Package: singleCellTK 2.17.0

Joshua David Campbell

singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.

Authors:Yichen Wang [aut], Irzam Sarfraz [aut], Rui Hong [aut], Yusuke Koga [aut], Salam Alabdullatif [aut], Nida Pervaiz [aut], David Jenkins [aut], Vidya Akavoor [aut], Xinyun Cao [aut], Shruthi Bandyadka [aut], Anastasia Leshchyk [aut], Tyler Faits [aut], Mohammed Muzamil Khan [aut], Zhe Wang [aut], W. Evan Johnson [aut], Ming Liu [aut], Joshua David Campbell [aut, cre]

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NEWS

# Install 'singleCellTK' in R:
install.packages('singleCellTK', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/compbiomed/singlecelltk/issues

Datasets:
  • MitoGenes - List of mitochondrial genes of multiple reference
  • SEG - Stably Expressed Gene (SEG) list obect, with SEG sets for human and mouse.
  • mouseBrainSubsetSCE - Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE60361 subset
  • msigdb_table - MSigDB gene get Category table
  • 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

On BioConductor:singleCellTK-2.15.2(bioc 3.21)singleCellTK-2.16.0(bioc 3.20)

singlecellgeneexpressiondifferentialexpressionalignmentclusteringimmunooncologybatcheffectnormalizationqualitycontroldataimportgui

10.12 score 177 stars 194 scripts 680 downloads 2 mentions 251 exports 376 dependencies

Last updated 23 days agofrom:65dee79069. Checks:OK: 1 NOTE: 1 ERROR: 2 WARNING: 3. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winWARNINGOct 31 2024
R-4.5-linuxNOTEOct 31 2024
R-4.4-winWARNINGOct 31 2024
R-4.4-macERROROct 31 2024
R-4.3-winWARNINGOct 31 2024
R-4.3-macERROROct 31 2024

