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. |
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 findMarkerDiffExp .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 |
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 |
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 object |
runMAST |
Perform differential expression analysis on SCE object |
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'. |
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 runTSCAN function to obtain pseudotime values for cells |
runTSCANClusterDEAnalysis |
Run runTSCANClusterDEAnalysis function to observe changes between paths and to obtain DE genes |
runTSCANDEG |
Run runTSCANDEG function to obtain changes along a trajectory |
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 |
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 |
setRowNames |
Set rownames of SCE with a character vector or a rowData column |
setSampleSummaryStatsTable<- |
Setter function which stores table of SCTK QC outputs to metadata. |
setSCTKDisplayRow |
Indicates which rowData to use for visualization |
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 |
.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 |