| Finds the effect sizes for all genes in the original dataset, regardless of significance. | calcEffectSizes |
| Combine a list of SingleCellExperiment objects as one SingleCellExperiment object | combineSCE |
| Computes heatmap for a set of features against dimensionality reduction components | computeHeatmap |
| Compute Z-Score | computeZScore |
| Create SingleCellExperiment object from csv or txt input | constructSCE |
| convertSCEToSeurat Converts sce object to seurat while retaining all assays and metadata | convertSCEToSeurat |
| convertSeuratToSCE Converts the input seurat object to a sce object | convertSeuratToSCE |
| Deduplicate the rownames of a matrix or SingleCellExperiment object | dedupRowNames |
| Detecting outliers within the SingleCellExperiment object. | detectCellOutlier |
| Calculate Differential Abundance with FET | diffAbundanceFET |
| Generate given number of color codes | discreteColorPalette |
| Generate a distinct palette for coloring different clusters | distinctColors |
| Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size | downSampleCells |
| Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect size | downSampleDepth |
| 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 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-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. | 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<-,ANY,character,CharacterOrNullOrMissing,logical-method |
| 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. | expDataNames,ANY-method |
| expDeleteDataTag Remove tag against an input data from the stored tag information in the metadata of the input object. | expDeleteDataTag |
| Export data in SingleCellExperiment object | exportSCE |
| Export a SingleCellExperiment R object as Python annData object | exportSCEtoAnnData |
| Export a SingleCellExperiment object to flat text files | exportSCEtoFlatFile |
| Export data in Seurat object | exportSCEToSeurat |
| expSetDataTag Set tag to an assay or a data item in the input SCE object. | expSetDataTag |
| expTaggedData Returns a list of names of data items from the input 'SingleCellExperiment' object based upon the input parameters. | expTaggedData |
| Retrieve row index for a set of features | featureIndex |
| Generate HTAN manifest file for droplet and cell count data | generateHTANMeta |
| Generate HTAN manifest file for droplet and cell count data | generateMeta |
| Generates a single simulated dataset, bootstrapping from the input counts matrix. | generateSimulatedData |
| Given a list of genes and a SingleCellExperiment object, return the binary or continuous expression of the genes. | getBiomarker |
| Get Top Table of a DEG analysis | getDEGTopTable |
| Get/Set diffAbundanceFET result table | getDiffAbundanceResults getDiffAbundanceResults,SingleCellExperiment-method getDiffAbundanceResults<- getDiffAbundanceResults<-,SingleCellExperiment-method |
| Get or Set EnrichR Result | getEnrichRResult getEnrichRResult,SingleCellExperiment-method getEnrichRResult<- getEnrichRResult<-,SingleCellExperiment-method |
| Fetch the table of top markers that pass the filtering | findMarkerTopTable getFindMarkerTopTable |
| List geneset names from geneSetCollection | getGenesetNamesFromCollection |
| Shows MSigDB categories | getMSigDBTable |
| List pathway analysis result names | getPathwayResultNames |
| Stores and returns table of SCTK QC outputs to metadata. | getSampleSummaryStatsTable getSampleSummaryStatsTable,SingleCellExperiment-method setSampleSummaryStatsTable<- setSampleSummaryStatsTable<-,SingleCellExperiment-method |
| Extract QC parameters from the SingleCellExperiment object | getSceParams |
| Get variable feature names after running runSeuratFindHVG function | getSeuratVariableFeatures |
| Get or Set SoupX Result | getSoupX getSoupX,SingleCellExperiment-method getSoupX<- getSoupX<-,SingleCellExperiment-method |
| Get or set top HVG after calculation | getTopHVG setTopHVG |
| getTSCANResults accessor function | getTSCANResults getTSCANResults,SingleCellExperiment-method getTSCANResults<- getTSCANResults<-,SingleCellExperiment-method listTSCANResults listTSCANResults,SingleCellExperiment-method listTSCANTerminalNodes listTSCANTerminalNodes,SingleCellExperiment-method |
| Construct SCE object from Salmon-Alevin output | importAlevin |
| Create a SingleCellExperiment Object from Python AnnData .h5ad files | importAnnData |
| Construct SCE object from BUStools output | importBUStools |
| Construct SCE object from Cell Ranger output | importCellRanger importCellRangerV2 importCellRangerV3 |
| Construct SCE object from Cell Ranger V2 output for a single sample | importCellRangerV2Sample |
