Package index • SCP

All functions

AnnotateFeatures(): AnnotateFeatures Annotate features in a Seurat object with additional metadata from databases or a GTF file.

BBKNN_integrate(): BBKNN_integrate

CC_GenePrefetch(): Prefetch cycle gene

CSS_integrate(): CSS_integrate

CellCorHeatmap(): CellCorHeatmap

CellDensityPlot(): CellDensityPlot

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

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

CellScoring(): CellScoring

CellStatPlot(): Statistical plot of cells

ComBat_integrate(): Combat_integrate

Conos_integrate(): Conos_integrate

CreateDataFile(): CreateDataFile

CreateMetaFile(): CreateMetaFile

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

DynamicHeatmap(): Heatmap plot for dynamic features along lineages

DynamicPlot(): DynamicPlot

EnrichmentPlot(): EnrichmentPlot

EnsureSeurat5(): Ensure Seurat object is V5-compatible

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

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

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

FeatureHeatmap(): FeatureHeatmap

FeatureStatPlot(): Statistical plot of features

FetchH5(): Fetch data from the hdf5 file

GSEAPlot(): GSEA Plot

GeneConvert(): Gene ID conversion function using biomart

GraphPlot(): GraphPlot

GroupHeatmap(): GroupHeatmap

Harmony_integrate(): Harmony_integrate

Integration_SCP(): Integration_SCP

IsSeurat5(): Check if a Seurat object is version 5

LIGER_integrate(): LIGER_integrate

LineagePlot(): LineagePlot

ListDB(): ListDB

MNN_integrate(): MNN_integrate

PAGAPlot(): PAGA plot

PrepareDB(): Prepare the gene annotation databases

PrepareEnv(): Install the SCP python environment

PrepareSCExplorer(): Prepare Seurat objects for the SCExplorer

ProjectionPlot(): Projection Plot

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

RemoveEnv(): Remove SCP Python environment

RenameClusters(): Rename clusters for the Seurat object

RenameFeatures(): Rename features for the Seurat object

RunALRA(): Run ALRA imputation

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

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

RunDEtest(): Differential gene test

RunDM(): Run DM (diffusion map)

RunDimReduction(): Run dimensionality reduction

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

RunDynamicEnrichment(): RunDynamicEnrichment

RunDynamicFeatures(): RunDynamicFeatures

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

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

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

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

RunHarmony2(): Run Harmony algorithm

RunImputation(): Run imputation methods on Seurat object

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

RunKNNPredict(): RunKNNPredict

RunKNNSmooth(): Run KNN-based smoothing imputation

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

RunMAGIC(): Run MAGIC imputation

RunMDS(): Run MDS (multi-dimensional scaling)

RunMonocle2(): Run Monocle2 analysis

RunMonocle3(): Run Monocle3 analysis

RunNMF(): Run NMF (non-negative matrix factorization)

RunPAGA(): Run PAGA analysis

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

RunPHATE(): Run PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding)

RunPaCMAP(): Run PaCMAP (Pairwise Controlled Manifold Approximation)

RunPalantir(): Run Palantir analysis

RunSCExplorer(): RunSCExplorer

RunSCVELO(): Run scVelo workflow

RunScmap(): Annotate single cells using scmap.

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

RunSingleR(): Annotate single cells using SingleR

RunSlingshot(): RunSlingshot

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

RunTriMap(): Run TriMap (Large-scale Dimensionality Reduction Using Triplets)

RunUMAP2(): Run UMAP (Uniform Manifold Approximation and Projection)

RunWOT(): Run WOT analysis

Scanorama_integrate(): Scanorama_integrate

Seurat_integrate(): Seurat_integrate

SrtAppend(): Append a Seurat object to another

SrtReorder(): Reorder idents by the gene expression

Standard_SCP(): Standard SCP

StatPlot(): StatPlot

Uncorrected_integrate(): Uncorrected_integrate

VelocityPlot(): Velocity Plot

VolcanoPlot(): VolcanoPlot

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

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

blendcolors(): Blend colors

check_DataType(): Check and report the type of data

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

check_srtMerge(): Check and preprocess a merged seurat object

choose_k(): Helper function to choose optimal k for ALRA

compute_velocity_on_grid(): Compute velocity on grid The original python code is on https://github.com/theislab/scvelo/blob/master/scvelo/plotting/velocity_embedding_grid.py

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

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

fastMNN_integrate(): fastMNN_integrate

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

get_feature_metadata(): Get feature metadata from Seurat object in a version-agnostic way

get_seurat_data(): Get data from Seurat object in a version-agnostic way

get_var_features(): Get variable features from Seurat object in a version-agnostic way

get_vars(): Get used vars in a ggplot object

ifnb_sub: A subsetted version of 'ifnb' datasets

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

lifemap_cell lifemap_compartment lifemap_organ: Embryonic Development Database from LifeMap Discovery

make_long(): make_long

matrix_power(): Helper function for matrix power

palette_list: A list of palettes for use in data visualization

palette_scp(): Color palettes collected in SCP.

panc8_sub: A subsetted version of human 'panc8' datasets

pancreas_sub: A subsetted version of mouse 'pancreas' datasets

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

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

scVI_integrate(): scVI_integrate

segementsDf(): Shorten and offset the segment

set_feature_metadata(): Set feature metadata in Seurat object in a version-agnostic way

set_seurat_data(): Set data in Seurat object in a version-agnostic way

show_palettes(): Show the color palettes

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

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

theme_blank(): Blank theme

theme_sankey() theme_alluvial() theme_sankey_bump(): sankey_themes

theme_scp(): SCP theme

words_excluded: Excluded words in keyword enrichment analysis and extraction