iaode 0.1.1

Interpretable Autoencoder with Ordinary Differential Equations for single-cell omics analysis

iAODE: Interpretable Autoencoder with Ordinary Differential Equations

A deep learning framework for single-cell omics data analysis that combines variational autoencoders (VAE) with neural ordinary differential equations (ODE) for trajectory inference and dimensionality reduction.

Features

• Interpretable Dimensionality Reduction: VAE-based architecture with multiple loss modes (MSE, NB, ZINB)
• Trajectory Inference: Neural ODE integration for continuous trajectory modeling
• scATAC-seq Peak Annotation: Comprehensive peak-to-gene annotation pipeline following best practices
• Comprehensive Evaluation: Built-in metrics for dimensionality reduction and latent space quality
• Benchmark Framework: Compare against state-of-the-art methods (scVI, PEAKVI, POISSONVI)
• Flexible Architecture: Support for multiple encoder types (MLP, Residual MLP, Linear, Transformer)

Installation

From PyPI (Recommended)

pip install iaode

From Source

git clone https://github.com/PeterPonyu/iAODE.git
cd iAODE
pip install -e .

Dependencies

• Python >= 3.8
• PyTorch >= 1.10.0
• AnnData >= 0.8.0
• Scanpy >= 1.8.0
• scvi-tools >= 0.16.0
• See requirements.txt for complete list

Quick Start

Basic Usage

import anndata as ad
import iaode

# Load your single-cell data
adata = ad.read_h5ad('your_data.h5ad')

# Create and train model
model = iaode.agent(
    adata,
    layer='counts',
    latent_dim=10,
    hidden_dim=128,
    use_ode=True,          # Enable neural ODE for trajectory
    loss_mode='nb',        # Negative binomial loss for count data
    encoder_type='mlp'     # MLP encoder architecture
)

# Train with early stopping
model.fit(
    epochs=100,
    patience=20,
    val_every=5,
    early_stop=True
)

# Get latent representation
latent = model.get_latent()

# Get interpretable embedding
iembed = model.get_iembed()

scATAC-seq Peak Annotation

import iaode

# Complete annotation pipeline
adata = iaode.annotation_pipeline(
    h5_file='filtered_peak_bc_matrix.h5',
    gtf_file='gencode.v44.annotation.gtf',
    output_h5ad='annotated_peaks.h5ad',
    
    # Annotation parameters
    promoter_upstream=2000,
    promoter_downstream=500,
    gene_body=True,
    gene_type='protein_coding',
    
    # TF-IDF normalization
    apply_tfidf=True,
    tfidf_scale_factor=1e4,
    
    # Highly variable peaks
    select_hvp=True,
    n_top_peaks=20000,
    hvp_method='signac'
)

Evaluation and Benchmarking

from iaode import (
    evaluate_dimensionality_reduction,
    evaluate_single_cell_latent_space,
    DataSplitter,
    train_scvi_models
)

# Evaluate dimensionality reduction quality
dr_metrics = evaluate_dimensionality_reduction(
    X_high=adata.X,
    X_low=latent,
    k=10,
    verbose=True
)

# Evaluate latent space for single-cell data
ls_metrics = evaluate_single_cell_latent_space(
    latent_space=latent,
    data_type='trajectory',  # or 'steady_state'
    verbose=True
)

# Benchmark against scVI models
splitter = DataSplitter(
    n_samples=adata.n_obs,
    test_size=0.15,
    val_size=0.15,
    random_state=42
)

scvi_results = train_scvi_models(
    adata,
    splitter,
    n_latent=10,
    n_epochs=400,
    batch_size=128
)

Model Architecture

Core Components

1. Encoder: Maps input data to latent distribution

   • Types: MLP, Residual MLP, Linear, Transformer
   • Outputs: mean, log-variance, sampled latent vector

2. Decoder: Reconstructs data from latent representation

   • Supports MSE, NB, ZINB loss modes
   • Learns library size and dispersion parameters

3. Neural ODE (Optional): Models continuous trajectories

   • Latent ODE function for trajectory inference
   • Time encoder predicts pseudo-time

4. Information Bottleneck: Additional interpretable layer

   • Projects latent space to lower-dimensional embedding
   • Enhances interpretability

Loss Components

• Reconstruction: MSE / NB / ZINB likelihood
• KL Divergence: Regularizes latent distribution
• β-TCVAE: Total correlation decomposition
• DIP: Disentanglement via learned projections
• MMD: Maximum mean discrepancy
• ODE Consistency: Aligns VAE and ODE latents

API Reference

Main Classes

iaode.agent

High-level interface for model training and inference.