| Construct SCE object from Cell Ranger V3 output for a single sample | importCellRangerV3Sample |
| Create a SingleCellExperiment Object from DropEst output | importDropEst |
| Retrieve example datasets | importExampleData |
| Create a SingleCellExperiment object from files | importFromFiles |
| Imports gene sets from a GeneSetCollection object | importGeneSetsFromCollection |
| Imports gene sets from a GMT file | importGeneSetsFromGMT |
| Imports gene sets from a list | importGeneSetsFromList |
| Imports gene sets from MSigDB | importGeneSetsFromMSigDB |
| Import mitochondrial gene sets | importMitoGeneSet |
| Imports samples from different sources and compiles them into a list of SCE objects | importMultipleSources |
| Construct SCE object from Optimus output | importOptimus |
| Construct SCE object from seqc output | importSEQC |
| Construct SCE object from STARsolo outputs | importSTARsolo |
| Returns significance data from a snapshot. | iterateSimulations |
| Lists the table of SCTK QC outputs stored within the metadata. | listSampleSummaryStatsTables listSampleSummaryStatsTables,SingleCellExperiment-method |
| Merging colData from two singleCellExperiment objects | mergeSCEColData |
| List of mitochondrial genes of multiple reference | MitoGenes |
| Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE60361 subset | mouseBrainSubsetSCE |
| MSigDB gene get Category table | msigdb_table |
| Plots for runBarcodeRankDrops outputs. | plotBarcodeRankDropsResults |
| Plots for runBarcodeRankDrops outputs. | plotBarcodeRankScatter |
| Plot comparison of batch corrected result against original assay | plotBatchCorrCompare |
| Plot the percent of the variation that is explained by batch and condition in the data | plotBatchVariance |
| Plots for runBcds outputs. | plotBcdsResults |
| Plot Bubble plot | plotBubble |
| Plot the differential Abundance | plotClusterAbundance |
| Plots for runCxds outputs. | plotCxdsResults |
| Plots for runDecontX outputs. | plotDecontXResults |
| Heatmap visualization of DEG result | plotDEGHeatmap |
| Create linear regression plot to show the expression the of top DEGs | plotDEGRegression |
| Generate violin plot to show the expression of top DEGs | plotDEGViolin |
| Generate volcano plot for DEGs | plotDEGVolcano |
| Plot dimensionality reduction from computed metrics including PCA, ICA, tSNE and UMAP | plotDimRed |
| Plots for runDoubletFinder outputs. | plotDoubletFinderResults |
| Plots for runEmptyDrops outputs. | plotEmptyDropsResults |
| Plots for runEmptyDrops outputs. | plotEmptyDropsScatter |
| Plot a heatmap to visualize the result of 'runFindMarker' | plotFindMarkerHeatmap plotMarkerDiffExp |
| MAST Identify adaptive thresholds | plotMASTThresholdGenes |
| Generate violin plots for pathway analysis results | plotPathway |
| Plot PCA run data from its components. | plotPCA |
| Plots for runPerCellQC outputs. | plotRunPerCellQCResults |
| 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 |
| Plots for runScDblFinder outputs. | plotScDblFinderResults |
| Plots for runCxdsBcdsHybrid outputs. | plotScdsHybridResults |
| Bar plot of assay data. | plotSCEBarAssayData |
| Bar plot of colData. | plotSCEBarColData |
| Plot mean feature value in each batch of a SingleCellExperiment object | plotSCEBatchFeatureMean |
| Density plot of any data stored in the SingleCellExperiment object. | plotSCEDensity |
| Density plot of assay data. | plotSCEDensityAssayData |
| Density plot of colData. | plotSCEDensityColData |
| Dimension reduction plot tool for colData | plotSCEDimReduceColData |
| Dimension reduction plot tool for assay data | plotSCEDimReduceFeatures |
| Plot heatmap of using data stored in SingleCellExperiment Object | plotSCEHeatmap |
| Dimension reduction plot tool for all types of data | plotSCEScatter |
| Violin plot of any data stored in the SingleCellExperiment object. | plotSCEViolin |
| Violin plot of assay data. | plotSCEViolinAssayData |
| Violin plot of colData. | plotSCEViolinColData |
| Plots for runScrublet outputs. | plotScrubletResults |
| plotSeuratElbow Computes the plot object for elbow plot from the pca slot in the input sce object | plotSeuratElbow |
| Compute and plot visualizations for marker genes | plotSeuratGenes |
| plotSeuratHeatmap Modifies the heatmap plot object so it contains specified number of heatmaps in a single plot | plotSeuratHeatmap |
| plotSeuratHVG Plot highly variable genes from input sce object (must have highly variable genes computations stored) | plotSeuratHVG |
| plotSeuratJackStraw Computes the plot object for jackstraw plot from the pca slot in the input sce object | plotSeuratJackStraw |
