Package index • scop

Single-Cell Omics analysis Pipeline

scop scop-package: Single-Cell Omics analysis Pipeline

scop_logo(): scop logo

Package Management

check_python(): Check and install python packages

check_r(): Check and install R packages

conda_install(): Enhanced conda installation

conda_python(): Find the path to Python associated with a conda environment

env_exist(): Check if a conda environment exists

env_info(): Print environment information

env_requirements(): Python environment requirements

exist_python_pkgs(): Check if the python package exists in the environment

find_conda(): Find an appropriate conda binary

install_miniconda2(): Enhanced miniconda installation

installed_python_pkgs(): Show all the python packages in the environment

ListEnv(): List conda environments

PrepareEnv(): Prepare the virtual environment

remove_python(): Remove Python packages from conda environment

remove_r(): Check and remove R packages

RemoveEnv(): Remove a conda environment

scop pipeline

integration_scop(): The integration_scop function

standard_scop(): Standard workflow for scop

Data Processing

adata_to_srt(): Convert an anndata object to a seurat object using reticulate

CellScoring(): Cell scoring

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

CycGenePrefetch(): Prefetch cell cycle genes

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

GetAssayData5(): Get expression data from Assay5 or Seurat object

is_outlier(): Detect outliers using MAD(Median Absolute Deviation) method

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_to_adata(): Convert a Seurat object to an AnnData object

srt_reorder(): Reorder idents by the gene expression

Feature Processing

AddFeaturesData(): AddFeaturesData

AnnotateFeatures(): Annotate Features

GetFeaturesData(): GetFeaturesData

GetSimilarFeatures(): Find genes with expression patterns similar to the genes you've specified.

RenameFeatures(): Rename features for the Seurat object

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

RunCellQC(): Run cell-level quality control for single cell RNA-seq data.

RunDoubletCalling(): Run doublet-calling for single cell RNA-seq data.

Dimensionality Reduction and Clustering

RunDimReduction(): Run dimensionality reduction

RunDM(): Run DM (diffusion map)

RunFR(): Run Force-Directed Layout (Fruchterman-Reingold algorithm)

RunGLMPCA(): Run GLMPCA (generalized version of principal components analysis)

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)

RunKNNPredict(): Run KNN prediction

Data Integration and Batch Effect Removal

RunCSSMap(): Single-cell reference mapping with CSS method

RunHarmony2(): Run Harmony algorithm

RunKNNMap(): Single-cell reference mapping with KNN method

RunPCAMap(): Single-cell reference mapping with PCA method

RunScmap(): Annotate single cells using scmap.

RunSeuratMap(): Single-cell reference mapping with Seurat method

RunSingleR(): Annotate single cells using SingleR

RunSymphonyMap(): Single-cell reference mapping with Symphony method

BBKNN_integrate(): BBKNN_integrate

CSS_integrate(): CSS_integrate

ComBat_integrate(): Combat_integrate

Conos_integrate(): Conos_integrate

Harmony_integrate(): Harmony_integrate

LIGER_integrate(): LIGER_integrate

MNN_integrate(): Integrate data using MNN

Scanorama_integrate(): Scanorama_integrate

Seurat_integrate(): Seurat_integrate

scVI_integrate(): scVI_integrate

Uncorrected_integrate(): Uncorrected_integrate

fastMNN_integrate(): fastMNN_integrate

Differential Analysis and Enrichment

FindExpressedMarkers(): FindExpressedMarkers

RunDEtest(): Differential gene test

RunGSEA(): Perform the enrichment analysis (GSEA) on the genes

RunEnrichment(): Perform the enrichment analysis (over-representation) on the genes

RunDynamicEnrichment(): RunDynamicEnrichment

Trajectory Analysis

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(): RunDynamicFeatures

Visualization Functions

CellDimPlot(): Visualize cell groups on a 2-dimensional reduction plot

CellDimPlot3D(): 3D-Dimensional reduction plot for cell classification visualization.

CellDensityPlot(): CellDensityPlot

CellCorHeatmap(): The Cell Correlation Heatmap

CellStatPlot(): Statistical plot of cells

DynamicPlot(): Plot dynamic features across pseudotime

DynamicHeatmap(): Heatmap plot for dynamic features along lineages

EnrichmentPlot(): Enrichment Plot

FeatureDimPlot(): Visualize feature values on a 2-dimensional reduction plot

FeatureDimPlot3D(): 3D-Dimensional reduction plot for gene expression visualization.

FeatureHeatmap(): Feature Heatmap

FeatureCorPlot(): Features correlation plot

FeatureStatPlot(): Statistical plot of features

GraphPlot(): Graph Plot

GroupHeatmap(): The Group Heatmap

GSEAPlot(): GSEA Plot

LineagePlot(): Lineage Plot

PAGAPlot(): PAGA plot

ProjectionPlot(): Projection Plot

StatPlot(): Statistic Plot

TACSPlot(): Transcript-averaged cell scoring (TACS)

VelocityPlot(): Velocity Plot

VolcanoPlot(): Volcano Plot

Plotting Functions

adjcolors(): Convert a color with specified alpha level

blendcolors(): Blends a list of colors using the specified blend mode

compute_velocity_on_grid(): Compute velocity on grid

drop_data(): Drop unused data in the plot

get_vars(): Get used vars in a ggplot object

geom_alluvial(): geom_alluvial

geom_alluvial_text() geom_alluvial_label(): geom_alluvial_label

geom_sankey(): geom_sankey

geom_sankey_bump(): geom_sankey_bump

geom_sankey_label() geom_sankey_text(): geom_sankey_label

make_long(): make_long

palette_colors(): Color palettes collected

palette_list: A list of palettes for use in data visualization

panel_fix() panel_fix_overall(): Set the panel width/height of a plot to a fixed value

print(<scop_logo>): print scop logo

segements_df(): Shorten and offset the segment

slim_data(): Slim unused data in the plot

show_palettes(): Show the color palettes

theme_blank(): Blank theme

theme_scop(): The default theme for scop plot function.

theme_sankey() theme_alluvial() theme_sankey_bump(): sankey_themes

SCExplorer

CreateDataFile(): Create data file

CreateMetaFile(): Create Meta File

FetchH5(): Fetch data from the hdf5 file

PrepareSCExplorer(): Prepare Seurat objects for the SCExplorer

RunSCExplorer(): Run SCExplorer

Database Operations

ListDB(): List cached databases

PrepareDB(): Prepare the gene annotation databases

Data

ifnb_sub: A subsetted version of 'ifnb' datasets

panc8_sub: A subsetted version of human 'panc8' datasets

pancreas_sub: A subsetted version of mouse 'pancreas' datasets

ref_scHCL ref_scMCA ref_scZCL: Reference datasets for cell type annotation in single-cell RNA data

words_excluded: Excluded words in keyword enrichment analysis and extraction