Package index • scop

Spatial and Cellular Omics Analysis Pipeline

scop scop-package: Spatial and single-cell omics analysis pipeline
scop_logo(): scop logo
print(<scop_logo>): print scop logo

Package and Environment Management

check_python(): Check and install python packages
env_info(): Print environment information
env_requirements(): Python environment requirements
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

Feature Processing

AddFeaturesData(): Add features data
AnnotateFeatures(): Annotate Features
GetFeaturesData(): Get features data
GetSimilarFeatures(): Find features with expression patterns similar to provided features
RenameFeatures(): Rename features for the Seurat object

Feature and Statistical Plots

CellCorHeatmap(): The Cell Correlation Heatmap
CellStatPlot(): Statistical plot of cells
FeatureCorPlot(): Features correlation plot
FeatureHeatmap(): Feature Heatmap
FeatureStatPlot(): Statistical plot of features
GroupHeatmap(): The Group Heatmap

Quality Control

db_DoubletDetection(): Run doublet-calling with DoubletDetection
db_scDblFinder(): Run doublet-calling with scDblFinder
db_scds(): Run doublet-calling with scds
db_Scrublet(): Run doublet-calling with Scrublet
RunATACQC(): Run scATAC quality control metrics
RunCellQC(): Run cell-level quality control
RunDecontX(): Run ambient RNA decontamination with decontX
RunDoubletCalling(): Run doublet-calling for single cell RNA-seq data.
RunSpotQC(): Run spot-level quality control

Cell Cycle Analysis

CycGenePrefetch(): Prefetch cell cycle genes
RunCellCycle(): Run cell cycle scoring

scop Pipeline

standard_scop(): Standard workflow for scop
integration_scop(): The integration workflow

Metacell Analysis

RunMetaCell(): Run metacell partitioning for single-cell data
RunmcRigor(): Run mcRigor metacell partition assessment

Metacell Plots

MetaCellPlot(): Visualize metacell partitions on a dimensionality reduction

Data Integration and Batch Effect Removal

Uncorrected_integrate(): The Uncorrected integration function
Seurat_integrate(): The Seurat integration function
CCA_integrate(): Seurat v5 CCA integration
RPCA_integrate(): Seurat v5 RPCA integration
fastMNN_integrate(): The fastMNN integration function
fastMNN5_integrate(): Seurat v5 fastMNN integration
Harmony_integrate(): The Harmony integration function
Harmony5_integrate(): Seurat v5 Harmony integration
MNN_integrate(): The MNN integration function
Scanorama_integrate(): The Scanorama integration function
BBKNN_integrate(): The BBKNN integration function
CSS_integrate(): The CSS integration function
GLUE_integrate(): The GLUE integration function
LIGER_integrate(): The LIGER integration function
scVI_integrate(): The scVI integration function
scVI5_integrate(): Seurat v5 scVI integration
MultiMAP_integrate(): The MultiMAP integration function
Conos_integrate(): The Conos integration function
ComBat_integrate(): The ComBat integration function
Coralysis_integrate(): The Coralysis integration function
WNN_integrate(): The WNN integration function

Integration Mapping

RunCSSMap(): Single-cell reference mapping with CSS method
RunHarmony2(): Run Harmony algorithm
RunLISI(): Compute LISI scores on a Seurat object
RunPCAMap(): Single-cell reference mapping with PCA method
RunSeuratMap(): Single-cell reference mapping with Seurat method
RunSymphonyMap(): Single-cell reference mapping with Symphony method

