SingleCellGeneExplore

Overview

SingleCellGeneExplore is a comprehensive R package designed for single-cell RNA sequencing (scRNA-seq) data analysis. This package provides a collection of robust, publication-ready functions for data processing, differential expression analysis, pathway enrichment analysis, and advanced visualization.

Key Features

Core Analysis Functions

• Differential Expression Analysis: Advanced Wilcoxon rank-sum tests with multiple correction methods
• Pseudobulk Analysis: Comprehensive workflow for pseudobulk differential expression
• Cell Cycle Analysis: Tools for cell cycle phase assignment and visualization
• Marker Gene Discovery: Identification and visualization of cell type-specific markers

Pathway and Enrichment Analysis

• Multi-platform Enrichment: Integration with Enrichr, Gene Ontology, KEGG, and Reactome
• Pathway Visualization: Advanced pathview integration for KEGG pathway mapping
• Parallel Processing: Optimized enrichment analysis with parallel computing support

Advanced Visualization

• Volcano Plots: Publication-ready differential expression volcano plots with caching
• Heatmaps: Unity-normalized and Z-score heatmaps with ComplexHeatmap integration
• Violin Plots: Split violin plots for comparative analysis
• Dot Plots: Faceted dot plots for multi-condition comparisons
• Box Plots: Cell frequency and gene expression box plots

Data Processing Utilities

• Data Splitting: Efficient cell type-based data partitioning
• Tibble Processing: Batch processing of analysis results with progress tracking
• File Management: Automated result saving and organization

Installation

You can install the development version of SingleCellGeneExplore from GitHub:

# Install devtools if you haven't already
if (!require(devtools)) install.packages("devtools")

# Install SingleCellGeneExplore
devtools::install_github("harshameghadri/SingleCellGeneExplore")

Dependencies

Required R Packages

The package depends on several key R packages:

• Core: Seurat (≥ 4.0.0), SeuratObject, Matrix
• Data Manipulation: dplyr (≥ 1.0.0), tidyr, rlang
• Visualization: ggplot2 (≥ 3.3.0), ComplexHeatmap, patchwork
• Statistical Analysis: broom, rstatix, epitools
• Utilities: progress, parallel, scales, viridis

Optional Dependencies (Suggested)

For full functionality, consider installing:

# Enrichment analysis
BiocManager::install(c("clusterProfiler", "org.Hs.eg.db", "pathview"))
install.packages("enrichR")

# Enhanced visualization
install.packages(c("EnhancedVolcano", "cowplot"))

# Parallel processing
install.packages("future.apply")

Quick Start

library(SingleCellGeneExplore)

# Load your Seurat object
# seurat_obj <- readRDS("path/to/your/seurat_object.rds")

# 1. Perform differential expression analysis
de_results <- test_wilcox_on_average(your_data)

# 2. Generate volcano plots
volcano_plots <- generate_differential_volcano_plot_with_caching(
  seurat_obj = seurat_obj,
  cell_type_to_analyze = c("T_cells", "B_cells")
)

# 3. Run comprehensive enrichment analysis
enrichment_results <- perform_enrichment_dual_pathview(
  de_results_list = de_results,
  outdir = "enrichment_output"
)

# 4. Create unity heatmaps
heatmap_result <- generate_unity_heatmap(
  seurat_obj = seurat_obj,
  genes_to_plot = top_genes
)

# 5. Process multiple datasets
results_list <- calculate_wilcox_on_tibble_list(
  tibble_list = your_tibble_list,
  test_wilcox_fn = test_wilcox_on_average
)

Main Functions

Differential Expression

• test_wilcox_on_average(): Wilcoxon test with fold change calculation
• process_tibbles_calculate_average_wilcox(): Batch processing of multiple datasets

Visualization

• generate_differential_volcano_plot_with_caching(): Advanced volcano plots
• generate_unity_heatmap(): Unity-normalized heatmaps
• plot_cell_frequency_boxplots(): Cell frequency analysis
• plot_faceted_dotplots_cell_types(): Multi-condition dot plots
• split_violin_plot(): Comparative violin plots

Enrichment Analysis

• perform_enrichment_dual_pathview(): Comprehensive pathway analysis
• find_cluster_markers_with_dor(): Marker gene identification

Data Processing

• split_data_by_celltype(): Cell type-based data partitioning
• run_unified_pseudobulk_workflow(): End-to-end pseudobulk analysis
• calculate_wilcox_on_tibble_list(): Batch tibble processing

Backward Compatibility

This package maintains backward compatibility with previous function names:

# New (recommended)
results <- test_wilcox_on_average(data)

# Old (still works)
results <- Test.wilcox.onAverage(data)

Documentation

Comprehensive documentation is available for all functions:

# View function help
?test_wilcox_on_average
?perform_enrichment_dual_pathview

# Browse package vignettes
browseVignettes("SingleCellGeneExplore")

Citation

If you use SingleCellGeneExplore in your research, please cite:

Meghadri, S.H. (2025). SingleCellGeneExplore: Comprehensive Tools for Single-Cell 
RNA-seq Analysis. R package version 1.0.0. 
https://github.com/harshameghadri/SingleCellGeneExplore

Contributing

We welcome contributions! Please see our Contributing Guidelines for details on how to contribute to this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Support

• Issues: Report bugs or request features via GitHub Issues
• Documentation: Comprehensive help available via ?function_name
• Examples: See function documentation and package vignettes

Development Status

This package is actively maintained and developed. Version 1.0.0 represents a major refactoring for publication readiness with:

• ✅ Standardized function naming (snake_case)
• ✅ Comprehensive documentation
• ✅ Backward compatibility
• ✅ Extensive test coverage
• ✅ Publication-ready visualizations
• ✅ Optimized performance with parallel processing

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SingleCellGeneExplore: Making single-cell RNA-seq analysis accessible and reproducible.