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 runFindMarker .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 |
Run Variable Feature Detection Methods |
runFindMarker |
Find the marker gene set for each cluster |
runGSVA |
Run GSVA analysis on a SingleCellExperiment object |
runHarmony |
Apply Harmony 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 object |
runMAST |
Perform differential expression analysis on SCE object |
runMNNCorrect |
Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object |
runModelGeneVar |
Calculate Variable Genes with Scran modelGeneVar |
runNormalization |
Run normalization/transformation with various methods |
runPerCellQC |
Wrapper for calculating QC metrics with scater. |
runQuickTSNE |
Run t-SNE embedding with Rtsne method |
runQuickUMAP |
Run UMAP embedding with scater method |
runSCANORAMA |
Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment object |
runScanpyFindClusters |
runScanpyFindClusters Computes the clusters from the input sce object and stores them back in sce object |
runScanpyFindHVG |
runScanpyFindHVG Find highly variable genes and store in the input sce object |
runScanpyFindMarkers |
runScanpyFindMarkers |
runScanpyNormalizeData |
runScanpyNormalizeData Wrapper for NormalizeData() function from scanpy library Normalizes the sce object according to the input parameters |
runScanpyPCA |
runScanpyPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object |
runScanpyScaleData |
runScanpyScaleData Scales the input sce object according to the input parameters |
runScanpyTSNE |
runScanpyTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object |
runScanpyUMAP |
runScanpyUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce 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 TSCAN to obtain pseudotime values for cells |
runTSCANClusterDEAnalysis |
Find DE genes between all TSCAN paths rooted from given cluster |
runTSCANDEG |
Test gene expression changes along a TSCAN trajectory path |
runTSNE |
Run t-SNE embedding with Rtsne method |
runUMAP |
Run UMAP embedding with scater method |
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 scater PCA on a SingleCellExperiment Object |
sce |
Example Single Cell RNA-Seq data in SingleCellExperiment Object, subset of 10x public dataset |
sceBatches |
Example Single Cell RNA-Seq data in SingleCellExperiment object, with different batches annotated |
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<- |
Stores and returns table of SCTK QC outputs to metadata. |
setSampleSummaryStatsTable<--method |
Stores and returns table of SCTK QC outputs to metadata. |
setSCTKDisplayRow |
Indicates which rowData to use for visualization |
setTopHVG |
Get or set top HVG after calculation |
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 |