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.

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:

# 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

Download the model

from deepcell_types.utils import download_model
download_model()

Running

The deepcell-types cell-type inference functionality is provided via a simple functional interface, deepcell_types.predict.

Canonical checkpoints read their marker and cell-type registry from a TissueNet zarr archive at inference time. Pass the archive with zarr_path=... or set DEEPCELL_TYPES_ZARR_PATH before calling deepcell_types.predict.

For a complete example of the cell-type inference pipeline, check out the tutorial.

Training

To retrain or fine-tune, install the [train] extra (pulls in wandb, zarr, pandas, scikit-learn, torchvision, torchmetrics, etc.):

pip install "deepcell-types[train] @ git+https://github.com/vanvalenlab/deepcell-types@master"

Training entry points live under scripts/:

• scripts/train.py — main training loop.
• scripts/pretrain.py — masked-marker pretraining.
• scripts/predict.py — batched evaluation over a zarr archive.
• scripts/benchmark_gold_standard.py — gold-standard benchmark suite.

All training scripts read configuration from a TissueNet zarr v3 archive. Pass --zarr_path or set DEEPCELL_TYPES_ZARR_PATH. The training-side modules under deepcell_types.training (e.g. TissueNetConfig, FullImageDataset, FocalLoss, HierarchicalLoss) are stable enough to import directly for custom training scripts.

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}
}