Exports:calcEffectSizescombineSCEcomputeHeatmapcomputeZScoreconstructSCEconvertSCEToSeuratconvertSeuratToSCEdedupRowNamesdetectCellOutlierdiffAbundanceFETdiscreteColorPalettedistinctColorsdownSampleCellsdownSampleDepthexpDataexpData<-expDataNamesexpDeleteDataTagexportSCEexportSCEtoAnnDataexportSCEtoFlatFileexportSCEToSeuratexpSetDataTagexpTaggedDatafeatureIndexfindMarkerDiffExpfindMarkerTopTablegenerateHTANMetagenerateMetagenerateSimulatedDatagetBiomarkergetDEGTopTablegetDiffAbundanceResultsgetDiffAbundanceResults<-getEnrichRResultgetEnrichRResult<-getFindMarkerTopTablegetGenesetNamesFromCollectiongetMSigDBTablegetPathwayResultNamesgetSampleSummaryStatsTablegetSceParamsgetSeuratVariableFeaturesgetSoupXgetSoupX<-getTopHVGgetTSCANResultsgetTSCANResults<-getTSNEgetUMAPimportAlevinimportAnnDataimportBUStoolsimportCellRangerimportCellRangerV2importCellRangerV2SampleimportCellRangerV3importCellRangerV3SampleimportDropEstimportExampleDataimportFromFilesimportGeneSetsFromCollectionimportGeneSetsFromGMTimportGeneSetsFromListimportGeneSetsFromMSigDBimportMitoGeneSetimportMultipleSourcesimportOptimusimportSEQCimportSTARsoloiterateSimulationslistSampleSummaryStatsTableslistTSCANResultslistTSCANTerminalNodesmergeSCEColDataplotBarcodeRankDropsResultsplotBarcodeRankScatterplotBatchCorrCompareplotBatchVarianceplotBcdsResultsplotBubbleplotClusterAbundanceplotCxdsResultsplotDecontXResultsplotDEGHeatmapplotDEGRegressionplotDEGViolinplotDEGVolcanoplotDimRedplotDoubletFinderResultsplotEmptyDropsResultsplotEmptyDropsScatterplotFindMarkerHeatmapplotMarkerDiffExpplotMASTThresholdGenesplotPathwayplotPCAplotRunPerCellQCResultsplotScanpyDotPlotplotScanpyEmbeddingplotScanpyHeatmapplotScanpyHVGplotScanpyMarkerGenesplotScanpyMarkerGenesDotPlotplotScanpyMarkerGenesHeatmapplotScanpyMarkerGenesMatrixPlotplotScanpyMarkerGenesViolinplotScanpyMatrixPlotplotScanpyPCAplotScanpyPCAGeneRankingplotScanpyPCAVarianceplotScanpyViolinplotScDblFinderResultsplotScdsHybridResultsplotSCEBarAssayDataplotSCEBarColDataplotSCEBatchFeatureMeanplotSCEDensityplotSCEDensityAssayDataplotSCEDensityColDataplotSCEDimReduceColDataplotSCEDimReduceFeaturesplotSCEHeatmapplotSCEScatterplotSCEViolinplotSCEViolinAssayDataplotSCEViolinColDataplotScrubletResultsplotSeuratElbowplotSeuratGenesplotSeuratHeatmapplotSeuratHVGplotSeuratJackStrawplotSeuratReductionplotSoupXResultsplotTopHVGplotTSCANClusterDEGplotTSCANClusterPseudoplotTSCANDimReduceFeaturesplotTSCANPseudotimeGenesplotTSCANPseudotimeHeatmapplotTSCANResultsplotTSNEplotUMAPqcInputProcessreadSingleCellMatrixreportCellQCreportClusterAbundancereportDiffAbundanceFETreportDiffExpreportDropletQCreportFindMarkerreportQCToolreportSeuratreportSeuratClusteringreportSeuratDimRedreportSeuratFeatureSelectionreportSeuratMarkerSelectionreportSeuratNormalizationreportSeuratResultsreportSeuratRunreportSeuratScalingretrieveSCEIndexrunANOVArunBarcodeRankDropsrunBBKNNrunBcdsrunCellQCrunClusterSummaryMetricsrunComBatSeqrunCxdsrunCxdsBcdsHybridrunDEAnalysisrunDecontXrunDESeq2runDimReducerunDoubletFinderrunDropletQCrunEmptyDropsrunEnrichRrunFastMNNrunFeatureSelectionrunFindMarkerrunGSVArunHarmonyrunKMeansrunLimmaBCrunLimmaDErunMASTrunMNNCorrectrunModelGeneVarrunNormalizationrunPerCellQCrunQuickTSNErunQuickUMAPrunSCANORAMArunScanpyFindClustersrunScanpyFindHVGrunScanpyFindMarkersrunScanpyNormalizeDatarunScanpyPCArunScanpyScaleDatarunScanpyTSNErunScanpyUMAPrunScDblFinderrunSCMergerunScranSNNrunScrubletrunSeuratFindClustersrunSeuratFindHVGrunSeuratFindMarkersrunSeuratHeatmaprunSeuratICArunSeuratIntegrationrunSeuratJackStrawrunSeuratNormalizeDatarunSeuratPCArunSeuratScaleDatarunSeuratSCTransformrunSeuratTSNErunSeuratUMAPrunSingleRrunSoupXrunTSCANrunTSCANClusterDEAnalysisrunTSCANDEGrunTSNErunUMAPrunVAMrunWilcoxrunZINBWaVEsampleSummaryStatsscaterCPMscaterlogNormCountsscaterPCAsctkListGeneSetCollectionssctkPythonInstallCondasctkPythonInstallVirtualEnvselectSCTKCondaselectSCTKVirtualEnvironmentsetRowNamessetSCTKDisplayRowsetTopHVGsingleCellTKsubDiffExsubDiffExANOVAsubDiffExttestsubsetSCEColssubsetSCERowssummarizeSCEtrimCounts

