Package index • scSidekick

Setup & Colors

Prepare objects and manage color palettes

GenerateDirectories(): Create a standard project directory tree

PrepObject(): Prepare a Seurat Object for Uniform Downstream Analysis

ShowColors(): Display All Stored Color Assignments

GetColors(): Retrieve Stored Color Vectors from a Prepared Seurat Object

SelectColors(): Generate a named color vector from a variable

SetLevels(): Relevel a factor or vector concisely

RenameClusters(): Rename numeric cluster IDs to prefixed labels

NumberLabels(): Prepend or append sequential numbers to factor levels

SummarizeMetadata(): Summarize cell-level metadata to donor / patient level

PlotMetaSummary(): Faceted bar chart summarizing metadata distributions across patients

Nour_pal(): Return an interpolated scSidekick color palette function

Nour_cols(): Access colors from the Nour18 palette by name

Nour_cols20(): Access colors from the Nour20 palette by name

Nour18: Nour18 color palette

Nour20: Nour20 full-spectrum color palette

Nour_palettes: Named list of scSidekick color palettes

scale_color_Nour(): ggplot2 color scale using scSidekick palettes

scale_fill_Nour(): ggplot2 fill scale using scSidekick palettes

Dimensionality Reduction

Dimension selection helpers

Determine_nDims(): Determine the optimal number of dimensions for downstream analysis

UMAP & Composition

Visualize dimensionality reductions and cell-type composition trends

PlotDimPlots() PlotGridUMAP() PlotTrendAndUMAP(): UMAP grid with optional composition trend panel

PlotTrendLabeled(): Labeled stacked composition trend plot

PlotComposition(): Cell Composition Bar Charts

PlotPieUMAP(): UMAP with Pie Charts at Cluster Centroids

PlotAlluvial(): Parallel Sets (Alluvial) Plot

PlotRose(): Rose (Polar Bar) Composition Chart

PlotChord(): Chord Diagram Across Two or More Categorical Variables

PlotAtlasWheel(): Dataset Overview Wheel

Feature Expression

Gene expression overlay on embeddings

PlotFeature(): Violin / box / bar plots for gene expression and numeric metadata

PlotPseudoBulk(): Pseudobulk box + dot plots for gene expression

ComputePseudobulk(): Compute pseudobulk (donor-level mean) expression

PlotFeaturePlots() GenerateFeatureMaps(): Batch single-cell feature plots with shared color limits

PlotMultiFeature(): Plot multiple features on a single embedding

Heatmaps & Dot Plots

Group-level expression summaries

GroupHeatmap(): Group-Level Expression Heatmap with Optional Dot Overlay

SplitDotPlot(): Dot plot split by a metadata variable with numbered group labels

SplitDotPlot2(): Dot plot with both group and gene-axis hierarchical clustering

FastDotPlot(): Fast dot plot with regex gene selection and k-group gene clustering

FastDotPlot2(): FastDotPlot with optional EnrichR enrichment header

StackedVlnPlot(): Stacked Violin Plot for Multiple Features

CorrelateGene(): Correlate one gene against all others within each cell type

SplitByGene(): Split cells into expression groups per cell type

PlotCorrelation(): Volcano plots of CorrelateGene results

Copy Number Variation

inferCNV and CopyKAT wrappers for CNV inference from scRNA-seq

RunInferCNV(): Run inferCNV copy-number inference

RunCopyKAT(): Run CopyKAT copy-number inference

Feature Annotation

Annotate genes with genomic coordinates, biotype, and Ensembl IDs; filter by chromosome, biotype, or pattern

AnnotateFeatures(): Annotate Seurat features with genomic metadata

GetFeatures(): Filter features by genomic annotation

GetGeneOrder(): Get genomically-ordered gene position table

ClearAnnotationCache(): Clear the local feature annotation cache

Marker Discovery

Fast per-cluster Wilcoxon rank-sum tests compatible with BPCells and Seurat v5

RunWilcoxAUC(): Fast Wilcoxon Rank-Sum + AUC Marker Test (BPCells / Seurat-v5 Compatible)

Differential Expression

Volcano plots, enrichment visualization, leading edge

PlotVolcano(): Volcano Plot of Differential Expression Results

PlotEnrichment(): Over-Representation Analysis Visualization

PlotGSEAEnrichment(): GSEA Running Score Mountain Plots - Direct or from RunGSEA Output Directory

VisualizeLeadingEdge(): Visualize Leading-Edge Genes from GSEA Results

Cell Annotation

Automated cell-type assignment and metadata enrichment

AssignCellTypes(): Assign cell-type labels from a cluster map

AddClinicalMeta(): Add clinical or sample metadata from a data frame to a Seurat object

