Package index • scop

Overview and Workflows

Package and Standard Workflows

scop scop-package: Single-cell and Spatial omics analysis pipeline
scop_logo() print(<scop_logo>): scop logo
RunStandardWorkflow(): Standard single-cell and spatial workflow

Quality Control and Preprocessing

Cell Quality and Contamination Control

RunCellQC(): Cell-level quality control
RunDoubletCalling(): Doublet calling
RunDoubletDetection(): Run doublet-calling with DoubletDetection
RunscDblFinder(): Run doublet-calling with scDblFinder
Runscds(): Run doublet-calling with scds
RunScrublet(): Run doublet-calling with Scrublet
RunDecontX(): Ambient RNA decontamination with decontX

Normalization and Feature Selection

NormalizeData(): Normalize a single-cell object
FindVariableFeatures(): Find variable features
ScaleData(): Scale expression data

Cell Cycle Analysis

CellCycleScoring(): Cell-cycle scoring with native sparse module scoring
CycGenePrefetch(): Prefetch cell cycle genes
RunCellCycle(): Run cell cycle scoring

Dimensionality Reduction and Clustering

Reduction Selection and Linear Embeddings

RunDimsEstimate(): Estimate useful dimensions from a reduction
DimsEstimatePlot(): Dimension estimate diagnostic plot
RunDimsReduction(): Run dimension reduction
RunPCA(): Run principal component analysis
RunCCA(): Run canonical correlation analysis
RunGLMPCA(): Run generalized principal components analysis (GLMPCA)
RunMDS(): Run MDS (multi-dimensional scaling)
RunNMF(): Run NMF
NMFHeatmap(): NMF similarity heatmap

Nonlinear Embeddings

RunUMAP(): Run UMAP
RunUMAP2(): Run UMAP
RunDM(): Run diffusion map (DM)
RunFR(): Run Force-Directed Layout (Fruchterman-Reingold algorithm)
RunPaCMAP(): Run PaCMAP
RunTriMap(): Run TriMap
RunPHATE(): Run PHATE
RunLargeVis(): Run LargeVis (Dimensionality Reduction with a LargeVis-like method)

Clustering and Neighborhoods

FindNeighbors(): Find nearest neighbors
FindClusters(): Seurat clustering with transparent backend delegation
ClusterTreePlot(): Cluster tree plot
CellStatePlot(): Composite cell-state plot
RunCHOIR(): Run CHOIR clustering
RunRareQ(): RareQ rare-cell population detection

Embedding Visualization

CellDimPlot(): Cell Dimensional Plot
CellDimPlot3D(): 3D-Dimensional reduction plot for cell classification visualization.
CellDensityPlot(): Cell density plot
FeatureDimPlot(): Feature values on a 2D reduction
FeatureDimPlot3D(): Feature values on a 3D reduction
ProjectionPlot(): Projection Plot
TACSPlot(): Transcript-averaged cell scoring (TACS)

Data Integration and Reference Mapping

Integration Workflows and Feature Preparation

RunIntegration(): Integration workflow
SelectIntegrationFeatures(): Select integration features with exact vectorized ranking
Uncorrected_integrate(): Uncorrected integration function

Anchor and Neighbor-Based Integration

Seurat_integrate(): Seurat integration function
CCA_integrate(): Seurat v5 CCA integration
RPCA_integrate(): Seurat v5 RPCA integration
MNN_integrate(): MNN integration function
fastMNN_integrate(): FastMNN integration function
fastMNN5_integrate(): Seurat v5 fastMNN integration
Scanorama_integrate(): Scanorama integration function

Embedding and Factor Integration

Harmony_integrate(): Harmony integration function
Harmony5_integrate(): Seurat v5 Harmony integration
RunHarmony2(): Run Harmony algorithm
LIGER_integrate(): LIGER integration function
scVI_integrate(): ScVI integration function
scVI5_integrate(): Seurat v5 scVI integration
Coralysis_integrate(): Coralysis integration function

Graph and Expression-Space Integration

BBKNN_integrate(): BBKNN integration function
Conos_integrate(): Conos integration function
CSS_integrate(): CSS integration function
ComBat_integrate(): ComBat integration function