Dependencies:abindalabaster.basealabaster.matrixalabaster.rangesalabaster.scealabaster.schemasalabaster.seanndataannotateAnnotationDbiAnnotationFilterAnnotationHubapeaplotaskpassassertthatassortheadbabelgenebackportsbase64encbasiliskbasilisk.utilsbatchelorbbmlebdsmatrixbeachmatbeeswarmBHBiobaseBiocFileCacheBiocGenericsBiocIOBiocManagerBiocNeighborsBiocParallelBiocSingularBiocVersionBiostringsbitbit64bitopsblobblusterbslibcachemCairocallrcaToolsceldacelldexcheckmatecirclizecliclueclustercodetoolscolorspacecolourpickercombinatcommonmarkComplexHeatmapcowplotcpp11crayoncrosstalkcurlcvToolsdata.tableDBIdbplyrdbscanDelayedArrayDelayedMatrixStatsdeldirdensEstBayesDEoptimRdescDESeq2digestdir.expirydistrdistributionaldoParalleldotCall64dplyrdqrngDropletUtilsDTedgeRedsenrichRensembldbevaluateExperimentHubfansifarverfastDummiesfastICAfastmapfieldsfilelockfitdistrplusFNNfontawesomeforeachforeignformatRFormulafsfutile.loggerfutile.optionsfuturefuture.applygenefiltergenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicFeaturesGenomicRangesGetoptLongggbeeswarmggfunggplot2ggplotifyggrastrggrepelggridgesggtreeGlobalOptionsglobalsgluegoftestgplotsgraphgridExtragridGraphicsGSEABaseGSVAGSVAdatagtablegtoolsgypsumHDF5Arrayherehgu95a.dbhighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvhttrhttr2icaigraphinlineIRangesirlbaisobanditeratorsjquerylibjsonlitejsonvalidateKEGGRESTKernSmoothknitrlabelinglambda.rlaterlatticelazyevalleidenlifecyclelimmalistenvlmtestlocfitlooM3DropmagickmagrittrmapsMASSMASTmathjaxrMatrixMatrixGenericsMatrixModelsmatrixStatsmclustMCMCprecisionmemoisemetapmetapodmgcvmimeminiUImnormtmsigdbrmultcompmulttestmunsellmutossmvtnormnlmennetnumDerivopensslorg.Hs.eg.dbparallellypatchworkpbapplypheatmappillarpkgbuildpkgconfigplogrplotlyplotrixplyrpngpolyclipposteriorprettyunitspROCprocessxprogressprogressrpromisesProtGenericsproxyCpspurrrqqconfquantregQuickJSRR.methodsS3R.ooR.utilsR6raggRANNrappdirsrbibutilsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppMLRcppParallelRcppProgressRcppTOMLRCurlRdpackreldistreshape2ResidualMatrixrestfulrreticulaterhdf5rhdf5filtersRhdf5libRhtslibrjsonrlangrmarkdownrobustbaseROCRrpartrprojrootRsamtoolsRSpectraRSQLiterstanrstantoolsrstudioapirsvdrtracklayerRtsneruvS4ArraysS4VectorssandwichsassScaledMatrixscalesscaterscattermorescDblFinderscdsscMergescranscRNAseqsctransformscuttleSeuratSeuratObjectsfsmiscshapeshinyshinyalertshinycssloadersshinyjsSingleCellExperimentSingleRsitmosnsnowsoftImputeSoupXsourcetoolsspspamSparseArraySparseMsparseMatrixStatsSpatialExperimentspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsStanHeadersstartupmsgstatmodstringistringrSummarizedExperimentsurvivalsvasyssystemfontstensortensorATENxPBMCDatatextshapingTFisherTH.datatibbletidyrtidyselecttidytreetinytexTrajectoryUtilstreeioTSCANtximportUCSC.utilsutf8uuiduwotV8VAMvctrsviporviridisviridisLitewithrWriteXLSxfunxgboostXMLxtableXVectoryamlyulab.utilszellkonverterzinbwavezlibbioczoo

Introduction to singleCellTK

Rendered fromsingleCellTK.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2023-07-26
Started: 2020-10-12