Dimensionality Reduction and Clustering

RunDimsEstimate(): Estimate useful dimensions from a reduction
RunDimsReduction(): Run dimension reduction
RunDM(): Run diffusion map (DM)
RunFR(): Run Force-Directed Layout (Fruchterman-Reingold algorithm)
RunGLMPCA(): Run generalized principal components analysis (GLMPCA)
RunMDS(): Run MDS (multi-dimensional scaling)
RunNMF(): Run NMF (non-negative matrix factorization)
RunUMAP2(): Run UMAP (Uniform Manifold Approximation and Projection)
RunPaCMAP(): Run PaCMAP (Pairwise Controlled Manifold Approximation)
RunTriMap(): Run TriMap (Large-scale Dimensionality Reduction Using Triplets)
RunPHATE(): Run PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding)
RunLargeVis(): Run LargeVis (Dimensionality Reduction with a LargeVis-like method)

Dimensionality Reduction Plots

CellDimPlot(): Cell Dimensional Plot
CellDimPlot3D(): 3D-Dimensional reduction plot for cell classification visualization.
CellDensityPlot(): Cell density plot
ClusterTreePlot(): Cluster tree plot
DimsEstimatePlot(): Dimension estimate diagnostic plot
FeatureDimPlot(): Visualize feature values on a 2-dimensional reduction plot
FeatureDimPlot3D(): 3D-Dimensional reduction plot for gene expression visualization.
NMFHeatmap(): NMF similarity heatmap
ProjectionPlot(): Projection Plot
TACSPlot(): Transcript-averaged cell scoring (TACS)

Spatial Analysis

RunBayesSpace(): Run BayesSpace spatial clustering
RunCytoSPACE(): Run CytoSPACE spatial assignment
RunRCTD(): Run RCTD spatial deconvolution
RunSpatialGradientFeatures(): Run spatial gradient feature screening
RunSpatialVariableFeatures(): Run spatial variable feature detection

Spatial Visualization

SpatialGradientPlot(): Plot spatial gradient screening results
SpatialSpotPlot(): Spatial spot plot

Cell Type Annotation

CellTypistModels(): Get available CellTypist models
RunCellTypist(): Run CellTypist cell type annotation
RunCoEmbedding(): Co-embed reference and query cells
RunKNNMap(): Single-cell reference mapping with KNN method
RunKNNPredict(): Run KNN prediction
RunLabelTransfer(): Transfer reference labels to query cells
RunReferenceMapping(): Map query cells into a reference space
RunScmap(): Annotate single cells using scmap.
RunSciBet(): Annotate single cells using native SciBet
RunSingleR(): Annotate single cells using SingleR
TrainCellTypist(): Train a CellTypist model

Differential Expression and Perturbation

FindExpressedMarkers(): Find Expressed Markers
RunAugur(): Prioritize perturbed cell types using Augur
RunDEtest(): Differential gene test
RunRareQ(): RareQ rare-cell population detection
RunScissor(): Run Scissor phenotype-associated cell selection
RunscTenifoldKnk(): Run scTenifoldKnk in-silico knockout analysis
RunscTenifoldNet(): Run scTenifoldNet network comparison

Differential Expression and Perturbation Plots

DEtestPlot(): Differential Expression Test Plot
DEtestManhattanPlot(): DEtest Manhattan Plot
DEtestRingPlot(): DEtest Ring Plot
VolcanoPlot(): Volcano Plot
ScissorPlot(): Plot Scissor results
scTenifoldKnkPlot(): scTenifoldKnk Plot
scTenifoldNetPlot(): scTenifoldNet Plot

Bulk and Composition Analysis

RunDeconvolution(): Run bulk or pseudobulk deconvolution
RunCIBERSORT(): Run CIBERSORT deconvolution
RunMilo(): Milo differential abundance wrapper
RunPermutation(): Permutation-based proportion test
RunPropeller(): Propeller differential abundance wrapper
RunProportionTest(): Proportion Test
RunscCODA(): scCODA differential abundance

Bulk and Composition Plots

DeconvolutionPlot(): Plot deconvolution results
ImmuneAbundancePlot(): Immune abundance plots
GeneImmuneCorPlot(): Gene-immune correlation butterfly plot
ProportionTestPlot(): Proportion Test Plot

