contrastiveVI

contrastiveVI is a generative model designed to isolate factors of variation specific to a group of "target" cells (e.g. from specimens with a given disease) from those shared with a group of "background" cells (e.g. from healthy specimens). contrastiveVI is implemented in scvi-tools.

User guide

Note: This implementation of contrastiveVI is no longer maintained. Please see the main scvi-tools repository for the most up to date version of contrastiveVI, along with an updated tutorial here.

Installation

To install the latest version of contrastiveVI via pip

pip install contrastive-vi

Installation should take no more than 5 minutes.

What you can do with contrastiveVI

• If you have a dataset with cells in a background condition (e.g. from healthy controls) and a target condition (e.g. from diseased patients), you can train contrastiveVI to isolate latent factors of variation specific to the target cells from those shared with a background into separate latent spaces.
• Run clustering algorithms on the target-specific latent space to discover sub-groups of target cells
• Perform differential expression testing for discovered sub-groups of target cells using a procedure similar to that of scVI .

Colab Notebook Examples

• Applying contrastiveVI to see the effects of stem cell transplants for leukemia patients
• Applying contrastiveVI to separate mouse intestinal epithelial cells infected with different pathogens by pathogen type
• Applying contrastiveVI to better understand the results of a MIX-Seq small-molecule drug perturbation experiment
• Applying contrastiveVI to better understand heterogeneity in cellular responses to Alzheimer's disease

Development guide

Set up the environment

1. Git clone this repository.
2. cd contrastive-vi.
3. Create and activate the specified conda environment by running

   conda env create -f environment.yml
   conda activate contrastive-vi-env
   

4. Install the constrative_vi package and necessary dependencies for development by running pip install -e ".[dev]".
5. Git pre-commit hooks (https://pre-commit.com/) are used to automatically check and fix formatting errors before a Git commit happens. Run pre-commit install to install all the hooks.
6. Test that the pre-commit hooks work by running pre-commit run --all-files.

Testing

It's a good practice to include unit tests during development. Run pytest tests to verify existing tests.

References

If you find contrastiveVI useful for your work, please consider citing our preprent:

@article{contrastiveVI,
  title={Isolating salient variations of interest in single-cell transcriptomic data with contrastiveVI},
  author={Weinberger, Ethan and Lin, Chris and Lee, Su-In},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}