CellTypeAssignmentHelper(): Automated cell-type annotation with rcellmarker, scType, and SingleR

Spatial Transcriptomics

Visualize spatial gene expression and cell-type distributions

PlotSpatialDimPlots() GenerateSpatialDimMaps(): Batch spatial dimensionality-reduction (cell-type) plots

PlotSpatialFeaturePlots() GenerateSpatialFeatureMaps(): Batch spatial feature plots for genes and/or metadata columns

PlotMasterMaps() GenerateMasterGeneMaps(): 4-column master gene map: UMAP clusters + UMAP expression + Spatial clusters + Spatial expression

Upstream Regulator Analysis

TF activity inference via decoupleR + CollecTRI

RunURA(): Upstream Regulator Analysis via decoupleR + CollecTRI

Pathway Analysis

GSEA and gene set enrichment

RunGSEA(): Run GSEA pathway analysis on a Seurat object

RunGSEA_pseudobulk(): Pseudobulk GSEA with limma contrasts and per-patient ssGSEA

RunSCssGSEA(): Single-cell ssGSEA with significance testing and heatmap visualization

RunEnrichment(): Run Over-Representation Analysis and Plot Results

PlotPathwayButterfly(): Pathway state hierarchy butterfly plot

Gene Expression Programs

Consensus NMF (cNMF) program discovery via reticulate

RunCNMF(): Run consensus NMF (cNMF) to discover gene expression programs

GetCNMFPrograms(): Extract cNMF consensus programs for a chosen K

GetCNMFTopGenes(): Top genes per cNMF program

Pseudotime & Trajectory

Slingshot pseudotime fit directly on any Seurat reduction (Harmony-aware), with matching trajectory/feature visualizations

RunSlingshot(): Fit Slingshot pseudotime directly on a Seurat reduction

SlingshotLineages(): Summarize Slingshot lineages found by RunSlingshot()

PlotTrajectory(): Overlay a Slingshot trajectory on a DimPlot

PlotPseudotime(): Plot the distribution of pseudotime across clusters

PlotFeatureTrend(): Plot gene/feature expression trends across pseudotime

PlotFeatureDistribution(): Density or ridge plot for any gene or numeric metadata feature

Cell-Cell Communication

CellChat pipeline wrappers

RunCellChat(): Run the full CellChat analysis pipeline per group

CompareCellChat(): Compare CellChat objects across groups for all shared pathways

RankCellChatPathways(): Rank and visualize differentially active CellChat pathways across conditions

RenameCellTypeInCC(): Rename a cell type label inside a CellChat object without re-running analysis

Survival Analysis

Kaplan-Meier survival analysis for TCGA and custom bulk expression cohorts

LoadTCGA(): Download and cache a TCGA or TARGET expression dataset

EnrichTCGAMeta(): Enrich TCGA metadata with GDC clinical and molecular subtype columns

SurvMetaSummary(): Summarize clinical metadata for survival analysis

SurvPlot(): Kaplan-Meier survival plots for bulk expression data

Quality Control

QC metrics, data loading, and sample validation

PlotQCMetrics(): Compute QC metrics and generate pre-filtration QC plots

LoadSamplesRNA(): Load, QC, and merge 10X scRNA-seq samples

CheckSex(): Verify sample sex annotation using sex-specific gene expression

FilterGenesByPct(): Filter genes by percent expression across groups

PlotPctHeatmap(): Heatmap of per-group gene expression percentages

Reporting & Utilities

Methods text, PPTX builder, theme, and session tools

ChartBuilder(): Open the Chart Builder app pre-loaded with an R data frame

ExtractMethods(): Extract analysis parameters and generate a methods text from a Seurat object

InspectPlot(): Inspect a ggplot2 object and describe what it shows

create_analysis_pptx(): Build a PowerPoint summary from an analysis output folder

log_analysis_params(): Log Seurat analysis parameters to a JSON file

log_figure_legend(): Manually log a figure legend for a saved PDF

SummarizeMethods(): Collect every saved methods paragraph into one quotable text file

theme_NourMin(): scSidekick minimal ggplot2 theme

roundUpNice(): Round a number up to the next "nice" value

AI Assistant

A lightweight, documentation-grounded AI assistant for navigating scSidekick

AskSidekick(): Ask the scSidekick help assistant a question

RebuildSidekickContext(): Force a rebuild of AskSidekick()'s documentation cache

Session Logger

Passive R session logging and summarization

start_session_logger(): Start the scSidekick passive session logger

summarize_r_session(): Summarize an R session log

logger_log_file(): Return the current session log file path

logger_plot_dir(): Return the current session plot directory