generateMeta |
Generate 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. |
getMSigDBTable |
Shows MSigDB categories |
getPCA |
Perform PCA on a SingleCellExperiment Object A wrapper to runPCA function to compute principal component analysis (PCA) from a given SingleCellExperiment object. |
getSceParams |
Extract QC parameters from the SingleCellExperiment object |
getTopHVG |
getTopHVG Extracts the top variable genes from an input singleCellExperiment object |
getTSNE |
Run t-SNE dimensionality reduction method on a SingleCellExperiment Object |
getUMAP |
Uniform Manifold Approximation and Projection(UMAP) algorithm for dimension reduction. |
gsvaPlot |
Run GSVA analysis on a SingleCellExperiment object |
gsvaSCE |
Run GSVA analysis on a SingleCellExperiment object |
readSingleCellMatrix |
Read single cell expression matrix |
reportCellQC |
Get runCellQC .html report |
reportDiffExp |
Get runDEAnalysis .html report |
reportDropletQC |
Get runDropletQC .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 |
runComBat |
Apply ComBat 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. |
runDoubletCells |
Detect doublet cells using scDblFinder. |
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 |
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 |
runPerCellQC |
Wrapper for calculating QC metrics with scater. |
runSCANORAMA |
Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object |
runSCMerge |
Apply scMerge batch effect correction method to SingleCellExperiment object |
runScranSNN |
Get clustering with SNN graph |
runScrublet |
Find doublets using 'scrublet'. |
runZINBWaVE |
Apply ZINBWaVE Batch effect correction method to SingleCellExperiment object |
sampleSummaryStats |
Generate table of SCTK QC outputs. |
scater_logNormCounts |
scater_logNormCounts Uses logNormCounts to log normalize input data |
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 |
scran_modelGeneVar |
scran_modelGeneVar 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 |
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 |
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 |
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 |
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 |