Single-Cell RNA Sequencing Analysis with Hybrid Machine Learning Models

🎯 Project Overview

Title: Comprehensive Analysis of Single-Cell RNA Sequencing Data Using Hybrid Machine Learning Models
Domain: Bioinformatics, Computational Biology, Machine Learning
Data: 14.7M cells, 30 features, Brain tissue scRNA-seq data

This dissertation project implements a comprehensive analysis pipeline for single-cell RNA sequencing data using a hybrid approach combining traditional machine learning, deep learning, and ensemble methods.

📊 Dataset Information

• File: data/raw/combined_data.csv
• Size: 2.0 GB
• Cells: 14,738,154
• Features: 30
• Tissue: Brain
• Cell Types: Oligodendrocytes, Astrocytes, Neurons, etc.

🏗️ Project Structure

single-cell-rna-analysis/
├── 📁 data/                    # Data storage
│   ├── raw/                   # Original data
│   ├── processed/             # Processed data
│   ├── external/              # Reference data
│   └── intermediate/          # Intermediate files
├── 📁 src/                    # Source code
│   ├── data_processing/       # Data processing pipeline
│   ├── models/                # Machine learning models
│   ├── analysis/              # Analysis modules
│   ├── visualization/         # Visualization modules
│   └── utils/                 # Utility functions
├── 📁 notebooks/              # Jupyter notebooks
├── 📁 results/                # Analysis results
├── 📁 docs/                   # Documentation
├── 📁 scripts/                # Standalone scripts
├── 📁 tests/                  # Unit tests
└── 📁 config/                 # Configuration files

🎯 Research Objectives

Primary Objectives:

1. Cell Type Classification: Accurate prediction of cell types using hybrid models
2. Quality Assessment: Identify high-quality cells and remove low-quality ones
3. Feature Selection: Identify important genes/features for classification
4. Model Comparison: Compare traditional ML, deep learning, and hybrid approaches

Secondary Objectives:

1. Interpretability: Understand model decisions and feature importance
2. Scalability: Handle large-scale data efficiently
3. Reproducibility: Ensure reproducible results
4. Novel Insights: Discover new biological patterns

🧬 Hybrid Model Strategy

Model 1: Traditional Machine Learning

• Random Forest: Robust, interpretable, handles high-dimensional data
• Support Vector Machine: Good for binary classification, kernel methods
• Gradient Boosting: High performance, feature importance
• Ensemble Methods: Combine multiple traditional models

Model 2: Deep Learning

• Feed-forward Neural Network: Baseline deep learning approach
• Autoencoder: Dimensionality reduction, feature learning
• Variational Autoencoder: Probabilistic modeling, latent space
• Attention Mechanisms: Focus on important features

Model 3: Hybrid Approach

• Stacked Generalization: Combine traditional and deep learning
• Voting Classifiers: Majority voting across models
• Weighted Ensembles: Optimize weights for best performance
• Pipeline Integration: Sequential model combination

🛠️ Installation & Setup

Prerequisites

• Python 3.8+
• 16GB+ RAM (for large dataset processing)
• Git

Installation

# Clone the repository
git clone <repository-url>
cd single-cell-rna-analysis

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Setup the project
python scripts/setup_environment.py

Dependencies

Key packages include:

• pandas, numpy - Data manipulation
• scikit-learn - Traditional ML models
• tensorflow, pytorch - Deep learning
• matplotlib, seaborn - Visualization
• scanpy, anndata - Single-cell analysis
• jupyter - Interactive notebooks

🚀 Quick Start

1. Data Exploration

python scripts/run_complete_analysis.py --mode exploration

2. Model Training

python scripts/run_model_training.py --models all

3. Hybrid Analysis

python scripts/run_hybrid_analysis.py

4. Generate Reports

python scripts/generate_report.py

📈 Expected Outcomes

Technical Outcomes:

• Comprehensive model comparison
• Best-performing hybrid approach
• Feature importance analysis
• Model interpretability insights

Biological Outcomes:

• Improved cell type classification
• Quality control metrics
• Novel cell type markers
• Biological pathway insights

Dissertation Contributions:

• Novel hybrid model approach
• Comprehensive scRNA-seq analysis pipeline
• Benchmarking of different methods
• Practical implementation guidelines

📊 Results Structure

Generated Files:

• Figures: Quality control plots, cell type distributions, model performance
• Tables: Summary statistics, model metrics, feature importance
• Models: Trained model files for each approach
• Reports: Comprehensive analysis reports

Key Visualizations:

• Quality metrics plots
• Cell type comparison charts
• Clustering analysis
• Model performance comparisons
• Feature importance rankings

🔬 Methodology

Phase 1: Data Processing (Week 1-2)

• Data loading and validation
• Quality control implementation
• Feature engineering
• Data preprocessing

Phase 2: Model Development (Week 3-6)

• Traditional ML models
• Deep learning models
• Hybrid model development
• Model training and validation

Phase 3: Analysis & Evaluation (Week 7-8)

• Model comparison
• Performance evaluation
• Statistical analysis
• Results interpretation

Phase 4: Documentation (Week 9-10)

• Report generation
• Documentation
• Code cleanup
• Final presentation

📚 Documentation

• docs/methodology.md - Detailed methodology
• docs/model_architecture.md - Model architecture details
• docs/results_summary.md - Results summary
• docs/technical_notes.md - Technical implementation notes

🧪 Testing

Run the test suite:

python -m pytest tests/

📝 Contributing

This is a dissertation project. For academic collaboration:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

📄 License

This project is for academic research purposes.

👨‍🎓 Author

Student Name
Institution
Supervisor: [Supervisor Name]
Year: 2024

📞 Contact

For questions about this dissertation project, please contact:

• Email: [your.email@institution.edu]
• Institution: [Your Institution]

---

Last Updated: December 2024
Version: 1.0.0
Status: In Progress

Running the Complete Pipeline

1. Edit the config file

   • Open config/config.yaml and set data_path to the full path of your input data file (especially important for cluster use).
   • Adjust output directories as needed.

2. Run the pipeline

   python scripts/run_complete_analysis.py --config config/config.yaml

   This will execute all steps in order, passing the config to each script.

3. Cluster Usage

   • Submit the above command as a job on your cluster (e.g., using SLURM, PBS, etc.).
   • Make sure all paths in config.yaml are accessible from the cluster compute nodes.

4. Customizing Steps

   • You can run individual scripts with the same --config argument, e.g.:

     python scripts/05_train_deep_learning_models.py --config config/config.yaml

5. Outputs

   • All results, logs, and reports will be saved in the directories specified in config.yaml.

Machine Learning-Based Prediction of Cellular Mechanisms in Alzheimer’s Disease

Project Overview

This project analyzes single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to predict cellular mechanisms involved in Alzheimer’s Disease. It includes data inspection, preprocessing, feature extraction, visualization, and machine learning modeling.

Folder Structure

• data/
  • raw/: Original data files (CSV format)
  • processed/: Cleaned and processed data
• results/: All output files, plots, logs, and model artifacts
• scripts/: Python scripts for each analysis step
• config/: Configuration files (YAML, Python)
• notebooks/: Jupyter notebooks for exploration

Setup Instructions

1. Clone the repository
2. Install dependencies

   & .venv\Scripts\Activate.ps1
   pip install -r requirements.txt

3. Configure paths
   • Edit config/config.yaml to set your data and results directories.

Data Source

• Download scRNA-seq and spatial datasets from: BMBLx Data Portal
• After downloading, convert the data to CSV format using your preferred tool (e.g., Excel, Python pandas):

  import pandas as pd
  df = pd.read_excel('your_downloaded_file.xlsx')
  df.to_csv('data/raw/your_data.csv', index=False)

Typical Workflow

1. Inspect and fix raw CSV:

   python scripts/01_run_csv_inspection.py --config config/config.yaml

2. Feature extraction:

   python scripts/02_feature_extraction.py --config config/config.yaml

3. Preprocessing:

   python scripts/02_preprocess_data.py --config config/config.yaml

4. Analysis and visualization:

   python scripts/03_analysis_and_visualization.py --type general --config config/config.yaml

5. Model training and evaluation:
   • Run subsequent scripts in scripts/ as needed for modeling and analysis.

Output

• All results, plots, and logs are saved in the results/ folder, organized by analysis step.

Troubleshooting

• If plots are empty, check your data for missing or constant values.
• Ensure all dependencies are installed in your virtual environment.
• Review logs in results/logs/ for error details.

Contact

For questions or issues, please contact the repository owner.