Parameters:

• adata (AnnData): Input single-cell data
• layer (str): Data layer to use (default: 'counts')
• latent_dim (int): Latent space dimension (default: 10)
• hidden_dim (int): Hidden layer dimension (default: 128)
• use_ode (bool): Enable neural ODE (default: False)
• loss_mode (str): Loss function ('mse', 'nb', 'zinb')
• encoder_type (str): Encoder architecture ('mlp', 'mlp_residual', 'linear', 'transformer')
• lr (float): Learning rate (default: 1e-4)
• beta (float): KL divergence weight (default: 1.0)
• recon (float): Reconstruction loss weight (default: 1.0)

Methods:

• fit(epochs, patience, val_every, early_stop): Train model
• get_latent(): Get latent representation
• get_iembed(): Get interpretable embedding
• get_test_latent(): Get test set latent representation

Annotation Functions

iaode.annotation_pipeline

Complete scATAC-seq peak annotation and preprocessing.

Parameters:

• h5_file (str): Path to 10X H5 file
• gtf_file (str): Path to GTF annotation
• promoter_upstream (int): TSS upstream extension (default: 2000)
• promoter_downstream (int): TSS downstream extension (default: 500)
• apply_tfidf (bool): Apply TF-IDF normalization (default: True)
• select_hvp (bool): Select highly variable peaks (default: True)
• n_top_peaks (int): Number of HVPs (default: 20000)

Evaluation Functions

iaode.evaluate_dimensionality_reduction

Evaluate dimensionality reduction quality.

Returns: Dict with distance_correlation, Q_local, Q_global, K_max

iaode.evaluate_single_cell_latent_space

Evaluate single-cell latent space quality.

Returns: Dict with manifold_dimensionality, spectral_decay_rate, participation_ratio, etc.

Advanced Usage

Custom Encoder Architecture

model = iaode.agent(
    adata,
    encoder_type='transformer',
    encoder_num_layers=4,
    encoder_n_heads=8,
    encoder_d_model=256,
    hidden_dim=512
)

Multiple Loss Terms

model = iaode.agent(
    adata,
    recon=1.0,      # Reconstruction loss
    beta=1.0,       # KL divergence
    tc=0.5,         # Total correlation
    dip=0.1,        # Disentanglement
    info=0.05       # MMD loss
)

Custom Training Loop

# Manual training with custom logic
for epoch in range(100):
    train_loss = model.train_epoch()
    
    if epoch % 5 == 0:
        val_loss, val_score = model.validate()
        print(f"Epoch {epoch}: Val Loss={val_loss:.4f}")
        
    # Custom early stopping logic
    if should_stop(val_loss):
        model.load_best_model()
        break

Citing iAODE

If you use iAODE in your research, please cite:

@software{iaode2024,
  author = {Fu, Zeyu},
  title = {iAODE: Interpretable Autoencoder with Ordinary Differential Equations},
  year = {2024},
  url = {https://github.com/PeterPonyu/iAODE}
}

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/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

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

Acknowledgments

• Built on top of PyTorch, AnnData, and Scanpy ecosystems
• Inspired by scVI-tools, Signac, and SnapATAC2 best practices
• Neural ODE implementation using torchdiffeq

Contact

For questions and feedback:

• GitHub Issues: https://github.com/PeterPonyu/iAODE/issues
• Email: [e-mail]

Changelog

v0.1.0 (2024-11-19)

• Initial release
• VAE with neural ODE support
• scATAC-seq peak annotation pipeline
• Comprehensive evaluation metrics
• Benchmark framework for scVI models