Multimodal Integration

WNN_integrate(): WNN integration function
FindMultiModalNeighbors(): Seurat weighted-nearest-neighbor construction
GLUE_integrate(): GLUE integration function
MultiMAP_integrate(): MultiMAP integration function

Reference Mapping and Label Transfer

RunReferenceMapping(): Map query cells into a reference space
RunLabelTransfer(): Transfer reference labels to query cells
RunSeuratMap(): Single-cell reference mapping with Seurat method
RunSymphonyMap(): Single-cell reference mapping with Symphony method
RunPCAMap(): Single-cell reference mapping with PCA method
RunCSSMap(): Single-cell reference mapping with CSS method
RunKNNMap(): Single-cell reference mapping with KNN method
RunCoEmbedding(): Co-embed reference and query cells

Integration Evaluation

RunIntegrationBenchmark(): Benchmark single-cell integration methods
IntegrationBenchmarkPlot(): Plot integration benchmark results
RunLISI(): Compute LISI scores on a Seurat object

Cell Type Annotation

Annotation Visualization

CellTreePlot(): Plot a circular cell annotation tree

Reference-Based Annotation and Models

CellTypistModels(): Get available CellTypist models
TrainCellTypist(): Train a CellTypist model
RunCellTypist(): Run CellTypist cell type annotation
RunSingleR(): Annotate single cells using SingleR
RunSciBet(): Annotate single cells with a C++ SciBet-compatible classifier
RunScmap(): Annotate single cells using scmap
RunKNNPredict(): Run KNN prediction

Specialized Cell State Annotation

RunscMalignantFinder(): Run scMalignantFinder malignant cell identification
RunscMalignantRegion(): Run scMalignantFinder malignant spatial region identification
RunscMalignantStates(): Run scMalignantFinder cancer cell state scoring

Differential Expression and Cell Composition

Markers and Differential Expression

FindAllMarkers(): Find markers for all groups
FindMarkers(): Find markers between groups
FindExpressedMarkers(): Find Expressed Markers
FoldChange(): Calculate expression fold change
RunDEtest(): Differential gene test
DEtestPlot(): Differential Expression Test Plot
DEtestManhattanPlot(): DEtest Manhattan Plot
DEtestRingPlot(): DEtest Ring Plot
VolcanoPlot(): Volcano Plot

Expression Aggregation and Metacells

AggregateExpression(): Aggregate expression by cell group
AverageExpression(): Average expression by cell group
RunMetaCell(): Run metacell partitioning for single-cell data
MetaCellPlot(): Visualize metacell partitions on a dimensionality reduction
RunmcRigor(): Run mcRigor metacell partition assessment

Cell Composition and Abundance

RunMilo(): Milo differential abundance wrapper
RunPropeller(): Propeller differential abundance wrapper
RunscCODA(): scCODA differential abundance
RunProportionTest(): Proportion Test
ProportionTestPlot(): Proportion Test Plot
RunPermutation(): Permutation-based proportion test

Bulk Deconvolution and Immune Scores

RunDeconvolution(): Run bulk or pseudobulk deconvolution
DeconvolutionPlot(): Plot deconvolution results
RunCIBERSORT(): Run CIBERSORT deconvolution
ImmuneAbundancePlot(): Immune abundance plots
RunESTIMATE(): Run ESTIMATE tumor microenvironment scoring
EstimateScorePlot(): ESTIMATE score plots
EstimateGenePlot(): Gene and ESTIMATE score relationship plots
GeneImmuneCorPlot(): Gene-immune correlation butterfly plot
estimate_signatures: ESTIMATE gene signatures

Functional and Pathway Analysis

Enrichment Testing and Visualization

RunEnrichment(): Perform the enrichment analysis (over-representation) on the genes
EnrichmentPlot(): Enrichment Plot
EnrichmentMirrorPlot(): Mirror stored enrichment results for two groups
FerrisWheelPlot(): Ferris Wheel Plot
RunGSEA(): Perform the enrichment analysis (GSEA) on the genes
GSEAPlot(): GSEA Plot

Gene Set and Pathway Scoring

AddModuleScore(): Add expression-program scores with a sparse native fast path
CellScoring(): Cell scoring
CellScoringPlot(): Plot CellScoring results from a Seurat object
RunGSVA(): Perform Gene Set Variation Analysis (GSVA)
GSVAPlot(): Plots for GSVA (Gene Set Variation Analysis)