Readme and manuals

Help Manual

Help pageTopics
Finds the effect sizes for all genes in the original dataset, regardless of significance.calcEffectSizes
Combine a list of SingleCellExperiment objects as one SingleCellExperiment objectcombineSCE
Computes heatmap for a set of features against dimensionality reduction componentscomputeHeatmap
Compute Z-ScorecomputeZScore
Create SingleCellExperiment object from csv or txt inputconstructSCE
convertSCEToSeurat Converts sce object to seurat while retaining all assays and metadataconvertSCEToSeurat
convertSeuratToSCE Converts the input seurat object to a sce objectconvertSeuratToSCE
Deduplicate the rownames of a matrix or SingleCellExperiment objectdedupRowNames
Detecting outliers within the SingleCellExperiment object.detectCellOutlier
Calculate Differential Abundance with FETdiffAbundanceFET
Generate given number of color codesdiscreteColorPalette
Generate a distinct palette for coloring different clustersdistinctColors
Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect sizedownSampleCells
Estimate numbers of detected genes, significantly differentially expressed genes, and median significant effect sizedownSampleDepth
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 objectexportSCE
Export a SingleCellExperiment R object as Python annData objectexportSCEtoAnnData
Export a SingleCellExperiment object to flat text filesexportSCEtoFlatFile
Export data in Seurat objectexportSCEToSeurat
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 featuresfeatureIndex
Generate HTAN manifest file for droplet and cell count datagenerateHTANMeta
Generate HTAN manifest file for droplet and cell count datagenerateMeta
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 analysisgetDEGTopTable
Get/Set diffAbundanceFET result tablegetDiffAbundanceResults getDiffAbundanceResults,SingleCellExperiment-method getDiffAbundanceResults<- getDiffAbundanceResults<-,SingleCellExperiment-method
Get or Set EnrichR ResultgetEnrichRResult getEnrichRResult,SingleCellExperiment-method getEnrichRResult<- getEnrichRResult<-,SingleCellExperiment-method
Fetch the table of top markers that pass the filteringfindMarkerTopTable getFindMarkerTopTable
List geneset names from geneSetCollectiongetGenesetNamesFromCollection
Shows MSigDB categoriesgetMSigDBTable
List pathway analysis result namesgetPathwayResultNames
Stores and returns table of SCTK QC outputs to metadata.getSampleSummaryStatsTable getSampleSummaryStatsTable,SingleCellExperiment-method setSampleSummaryStatsTable<- setSampleSummaryStatsTable<-,SingleCellExperiment-method
Extract QC parameters from the SingleCellExperiment objectgetSceParams
Get variable feature names after running runSeuratFindHVG functiongetSeuratVariableFeatures
Get or Set SoupX ResultgetSoupX getSoupX,SingleCellExperiment-method getSoupX<- getSoupX<-,SingleCellExperiment-method
Get or set top HVG after calculationgetTopHVG setTopHVG
getTSCANResults accessor functiongetTSCANResults getTSCANResults,SingleCellExperiment-method getTSCANResults<- getTSCANResults<-,SingleCellExperiment-method listTSCANResults listTSCANResults,SingleCellExperiment-method listTSCANTerminalNodes listTSCANTerminalNodes,SingleCellExperiment-method
Construct SCE object from Salmon-Alevin outputimportAlevin
Create a SingleCellExperiment Object from Python AnnData .h5ad filesimportAnnData
Construct SCE object from BUStools outputimportBUStools
Construct SCE object from Cell Ranger outputimportCellRanger importCellRangerV2 importCellRangerV3
Construct SCE object from Cell Ranger V2 output for a single sampleimportCellRangerV2Sample
Construct SCE object from Cell Ranger V3 output for a single sampleimportCellRangerV3Sample
Create a SingleCellExperiment Object from DropEst outputimportDropEst
Retrieve example datasetsimportExampleData
Create a SingleCellExperiment object from filesimportFromFiles
Imports gene sets from a GeneSetCollection objectimportGeneSetsFromCollection
Imports gene sets from a GMT fileimportGeneSetsFromGMT
Imports gene sets from a listimportGeneSetsFromList
Imports gene sets from MSigDBimportGeneSetsFromMSigDB
Import mitochondrial gene setsimportMitoGeneSet
Imports samples from different sources and compiles them into a list of SCE objectsimportMultipleSources
Construct SCE object from Optimus outputimportOptimus
Construct SCE object from seqc outputimportSEQC
Construct SCE object from STARsolo outputsimportSTARsolo
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 objectsmergeSCEColData
List of mitochondrial genes of multiple referenceMitoGenes
Example Single Cell RNA-Seq data in SingleCellExperiment Object, GSE60361 subsetmouseBrainSubsetSCE
MSigDB gene get Category tablemsigdb_table
Plots for runBarcodeRankDrops outputs.plotBarcodeRankDropsResults
Plots for runBarcodeRankDrops outputs.plotBarcodeRankScatter
