liora 0.4.1

Liora: Lorentz Information ODE-Regularized Variational Autoencoder for single-cell RNA-seq

Liora

Lorentz Information ODE-Regularized Variational Autoencoder for single-cell RNA-seq

Liora is a deep learning framework for single-cell RNA-seq analysis that combines:

• Variational Autoencoder (VAE) for dimensionality reduction
• Lorentz (hyperbolic) or Euclidean manifold regularization for capturing hierarchical structure
• Information bottleneck for hierarchical representation learning
• Neural ODE dynamics for continuous trajectory inference (optional)
• Count-based likelihoods: Negative Binomial (NB), Zero-Inflated NB (ZINB), Poisson, Zero-Inflated Poisson (ZIP)
• Flexible encoder architectures: MLP (default) or Transformer-based with self-attention
• Multiple ODE function types: Legacy MLP, time-conditioned MLP, or GRU-based with memory

Features

Encoder Options

• MLP Encoder (default): Two-layer fully connected network with ReLU activations
• Transformer Encoder: Self-attention mechanism with configurable heads, layers, and embedding dimensions
  • Projects features into token sequences
  • Applies multi-head attention across feature space
  • Mean-pools output for latent representation

ODE Function Types

• Legacy: Time-invariant MLP (backward compatibility)
• Time-conditioned MLP: Time-aware dynamics with multiple conditioning strategies
  • concat: Concatenate time to latent state
  • film: Feature-wise Linear Modulation (scale and shift)
  • add: Additive time embedding
• GRU-based: Recurrent dynamics with trajectory memory for smooth developmental processes

ODE Solvers

Supports all torchdiffeq methods:

• Fixed-step: rk4, euler, midpoint (use ode_step_size)
• Adaptive: dopri5, adams, bosh3 (use ode_rtol, ode_atol)

Installation

From PyPI

pip install liora

From Source

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

With Test Dependencies

pip install -e .[test]

Quick Start

import scanpy as sc
from liora import Liora

# Load your data
adata = sc.read_h5ad('data.h5ad')

# Basic usage with default MLP encoder
model = Liora(
    adata,
    layer='counts',
    hidden_dim=128,
    latent_dim=10,
    i_dim=2,
)
model.fit(epochs=100)
latent = model.get_latent()

# Advanced: Transformer encoder + ODE trajectory inference
model = Liora(
    adata,
    layer='counts',
    hidden_dim=128,
    latent_dim=10,
    i_dim=2,
    # Encoder configuration
    encoder_type='transformer',
    attn_embed_dim=64,
    attn_num_heads=4,
    attn_num_layers=2,
    attn_seq_len=32,
    # ODE configuration
    use_ode=True,
    ode_type='time_mlp',
    ode_time_cond='concat',
    ode_hidden_dim=64,
    ode_solver_method='dopri5',
    ode_rtol=1e-5,
    ode_atol=1e-7,
    # Loss weights
    lorentz=5.0,
    beta=1.0,
)
model.fit(epochs=200, patience=20)

# Extract results
latent = model.get_latent()           # Latent embeddings
bottleneck = model.get_bottleneck()   # Information bottleneck
pseudotime = model.get_time()         # Predicted pseudotime (ODE mode)
transitions = model.get_transition()  # Transition matrix (ODE mode)

API Reference

Main Parameters

Model Architecture

• hidden_dim (int, default=128): Hidden layer dimension in encoder/decoder
• latent_dim (int, default=10): Primary latent space dimensionality
• i_dim (int, default=2): Information bottleneck dimension (must be < latent_dim)

Encoder Configuration

• encoder_type (str, default='mlp'): Encoder architecture
  • 'mlp': Standard multi-layer perceptron
  • 'transformer': Self-attention based encoder
• attn_embed_dim (int, default=64): Embedding dimension for transformer tokens
• attn_num_heads (int, default=4): Number of attention heads
• attn_num_layers (int, default=2): Number of transformer encoder layers
• attn_seq_len (int, default=32): Number of token sequences