Immune Response Programs

PrepareIREA(): Prepare Immune Dictionary references
RunIREA(): Analyse cytokine responses or immune-cell polarization
IREAPlot(): Plot IREA response or polarization results

Metabolic Activity and Flux

RunMetabolism(): Run metabolism pathway scoring
MetabolismPlot(): Plots for metabolism pathway scoring
RunscFEA(): Run scFEA flux estimation for a Seurat object
scFEAHeatmap(): Plot scFEA module flux heatmap
scFEAVolcanoPlot(): Plot scFEA flux Cohen's d volcano plots
scFEABalanceBarPlot(): Plot scFEA metabolite balance changes

Trajectory and Cell Fate

Trajectory and Pseudotime Inference

RunMonocle2(): Run Monocle2 analysis
RunMonocle3(): Run Monocle3 analysis
RunPAGA(): Run PAGA analysis
PAGAPlot(): PAGA plot
RunPalantir(): Run Palantir analysis
PalantirTrajectoryPlot(): Plot Palantir trajectories
RunSlingshot(): RunSlingshot

RNA Velocity and Vector Fields

RunCell2fate(): Run Cell2fate RNA velocity analysis
RunSCVELO(): Run scVelo workflow
VelocityPlot(): Velocity Plot
RunVECTOR(): Run VECTOR developmental direction inference
VECTORPlot(): Plot VECTOR results

Fate Mapping and Lineages

RunCellRank(): Run CellRank analysis
CellRankPlot(): Plot CellRank results
RunWOT(): Run WOT analysis
LineagePlot(): Lineage Plot
BranchStreamPlot(): Branch Stream Plot
PseudotimeProjectionPlot(): Pseudotime Projection Plot

Dynamic Expression and Pathways

RunDynamicFeatures(): Calculates dynamic features for lineages
DynamicPlot(): Plot dynamic features across pseudotime
DynamicHeatmap(): Heatmap plot for dynamic features along lineages
RunDynamicEnrichment(): RunDynamicEnrichment
RunCellRankTrends(): Fit and cluster CellRank lineage trends
RunCellRankEnrichment(): Enrich CellRank trend modules

Cell Potency and Aging

PrepareCytoTRACE2(): Prepare CytoTRACE2 model resources
RunCytoTRACE(): Run CytoTRACE 2
CytoTRACEPlot(): Plot CytoTRACE 2 Results
RunFitDevo(): Run FitDevo developmental potential scoring
FitDevoPlot(): Plot FitDevo results
RuntAge(): Run tAge transcriptomic aging-clock prediction
tAgePlot(): Plot tAge transcriptomic aging-clock predictions

Cell-Cell Communication

Communication Inference

RunCCC(): Run common cell-cell communication analyses
RunCellChat(): Run CellChat analysis
RunCellphoneDB(): Run CellphoneDB analysis
RunLIANA(): Run LIANA cell-cell communication analysis
RunMDIC3(): Run MDIC3 cell-cell communication inference
RunscOMM(): Run scOMM label prediction
RunNichenetr(): Run NicheNet analysis
RunMultiNichenetr(): Run MultiNicheNet analysis

Spatial Communication

RunSpaTalk(): Run official SpaTalk spatial communication analysis
SpaTalkPlot(): Plot stored SpaTalk results
RunCOMMOT(): Run official COMMOT spatial communication analysis
COMMOTPlot(): Plot stored COMMOT results
RunSpatialDM(): Run official SpatialDM spatial ligand-receptor association analysis
SpatialDMPlot(): Plot stored SpatialDM results
GetSpatialDMResult(): Access stored SpatialDM results
RunSpatialCellChat(): Run Spatial CellChat analysis
SpatialCellChatPlot(): Plot stored SpatialCellChat results

Signaling Activity and Dynamics

RunSecAct(): Run SecAct secreted protein activity inference
RunSecActCCC(): Run SecAct cell-cell communication analysis
RunSecActSignalingPattern(): Run SecAct spatial signaling pattern analysis
RunSecActPatternGenes(): Extract SecAct pattern-associated secreted proteins
RunSecActVelocity(): Run SecAct spatial signaling velocity