Enrichment Analysis

CellScoring(): Cell scoring
RunDorothea(): Run DoRothEA transcription factor activity inference
RunDynamicEnrichment(): RunDynamicEnrichment
RunEnrichment(): Perform the enrichment analysis (over-representation) on the genes
RunGSEA(): Perform the enrichment analysis (GSEA) on the genes
RunGSVA(): Perform Gene Set Variation Analysis (GSVA)
RunMetabolism(): Run metabolism pathway scoring

Enrichment Analysis Plot

DorotheaPlot(): Plot differential DoRothEA TF activity
EnrichmentPlot(): Enrichment Plot
FerrisWheelPlot(): Ferris Wheel Plot
GSEAPlot(): GSEA Plot
GSVAPlot(): Plots for GSVA (Gene Set Variation Analysis)
MetabolismPlot(): Plots for metabolism pathway scoring

Metabolic Flux Analysis

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

Dynamic Trajectory Analysis

RunCellRank(): Run CellRank analysis
RunCytoTRACE(): Run CytoTRACE 2
RunMonocle2(): Run Monocle2 analysis
RunMonocle3(): Run Monocle3 analysis
RunPAGA(): Run PAGA analysis
RunPalantir(): Run Palantir analysis
RunSlingshot(): RunSlingshot
RunSCVELO(): Run scVelo workflow
RunWOT(): Run WOT analysis
RunDynamicFeatures(): Calculates dynamic features for lineages

Dynamic Trajectory Plots

BranchStreamPlot(): Branch Stream Plot
CytoTRACEPlot(): Plot CytoTRACE 2 Results
DynamicPlot(): Plot dynamic features across pseudotime
DynamicHeatmap(): Heatmap plot for dynamic features along lineages
LineagePlot(): Lineage Plot
PAGAPlot(): PAGA plot
PalantirTrajectoryPlot(): Plot Palantir trajectories
PseudotimeProjectionPlot(): Pseudotime Projection Plot
VelocityPlot(): Velocity Plot

Cell-Cell Communication Analysis

RunCCC(): Run common cell-cell communication analyses
RunCellChat(): Run CellChat analysis
RunCellphoneDB(): Run CellphoneDB analysis
RunLIANA(): Run LIANA cell-cell communication analysis
RunNichenetr(): Run NicheNet analysis
RunMultiNichenetr(): Run MultiNicheNet analysis
RunscOMM(): Run scOMM label prediction
ccc_to_adata(): Convert CCC results to OmicVerse communication AnnData
ccc_to_liana(): Convert CCC results to a LIANA-like table

Cell-Cell Communication Plots

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

Gene Regulatory Network Inference

RunGRNBoost2(): Infer gene regulatory networks with GRNBoost2
RunGENIE3(): Infer gene regulatory networks with GENIE3
RunSCENIC(): Run SCENIC gene regulatory network analysis
RunSCENICPlus(): Run SCENICPlus-style eGRN analysis

Gene Regulatory Network Plots

SCENICPlot(): Plot top regulon specificity scores from SCENIC results

Other

BenchmarkPlot(): Plot benchmark metrics
CoverageTrackPlot(): Coverage track plot for ATAC data
LISIPlot(): Plot LISI scores
tAgePlot(): Plot tAge transcriptomic aging-clock predictions
RuntAge(): Run tAge transcriptomic aging-clock prediction

SCExplorer

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

Data Conversion

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

Data Processing

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
DefaultReduction(): Find the default reduction name in a Seurat object
FetchDataZero(): FetchData but with zeroes for unavailable genes
GeneConvert(): Gene ID conversion function using biomart
ConvertHomologs(): Convert homologous gene symbols in expression objects
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

Database Operations

ListDB(): List cached databases
PrepareDB(): Prepare the gene annotation databases
RunCisTarget(): Run cisTarget motif enrichment on a GRN adjacency table

Data

islet_bulk: Human pancreatic islet bulk RNA-seq example dataset
panc8_sub: A subsetted version of human 'panc8' datasets
pancreas_sub: A subsetted version of 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