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.
`reportDropletQC()`	Get runDropletQC .html report
`reportCellQC()`	Get runCellQC .html report
`plotRunPerCellQCResults()`	Plots for runPerCellQC outputs.
Decontamination
`runDecontX()`	Detecting contamination with DecontX.
`plotDecontXResults()`	Plots for runDecontX outputs.
`runSoupX()`	Detecting and correct contamination with SoupX
`getSoupX<-`() `getSoupX()`	Get or Set SoupX Result
`plotSoupXResults()`	Plot SoupX Result
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`.
`plotBarcodeRankDropsResults()`	Plots for runBarcodeRankDrops outputs.
`plotEmptyDropsResults()`	Plots for runEmptyDrops outputs.
`plotBcdsResults()`	Plots for runBcds outputs.
`plotCxdsResults()`	Plots for runCxds outputs.
`plotScdsHybridResults()`	Plots for runCxdsBcdsHybrid outputs.
`plotScDblFinderResults()`	Plots for runScDblFinder outputs.
`plotDoubletFinderResults()`	Plots for runDoubletFinder outputs.
`plotScrubletResults()`	Plots for runScrublet outputs.
Normalization
`runNormalization()`	Run normalization/transformation with various methods
`scaterlogNormCounts()`	scaterlogNormCounts Uses logNormCounts to log normalize input data
`scaterCPM()`	scaterCPM Uses CPM from scater library to compute counts-per-million.
`runSeuratNormalizeData()`	runSeuratNormalizeData Wrapper for NormalizeData() function from seurat library Normalizes the sce object according to the input parameters
`runSeuratScaleData()`	runSeuratScaleData Scales the input sce object according to the input parameters
`runSeuratSCTransform()`	runSeuratSCTransform Runs the SCTransform function to transform/normalize the input data
`computeZScore()`	Compute Z-Score
`trimCounts()`	Trim Counts
Batch Effect Correction
`runMNNCorrect()`	Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object
`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
`runHarmony()`	Apply Harmony 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
`runSeuratIntegration()`	runSeuratIntegration 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
`plotBatchCorrCompare()`	Plot comparison of batch corrected result against original assay
`plotSCEBatchFeatureMean()`	Plot mean feature value in each batch of a SingleCellExperiment object
Feature Selection
`runFeatureSelection()`	Run Variable Feature Detection Methods
`runModelGeneVar()`	Calculate Variable Genes with Scran modelGeneVar
`runSeuratFindHVG()`	runSeuratFindHVG Find highly variable genes and store in the input sce object
`getTopHVG()` `setTopHVG()`	Get or set top HVG after calculation
`plotTopHVG()`	Plot highly variable genes
Dimensionality Reduction & Embedding
`runDimReduce()`	Generic Wrapper function for running dimensionality reduction
`scaterPCA()`	Perform scater PCA on a SingleCellExperiment Object
`runUMAP()` `runQuickUMAP()` `getUMAP()`	Run UMAP embedding with scater method
`runTSNE()` `runQuickTSNE()` `getTSNE()`	Run t-SNE embedding with Rtsne method
`runSeuratICA()`	runSeuratICA Computes ICA on the input sce object and stores the calculated independent components within the sce object
`runSeuratPCA()`	runSeuratPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object
`runSeuratUMAP()`	runSeuratUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object
`runSeuratTSNE()`	runSeuratTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object
`plotPCA()`	Plot PCA run data from its components.
`plotUMAP()`	Plot UMAP results either on already run results or run first and then plot.
`plotTSNE()`	Plot t-SNE plot on dimensionality reduction data run from t-SNE method.
`plotDimRed()`	Plot dimensionality reduction from computed metrics including PCA, ICA, tSNE and UMAP
`plotSCEDimReduceColData()`	Dimension reduction plot tool for colData
`plotSCEDimReduceFeatures()`	Dimension reduction plot tool for assay data
Clustering
`runScranSNN()`	Get clustering with SNN graph
`runSeuratFindClusters()`	runSeuratFindClusters 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
`getDEGTopTable()`	Get Top Table of a DEG analysis
`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
Find Marker
`runFindMarker()` `findMarkerDiffExp()`	Find the marker gene set for each cluster