ODE Configuration

• use_ode (bool, default=False): Enable Neural ODE regularization
• ode_type (str, default='time_mlp'): ODE function architecture
  • 'legacy': Time-invariant MLP (not recommended)
  • 'time_mlp': Time-conditioned MLP (recommended)
  • 'gru': GRU-based with recurrent memory
• ode_time_cond (str, default='concat'): Time conditioning for 'time_mlp'
  • 'concat': Concatenate time to state
  • 'film': Feature-wise Linear Modulation
  • 'add': Additive time embedding
• ode_hidden_dim (int, optional): Hidden dimension for ODE networks (defaults to hidden_dim)
• ode_solver_method (str, default='rk4'): Torchdiffeq solver method
• ode_step_size (float or 'auto', optional): Step size for fixed-step solvers
• ode_rtol (float, optional): Relative tolerance for adaptive solvers
• ode_atol (float, optional): Absolute tolerance for adaptive solvers

Loss Configuration

• recon (float, default=1.0): Reconstruction loss weight
• irecon (float, default=0.0): Information bottleneck reconstruction weight
• lorentz (float, default=0.0): Manifold regularization weight
• beta (float, default=1.0): KL divergence weight (β-VAE)
• dip (float, default=0.0): DIP-VAE loss weight
• tc (float, default=0.0): Total Correlation loss weight
• info (float, default=0.0): MMD loss weight
• loss_type (str, default='nb'): Count likelihood model
  • 'nb': Negative Binomial (recommended for UMI data)
  • 'zinb': Zero-Inflated Negative Binomial
  • 'poisson': Poisson
  • 'zip': Zero-Inflated Poisson

Training Configuration

• lr (float, default=1e-4): Learning rate for Adam optimizer
• batch_size (int, default=128): Mini-batch size
• train_size (float, default=0.7): Proportion of training data
• val_size (float, default=0.15): Proportion of validation data
• test_size (float, default=0.15): Proportion of test data

Methods

• fit(epochs=400, patience=25, val_every=5, early_stop=True): Train the model
• get_latent(): Extract latent representations
• get_bottleneck(): Extract information bottleneck embeddings
• get_time(): Get predicted pseudotime (ODE mode only)
• get_transition(): Get cell-to-cell transition probabilities (ODE mode only)

Testing

Run the test suite:

pytest

Or with coverage:

pytest --cov=liora --cov-report=html

Tests cover:

• ODE function construction for all types
• Fixed-step and adaptive ODE solvers
• VAE forward passes with different configurations
• Encoder variants (MLP and Transformer)

Examples

See the examples/ directory for:

• encoder_example.py: Demonstrates MLP vs Transformer encoders
• More examples coming soon!

Technical Notes

ODE Solver Selection

Fixed-step solvers (rk4, euler, midpoint):

• Require ode_step_size parameter
• Use 'auto' for uniform discretization based on time points
• Good for when trajectory is well-behaved

Adaptive solvers (dopri5, adams):

• Automatically adjust step size based on error tolerance
• Use ode_rtol and ode_atol for control
• Better for stiff or complex dynamics

GRU ODE Notes

The GRU-based ODE maintains hidden state across time steps:

• Hidden state automatically resets before each trajectory
• Provides smooth dynamics with memory of past states
• Best for modeling developmental processes

CPU vs GPU for ODE

ODE solving runs on CPU for computational efficiency:

• torchdiffeq is CPU-optimized for small latent dimensions
• Observed 2-3x speedup vs GPU for typical use cases
• Avoids GPU memory pressure

Citation

If you use Liora in your research, please cite:

@software{liora2025,
  title = {Liora: Lorentz Information ODE-Regularized Variational Autoencoder},
  author = {Zeyu Fu},
  year = {2025},
  url = {https://github.com/PeterPonyu/liora}
}

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Contact

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