGenes`	List of mitochondrial genes of multiple reference
`msigdb_table`	MSigDB gene get Category table
`scExample`	Example Single Cell RNA-Seq data in SingleCellExperiment Object, subset of 10x public dataset https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k A subset of 390 barcodes and top 200 genes were included in this example. Within 390 barcodes, 195 barcodes are empty droplet, 150 barcodes are cell barcode and 45 barcodes are doublets predicted by scrublet and doubletFinder package. This example only serves as a proof of concept and a tutoriol on how to run the functions in this package. The results should not be used for drawing scientific conclusions.
`SEG`	Stably Expressed Gene (SEG) list obect, with SEG sets for human and mouse.
Other Data Processing
`expData()`	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.
`expData<-`()	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.
`expDataNames()`	expDataNames Get names of all the data items in the input `SingleCellExperiment` object including assays, altExps and reducedDims.
`expDeleteDataTag()`	expDeleteDataTag Remove tag against an input data from the stored tag information in the metadata of the input object.
`expSetDataTag()`	expSetDataTag Set tag to an assay or a data item in the input SCE object.
`expTaggedData()`	expTaggedData Returns a list of names of data items from the input `SingleCellExperiment` object based upon the input parameters.
`combineSCE()`	Combine a list of SingleCellExperiment objects as one SingleCellExperiment object
`convertSCEToSeurat()`	convertSCEToSeurat Converts sce object to seurat while retaining all assays and metadata
`convertSeuratToSCE()`	convertSeuratToSCE Converts the input seurat object to a sce object
`dedupRowNames()`	Deduplicate the rownames of a matrix or SingleCellExperiment object 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.
`detectCellOutlier()`	Detecting outliers within the SingleCellExperiment object.
`featureIndex()`	Retrieve row index for a set of features
`getGenesetNamesFromCollection()`	List geneset names from geneSetCollection
`retrieveSCEIndex()`	Retrieve cell/feature index by giving identifiers saved in col/rowData
`sampleSummaryStats()`	Generate table of SCTK QC outputs.
`sctkListGeneSetCollections()`	Lists imported GeneSetCollections
`setSCTKDisplayRow()`	Indicates which rowData to use for visualization
`subsetSCECols()`	Subset a SingleCellExperiment object by columns
`subsetSCERows()`	Subset a SingleCellExperiment object by rows
Python Environment Setting
`sctkPythonInstallConda()`	Installs Python packages into a Conda environment
`selectSCTKConda()`	Selects a Conda environment
`sctkPythonInstallVirtualEnv()`	Installs Python packages into a virtual environment
`selectSCTKVirtualEnvironment()`	Selects a virtual environment

Reference

Start the Shiny APP

Importing scRNA-seq Data

Quality Control & Preprocessing

Decontamination

Doublet/Empty Droplet Detection

Normalization

Batch Effect Correction

Feature Selection

Dimensionality Reduction & Embedding

Clustering

Differential Expression

Find Marker

Cell Type Labeling

Enrichment & Pathway Analysis

Seurat Curated Workflow

Visualization

Report Generation

Exporting Results

Datasets

Other Data Processing

Python Environment Setting