Communication Resources and Interoperability

GetCCCObject(): Get an upstream CellChat-family object
ListCCCDB(): List CCC ligand-receptor and prior-model databases
PrepareCCCDB(): Prepare cell-cell communication databases
ccc_to_adata(): Convert CCC results to OmicVerse communication AnnData
ccc_to_liana(): Convert CCC results to a LIANA-like table

Communication Visualization

CCCHeatmap(): CCC heatmap and dot matrix plot
CCCNetworkPlot(): CCC network and flow plots
CCCStatPlot(): CCC statistical distribution and summary plots

Gene Regulatory Networks

Network Inference

RunGRN(): Infer gene regulatory networks with a selected method
RunGRNBoost2(): Infer gene regulatory networks with GRNBoost2
RunGENIE3(): Infer gene regulatory networks with GENIE3
RunGNIPLR(): Infer gene regulatory networks with GNIPLR

Regulon Inference and Activity

RunSCENIC(): Run SCENIC gene regulatory network analysis
SCENICPlot(): Plot SCENIC or SCENIC+ regulon activity
RunCisTarget(): Run cisTarget motif enrichment on a GRN adjacency table
RunDorothea(): Run transcription factor activity inference
DorotheaPlot(): Plot transcription factor activity

Multiomic Regulatory Networks

RunSCENICPlus(): Read SCENIC+ results or run an approximate SCENICPlus-compatible eGRN analysis
SCENICPlusPlot(): Plot SCENIC+ eRegulon activity

Phenotype and Perturbation Analysis

Phenotype Association

RunScissor(): Run Scissor phenotype-associated cell selection
ScissorPlot(): Plot Scissor results
RunAugur(): Prioritize perturbed cell types using Augur
RunFWP(): Run FWP feature-weight phenotype scoring

Network Perturbation

RunCellOracle(): Run official CellOracle perturbation analysis
CellOraclePlot(): Plot stored CellOracle results
RunscTenifoldKnk(): Run scTenifoldKnk in-silico knockout analysis
scTenifoldKnkPlot(): scTenifoldKnk Plot
RunscTenifoldNet(): Run scTenifoldNet network comparison
scTenifoldNetPlot(): scTenifoldNet Plot

ATAC and Multiome Analysis

Accessibility Quality and Coverage

RunATACQC(): Run scATAC quality control metrics
ATACDepthCorPlot(): Plot ATAC reduction-depth correlations
ATACTSSPlot(): Plot ATAC TSS enrichment profiles
ATACFragmentPlot(): Plot ATAC fragment lengths
CoverageTrackPlot(): Coverage track plot for ATAC data
RunATACRegionMatrix(): Count strand-oriented insertion endpoints around ATAC regions
ATACRegionHeatmap() ATACRegionPlot(): Plot positional ATAC region signal

Peak Definition and Annotation

CallATACPeaks(): Call MACS2 peaks for explicitly selected capture groups
CombineATACPeaks(): Combine explicit peak sets into one common interval universe
RecountATAC(): Recount fragments over an explicit common ATAC peak universe
RunGenomeTiles(): Count fragments in fixed-width genome tiles
FilterATACPeaks(): Filter an ATAC peak universe with explicit criteria
AnnotateATACPeaks(): Annotate ATAC peaks with nearest genomic features

Motifs and Accessibility Deviations

RunATACFootprint(): Bias-corrected ATAC footprinting
ATACFootprintPlot(): Plot bias-corrected ATAC footprints
ATACMotifPlot(): Plot motif sequence logos from a selected ATAC assay
AddATACMotifs(): Add motif matches and sequence statistics
RunMotifEnrichment(): Test motif enrichment within an explicit peak universe
RunChromVAR(): Compute chromVAR deviations from an explicit sequence reference

Gene Activity and Peak-Gene Associations

RunATACCoaccessibility(): Infer peak coaccessibility with Cicero
RunGeneActivity(): Quantify gene activity from indexed ATAC fragments
RunLinkPeaks(): Link ATAC peaks to paired measured expression

Spatial Omics Analysis

Spatial Quality Control and Normalization

RunSpotQC(): Run spot-level quality control
RunSpotSweeper(): Run SpotSweeper spatial quality control
RunSpaNorm(): Run SpaNorm spatial normalization