Plot comparison of batch corrected result against original assayplotBatchCorrCompare
Plot the percent of the variation that is explained by batch and condition in the dataplotBatchVariance
Plots for runBcds outputs.plotBcdsResults
Plot Bubble plotplotBubble
Plot the differential AbundanceplotClusterAbundance
Plots for runCxds outputs.plotCxdsResults
Plots for runDecontX outputs.plotDecontXResults
Heatmap visualization of DEG resultplotDEGHeatmap
Create linear regression plot to show the expression the of top DEGsplotDEGRegression
Generate violin plot to show the expression of top DEGsplotDEGViolin
Generate volcano plot for DEGsplotDEGVolcano
Plot dimensionality reduction from computed metrics including PCA, ICA, tSNE and UMAPplotDimRed
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 thresholdsplotMASTThresholdGenes
Generate violin plots for pathway analysis resultsplotPathway
Plot PCA run data from its components.plotPCA
Plots for runPerCellQC outputs.plotRunPerCellQCResults
plotScanpyDotPlotplotScanpyDotPlot
plotScanpyEmbeddingplotScanpyEmbedding
plotScanpyHeatmapplotScanpyHeatmap
plotScanpyHVGplotScanpyHVG
plotScanpyMarkerGenesplotScanpyMarkerGenes
plotScanpyMarkerGenesDotPlotplotScanpyMarkerGenesDotPlot
plotScanpyMarkerGenesHeatmapplotScanpyMarkerGenesHeatmap
plotScanpyMarkerGenesMatrixPlotplotScanpyMarkerGenesMatrixPlot
plotScanpyMarkerGenesViolinplotScanpyMarkerGenesViolin
plotScanpyMatrixPlotplotScanpyMatrixPlot
plotScanpyPCAplotScanpyPCA
plotScanpyPCAGeneRankingplotScanpyPCAGeneRanking
plotScanpyPCAVarianceplotScanpyPCAVariance
plotScanpyViolinplotScanpyViolin
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 objectplotSCEBatchFeatureMean
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 colDataplotSCEDimReduceColData
Dimension reduction plot tool for assay dataplotSCEDimReduceFeatures
Plot heatmap of using data stored in SingleCellExperiment ObjectplotSCEHeatmap
Dimension reduction plot tool for all types of dataplotSCEScatter
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 objectplotSeuratElbow
Compute and plot visualizations for marker genesplotSeuratGenes
plotSeuratHeatmap Modifies the heatmap plot object so it contains specified number of heatmaps in a single plotplotSeuratHeatmap
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 objectplotSeuratJackStraw
plotSeuratReduction Plots the selected dimensionality reduction methodplotSeuratReduction
Plot SoupX ResultplotSoupXResults
Plot highly variable genesplotTopHVG
Plot features identified by 'runTSCANClusterDEAnalysis' on cell 2D embedding with MST overlaidplotTSCANClusterDEG
Plot TSCAN pseudotime rooted from given clusterplotTSCANClusterPseudo
Plot feature expression on cell 2D embedding with MST overlaidplotTSCANDimReduceFeatures
Plot expression changes of top features along a TSCAN pseudotime pathplotTSCANPseudotimeGenes
Plot heatmap of genes with expression change along TSCAN pseudotimeplotTSCANPseudotimeHeatmap
Plot MST pseudotime values on cell 2D embeddingplotTSCANResults
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 argumentsqcInputProcess
Read single cell expression matrixreadSingleCellMatrix
Get runCellQC .html reportreportCellQC
Get plotClusterAbundance .html reportreportClusterAbundance
Get diffAbundanceFET .html reportreportDiffAbundanceFET
Get runDEAnalysis .html reportreportDiffExp
Get runDropletQC .html reportreportDropletQC
Get runFindMarker .html reportreportFindMarker
Get .html report of the output of the selected QC algorithmreportQCTool
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/rowDataretrieveSCEIndex
Identify empty droplets using barcodeRanks.runBarcodeRankDrops
Apply BBKNN batch effect correction method to SingleCellExperiment objectrunBBKNN
Find doublets/multiplets using bcds.runBcds
Perform comprehensive single cell QCrunCellQC
Run Cluster Summary MetricsrunClusterSummaryMetrics
Apply ComBat-Seq batch effect correction method to SingleCellExperiment objectrunComBatSeq
Find doublets/multiplets using cxds.runCxds
Find doublets/multiplets using cxds_bcds_hybrid.runCxdsBcdsHybrid
Perform differential expression analysis on SCE objectrunANOVA runDEAnalysis runDESeq2 runLimmaDE runMAST runWilcox
Detecting contamination with DecontX.runDecontX
Generic Wrapper function for running dimensionality reductionrunDimReduce
Generates a doublet score for each cell via doubletFinderrunDoubletFinder
Perform comprehensive droplet QCrunDropletQC
Identify empty droplets using emptyDrops.runEmptyDrops
Run EnrichR on SCE objectrunEnrichR
Apply a fast version of the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment objectrunFastMNN
Run Variable Feature Detection MethodsrunFeatureSelection
Find the marker gene set for each clusterfindMarkerDiffExp runFindMarker
Run GSVA analysis on a SingleCellExperiment objectrunGSVA
