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

## Documentation for package ‘singleCellTK’ version 2.4.0

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

## Help Pages

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

## -- 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 | 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.
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 --

dataAnnotationColor | Generate distinct colors for all categorical col/rowData entries. Character columns will be considered as well as all-integer columns. Any column with all-distinct values will be excluded.
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.
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 --

enrichRSCE | enrichR Given a list of genes this function runs the enrichR() to perform Gene enrichment
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 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

## -- 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
getMSigDBTable | Shows MSigDB categories
getSceParams | Extract QC parameters from the SingleCellExperiment 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.
getTSNE | Run t-SNE dimensionality reduction method on a SingleCellExperiment Object
getUMAP | Uniform Manifold Approximation and Projection(UMAP) algorithm for dimension reduction.

## -- 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.

## -- 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 Cctegory table

## -- P --

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 | plot the linear regression to show visualize the expression the of DEGs identified by differential expression analysis
plotDEGViolin | plot the violin plot to show visualize the expression distribution of DEGs identified by differential expression analysis
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 | Plot a heatmap to visualize the result of 'findMarkerDiffExp'
plotMASTThresholdGenes | MAST Identify adaptive thresholds
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 | 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.
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.

## -- Q --

qcInputProcess | Create SingleCellExperiment object from command line input arguments

## -- R --

readSingleCellMatrix | Read single cell expression matrix
reportCellQC | Get runCellQC .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
retrieveSCEIndex | Retrieve cell/feature index by giving identifiers saved in col/rowData
runANOVA | Perform differential expression analysis on SCE with ANOVA
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
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 with specified method Method supported: 'MAST', 'DESeq2', 'Limma', 'ANOVA'
runDecontX | Detecting contamination with DecontX.
runDESeq2 | Perform differential expression analysis on SCE with DESeq2.
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.
runFastMNN | Apply a fast version of the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object
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.
runGSVA | Run GSVA analysis on a 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 with Limma.
runMAST | Perform differential expression analysis on SCE with MAST
runMNNCorrect | Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object
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.
runPerCellQC | Wrapper for calculating QC metrics with scater.
runSCANORAMA | Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment 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'.
runSingleR | Label cell types with SingleR
runVAM | Run VAM to score gene sets in single cell data
runWilcox | Perform differential expression analysis on SCE with Wilcoxon test
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 PCA on a SingleCellExperiment Object A wrapper to runPCA function to compute principal component analysis (PCA) from a given SingleCellExperiment object.
sce | 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.
sceBatches | Example Single Cell RNA-Seq data in SingleCellExperiment object, with different batches annotated
scranModelGeneVar | scranModelGeneVar Generates and stores variability data from scran::modelGeneVar in the input singleCellExperiment object
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
setSCTKDisplayRow | Indicates which rowData to use for visualization
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
simpleLog | A decorator that prints the arguments to the decorated function
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

## -- misc --

.addSeuratToMetaDataSCE | .addSeuratToMetaDataSCE Adds the input seurat object to the metadata slot of the input sce object (after removing the data matrices)
.checkDiffExpResultExists | Check if the specified MAST result in SingleCellExperiment object is complete. But does not garantee the biological correctness.
.computeSignificantPC | .computeSignificantPC Computes the significant principal components from an input sce object (must contain pca slot) using stdev
.extractSCEAnnotation | Extract columns from row/colData and transfer to factors
.formatDEAList | Helper function for differential expression analysis methods that accepts multiple ways of conditional subsetting and returns stable index format. Meanwhile it does all the input checkings.
.getComponentNames | .getComponentNames Creates a list of PC/IC components to populate the picker for PC/IC heatmap generation
.ggBar | Bar plot plotting tool.
.ggDensity | Density plot plotting tool.
.ggScatter | Plot results of reduced dimensions data.
.ggViolin | Violin plot plotting tool.
.sce2adata | Coverts SingleCellExperiment object from R to anndata.AnnData object in Python
.seuratGetVariableFeatures | .seuratGetVariableFeatures Retrieves the requested number of variable feature names
.seuratInvalidate | .seuratInvalidate Removes seurat data from the input SingleCellExperiment object specified by the task in the Seurat workflow.
.updateAssaySCE | .updateAssaySCE Update/Modify/Add an assay in the provided SingleCellExperiment object from a Seurat object