Metadata-Version: 2.4
Name: peptcrnet
Version: 1.0.0
Summary: A Deep Learning Framework for TCR-Peptide Recognition Prediction
Home-page: https://github.com/mlizhangx/Pep-TCRNet
Author: PepTCRNet Team
Author-email: mlizhang@gmail.com
Project-URL: Bug Reports, https://github.com/mlizhangx/Pep-TCRNet/issues
Project-URL: Source, https://github.com/mlizhangx/Pep-TCRNet
Project-URL: Documentation, https://peptcrnet.readthedocs.io
Keywords: TCR peptide recognition deep-learning bioinformatics immunology
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
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# PepTCRNet: Deep Learning for TCR-Peptide Recognition Prediction

<p align="center">
  <img src="figures/Pipeline.jpg" alt="PepTCRNet Pipeline" width="600"/>
</p>

[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![TensorFlow 2.13+](https://img.shields.io/badge/tensorflow-2.13+-orange.svg)](https://www.tensorflow.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**PepTCRNet** is a state-of-the-art deep learning framework for predicting T-cell receptor (TCR) recognition of peptide antigens. It combines advanced neural network architectures with comprehensive feature engineering to achieve high-accuracy predictions with uncertainty quantification.

## 🌟 Key Features

- **Multi-modal Integration**: Seamlessly combines sequence, categorical, and network-based features
- **Advanced Embeddings**: Utilizes autoencoders, position encoding, and Atchley factors for sequence representation
- **Bayesian Neural Networks**: Provides uncertainty quantification for predictions
- **Comprehensive Pipeline**: End-to-end solution from data preprocessing to model deployment
- **Flexible Architecture**: Modular design allows easy customization and extension
- **Class Imbalance Handling**: Built-in support for imbalanced datasets
- **Rich Visualizations**: Extensive plotting utilities for model interpretation

## 🚀 Quick Start

### Run the Complete Demo (Easiest!)
```bash
# One-click demo launcher
./run_demo.sh
```

This launches the complete **Scenario 17** demo using all features!

### Installation

#### From Source (Current Setup)
```bash
cd /Users/lung/Documents/Projects/peptcrnet/PepTCR-Net

# Install in development mode
pip install -e .

# Run the demo
conda activate tfBNN
jupyter notebook DEMO_Complete_Pipeline.ipynb
```

#### Future: From PyPI (After Publishing)
```bash
pip install peptcrnet
```

### Basic Usage

```python
import peptcrnet
from peptcrnet import PepTCRNetPipeline

# Initialize pipeline
pipeline = PepTCRNetPipeline(data_path='your_data.csv')

# Load and prepare data
pipeline.load_data()
pipeline.split_data(test_size=0.2, val_size=0.1)

# Prepare features
pipeline.prepare_features(feature_types=['sequences', 'categorical'])

# Train model
history = pipeline.train(epochs=100, batch_size=128)

# Evaluate with uncertainty
results = pipeline.evaluate_with_uncertainty(n_samples=200)

# Make predictions
predictions = pipeline.predict(new_data)
```

## 📊 Data Format

PepTCRNet expects input data in CSV format with the following columns:

| Column | Description | Example |
|--------|-------------|---------|
| `CDR3` | TCR CDR3β sequence | `CASSRGQGNEQFF` |
| `Peptide` | Peptide sequence or class label | `GILGFVFTL` |
| `V` | V gene segment | `TRBV7-2` |
| `J` | J gene segment | `TRBJ2-1` |
| `HLA-A` | HLA-A allele | `A*02:01` |
| `HLA-B` | HLA-B allele | `B*07:02` |
| `HLA-C` | HLA-C allele | `C*07:01` |

## 🧪 Demo Notebook

Try our interactive demo notebook to see PepTCRNet in action:

```bash
jupyter notebook demo_pipeline.ipynb
```

The demo includes:
- Sample data generation
- Step-by-step pipeline walkthrough
- Model training and evaluation
- Uncertainty quantification
- Visualization examples

## 📚 Documentation

### Pipeline Components

#### 1. Data Loading and Preprocessing
```python
from peptcrnet.data import DataLoader

loader = DataLoader('data.csv', atchley_path='atchley_factors.txt')
stats = loader.get_summary_stats()
splits = loader.split_data()
```

