Gene Expression Analysis Tool 🧬

A professional-grade gene expression analysis toolkit implementing state-of-the-art statistical methods and machine learning algorithms, optimized for high-performance computing environments.

📚 Table of Contents

• Features
• Project Structure
• Technical Overview
• Installation
• Quick Start
• Usage Example
• Configuration
• Changelog
• Roadmap
• Contributing
• License
• Citation
• Contact

✨ Features

• High Performance: GPU-accelerated computations with CUDA support
• Robust Statistics: Implements TMM/RLE/Quantile normalization
• Advanced ML: Multi-algorithm clustering with automatic hyperparameter optimization
• Quality Control: Comprehensive metrics and validation tools
• Scalability: Parallel processing and distributed computing support

📁 Project Structure

GeneExpressionAnalysisTool/
├── Core/
│   ├── utils.py           # Core statistical and preprocessing utilities
│   ├── model_training.py  # ML model implementations and training logic
│   ├── model_evaluation.py # Evaluation metrics and validation tools
│   ├── main.py           # Pipeline orchestration and CLI interface
│   └── hyperparams.yaml  # Configuration and hyperparameters

Technical Overview

Core Components

1. Statistical Engine (utils.py)

   • TMM/RLE/Quantile normalization
   • Robust outlier detection
   • Quality metrics computation
   • Expression data validation

2. Model Training (model_training.py)

   • GPU-accelerated PCA implementation
   • Multi-algorithm clustering (K-means, DBSCAN, Spectral)
   • Automatic hyperparameter optimization
   • CUDA support for large-scale computations

3. Model Evaluation (model_evaluation.py)

   • Bootstrap-based stability assessment
   • GSEA implementation
   • Comprehensive clustering metrics
   • Permutation testing for PCA

4. Pipeline Orchestration (main.py)

   • Parallel processing support
   • GPU acceleration
   • Progress logging
   • Error handling

🚀 Installation

1. Clone the repository:

git clone https://github.com/username/GeneExpressionAnalysisTool.git

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows

3. Install dependencies:

pip install -r requirements.txt

⚡ Quick Start

from Core.main import GeneExpressionAnalysis

# One-line analysis
results = GeneExpressionAnalysis().analyze("data.csv")

# Advanced usage with custom settings
analysis = GeneExpressionAnalysis(
    config_path="Core/hyperparams.yaml",
    use_gpu=True
)
results = analysis.run_pipeline(
    data_path="expression_data.csv",
    metadata_path="metadata.csv",
    output_dir="results"
)

Usage Example

from Core.main import GeneExpressionAnalysis

# Initialize with GPU support
analysis = GeneExpressionAnalysis(
    config_path="Core/hyperparams.yaml",
    use_gpu=True
)

# Run analysis pipeline
results = analysis.run_pipeline(
    data_path="expression_data.csv",
    metadata_path="metadata.csv",
    output_dir="results"
)

Configuration

The hyperparams.yaml file controls all major parameters:

data_processing:
  normalization_method: 'tmm'
  missing_value_threshold: 0.3

pca:
  min_variance_explained: 0.9
  max_components: 50

clustering:
  kmeans:
    max_clusters: 10
    n_init: 10

📋 Changelog

v3.5.0 (current)

🔨 Maintenance Release

• Fixed numerous inconsistencies
• Better integrated project structure and modules
• Updated changelog with v4.0.0 improvements

v3.0.0 (01/05)

• Implemented GPU-accelerated PCA
• Added parallel processing support
• Enhanced clustering algorithms
• Introduced GSEA functionality
• Added comprehensive error handling
• Improved documentation and type hints

v2.0.0 (12/2024)

• ComBat-based batch effect correction
• Comprehensive QC metrics suite
• Hierarchical clustering with dendrograms
• Enhanced visualization pipeline
• Multiple testing correction (FDR)
• Silhouette score analysis for clustering
• Type hints for all functions
• Comprehensive error handling
• Input validation checks
• Improved PCA visualization
• Enhanced documentation
• Optimized data preprocessing
• Updated dependency requirements

🗺️ Roadmap

v4.0.0 (Planned)

🎯 Major Release Focus: Complete Framework Overhaul

🏗️ Improve Project Structure

GeneExpressionAnalysisTool/
├── tests/                    # Comprehensive test suite
├── examples/                 # Example notebooks
├── docs/                    # Detailed documentation
└── requirements/            # Environment-specific requirements

Add Essential Bioinformatics Features:

• Batch effect correction
• Multiple testing correction
• Gene set enrichment analysis
• Quality control metrics
• Differential expression analysis

Enhance Testing:

• Unit tests for all components
• Integration tests
• Test with real RNA-seq datasets

Improve Documentation:

• Add docstrings with biological context
• Create user guide
• Add example workflows
• Document statistical methods

Optimize Performance:

• Implement parallel processing
• Add progress tracking
• Optimize memory usage
• Add checkpointing

Add Visualization:

• PCA plots
• Heatmaps
• Volcano plots
• Quality control visualizations

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

📜 License

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

📝 Citation

@software{gene_expression_tool,
  author = {Cotta, Yan P.},
  title = {Gene Expression Analysis Tool},
  version = {3.0.0},
  year = {2025},
  url = {https://github.com/YanCotta/GeneExpressionAnalysisTool}
}

📫 Contact

• Author: Yan P. Cotta
• Email: yanpcotta@gmail.com
• GitHub: @YanCotta

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_{Built with ❤️ using Python 3.8+, PyTorch, and scikit-learn.}