ACE (Aging Cell Embedding) is an explainable deep generative model for disentangling aging-related signals from background biological variation in single-cell transcriptomic data.
ACE builds two separate latent spaces:
- Aging latent space – captures gene expression patterns related to aging
- Background latent space – models confounding factors such as tissue, cell type, or species differences
This enables ACE to identify both global aging markers (shared across tissues and cell types) and local, tissue- or cell-type-specific aging signals, and supports cross-species alignment of aging trajectories.
ACE is implemented on top of the scvi-tools framework.
ACE is not yet published to PyPI. You can install it locally for development and testing.
- Clone the repository
git clone https://github.com/your-username/ace.git cd ace - Create and activate the Conda environment
conda env create -f environment.yml conda activate ace
- Install in editable mode
pip install -e .