# Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

## Documentation for package ‘singleCellTK’ version 2.16.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

C D E F G I L M P Q R S T

## -- C --

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
computeHeatmap | Computes heatmap for a set of features against dimensionality reduction components
computeZScore | Compute Z-Score
constructSCE | Create SingleCellExperiment object from csv or txt input
convertSCEToSeurat | convertSCEToSeurat Converts sce object to seurat while retaining all assays and metadata
convertSeuratToSCE | convertSeuratToSCE Converts the input seurat object to a sce object

## -- D --

dedupRowNames | Deduplicate the rownames of a matrix or SingleCellExperiment object
detectCellOutlier | Detecting outliers within the SingleCellExperiment object.
diffAbundanceFET | Calculate Differential Abundance with FET
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

## -- E --

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-method | 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.
expData<--method | 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.
expDataNames-method | 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.
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
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.

## -- F --

featureIndex | Retrieve row index for a set of features
findMarkerDiffExp | Find the marker gene set for each cluster
findMarkerTopTable | Fetch the table of top markers that pass the filtering

## -- G --

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.
getDEGTopTable | Get Top Table of a DEG analysis
getDiffAbundanceResults | Get/Set diffAbundanceFET result table
getDiffAbundanceResults-method | Get/Set diffAbundanceFET result table
getDiffAbundanceResults<- | Get/Set diffAbundanceFET result table
getDiffAbundanceResults<--method | Get/Set diffAbundanceFET result table
getEnrichRResult | Get or Set EnrichR Result
getEnrichRResult-method | Get or Set EnrichR Result
getEnrichRResult<- | Get or Set EnrichR Result
getEnrichRResult<--method | Get or Set EnrichR Result
getFindMarkerTopTable | Fetch the table of top markers that pass the filtering
getGenesetNamesFromCollection | List geneset names from geneSetCollection
getMSigDBTable | Shows MSigDB categories
getPathwayResultNames | List pathway analysis result names
getSampleSummaryStatsTable | Stores and returns table of SCTK QC outputs to metadata.
getSampleSummaryStatsTable-method | Stores and returns table of SCTK QC outputs to metadata.
getSceParams | Extract QC parameters from the SingleCellExperiment object
getSeuratVariableFeatures | Get variable feature names after running runSeuratFindHVG function
getSoupX | Get or Set SoupX Result
getSoupX-method | Get or Set SoupX Result
getSoupX<- | Get or Set SoupX Result
getSoupX<--method | Get or Set SoupX Result
getTopHVG | Get or set top HVG after calculation
getTSCANResults | getTSCANResults accessor function
getTSCANResults-method | getTSCANResults accessor function
getTSCANResults<- | getTSCANResults accessor function
getTSCANResults<--method | getTSCANResults accessor function
getTSNE | Run t-SNE embedding with Rtsne method
getUMAP | Run UMAP embedding with scater method

## -- I --

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 | Construct SCE object from Cell Ranger output
importCellRangerV2 | Construct SCE object from Cell Ranger output
importCellRangerV2Sample | Construct SCE object from Cell Ranger V2 output for a single sample
importCellRangerV3 | Construct SCE object from Cell Ranger output
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
iterateSimulations | Returns significance data from a snapshot.

## -- L --

listSampleSummaryStatsTables | Lists the table of SCTK QC outputs stored within the metadata.
listSampleSummaryStatsTables-method | Lists the table of SCTK QC outputs stored within the metadata.
listTSCANResults | getTSCANResults accessor function
listTSCANResults-method | getTSCANResults accessor function
listTSCANTerminalNodes | getTSCANResults accessor function
listTSCANTerminalNodes-method | getTSCANResults accessor function

## -- M --

mergeSCEColData | Merging colData from two singleCellExperiment objects
MitoGenes | List of mitochondrial genes of multiple reference
mouseBrainSubsetSCE | Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE60361 subset
msigdb_table | MSigDB gene get Category table

## -- P --

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.
plotBubble | Plot Bubble plot
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.
plotFindMarkerHeatmap | Plot a heatmap to visualize the result of 'runFindMarker'
plotMarkerDiffExp | Plot a heatmap to visualize the result of 'runFindMarker'
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.
plotScanpyDotPlot | plotScanpyDotPlot
plotScanpyEmbedding | plotScanpyEmbedding
plotScanpyHeatmap | plotScanpyHeatmap
plotScanpyHVG | plotScanpyHVG
plotScanpyMarkerGenes | plotScanpyMarkerGenes
plotScanpyMarkerGenesDotPlot | plotScanpyMarkerGenesDotPlot
plotScanpyMarkerGenesHeatmap | plotScanpyMarkerGenesHeatmap
plotScanpyMarkerGenesMatrixPlot | plotScanpyMarkerGenesMatrixPlot
plotScanpyMarkerGenesViolin | plotScanpyMarkerGenesViolin
plotScanpyMatrixPlot | plotScanpyMatrixPlot
plotScanpyPCA | plotScanpyPCA
plotScanpyPCAGeneRanking | plotScanpyPCAGeneRanking
plotScanpyPCAVariance | plotScanpyPCAVariance
plotScanpyViolin | plotScanpyViolin
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 | 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.
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
plotSeuratHeatmap | plotSeuratHeatmap Modifies the heatmap plot object so it contains specified number of heatmaps in a single plot
plotSeuratHVG | plotSeuratHVG Plot highly variable genes from input sce object (must have highly variable genes computations stored)
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
plotTopHVG | Plot highly variable genes
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.
plotUMAP | Plot UMAP results either on already run results or run first and then plot.

