LiVAE: Lorentzian Interpretable Variational Autoencoder

LiVAE (Lorentzian Interpretable Variational Autoencoder) learns interpretable latent representations for single-cell RNA-seq data using Lorentzian (hyperbolic) geometry and multi-component regularization.

Installation

# Install from PyPI (recommended)
pip install livae

# Or install from source for development
git clone https://github.com/PeterPonyu/LiVAE.git
cd LiVAE
pip install -e .

Core Requirements

torch>=2.3.0,<2.5.0
torchvision>=0.18.0,<0.20.0
scanpy>=1.10.0,<1.11.0
scvi-tools>=1.1.0,<1.2.0
anndata>=0.10.0,<0.11.0
scib>=1.0.0
numpy>=1.26.0,<1.27.0
pandas>=2.2.0,<2.3.0
scipy>=1.12.0,<1.13.0
scikit-learn>=1.5.0,<1.6.0
tqdm>=4.66.0,<5.0.0
fastapi>=0.117.0,<0.118.0
uvicorn[standard]>=0.36.0,<0.37.0
python-multipart>=0.0.6

Python Quick Start

import scanpy as sc
from livae import agent

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

# Initialize LiVAE agent
model = agent(
    adata=adata,
    layer='counts',         # Data layer to use
    latent_dim=10,          # Primary latent dimension
    i_dim=2,                # Interpretable latent dimension
    hidden_dim=128,         # Hidden layer dimension
    lr=1e-4,                # Learning rate
    # Regularization weights
    beta=1.0,               # β-VAE weight
    lorentz=1.0,            # Lorentzian regularization
    irecon=1.0,             # Interpretable reconstruction
)

# Train the model
model.fit(epochs=100)

# Extract embeddings
latent = model.get_latent()          # Primary latent representation
interpretable = model.get_iembed()   # Interpretable embedding (i_dim)

# Access results
print(f"Latent shape: {latent.shape}")
print(f"Interpretable embedding shape: {interpretable.shape}")

Web Interface

LiVAE includes an integrated web interface for interactive training and visualization.

Launch the Application

# Start the FastAPI server (serves both API and frontend)
uvicorn api.main:app --host 0.0.0.0 --port 8000

Access the web interface at http://localhost:8000

Features

• Data Upload: Upload single-cell datasets (H5AD format)
• Training Configuration: Configure model parameters and hyperparameters
• Real-time Monitoring: Track loss curves and metrics during training
• Results Visualization: Download embeddings and view training summaries

API Endpoints

• POST /upload - Upload dataset
• POST /train/start - Start training
• GET /train/metrics - Get training metrics
• GET /embeddings/interpretable - Get interpretable embeddings
• GET /embeddings/latent - Get latent embeddings
• GET /download/embeddings/{type} - Download embeddings as CSV

Architecture Overview

LiVAE consists of three main components working in concert:

1. Encoder Network

Input (gene expression) → Hidden → Hidden → μ, σ (latent parameters)

• Maps high-dimensional gene expression to latent distribution parameters
• Uses reparameterization trick for differentiable sampling

2. Hyperbolic Transformation

Latent Sample → Tangent Space → Exponential Map → Lorentzian Manifold

• Projects latent vectors onto hyperbolic manifold
• Enables natural representation of hierarchical relationships

3. Dual Decoder Pathway

Primary:      Latent → Decoder → Reconstruction
Interpretable: Latent → Compress → Decompress → Decoder → Reconstruction

• Dual reconstruction paths for enhanced representation learning
• Compression bottleneck encourages essential feature extraction

Loss Function

LiVAE optimizes a composite loss function:

L_total = L_recon + L_irecon + L_lorentz + L_KL

Where:

• L_recon: Negative binomial reconstruction loss
• L_irecon: Interpretable reconstruction loss
• L_lorentz: Lorentz distance regularization
• L_KL: KL divergence

Evaluation Metrics

Clustering Quality

• ARI (Adjusted Rand Index): Clustering agreement with ground truth
• NMI (Normalized Mutual Information): Information-theoretic clustering measure
• ASW (Average Silhouette Width): Cluster cohesion and separation

Cluster Validity

• C_H (Calinski-Harabasz): Ratio of between to within cluster variance
• D_B (Davies-Bouldin): Average similarity between clusters
• P_C (Graph Connectivity): Connectivity within clusters

Batch Integration

• cLISI: Cluster-specific Local Inverse Simpson Index
• iLISI: Integration LISI for batch mixing
• bASW: Batch-corrected Average Silhouette Width

Performance Tips

Memory Optimization

# For large datasets, reduce batch size
model = agent(adata, percent=0.005)  # Use 0.5% of data per batch

# Use CPU if GPU memory is limited
import torch
model = agent(adata, device=torch.device('cpu'))

Training Efficiency

# Progressive training with increasing regularization
model = agent(adata, beta=0.1, lorentz=0.0)
model.fit(epochs=500)

# Increase regularization for fine-tuning
model.beta = 1.0
model.lorentz = 0.1
model.fit(epochs=500)

License

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