Function reference • singleCellTK

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
Decontamination
`runDecontX()`	Detecting contamination with DecontX.
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
`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
`seuratPlotHVG()`	seuratPlotHVG Plot highly variable genes from input sce object (must have highly variable genes computations stored)
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()`	Perform differential expression analysis on SCE with specified method Method supported: 'MAST', 'DESeq2', 'Limma', 'ANOVA'
`runWilcox()`	Perform differential expression analysis on SCE with Wilcoxon test
`runMAST()`	Perform differential expression analysis on SCE with MAST
`runDESeq2()`	Perform differential expression analysis on SCE with DESeq2.
`runLimmaDE()`	Perform differential expression analysis on SCE with Limma.
`runANOVA()`	Perform differential expression analysis on SCE with ANOVA
`plotDEGViolin()`	plot the violin plot to show visualize the expression distribution of DEGs identified by differential expression analysis
`plotDEGRegression()`	plot the linear regression to show visualize the expression the of DEGs identified by differential expression analysis
`plotDEGHeatmap()`	Heatmap visualization of DEG result
`plotMASTThresholdGenes()`	MAST Identify adaptive thresholds
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.
`plotMarkerDiffExp()`	Plot a heatmap to visualize the result of `findMarkerDiffExp`
Visualization
`plotBarcodeRankDropsResults()`	Plots for runEmptyDrops outputs.
`plotBarcodeRankScatter()`	Plots for runBarcodeRankDrops outputs.
`plotBatchVariance()`	Plot the percent of the variation that is explained by batch and condition in the data
`plotBcdsResults()`	Plots for runBcds outputs.
`plotBiomarker()`	Given a set of genes, return a ggplot of expression values.
`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.
`plotHeatmapMulti()`	plotHeatmapMulti
`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.
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
Other Data processing
`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
`subsetSCECols()`	Subset a SingleCellExperiment object by columns
`subsetSCERows()`	Subset a SingleCellExperiment object by rows
`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.

Reference

Importing scRNA-seq data

Quality Control & Preprocessing

Decontamination

Doublet/Empty Droplet Detection

Normalization

Batch Effect Correction

Feature Selection

Dimensionality Reduction

Clustering

Differential Expression

Find Marker

Visualization

Exporting Results

Other Data processing