Installation#

Requirements#

Peach requires Python 3.10 or later and is compatible with the scverse ecosystem.

Core Dependencies#

  • anndata >=0.8.0 - Single-cell data structures

  • scanpy >=1.9.0 - Single-cell analysis toolkit

  • torch >=2.0.0 - Deep learning framework

  • scikit-learn >=1.0.0 - Machine learning utilities

  • pandas >=2.0.0 - Data manipulation

  • numpy >=1.24.0 - Numerical computing

  • plotly >=5.0.0 - Interactive visualization

  • scipy >=1.10.0 - Scientific computing

  • statsmodels >=0.14.0 - Statistical analysis

Installation Options#

Option 2: Development Installation#

For the latest features and development:

git clone https://github.com/xhonkala/PEACH.git
cd peach
pip install -e ".[dev]"

Verify Installation#

Test your installation:

import peach as pc
print(pc.__version__)

# Quick test with synthetic data
adata = pc.pp.generate_synthetic(n_points=100, n_dimensions=50, n_archetypes=3)
results = pc.tl.train_archetypal(adata, n_archetypes=3, n_epochs=5)
print(f"Test R²: {results['history']['archetype_r2'][-1]:.3f}")

GPU Support (Optional)#

For GPU acceleration with large datasets:

NB: this is not yet fully supported but will be included in the next release.

# Install PyTorch with CUDA support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Verify GPU availability
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"

Troubleshooting#

Common Issues#

  1. Import errors: Ensure all dependencies are installed with correct versions

  2. CUDA errors: Make sure PyTorch CUDA version matches your system

  3. Memory errors: Consider using CPU for large datasets or reducing batch size

Getting Help#

System Requirements#

  • Memory: Minimum 4GB RAM, 16GB+ recommended for large datasets

  • Storage: ~1GB for installation plus data storage

  • CPU: Multi-core recommended for optimal performance

  • GPU: Optional but recommended for datasets >10k cells