bsvae 0.1.1

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BSVAE: Biologically Structured Variational Autoencoder

BSVAE is a PyTorch package for Structured Factor Variational Autoencoders (StructuredFactorVAE).
It is designed for gene expression modeling with biological priors, integrating protein–protein interaction (PPI) networks and sparsity constraints for interpretable latent representations.

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Features

• Structured VAE architecture (StructuredFactorVAE)
  • Factorized encoder/decoder with group sparsity
  • Optional Laplacian regularization from PPI networks
• Dataset utilities
  • Load gene expression matrices (GeneExpression)
  • Support for CSV full-matrix mode or pre-split train/test mode
• Biological priors
  • Fetch and cache STRING PPI networks by NCBI TaxID
  • Map gene symbols / Ensembl IDs to protein IDs using MyGene.info or BioMart
• Training and evaluation
  • Unified training loop (Trainer)
  • Evaluation with reconstruction, KL, sparsity, and Laplacian penalties (Evaluator)
• Reproducibility
  • Save/load models + metadata (modelIO)
  • Configurable hyperparameters via hyperparam.ini

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Installation

Clone the repo and install with Poetry (recommended):

git clone https://github.com/YOUR-LAB/BSVAE.git
cd BSVAE
poetry install

Or using pip:

pip install -e .

Dependencies:

• Python 3.9+
• PyTorch ≥ 2.0
• pandas, numpy, scikit-learn
• networkx, scipy
• mygene (for gene annotation)

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Quickstart

1. Prepare gene expression data

BSVAE expects genes × samples CSVs.

• Full-matrix mode: Provide expr.csv with all samples → 10-fold CV split is created.
• Pre-split mode: Provide directory with X_train.csv and X_test.csv.

2. Train a model

poetry run python -m bsvae.main exp1 \
    --gene-expression-filename data/expr.csv \
    --epochs 50 \
    --latent-dim 10 \
    --ppi-taxid 9606

• Results (checkpoints, logs, metadata) saved under results/exp1/.

3. Evaluate a trained model

poetry run python -m bsvae.main exp1 \
    --gene-expression-filename data/expr.csv \
    --is-eval-only

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⚙Configuration

Hyperparameters can be set via hyperparam.ini:

[Custom]
seed = 42
no_cuda = False
epochs = 100
batch_size = 64
latent_dim = 10
hidden_dims = [128, 64]
dropout = 0.1
l1_strength = 1e-3
lap_strength = 1e-4

Override from CLI if needed:

--epochs 50 --latent-dim 20

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PPI Priors

BSVAE supports automatic download & caching of STRING v12.0 PPI networks.

• Supported species (via NCBI TaxID):

  • Human (9606)
  • Mouse (10090)
  • Rat (10116)
  • Fly (7227)

• Cache location defaults to ~/.bsvae/ppi (override via --ppi-cache).

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Citation

If you use BSVAE in your research, please cite:

@article{Benjamin2025bsvae,
  title={Structured Factor Variational Autoencoder with Biological Priors},
  author={Kynon J. M. Benjamin},
  year={2025},
  journal={N/A}
}

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License

This project is licensed under the MIT License.