`getFindMarkerTopTable()` `findMarkerTopTable()`	Fetch the table of top markers that pass the filtering
`plotFindMarkerHeatmap()` `plotMarkerDiffExp()`	Plot a heatmap to visualize the result of `runFindMarker`
Differential Abundance
`diffAbundanceFET()`	Calculate Differential Abundance with FET
`getDiffAbundanceResults()` `getDiffAbundanceResults<-`()	Get/Set diffAbundanceFET result table
`plotClusterAbundance()`	Plot the differential Abundance
Cell Type Labeling
`runSingleR()`	Label cell types with SingleR
Enrichment & Pathway Analysis
`getMSigDBTable()`	Shows MSigDB categories
`runEnrichR()`	Run EnrichR on SCE object
`getEnrichRResult<-`() `getEnrichRResult()`	Get or Set EnrichR Result
`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
Trajectory Analysis
`runTSCAN()`	Run TSCAN to obtain pseudotime values for cells
`runTSCANClusterDEAnalysis()`	Find DE genes between all TSCAN paths rooted from given cluster
`runTSCANDEG()`	Test gene expression changes along a TSCAN trajectory path
`plotTSCANClusterDEG()`	Plot features identified by `runTSCANClusterDEAnalysis` on cell 2D embedding with MST overlaid
`plotTSCANClusterPseudo()`	Plot TSCAN pseudotime rooted from given cluster
`plotTSCANDimReduceFeatures()`	Plot feature expression on cell 2D embedding with MST overlaid
`plotTSCANPseudotimeGenes()`	Plot expression changes of top features along a TSCAN pseudotime path
`plotTSCANPseudotimeHeatmap()`	Plot heatmap of genes with expression change along TSCAN pseudotime
`plotTSCANResults()`	Plot MST pseudotime values on cell 2D embedding
`getTSCANResults()` `getTSCANResults<-`() `listTSCANResults()` `listTSCANTerminalNodes()`	getTSCANResults accessor function
Seurat Curated Workflow
`runSeuratFindClusters()`	runSeuratFindClusters Computes the clusters from the input sce object and stores them back in sce object
`runSeuratFindHVG()`	runSeuratFindHVG Find highly variable genes and store in the input sce object
`runSeuratFindMarkers()`	runSeuratFindMarkers
`runSeuratHeatmap()`	runSeuratHeatmap Computes the heatmap plot object from the pca slot in the input sce object
`runSeuratICA()`	runSeuratICA Computes ICA on the input sce object and stores the calculated independent components within the sce object
`runSeuratIntegration()`	runSeuratIntegration A wrapper function to Seurat Batch-Correction/Integration workflow.
`runSeuratJackStraw()`	runSeuratJackStraw Compute jackstraw plot and store the computations in the input sce object
`runSeuratNormalizeData()`	runSeuratNormalizeData Wrapper for NormalizeData() function from seurat library Normalizes the sce object according to the input parameters
`runSeuratPCA()`	runSeuratPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object
`runSeuratSCTransform()`	runSeuratSCTransform Runs the SCTransform function to transform/normalize the input data
`runSeuratScaleData()`	runSeuratScaleData Scales the input sce object according to the input parameters
`runSeuratTSNE()`	runSeuratTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object
`runSeuratUMAP()`	runSeuratUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object
`computeHeatmap()`	Computes heatmap for a set of features against dimensionality reduction components
`getSeuratVariableFeatures()`	Get variable feature names after running runSeuratFindHVG function
Scanpy Curated Workflow
`runScanpyFindClusters()`	runScanpyFindClusters Computes the clusters from the input sce object and stores them back in sce object
`runScanpyFindHVG()`	runScanpyFindHVG Find highly variable genes and store in the input sce object
`runScanpyFindMarkers()`	runScanpyFindMarkers
`runScanpyNormalizeData()`	runScanpyNormalizeData Wrapper for NormalizeData() function from scanpy library Normalizes the sce object according to the input parameters
`runScanpyPCA()`	runScanpyPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object
`runScanpyScaleData()`	runScanpyScaleData Scales the input sce object according to the input parameters
`runScanpyTSNE()`	runScanpyTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object
`runScanpyUMAP()`	runScanpyUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object
Visualization
`plotBarcodeRankDropsResults()`	Plots for runBarcodeRankDrops 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.
