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[](https://opensource.org/licenses/MIT)
# DECIPHER
<div align="center">
[Installation](#Installation) • [Documentation](#Documentation) • [Citation](#Citation) • [FAQ](#FAQ) • [Acknowledgement](#Acknowledgement)
</div>
`DECIPHER` aims to learn cells’ disentangled embeddings from intracellular state and extracellular contexts view based on single-cell spatial omics data.
## Installation
> [!IMPORTANT]
> Requires Python >= 3.10 and CUDA-enabled GPU (CPU-only device is not recommended).
### PyPI
We recommend to install `cell-decipher` to a new conda environment with [RAPIDS](https://docs.rapids.ai/install) dependencies.
```sh
mamba create -n decipher -c conda-forge -c rapidsai -c nvidia python=3.11 rapids=24.04 cuda-version=11.8 cudnn cutensor cusparselt -y && conda activate decipher
pip install cell-decipher
install_pyg_dependencies
```
### Docker
Build docker image from [Dockerfile](./Dockerfile) or pull the latest image from Docker Hub by:
```sh
docker pull huhansan666666/decipher:latest
```
## Documentation
> Please check [**documentation**](https://slat.readthedocs.io/en/latest/) for detailed tutorial.
### Minimal example
Here is a minimal example for quick start:
```python
import scanpy as sc
from decipher import DECIPHER
# Init model
model = DECIPHER(work_dir='/path/to/work_dir')
# Register data
adata = sc.read_h5ad('/path/to/adata.h5ad')
model.register_data(adata)
# Fit model
model.fit_omics()
# Get disentangled omics and spatial embeddings
omics_emb = model.center_emb
spatial_emb = model.nbr_emb
```
### Demo
| Name | Description |
| --------------------------------------- | ------------------------------------------------------------ |
| [Basic Model Tutorial]() ([Colab](https://colab.research.google.com/drive/14PEtrgqlf-KbLOTfBLc9gbx0YvY6mi0S?usp=sharing)) | Tutorial on how to train DECIPHER |
| [Identify Localization Related Genes]() | Tutorial on how to identify cells’ localization related genes via DECIPHER embeddings|
| [Multi-slices with Batch Effects]() | Tutorial on how to remove batch effects across multiple slices |
| [DDP Training]() | Tutorial on how to use multi-GPUs on large datasets |
## Citation
TBD
> If you want to repeat our benchmarks and case studies, please check the [**benchmark**](./benchmark/README.md) and [**experiments**](./experiments/README.md) folder.
## FAQ
1. `CUDA out of memory` error
We do all experiments on A100-80G GPUs. We observed `model.train_gene_select()` may use ~40GB for 700,000 cells with 1,000 genes. If your GPU do not have enough memory, you can try running with CPU.
## Acknowledgement
We thank following great open-source projects for their help or inspiration:
- [SimCLR](https://github.com/sthalles/SimCLR)
- [vit-pytorch](https://github.com/lucidrains/vit-pytorch)
- [lightly](https://github.com/lightly-ai/lightly)
- [scib](https://github.com/theislab/scib)
- [rapids_singlecell](https://github.com/scverse/rapids_singlecell/)
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