Package index • SigBridgeR

Data Preprocessing

Functions for preprocessing inputs.

BulkPreProcess(): Bulk RNA-seq Data Preprocessing and Quality Control Function
SCPreProcess(): Single-Cell RNA-seq Preprocessing Pipeline
QCFilter(): Filter Seurat object cells by QC metrics
QCPatternDetect(): Calculate Percentage of Features Matching Patterns
Pattern2Colname(): convert regex patterns to column names (internal)
SymbolConvert(): Convert Ensembles Version IDs & TCGA Version IDs to Genes in Bulk Expression Data
AggregateDupRows() AggregateDupCols() AggregateDups(): Aggregate Rows or Columns with Duplicate Names
SCIntegrate() SCIntegrate.data.frame() SCIntegrate.Matrix() SCIntegrate.Seurat(): Integrate Single-Cell Datasets
CheckNA(): Check for Missing Values (NA) in Data
PhenoPreProcess(): Preprocess Phenotype Data
PhenoMap(): Map Phenotype Values Using Conditional Rules

Merge and visualize screening results across methods or conditions.

MergeResult(): Merge Multiple Screening Analysis Results
WeightedVote(): Weighted Voting Aggregation for Multi-Voter Classification
ScreenFractionPlot(): Visualization of Cell Screening Fractions
ScreenUpset(): ScreenUpset - Visualize cell type intersections from screened Seurat object

Built-in screening algorithms.

Screen(): Single-Cell Data Screening
DoLP_SGL(): Perform LP-SGL Screening Analysis
DoPIPET(): Perform PIPET Screening Analysis
DoScissor(): Perform Scissor Screening Analysis
DoscAB(): Perform scAB Screening Analysis
DoscPAS(): Perform scPAS Screening Analysis
DoscPP(): Perform scPP screening analysis
DoDEGAS(): Run DEGAS Analysis for Single-Cell and Bulk RNA-seq Data Integration
DoSIDISH(): Perform SIDISH Screening Analysis
DoSCIPAC(): Screen Single-Cell Data Using SCIPAC Algorithm

AddMetaFeature(): Add Gene-Level Metadata to Seurat Object (Vectorized, ...-based)
AddMisc(): Safely Add Miscellaneous Data to Seurat Object
FindRobustElbow(): Automatically determine optimal PCA dimensions using multiple robust methods
ChooseNormalization(): Data-Driven Selection of Single-Cell Normalization Methods

Built-in annotationa algorithms.

SCAnnotate(): Unified Interface for Single-Cell Annotation Methods
CellTypistAnnotate(): Annotate Cell Types Using CellTypist (Python Backend)
SingleRAnnotate(): Annotate Single-Cell Data Using SingleR
mLLMCelltypeAnnotate(): Annotate Cell Types Using Multi-LLM Consensus Approach

Load reference datasets.

Manage Python environments for integrated R/Python workflows.

InterceptStrategy(): Inspect Registered Strategy Environments
Register(): Unified Registration Interface for Strategy Methods
RegisterScreenMethod(): Register a Custom Screening Method for Phenotype-Driven Analysis
RegisterAnnoMethod(): Register an Annotation Method into the Strategy Registry
RegisterSeuratMethod(): Register a Seurat Processing Strategy
ValidateScreenFunc(): Validate Custom Screening Function Compliance
TemplateScreenFunc(): Generate a Template Screening Function with Optional Roxygen2 Documentation

Extended registry for SigBridgeR

SCPreProcessStrategy: Preprocessing Strategy Registry for Single-Cell Workflows
SCAnnotateStrategy: Registry of Cell Type Annotation Methods
ScreenStrategy: Registry of Phenotype-Associated Cell Screening Methods

Get and set global options for SigBridgeR behavior.