Package index • scop

Single-Cell Omics analysis Pipeline

scop scop-package: scop Single-Cell Omics analysis Pipeline

scop_logo(): scop logo

print(<logo>): print logo

Package Setup

PrepareEnv(): Prepare the scop Python environment by installing the required dependencies and setting up the environment.

conda_install(): Enhanced conda installation

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

find_conda(): Find an appropriate conda binary

env_requirements(): env_requirements function

check_python(): Check and install python packages

check_r(): Check and install R packages

env_exist(): Check if a conda environment exists

env_info(): Print environment information

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

install_miniconda2(): Enhanced miniconda installation

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

Data Processing and Quality Control

standard_scop(): Standard scop

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

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

db_DoubletDetection(): Run doublet-calling with DoubletDetection

db_Scrublet(): Run doublet-calling with Scrublet

db_scDblFinder(): Run doublet-calling with scDblFinder

db_scds(): Run doublet-calling with scds

check_data_type(): Check and report the type of data

check_srt_list(): Check and preprocess a list of seurat objects

check_srt_merge(): Check and preprocess a merged seurat object

AddFeaturesData(): AddFeaturesData

GetFeaturesData(): GetFeaturesData

RecoverCounts(): Attempt to recover raw counts from the normalized matrix.

CellScoring(): CellScoring

CC_GenePrefetch(): Prefetch cycle gene

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

DefaultReduction(): Find the default reduction name in a Seurat object.

Data Integration and Batch Effect Removal

integration_scop(): integration_scop

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(): MNN_integrate

Scanorama_integrate(): Scanorama_integrate

Seurat_integrate(): Seurat_integrate

scVI_integrate(): scVI_integrate

Uncorrected_integrate(): Uncorrected_integrate

fastMNN_integrate(): fastMNN_integrate

Dimensionality Reduction and Clustering

RunDimReduction(): Run dimensionality reduction

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)

RunDM(): Run DM (diffusion map)

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

RunLargeVis(): Run LargeVis (Dimensionality Reduction with a LargeVis-like method)

RunKNNPredict(): RunKNNPredict

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 and Dynamic Processes

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(): CellCorHeatmap

CellStatPlot(): Statistical plot of cells

DynamicPlot(): DynamicPlot

DynamicHeatmap(): Heatmap plot for dynamic features along lineages

EnrichmentPlot(): EnrichmentPlot

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

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

FeatureHeatmap(): FeatureHeatmap

FeatureCorPlot(): Features correlation plot This function creates a correlation plot to visualize the pairwise correlations between selected features in a Seurat object.

FeatureStatPlot(): Statistical plot of features

LineagePlot(): LineagePlot

GraphPlot(): GraphPlot

GroupHeatmap(): GroupHeatmap

GSEAPlot(): GSEA Plot

PAGAPlot(): PAGA plot

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

ProjectionPlot(): Projection Plot

StatPlot(): StatPlot

VelocityPlot(): Velocity Plot

VolcanoPlot(): VolcanoPlot

Themes

adjcolors(): Convert a color with arbitrary transparency to a fixed color

blendcolors(): Blend colors

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

palette_scop(): Color palettes collected in scop.

show_palettes(): Show the color palettes

theme_blank(): Blank theme

theme_scop(): scop theme

theme_sankey() theme_alluvial() theme_sankey_bump(): sankey_themes

Web SCExplorer

PrepareSCExplorer(): Prepare Seurat objects for the SCExplorer

RunSCExplorer(): RunSCExplorer

Utility Functions

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

AnnotateFeatures(): AnnotateFeatures

capitalize(): Capitalizes the characters Making the first letter uppercase

compute_velocity_on_grid(): Compute velocity on grid

download(): Download File from the Internet

drop_data(): Drop all data in the plot (only one observation is kept)

get_vars(): Get used vars in a ggplot object

GeneConvert(): Gene ID conversion function using biomart

GetAssayData5(): GetAssayData5

invoke_fun(): Invoke a function with a list of arguments

make_long(): make_long

RenameClusters(): Rename clusters for the Seurat object

RenameFeatures(): Rename features for the Seurat object

segements_df(): Shorten and offset the segment

slim_data(): Drop unused data from the plot to reduce the object size

srt_append(): Append a Seurat object to another

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

srt_reorder(): Reorder idents by the gene expression

try_get(): Try to evaluate an expression a set number of times before failing

unnest_fun(): Implement similar functions to the unnest function in the tidyr package

Database Operations

CreateDataFile(): CreateDataFile

CreateMetaFile(): CreateMetaFile

FetchH5(): Fetch data from the hdf5 file

ListDB(): ListDB

PrepareDB(): Prepare the gene annotation databases

Example Data

ifnb_sub: A subsetted version of 'ifnb' datasets

lifemap_cell lifemap_compartment lifemap_organ: Embryonic Development Database from LifeMap Discovery

palette_list: A list of palettes for use in data visualization

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