DeepCell Types is a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels.
from deepcell_types.utils import download_model
download_model()As with all Python packages, users are encouraged to use some form
of virtual environment for package installation.
Popular options include venv/virtualenv, conda/mamba, uv,
or pixi.
Users are encouraged to use whatever environment management toolchain
they are most comfortable with.
For those unsure, the quickest way to start is to use the venv module,
part of the Python standard library:
# Create a new virtual environment
python -m venv dct-env
# Enter the virtual environment
source dct-env/bin/activate
# Once inside the environment, install deepcell-types
pip install git+https://github.com/vanvalenlab/deepcell-types@masterThe deepcell-types cell-type inference functionality is provided via
a simple functional interface, deepcell_types.predict.
For a complete example of the cell-type inference pipeline, check out the tutorial.
@article{deepcelltypes,
title={Generalized cell phenotyping for spatial proteomics with language-informed vision models},
author={Wang, Xuefei and Dilip, Rohit and Bussi, Yuval and Brown, Caitlin and Pradhan, Elora and Jain, Yashvardhan and Yu, Kevin and Li, Shenyi and Abt, Martin and Borner, Katy and others},
journal={bioRxiv},
pages={2024--11},
year={2024},
publisher={Cold Spring Harbor Laboratory}
}