bsvae 0.2.0

Biologically structured variational autoencoder for gene expression data with PPI priors.

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
• Post-training analysis
  • Gene–gene network extraction via decoder similarity, propagated covariance, Graphical Lasso, and Laplacian refinement
  • Latent export (mu, logvar) to CSV or AnnData for downstream workflows

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Installation

Install from PyPI:

pip install bsvae

Or install from source with Poetry:

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

Dependencies:

• Python 3.11+
• PyTorch ≥ 2.8
• 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

bsvae-train 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

bsvae-train exp1 \
    --gene-expression-filename data/expr.csv \
    --is-eval-only

4. Extract networks and export latents

bsvae-networks extract-networks \
    --model-path results/exp1 \
    --dataset data/expr.csv \
    --output-dir results/exp1/networks
# optional: --methods latent_cov graphical_lasso laplacian

bsvae-networks export-latents \
    --model-path results/exp1 \
    --dataset data/expr.csv \
    --output results/exp1/latents.h5ad

The extractor writes adjacency matrices, edge lists, and optional heatmaps for each requested method. By default the decoder-loading cosine similarity (w_similarity) is computed; add other methods with --methods. Latent exports include per-sample mu and logvar as tidy CSV or AnnData files.

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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:

bsvae-train my_experiment --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).

Prefetch PPI cache from the CLI

Use the lightweight downloader to cache a STRING network ahead of training:

bsvae-download-ppi --taxid 9606 --cache-dir ~/.bsvae/ppi

Troubleshooting PPI downloads on HPC systems

Some clusters block HTTPS certificate resolution for outbound downloads. If bsvae-download-ppi cannot reach STRING, manually cache the file with wget (or curl) using --no-check-certificate and point --ppi-cache to the same directory:

OUTDIR="$HOME/.bsvae/ppi"
mkdir -p "${OUTDIR}"
wget --no-check-certificate \
  "https://stringdb-static.org/download/protein.links.detailed.v12.0/9606.protein.links.detailed.v12.0.txt.gz" \
  -O "${OUTDIR}/9606_string.txt.gz"

Use curl -k -L "<url>" -o "${OUTDIR}/9606_string.txt.gz" if wget is unavailable.

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Integration notes

• The bsvae-networks workflows reuse the same gene ordering as training. When loading a standalone expression file, ensure columns correspond to the genes seen by the checkpoint.
• The CLI automatically handles CPU/GPU placement based on availability; models are loaded in evaluation mode without modifying training metadata.
• Network extraction functions are written to be test-friendly: they accept PyTorch DataLoader instances, operate without global state, and persist outputs as CSV/TSV/NPY for interoperability with graph toolchains.

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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 GNU General Public License v3.0.