Deconvolution and Ecotypes

RunRCTD(): Run RCTD spatial deconvolution
RunCell2location(): Run cell2location spatial deconvolution
Cell2locationPlot(): Plot cell2location spatial results
RunCARD(): Run CARD spatial deconvolution
RunSPOTlight(): Run SPOTlight spatial deconvolution
RunSTdeconvolve(): Run STdeconvolve reference-free spatial deconvolution
STdeconvolvePlot(): Plot STdeconvolve topic proportions
RunSpatialEcoTyper(): Run SpatialEcoTyper spatial ecotype analysis
SpatialDeconvolutionPlot(): Plot stored spatial deconvolution proportions

Spatial Domains and Cell Mapping

RunBayesSpace(): Run BayesSpace spatial clustering
RunBANKSY(): Run BANKSY spatial clustering
RunCytoSPACE(): Run CytoSPACE spatial assignment
RunSmoothClust(): Run smoothclust spatial domain clustering

Spatial Feature Patterns

RunMERINGUE(): Run MERINGUE spatial autocorrelation analysis
RunSpatialVariableFeatures(): Run spatial variable feature detection
SpatialVariableFeaturePlot(): Plot spatial variable feature results
RunSpatialGradientFeatures(): Run spatial gradient feature screening
SpatialGradientPlot(): Plot spatial gradient screening results
RunCSIDE(): Run C-SIDE spatial differential expression

Spatial Neighborhoods and Interactions

RunSpatialNeighborhood(): Run spatial neighborhood statistics
SpatialNeighborhoodProfile(): Profile cell neighborhoods at several distances
SpatialNeighborhoodPlot(): Spatial neighborhood plot
RunSpatialNetwork(): Build a spatial network
SpatialNetworkPlot(): Plot a spatial network
RunMistyR(): Run mistyR multiview spatial modeling
MistyRPlot(): Plot stored MISTy results
RunStatialKontextual(): Run Statial Kontextual spatial relationships
StatialKontextualPlot(): Plot stored Statial Kontextual results

Spatial Integration and Evaluation

RunSpatialIntegration(): Run multi-sample spatial integration
SpatialIntegrationPlot(): Plot spatial integration results
RunSpatialBenchmark(): Benchmark spatial domain clustering methods
SpatialBenchmarkPlot(): Plot spatial domain clustering benchmarks

Spatial Maps

SpatialSpotPlot(): Spatial spot plot
SpatialCellPlot(): Plot spatial cell boundaries

Genomic Variation and Trait Association

Copy Number Variation

RunCNV(): Run copy-number alteration inference
CNVPlot(): Plot CNV results

GWAS and Trait Association

RunscPagwas(): Run scPagwas
PlotscPagwas(): Plot scPagwas Scores

Visualization and Interactive Exploration

Feature and Statistical Plots

CellStatPlot(): Statistical plot of cells
FeatureCorPlot(): Features correlation plot
FeatureStatPlot(): Feature statistical plots

Heatmaps and Themes

CellCorHeatmap(): Cell Correlation Heatmap
FeatureHeatmap(): Feature Heatmap
GroupHeatmap(): Group heatmap
theme_scop(): Default ggplot2 theme for scop plots
theme_spatial(): ggplot2 theme for spatial plots

Interactive Exploration

PrepareSCExplorer(): Prepare Seurat objects for the SCExplorer
RunSCExplorer(): Run SCExplorer

Data I/O and Object Management

Data Import and Validation

ReadATACData(): Read raw ATAC counts into a ChromatinAssay
ReadMultiomeData(): Read paired 10x RNA and ATAC counts
ReadSpatialData(): Load and validate Visium, Visium HD, or Xenium data
CheckATACData(): Check an ATAC object without changing it
CheckDataType(): Check and report the type of data in Seurat object
CheckDataList(): Check and preprocess a list of Seurat objects
CheckDataMerge(): Check and preprocess a merged seurat object
ExportATACResults(): Export an ATAC object and explicit result/resource tables

Object and File Conversion

adata_to_srt(): Convert an anndata object to a seurat object
srt_to_adata(): Convert a Seurat object to an AnnData object
h5ad_to_srt(): Read an .h5ad file and convert to a Seurat
srt_to_h5ad(): Convert a Seurat object to an .h5ad file
loom_to_adata(): Read a .loom file as an AnnData object
loom_to_srt(): Read a .loom file and convert to a Seurat