| plotSeuratReduction Plots the selected dimensionality reduction method | plotSeuratReduction |
| Plot SoupX Result | plotSoupXResults |
| Plot highly variable genes | plotTopHVG |
| Plot features identified by 'runTSCANClusterDEAnalysis' on cell 2D embedding with MST overlaid | plotTSCANClusterDEG |
| Plot TSCAN pseudotime rooted from given cluster | plotTSCANClusterPseudo |
| Plot feature expression on cell 2D embedding with MST overlaid | plotTSCANDimReduceFeatures |
| Plot expression changes of top features along a TSCAN pseudotime path | plotTSCANPseudotimeGenes |
| Plot heatmap of genes with expression change along TSCAN pseudotime | plotTSCANPseudotimeHeatmap |
| Plot MST pseudotime values on cell 2D embedding | plotTSCANResults |
| Plot t-SNE plot on dimensionality reduction data run from t-SNE method. | plotTSNE |
| Plot UMAP results either on already run results or run first and then plot. | plotUMAP |
| Create SingleCellExperiment object from command line input arguments | qcInputProcess |
| Read single cell expression matrix | readSingleCellMatrix |
| Get runCellQC .html report | reportCellQC |
| Get plotClusterAbundance .html report | reportClusterAbundance |
| Get diffAbundanceFET .html report | reportDiffAbundanceFET |
| Get runDEAnalysis .html report | reportDiffExp |
| Get runDropletQC .html report | reportDropletQC |
| Get runFindMarker .html report | reportFindMarker |
| Get .html report of the output of the selected QC algorithm | reportQCTool |
| Generates an HTML report for the complete Seurat workflow and returns the SCE object with the results computed and stored inside the object. | reportSeurat |
| Generates an HTML report for Seurat Clustering and returns the SCE object with the results computed and stored inside the object. | reportSeuratClustering |
| Generates an HTML report for Seurat Dimensionality Reduction and returns the SCE object with the results computed and stored inside the object. | reportSeuratDimRed |
| Generates an HTML report for Seurat Feature Selection and returns the SCE object with the results computed and stored inside the object. | reportSeuratFeatureSelection |
| 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. | reportSeuratMarkerSelection |
| Generates an HTML report for Seurat Normalization and returns the SCE object with the results computed and stored inside the object. | reportSeuratNormalization |
| 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. | reportSeuratResults |
| 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. | reportSeuratRun |
| Generates an HTML report for Seurat Scaling and returns the SCE object with the results computed and stored inside the object. | reportSeuratScaling |
| Retrieve cell/feature index by giving identifiers saved in col/rowData | retrieveSCEIndex |
| Identify empty droplets using barcodeRanks. | runBarcodeRankDrops |
| Apply BBKNN batch effect correction method to SingleCellExperiment object | runBBKNN |
| Find doublets/multiplets using bcds. | runBcds |
| Perform comprehensive single cell QC | runCellQC |
| Run Cluster Summary Metrics | runClusterSummaryMetrics |
| Apply ComBat-Seq batch effect correction method to SingleCellExperiment object | runComBatSeq |
| Find doublets/multiplets using cxds. | runCxds |
| Find doublets/multiplets using cxds_bcds_hybrid. | runCxdsBcdsHybrid |
| Perform differential expression analysis on SCE object | runANOVA runDEAnalysis runDESeq2 runLimmaDE runMAST runWilcox |
| Detecting contamination with DecontX. | runDecontX |
| Generic Wrapper function for running dimensionality reduction | runDimReduce |
| Generates a doublet score for each cell via doubletFinder | runDoubletFinder |
| Perform comprehensive droplet QC | runDropletQC |
| Identify empty droplets using emptyDrops. | runEmptyDrops |
| Run EnrichR on SCE object | runEnrichR |
| Apply a fast version of the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object | runFastMNN |
| Run Variable Feature Detection Methods | runFeatureSelection |
| Find the marker gene set for each cluster | findMarkerDiffExp runFindMarker |
| Run GSVA analysis on a SingleCellExperiment object | runGSVA |
| Apply Harmony batch effect correction method to SingleCellExperiment object | runHarmony |
| Get clustering with KMeans | runKMeans |
| Apply Limma's batch effect correction method to SingleCellExperiment object | runLimmaBC |
| Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object | runMNNCorrect |
| Calculate Variable Genes with Scran modelGeneVar | runModelGeneVar |
| Run normalization/transformation with various methods | runNormalization |
| Wrapper for calculating QC metrics with scater. | runPerCellQC |
| Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object | runSCANORAMA |
| runScanpyFindClusters Computes the clusters from the input sce object and stores them back in sce object | runScanpyFindClusters |
| runScanpyFindHVG Find highly variable genes and store in the input sce object | runScanpyFindHVG |
| runScanpyFindMarkers | runScanpyFindMarkers |
| runScanpyNormalizeData Wrapper for NormalizeData() function from scanpy library Normalizes the sce object according to the input parameters | runScanpyNormalizeData |
| runScanpyPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object | runScanpyPCA |
| runScanpyScaleData Scales the input sce object according to the input parameters | runScanpyScaleData |
| runScanpyTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object | runScanpyTSNE |
| runScanpyUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object | runScanpyUMAP |
| Detect doublet cells using scDblFinder. | runScDblFinder |
| Apply scMerge batch effect correction method to SingleCellExperiment object | runSCMerge |
| Get clustering with SNN graph | runScranSNN |
| Find doublets using 'scrublet'. | runScrublet |
| runSeuratFindClusters Computes the clusters from the input sce object and stores them back in sce object | runSeuratFindClusters |
| runSeuratFindHVG Find highly variable genes and store in the input sce object | runSeuratFindHVG |
| runSeuratFindMarkers | runSeuratFindMarkers |
| runSeuratHeatmap Computes the heatmap plot object from the pca slot in the input sce object | runSeuratHeatmap |
| runSeuratICA Computes ICA on the input sce object and stores the calculated independent components within the sce object | runSeuratICA |
| runSeuratIntegration A wrapper function to Seurat Batch-Correction/Integration workflow. | runSeuratIntegration |
| runSeuratJackStraw Compute jackstraw plot and store the computations in the input sce object | runSeuratJackStraw |
| runSeuratNormalizeData Wrapper for NormalizeData() function from seurat library Normalizes the sce object according to the input parameters | runSeuratNormalizeData |
| runSeuratPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object | runSeuratPCA |
| runSeuratScaleData Scales the input sce object according to the input parameters | runSeuratScaleData |
| runSeuratSCTransform Runs the SCTransform function to transform/normalize the input data | runSeuratSCTransform |
| runSeuratTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object | runSeuratTSNE |
| runSeuratUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce object | runSeuratUMAP |
| Label cell types with SingleR | runSingleR |
| Detecting and correct contamination with SoupX | runSoupX |
| Run TSCAN to obtain pseudotime values for cells | runTSCAN |
| Find DE genes between all TSCAN paths rooted from given cluster | runTSCANClusterDEAnalysis |
| Test gene expression changes along a TSCAN trajectory path | runTSCANDEG |
| Run t-SNE embedding with Rtsne method | getTSNE runQuickTSNE runTSNE |
| Run UMAP embedding with scater method | getUMAP runQuickUMAP runUMAP |
| Run VAM to score gene sets in single cell data | runVAM |
| Apply ZINBWaVE Batch effect correction method to SingleCellExperiment object | runZINBWaVE |
| Generate table of SCTK QC outputs. | sampleSummaryStats |
| scaterCPM Uses CPM from scater library to compute counts-per-million. | scaterCPM |
| scaterlogNormCounts Uses logNormCounts to log normalize input data | scaterlogNormCounts |
| Perform scater PCA on a SingleCellExperiment Object | scaterPCA |
| Example Single Cell RNA-Seq data in SingleCellExperiment Object, subset of 10x public dataset | sce |
| Example Single Cell RNA-Seq data in SingleCellExperiment object, with different batches annotated | sceBatches |
| Lists imported GeneSetCollections | sctkListGeneSetCollections |
| Installs Python packages into a Conda environment | sctkPythonInstallConda |
| Installs Python packages into a virtual environment | sctkPythonInstallVirtualEnv |
| Stably Expressed Gene (SEG) list obect, with SEG sets for human and mouse. | SEG |
| Selects a Conda environment | selectSCTKConda |
| Selects a virtual environment | selectSCTKVirtualEnvironment |
| Set rownames of SCE with a character vector or a rowData column | setRowNames |
| Indicates which rowData to use for visualization | setSCTKDisplayRow |
| Run the single cell analysis app | singleCellTK |
| 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. | subDiffEx subDiffExANOVA subDiffExttest |
| Subset a SingleCellExperiment object by columns | subsetSCECols |
| Subset a SingleCellExperiment object by rows | subsetSCERows |
| Summarize an assay in a SingleCellExperiment | summarizeSCE |
| Trim Counts | trimCounts |