## -- Q --

qcInputProcess | Create SingleCellExperiment object from command line input arguments

## -- R --

readSingleCellMatrix | Read single cell expression matrix
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.
retrieveSCEIndex | Retrieve cell/feature index by giving identifiers saved in col/rowData
runANOVA | Perform differential expression analysis on SCE object
runBarcodeRankDrops | Identify empty droplets using barcodeRanks.
runBBKNN | Apply BBKNN batch effect correction method to SingleCellExperiment object
runBcds | Find doublets/multiplets using bcds.
runCellQC | Perform comprehensive single cell QC
runClusterSummaryMetrics | Run Cluster Summary Metrics
runComBatSeq | Apply ComBat-Seq batch effect correction method to SingleCellExperiment object
runCxds | Find doublets/multiplets using cxds.
runCxdsBcdsHybrid | Find doublets/multiplets using cxds_bcds_hybrid.
runDEAnalysis | Perform differential expression analysis on SCE object
runDecontX | Detecting contamination with DecontX.
runDESeq2 | Perform differential expression analysis on SCE object
runDimReduce | Generic Wrapper function for running dimensionality reduction
runDoubletFinder | Generates a doublet score for each cell via doubletFinder
runDropletQC | Perform comprehensive droplet QC
runEmptyDrops | Identify empty droplets using emptyDrops.
runEnrichR | Run EnrichR on SCE object
runFastMNN | Apply a fast version of the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object
runFeatureSelection | Run Variable Feature Detection Methods
runFindMarker | Find the marker gene set for each cluster
runGSVA | Run GSVA analysis on a SingleCellExperiment object
runHarmony | Apply Harmony batch effect correction method to SingleCellExperiment object
runKMeans | Get clustering with KMeans
runLimmaBC | Apply Limma's batch effect correction method to SingleCellExperiment object
runLimmaDE | Perform differential expression analysis on SCE object
runMAST | Perform differential expression analysis on SCE object
runMNNCorrect | Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object
runModelGeneVar | Calculate Variable Genes with Scran modelGeneVar
runNormalization | Run normalization/transformation with various methods
runPerCellQC | Wrapper for calculating QC metrics with scater.
runQuickTSNE | Run t-SNE embedding with Rtsne method
runQuickUMAP | Run UMAP embedding with scater method
runSCANORAMA | Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object
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
runScDblFinder | Detect doublet cells using scDblFinder.
runSCMerge | Apply scMerge batch effect correction method to SingleCellExperiment object
runScranSNN | Get clustering with SNN graph
runScrublet | Find doublets using 'scrublet'.
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
runSeuratScaleData | runSeuratScaleData Scales the input sce object according to the input parameters
runSeuratSCTransform | runSeuratSCTransform Runs the SCTransform function to transform/normalize the input data
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
runSingleR | Label cell types with SingleR
runSoupX | Detecting and correct contamination with SoupX
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
runTSNE | Run t-SNE embedding with Rtsne method
runUMAP | Run UMAP embedding with scater method
runVAM | Run VAM to score gene sets in single cell data
runWilcox | Perform differential expression analysis on SCE object
runZINBWaVE | Apply ZINBWaVE Batch effect correction method to SingleCellExperiment object

## -- S --

sampleSummaryStats | Generate table of SCTK QC outputs.
scaterCPM | scaterCPM Uses CPM from scater library to compute counts-per-million.
scaterlogNormCounts | scaterlogNormCounts Uses logNormCounts to log normalize input data
scaterPCA | Perform scater PCA on a SingleCellExperiment Object
sce | Example Single Cell RNA-Seq data in SingleCellExperiment Object, subset of 10x public dataset
sceBatches | Example Single Cell RNA-Seq data in SingleCellExperiment object, with different batches annotated
sctkListGeneSetCollections | Lists imported GeneSetCollections
sctkPythonInstallConda | Installs Python packages into a Conda environment
sctkPythonInstallVirtualEnv | Installs Python packages into a virtual environment
SEG | Stably Expressed Gene (SEG) list obect, with SEG sets for human and mouse.
selectSCTKConda | Selects a Conda environment
selectSCTKVirtualEnvironment | Selects a virtual environment
setRowNames | Set rownames of SCE with a character vector or a rowData column
setSampleSummaryStatsTable<- | Stores and returns table of SCTK QC outputs to metadata.
setSampleSummaryStatsTable<--method | Stores and returns table of SCTK QC outputs to metadata.
setSCTKDisplayRow | Indicates which rowData to use for visualization
setTopHVG | Get or set top HVG after calculation
singleCellTK | Run the single cell analysis app
subDiffEx | 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.
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.
subDiffExttest | 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.
subsetSCECols | Subset a SingleCellExperiment object by columns
subsetSCERows | Subset a SingleCellExperiment object by rows
summarizeSCE | Summarize an assay in a SingleCellExperiment

## -- T --

trimCounts | Trim Counts