Spatial Framework Interoperability

srt_to_spe(): Convert Seurat to SpatialExperiment
spe_to_srt(): Convert SpatialExperiment to Seurat
srt_to_giotto(): Convert Seurat to a Giotto object
giotto_to_srt(): Convert Giotto to Seurat

Spatial Coordinates and Results

SpatialCoordinates(): Read spatial coordinates with an explicit coordinate contract
SetSpatialImageAxes(): Persist the coordinate axis convention of a VisiumV2 image
GetSpatialGraph(): Read or convert a stored spatial graph
GetSpatialResult(): Access spatial analysis results

Object Access and Organization

DefaultReduction(): Find the default reduction name in a Seurat object
FetchDataZero(): FetchData but with zeroes for unavailable genes
GetAssayData5(): Get expression data from Assay5 or Seurat object
RecoverCounts(): Attempt to recover raw counts from the normalized matrix
RenameClusters(): Rename clusters for the Seurat object
srt_append(): Append a Seurat object to another
srt_reorder(): Reorder idents by the gene expression

Feature Annotation and Identifiers

AddFeaturesData(): Add features data
GetFeaturesData(): Get features data
AnnotateFeatures(): Annotate Features
GetSimilarFeatures(): Find features with expression patterns similar to provided features
RenameFeatures(): Rename features for the Seurat object
GeneConvert(): Gene ID conversion function using biomart
ConvertHomologs(): Convert homologous gene symbols in expression objects

Interactive Data Files

CreateDataFile(): Create HDF5 data file from Seurat object
CreateMetaFile(): Create Meta File in HDF5 format from Seurat object
FetchH5(): Fetch data from the hdf5 file and returns a Seurat object

Resources and Environment

Resource Preparation

ListDB(): List cached gene annotation databases
PrepareDB(): Prepare databases and reference resources
PrepareCustomDB(): Prepare custom gene annotation databases

Pathway and Gene-Set Resources

PrepareGO(): Prepare GO databases
PrepareKEGG(): Prepare KEGG databases
PrepareWikiPathway(): Prepare WikiPathway databases
PrepareReactome(): Prepare Reactome databases
PrepareMSigDB(): Prepare MSigDB databases
PrepareCORUM(): Prepare CORUM databases

Annotation and Ontology Resources

PrepareMP(): Prepare MP databases
PrepareDO(): Prepare DO databases
PrepareHPO(): Prepare HPO databases
PreparePFAM(): Prepare PFAM databases
PrepareChromosome(): Prepare Chromosome databases
PrepareGeneType(): Prepare GeneType databases
PrepareEnzyme(): Prepare Enzyme databases

Regulatory Resources

PrepareTF(): Prepare TF databases
PrepareTFLink(): Prepare TFLink databases
PrepareHTFtarget(): Prepare hTFtarget databases
PrepareTRRUST(): Prepare TRRUST databases
PrepareJASPAR(): Prepare JASPAR databases
PrepareENCODE(): Prepare ENCODE databases

Cell Surface and Signaling Resources

PrepareCSPA(): Prepare CSPA databases
PrepareSurfaceome(): Prepare Surfaceome databases
PrepareSPRomeDB(): Prepare SPRomeDB databases
PrepareVerSeDa(): Prepare VerSeDa databases
PrepareCellTalk(): Prepare CellTalk databases
PrepareCellChat(): Prepare CellChat databases

Example Datasets

islet_bulk: Human pancreatic islet bulk RNA-seq example dataset
panc8_sub: A subsetted version of human 'panc8' datasets
pancreas_sub: A subsetted mouse 'pancreas' datasets
pbmcmultiome_sub: A small human PBMC multiome example dataset
ref_scMCA: Reference datasets for cell type annotation in single-cell RNA data
visium_human_pancreas_sub: A human pancreas Visium spatial example dataset
words_excluded: Excluded words in keyword enrichment analysis and extraction

Python Environment Management

check_python(): Check and install python packages
env_requirements(): Python environment requirements
env_info(): Print environment information
ListEnv(): List conda-compatible Python environments
PrepareEnv(): Prepare the python environment
remove_python(): Remove Python packages from a conda-compatible Python environment
RemoveEnv(): Remove a conda-compatible Python environment