Apply Harmony batch effect correction method to SingleCellExperiment objectrunHarmony
Get clustering with KMeansrunKMeans
Apply Limma's batch effect correction method to SingleCellExperiment objectrunLimmaBC
Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment objectrunMNNCorrect
Calculate Variable Genes with Scran modelGeneVarrunModelGeneVar
Run normalization/transformation with various methodsrunNormalization
Wrapper for calculating QC metrics with scater.runPerCellQC
Apply the mutual nearest neighbors (MNN) batch effect correction method to SingleCellExperiment objectrunSCANORAMA
runScanpyFindClusters Computes the clusters from the input sce object and stores them back in sce objectrunScanpyFindClusters
runScanpyFindHVG Find highly variable genes and store in the input sce objectrunScanpyFindHVG
runScanpyFindMarkersrunScanpyFindMarkers
runScanpyNormalizeData Wrapper for NormalizeData() function from scanpy library Normalizes the sce object according to the input parametersrunScanpyNormalizeData
runScanpyPCA Computes PCA on the input sce object and stores the calculated principal components within the sce objectrunScanpyPCA
runScanpyScaleData Scales the input sce object according to the input parametersrunScanpyScaleData
runScanpyTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce objectrunScanpyTSNE
runScanpyUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce objectrunScanpyUMAP
Detect doublet cells using scDblFinder.runScDblFinder
Apply scMerge batch effect correction method to SingleCellExperiment objectrunSCMerge
Get clustering with SNN graphrunScranSNN
Find doublets using 'scrublet'.runScrublet
runSeuratFindClusters Computes the clusters from the input sce object and stores them back in sce objectrunSeuratFindClusters
runSeuratFindHVG Find highly variable genes and store in the input sce objectrunSeuratFindHVG
runSeuratFindMarkersrunSeuratFindMarkers
runSeuratHeatmap Computes the heatmap plot object from the pca slot in the input sce objectrunSeuratHeatmap
runSeuratICA Computes ICA on the input sce object and stores the calculated independent components within the sce objectrunSeuratICA
runSeuratIntegration A wrapper function to Seurat Batch-Correction/Integration workflow.runSeuratIntegration
runSeuratJackStraw Compute jackstraw plot and store the computations in the input sce objectrunSeuratJackStraw
runSeuratNormalizeData Wrapper for NormalizeData() function from seurat library Normalizes the sce object according to the input parametersrunSeuratNormalizeData
runSeuratPCA Computes PCA on the input sce object and stores the calculated principal components within the sce objectrunSeuratPCA
runSeuratScaleData Scales the input sce object according to the input parametersrunSeuratScaleData
runSeuratSCTransform Runs the SCTransform function to transform/normalize the input datarunSeuratSCTransform
runSeuratTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce objectrunSeuratTSNE
runSeuratUMAP Computes UMAP from the given sce object and stores the UMAP computations back into the sce objectrunSeuratUMAP
Label cell types with SingleRrunSingleR
Detecting and correct contamination with SoupXrunSoupX
Run TSCAN to obtain pseudotime values for cellsrunTSCAN
Find DE genes between all TSCAN paths rooted from given clusterrunTSCANClusterDEAnalysis
Test gene expression changes along a TSCAN trajectory pathrunTSCANDEG
Run t-SNE embedding with Rtsne methodgetTSNE runQuickTSNE runTSNE
Run UMAP embedding with scater methodgetUMAP runQuickUMAP runUMAP
Run VAM to score gene sets in single cell datarunVAM
Apply ZINBWaVE Batch effect correction method to SingleCellExperiment objectrunZINBWaVE
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 datascaterlogNormCounts
Perform scater PCA on a SingleCellExperiment ObjectscaterPCA
Example Single Cell RNA-Seq data in SingleCellExperiment Object, subset of 10x public datasetsce
Example Single Cell RNA-Seq data in SingleCellExperiment object, with different batches annotatedsceBatches
Lists imported GeneSetCollectionssctkListGeneSetCollections
Installs Python packages into a Conda environmentsctkPythonInstallConda
Installs Python packages into a virtual environmentsctkPythonInstallVirtualEnv
Stably Expressed Gene (SEG) list obect, with SEG sets for human and mouse.SEG
Selects a Conda environmentselectSCTKConda
Selects a virtual environmentselectSCTKVirtualEnvironment
Set rownames of SCE with a character vector or a rowData columnsetRowNames
Indicates which rowData to use for visualizationsetSCTKDisplayRow
Run the single cell analysis appsingleCellTK
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 columnssubsetSCECols
Subset a SingleCellExperiment object by rowssubsetSCERows
Summarize an assay in a SingleCellExperimentsummarizeSCE
Trim CountstrimCounts