snRNA-seq Pipeline

A comprehensive single-nucleus RNA sequencing analysis pipeline designed for computational biologists and researchers. Built with Seurat, this tool provides both interactive web-based analysis and high-throughput command-line processing for multi-sample studies.

Quick Start

# Clone and setup
git clone https://github.com/VibhavSetlur/scRNA-seq_Data_Validation
cd snRNA-seq-Pipeline
./launch.sh setup

# Launch web interface
./launch.sh shiny

Access: http://localhost:3838

Overview

This pipeline streamlines single-nucleus RNA-seq analysis with:

• Multi-sample processing with parallel computing
• Sample-specific configurations for customized analysis
• Interactive web interface for exploratory analysis
• Command-line tools for batch processing
• Comprehensive QC and visualization
• Publication-ready outputs

Analysis Workflow

1. Data Input: H5 files (10X Genomics) or RDS files (Seurat objects)
2. Quality Control: Filtering based on features, counts, and mitochondrial content
3. Processing: Normalization, variable feature selection, scaling
4. Dimensionality Reduction: PCA and UMAP
5. Clustering: Multiple algorithms (Leiden, Louvain, etc.)
6. Marker Identification: Differential expression analysis
7. Visualization: Automated plot generation

Input Data

Supported Formats

• H5 files: 10X Genomics Cell Ranger output
• RDS files: Pre-processed Seurat objects
• Multiple samples: Parallel processing with sample-specific parameters

Data Requirements

• Single-nucleus RNA-seq count matrices
• Gene expression data with cell barcodes
• Optional: SouporCell output for doublet detection

Key Features

Multi-Sample Analysis

Process multiple samples simultaneously with:

• Parallel computing for efficiency
• Sample-specific parameter customization
• Automated sample comparisons
• Integrated results visualization

Quality Control

Comprehensive filtering options:

• Feature count thresholds
• UMI count filtering
• Mitochondrial content filtering
• Cell and gene quality metrics

Advanced Analysis

• Multiple clustering algorithms
• Differential expression analysis
• Dimensionality reduction
• Marker gene identification

Usage Options

Web Interface (Recommended for Exploration)

./launch.sh shiny

• Interactive parameter adjustment
• Real-time visualization
• Sample preview and validation
• Progress monitoring

Command Line (Recommended for Batch Processing)

# Single sample
Rscript scripts/run_pipeline_terminal.R \
  --h5_input data/sample.h5 \
  --project_name MyProject

# Multiple samples
Rscript scripts/run_multi_sample_pipeline.R \
  --h5_inputs data/sample1.h5 data/sample2.h5 \
  --project_name MultiProject \
  --parallel \
  --n_cores 2

Docker Deployment

./launch.sh deploy

Output Structure

ProjectName_outputs/
├── individual_samples/
│   └── sample_name/
│       ├── QC plots and summaries
│       ├── Processing results
│       ├── Clustering analysis
│       └── Marker gene tables
├── comparisons/
│   ├── Sample comparisons
│   └── Cross-sample analyses
├── combined_analysis/
└── reports/

Configuration

Sample-Specific Parameters

Create YAML files to customize analysis for individual samples:

samples:
  Control_1:
    qc:
      min_features: 200
      max_mt_percent: 20
    clustering:
      resolution: 0.5
      algorithm: "leiden"

See docs/sample_configuration.md for detailed examples.

Installation

System Requirements

• R 4.0+ with Bioconductor
• 8GB+ RAM recommended
• Multi-core CPU for parallel processing

Quick Setup

./launch.sh setup

Manual Installation

See docs/installation.md for detailed instructions.

Documentation

• Installation Guide: Detailed setup instructions
• User Guide: Step-by-step analysis workflow
• Sample Configuration: Customizing parameters
• Advanced Usage: Advanced features and options
• Troubleshooting: Common issues and solutions
• API Reference: Function documentation

Troubleshooting

Common Issues

1. Memory errors: Increase R_MAX_MEM_SIZE
2. Port conflicts: Use SHINY_PORT=8080 ./launch.sh shiny
3. Parallel processing: Reduce --n_cores parameter

Getting Help

• Check docs/troubleshooting.md
• Review logs: ./scripts/deployment/deploy.sh logs
• Open an issue on GitHub

Performance

Recommended Settings

• Small datasets (<10K cells): 2-4 cores
• Medium datasets (10K-100K cells): 4-8 cores
• Large datasets (>100K cells): 8+ cores, 16GB+ RAM

Memory Usage

• ~2GB per 10K cells
• Scale linearly with dataset size
• Adjust R_MAX_MEM_SIZE as needed

Contributing

We welcome contributions from the research community:

1. Fork the repository
2. Create a feature branch
3. Test thoroughly with your data
4. Submit a pull request

Citation

If you use this pipeline in your research, please cite:

snRNA-seq Pipeline v1.0
Single-nucleus RNA sequencing analysis pipeline
https://github.com/VibhavSetlur/scRNA-seq_Data_Validation

Support

• Documentation: Check the docs/ folder
• Issues: GitHub issue tracker
• Questions: Open a discussion on GitHub

License

MIT License - see LICENSE for details.

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For detailed documentation, examples, and advanced usage, see the docs/ folder.