https://github.com/vanvalenlab/deepcell-types
https://github.com/vanvalenlab/deepcell-types
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/vanvalenlab/deepcell-types
- Owner: vanvalenlab
- License: other
- Created: 2024-10-09T16:06:37.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2025-06-10T18:57:52.000Z (about 1 year ago)
- Last Synced: 2025-06-10T19:02:36.195Z (about 1 year ago)
- Language: Python
- Size: 6.09 MB
- Stars: 6
- Watchers: 2
- Forks: 3
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DeepCell Types
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.
## Download the model
```python
from deepcell_types.utils import download_model
download_model()
```
## Installation
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:
```bash
# 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@master
```
## Running
The `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](https://vanvalenlab.github.io/deepcell-types/site/tutorial.html).
## Citation
```
@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}
}
```