`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
`plotDecontXResults()`	Plots for runDecontX outputs.
`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.
`plotFindMarkerHeatmap()` `plotMarkerDiffExp()`	Plot a heatmap to visualize the result of `runFindMarker`
`plotMASTThresholdGenes()`	MAST Identify adaptive thresholds
`plotPCA()`	Plot PCA run data from its components.
`plotPathway()`	Generate violin plots for pathway analysis results
`plotRunPerCellQCResults()`	Plots for runPerCellQC 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()`	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.
`plotScDblFinderResults()`	Plots for runScDblFinder outputs.
`plotScanpyDotPlot()`	plotScanpyDotPlot
`plotScanpyEmbedding()`	plotScanpyEmbedding
`plotScanpyHVG()`	plotScanpyHVG
`plotScanpyHeatmap()`	plotScanpyHeatmap
`plotScanpyMarkerGenes()`	plotScanpyMarkerGenes
`plotScanpyMarkerGenesDotPlot()`	plotScanpyMarkerGenesDotPlot
`plotScanpyMarkerGenesHeatmap()`	plotScanpyMarkerGenesHeatmap
`plotScanpyMarkerGenesMatrixPlot()`	plotScanpyMarkerGenesMatrixPlot
`plotScanpyMarkerGenesViolin()`	plotScanpyMarkerGenesViolin
`plotScanpyMatrixPlot()`	plotScanpyMatrixPlot
`plotScanpyPCA()`	plotScanpyPCA
`plotScanpyPCAGeneRanking()`	plotScanpyPCAGeneRanking
`plotScanpyPCAVariance()`	plotScanpyPCAVariance
`plotScanpyViolin()`	plotScanpyViolin
`plotScdsHybridResults()`	Plots for runCxdsBcdsHybrid outputs.
`plotScrubletResults()`	Plots for runScrublet outputs.
`plotSeuratElbow()`	plotSeuratElbow Computes the plot object for elbow plot from the pca slot in the input sce object
`plotSeuratGenes()`	Compute and plot visualizations for marker genes
`plotSeuratHVG()`	plotSeuratHVG Plot highly variable genes from input sce object (must have highly variable genes computations stored)
`plotSeuratHeatmap()`	plotSeuratHeatmap Modifies the heatmap plot object so it contains specified number of heatmaps in a single plot
`plotSeuratJackStraw()`	plotSeuratJackStraw Computes the plot object for jackstraw plot from the pca slot in the input sce object
`plotSeuratReduction()`	plotSeuratReduction Plots the selected dimensionality reduction method
`plotSoupXResults()`	Plot SoupX Result
`plotTSCANClusterDEG()`	Plot features identified by `runTSCANClusterDEAnalysis` on cell 2D embedding with MST overlaid
`plotTSCANClusterPseudo()`	Plot TSCAN pseudotime rooted from given cluster
`plotTSCANDimReduceFeatures()`	Plot feature expression on cell 2D embedding with MST overlaid
`plotTSCANPseudotimeGenes()`	Plot expression changes of top features along a TSCAN pseudotime path
`plotTSCANPseudotimeHeatmap()`	Plot heatmap of genes with expression change along TSCAN pseudotime
`plotTSCANResults()`	Plot MST pseudotime values on cell 2D embedding
`plotTSNE()`	Plot t-SNE plot on dimensionality reduction data run from t-SNE method.
`plotTopHVG()`	Plot highly variable genes
`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 runFindMarker .html report
`reportQCTool()`	Get .html report of the output of the selected QC algorithm
`reportSeurat()`	Generates an HTML report for the complete Seurat workflow and returns the SCE object with the results computed and stored inside the object.
`reportSeuratClustering()`	Generates an HTML report for Seurat Clustering and returns the SCE object with the results computed and stored inside the object.
`reportSeuratDimRed()`	Generates an HTML report for Seurat Dimensionality Reduction and returns the SCE object with the results computed and stored inside the object.
`reportSeuratFeatureSelection()`	Generates an HTML report for Seurat Feature Selection and returns the SCE object with the results computed and stored inside the object.
`reportSeuratMarkerSelection()`	Generates an HTML report for Seurat Results (including Clustering & Marker Selection) and returns the SCE object with the results computed and stored inside the object.
`reportSeuratNormalization()`	Generates an HTML report for Seurat Normalization and returns the SCE object with the results computed and stored inside the object.
`reportSeuratResults()`	Generates an HTML report for Seurat Results (including Clustering & Marker Selection) and returns the SCE object with the results computed and stored inside the object.