#### 2. Feature Engineering
```python
from peptcrnet.embeddings import SequenceEmbedder, CategoricalEmbedder

# Sequence embeddings
seq_embedder = SequenceEmbedder(atchley_factors, max_length=30)
tcr_embeddings = seq_embedder.encode_sequences(tcr_sequences)

# Categorical embeddings
cat_embedder = CategoricalEmbedder()
cat_embeddings = cat_embedder.encode_features(categorical_data)
```

#### 3. Model Training
```python
from peptcrnet.models import BayesianClassifier

model = BayesianClassifier(
    input_shapes={'sequences': (100,), 'categorical': (50,)},
    num_classes=5,
    hidden_dims=[512, 256, 64]
)

history = model.train(X_train, y_train, X_val, y_val)
```

#### 4. Evaluation and Visualization
```python
from peptcrnet.evaluation import ModelEvaluator
from peptcrnet.visualization import plot_confusion_matrix, plot_roc_curves

evaluator = ModelEvaluator()
metrics = evaluator.compute_metrics(y_true, y_pred, y_proba)

plot_confusion_matrix(y_true, y_pred)
plot_roc_curves(y_true, y_proba)
```

## ⚙️ Configuration

PepTCRNet uses a centralized configuration system:

```python
from peptcrnet import config

# Access configuration
print(config.ModelParams.MAX_TCR_LENGTH)
print(config.TrainingParams.BATCH_SIZE)

# Save configuration
config.save_config('my_config.json')

# Load configuration
config.load_config('my_config.json')
```

## 🔬 Advanced Features

### Uncertainty Quantification

PepTCRNet provides Bayesian uncertainty estimation:

```python
# Multiple forward passes for uncertainty
predictions, uncertainty = pipeline.predict_with_uncertainty(
    test_data,
    n_samples=200
)

# Identify high-confidence predictions
high_confidence_mask = uncertainty < threshold
```

### Custom Feature Combinations

Experiment with different feature combinations:

```python
# Define feature cases
feature_cases = {
    1: ['TCR'],
    2: ['TCR', 'Peptide'],
    3: ['TCR', 'Peptide', 'HLA'],
    4: ['TCR', 'Peptide', 'HLA', 'VJ', 'Network']
}

# Train with specific features
pipeline.prepare_features(feature_types=feature_cases[3])
```

### Model Persistence

Save and load trained models:

```python
# Save complete pipeline
pipeline.save_pipeline('output_dir/')

# Load saved pipeline
new_pipeline = PepTCRNetPipeline()
new_pipeline.load_pipeline('output_dir/')
```

## 📈 Performance

PepTCRNet achieves state-of-the-art performance on TCR-peptide binding prediction:

- **Accuracy**: Up to 95% on benchmark datasets
- **AUC-ROC**: >0.90 for multi-class classification
- **Uncertainty Calibration**: Well-calibrated confidence scores

## 🤝 Contributing

We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.

```bash
# Fork the repository
# Create your feature branch
git checkout -b feature/amazing-feature

# Commit your changes
git commit -m 'Add amazing feature'

# Push to the branch
git push origin feature/amazing-feature

# Open a Pull Request
```

## 📝 Citation

If you use PepTCRNet in your research, please cite:

```bibtex
@article{peptcrnet2024,
  title={PepTCRNet: A Deep Learning Framework for TCR-Peptide Recognition Prediction},
  author={Your Name et al.},
  journal={Journal Name},
  year={2024}
}
```

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- Thanks to all contributors who have helped shape PepTCRNet
- Inspired by advances in deep learning for immunology
- Built with TensorFlow and the Python scientific computing ecosystem

## 📮 Contact

- **Issues**: [GitHub Issues](https://github.com/mlizhangx/Pep-TCRNet/issues)
- **Discussions**: [GitHub Discussions](https://github.com/mlizhangx/Pep-TCRNet/discussions)
- **Email**: peptcrnet@example.com

## 🗺️ Roadmap

- [ ] Support for TCRα chains
- [ ] Integration with single-cell RNA-seq data
- [ ] Web interface for predictions
- [ ] Pre-trained models for common peptides
- [ ] GPU optimization for large-scale predictions
- [ ] Docker containerization

---

<p align="center">
  Made with ❤️ by the PepTCRNet Team
</p>
