drvi-py 0.2.5

Unsupervised Deep Disentangled Representation of Single-Cell Omics

DRVI

Unsupervised Deep Disentangled Representation of Single-Cell Omics

Getting started

Please refer to the documentation. In particular, the

• Tutorials, specially
  • Train DRVI and interpret the latent dimensions.
  • Mapping query data into a DRVI reference.
• API documentation, specially
  • DRVI Model
  • DRVI utility functions (tools)
  • DRVI plotting functions

System requirements

We recommend running DRVI on a recent Linux distribution. DRVI is actively tested on the latest LTS version of Ubuntu (currently 24.04 LTS).

For optimal performance, we highly recommend using a GPU with CUDA capabilities. While CPU-based systems are supported, GPU-powered systems are strongly recommended for optimal performance.

Installation

You need to have Python (versions 3.10 to 3.14 supported) installed on your system. If you don't have Python installed, we recommend installing uv.

There are several alternative options to install drvi:

1. Install the latest release of drvi-py from PyPI, which should take around two minutes:

pip install drvi-py

1. Install the latest development version:

pip install git+https://github.com/theislab/drvi.git@main

Please be sure to install a version of PyTorch that is compatible with your GPU. Dependencies are installed automatically, please take a look at the versions for different dependencies in pyproject.toml if needed.

Release notes

See the changelog.

Contact

For questions and help requests, you can reach out in the scverse discourse. If you found a bug, please use the issue tracker.

Citation

If DRVI is helpful in your research, please consider citing the following paper:

Moinfar, A. A. & Theis, F. J. Disentangling cellular heterogeneity into interpretable biological factors through structured latent representations. bioRxiv 2024.11.06.622266 (2024) doi:10.1101/2024.11.06.622266.

Reproducibility

Code, notebooks, and instructions to reproduce the results from the paper are available at the reproducibility repository.