`reportSeuratRun()`	Generates an HTML report for Seurat Run (including Normalization, Feature Selection, Dimensionality Reduction & Clustering) and returns the SCE object with the results computed and stored inside the object.
`reportSeuratScaling()`	Generates an HTML report for Seurat Scaling and returns the SCE object with the results computed and stored inside the object.
Exporting Results
`exportSCE()`	Export data in SingleCellExperiment object
`exportSCEToSeurat()`	Export data in Seurat object
`exportSCEtoAnnData()`	Export a SingleCellExperiment R object as Python annData object
`exportSCEtoFlatFile()`	Export a SingleCellExperiment object to flat text files
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
`SEG`	Stably Expressed Gene (SEG) list obect, with SEG sets for human and mouse.
Other Data Processing
`expData(<ANY>,<character>)`	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<-`(<ANY>,<character>,<CharacterOrNullOrMissing>,<logical>)	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.
`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.
`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.
`expDataNames(<ANY>)`	expDataNames Get names of all the data items in the input `SingleCellExperiment` object including assays, altExps and reducedDims.
`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.
`calcEffectSizes()`	Finds the effect sizes for all genes in the original dataset, regardless of significance.
`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
`constructSCE()`	Create SingleCellExperiment object from csv or txt input
`dedupRowNames()`	Deduplicate the rownames of a matrix or SingleCellExperiment object
`detectCellOutlier()`	Detecting outliers within the SingleCellExperiment object.
`discreteColorPalette()`	Generate given number of color codes
`distinctColors()`	Generate a distinct palette for coloring different clusters
`downSampleCells()`	Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size
`downSampleDepth()`	Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size
`featureIndex()`	Retrieve row index for a set of features
`retrieveSCEIndex()`	Retrieve cell/feature index by giving identifiers saved in col/rowData
`sampleSummaryStats()`	Generate table of SCTK QC outputs.
`getSampleSummaryStatsTable()` `setSampleSummaryStatsTable<-`()	Stores and returns table of SCTK QC outputs to metadata.
`listSampleSummaryStatsTables()`	Lists the table of SCTK QC outputs stored within the metadata.
`sctkListGeneSetCollections()`	Lists imported GeneSetCollections
`getGenesetNamesFromCollection()`	List geneset names from geneSetCollection
`setSCTKDisplayRow()`	Indicates which rowData to use for visualization
`subsetSCECols()`	Subset a SingleCellExperiment object by columns
`subsetSCERows()`	Subset a SingleCellExperiment object by rows
`generateHTANMeta()`	Generate HTAN manifest file for droplet and cell count data
`generateMeta()`	Generate HTAN manifest file for droplet and cell count data
`generateSimulatedData()`	Generates a single simulated dataset, bootstrapping from the input counts matrix.
`getBiomarker()`	Given a list of genes and a SingleCellExperiment object, return the binary or continuous expression of the genes.
`getSceParams()`	Extract QC parameters from the SingleCellExperiment object
`iterateSimulations()`	Returns significance data from a snapshot.
`mergeSCEColData()`	Merging colData from two singleCellExperiment objects
`qcInputProcess()`	Create SingleCellExperiment object from command line input arguments
`setRowNames()`	Set rownames of SCE with a character vector or a rowData column
`subDiffEx()` `subDiffExttest()` `subDiffExANOVA()`	Passes the output of generateSimulatedData() to differential expression tests, picking either t-tests or ANOVA for data with only two conditions or multiple conditions, respectively.
`summarizeSCE()`	Summarize an assay in a SingleCellExperiment
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

Differential Abundance

Cell Type Labeling

Enrichment & Pathway Analysis

Trajectory Analysis

Seurat Curated Workflow

Scanpy Curated Workflow

Visualization

Report Generation

Exporting Results

Datasets

Other Data Processing

Python Environment Setting