scRNA-seq_analysis_app

scRNA-seq Explorer

A comprehensive Shiny application for analyzing single-cell RNA sequencing data using the Seurat framework. This interactive tool provides a user-friendly interface for performing common scRNA-seq analysis tasks without extensive coding knowledge.

Features

• Data Import: Load 10X Genomics data formats
• Quality Control: Interactive filtering based on metrics like mitochondrial percentage, number of genes, and UMI counts
• Doublet Detection: Identify and remove cell doublets using scDblFinder
• Dimensionality Reduction: UMAP visualizations
• Clustering: Flexible clustering options with multiple algorithms and resolution settings
• Gene Expression Visualization: Feature plots and violin plots for gene expression patterns
• Marker Gene Identification: Find differentially expressed genes between clusters

Installation

1. Clone this repository:

git clone https://github.com/iichelhadi/Shiny_apps/scRNA-seq-explorer.git
cd scRNA-seq-explorer

2. Install the required R packages:

# Install CRAN packages
install.packages(c("shiny", "shinythemes", "shinyFiles", "DT", "tidyverse", 
                  "ggplot2", "patchwork", "cowplot", "parallelly", "reticulate"))

# Install Bioconductor packages
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
    
BiocManager::install(c("scDblFinder", "BiocParallel", "MAST"))

# Install Seurat and scCustomize
install.packages("remotes")
remotes::install_version("Seurat", version = "4.3.0")
devtools::install_github("samuel-marsh/scCustomize")

3. For Leiden clustering, install the leidenalg Python package:

pip install leidenalg

4. Run the application:

shiny::runApp("path/to/scRNA-seq-explorer")

Usage

Data Input

1. Select a 10X Genomics output directory containing the matrix, barcodes, and features files
2. Set QC parameters to filter cells based on quality metrics
3. Optionally detect and filter out doublets

Preprocessing

1. Set parameters for variable feature selection and dimensionality reduction
2. Run the dimension reduction to visualize cells in UMAP space
3. Run clustering with desired resolution and algorithm

Visualization

1. Select genes of interest to visualize their expression on UMAP or as violin plots
2. Explore the relationship between gene expression and cell clusters

Marker Genes

1. Find marker genes for all clusters or between specific clusters
2. Visualize top marker genes in a heatmap
3. Download marker gene lists for further analysis

Cell Annotation

1. Perform manual cell type annotation
2. Visualize cell types on UMAP plot
3. Export annotated Seurat object for further analysis

Example Data

Example 10X Genomics datasets can be downloaded from:

• 10X Genomics Datasets
• Satija Lab

File Structure

scRNA-seq-explorer/
├── app.R             # Main application file
├── global.R          # Global variables and functions
├── www/              # Static assets
│   └── styles.css    # Custom CSS styles
├── data/             # Example datasets (optional)
└── README.md         # Documentation

Customization

The app can be customized by modifying the following files:

• app.R: UI layout and server logic
• global.R: Functions for data processing and analysis
• www/styles.css: Custom styling for the application

Performance Considerations

• The app performs memory-intensive operations. We recommend at least 16GB of RAM for analyzing datasets with more than 5,000 cells.
• For larger datasets (>20,000 cells), consider subsampling or using a server with more RAM.
• Some operations (dimensionality reduction, clustering, marker gene identification) may take several minutes to complete depending on dataset size.

Troubleshooting

Common issues:

• Memory errors: Increase the memory allocation for R and/or use a smaller dataset
• Missing leidenalg: Ensure Python is properly configured with reticulate and leidenalg is installed
• Slow performance: Adjust parameters (reduce number of variable features, use fewer PCs for analysis)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Citation

If you use this application in your research, please cite this repo:

https://github.com/iichelhadi/Shiny_apps/tree/main/scRNA-seq_analysis_app

And the core software packages:

• Seurat: Stuart et al. (2019). Comprehensive Integration of Single-Cell Data. Cell, 177(7), 1888-1902.
• scDblFinder: Germain et al. (2021). scDblFinder: a pipeline for filtering cells with artifactual doublet-like expression profiles. Bioinformatics, 37(19), 3333-3335.
• MAST: Finak et al. (2015). MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biology, 16, 278.