DECIPHER aims to learn cells’ disentangled intracellular molecular identity embedding and extracellular spatial context embedding from spatial omics data.
Please check our paper DECIPHER for learning disentangled cellular embeddings in large-scale heterogeneous spatial omics data on Nature Communications.
Important
Requires Python >= 3.10 and CUDA-enabled GPU (CPU-only device is not recommended).
We recommend to install cell-decipher to a new conda environment:
mamba create -n decipher python==3.11 -c conda-forge -y && conda activate decipher
pip install cell-decipher
install_pyg_dependencies(Optional) You can install RAPIDS to accelerate visualization.
mamba create -n decipher -c conda-forge -c rapidsai -c nvidia python=3.11 rapids=25.06 'cuda-version>=12.0,<=12.8' -y && conda activate decipher
pip install cell-decipher
install_pyg_dependenciesBuild docker image from Dockerfile or pull image from Docker Hub directly:
docker pull huhansan666666/decipher:latest
docker run --gpus all -it --rm huhansan666666/decipher:latestHere is a minimal example for quick start:
import scanpy as sc
from decipher import DECIPHER
from decipher.utils import scanpy_viz
# Init model
model = DECIPHER(work_dir='/path/to/work_dir')
# Register data (adata.X is raw counts, adata.obsm['spatial'] is spatial coordinates)
adata = sc.read_h5ad('/path/to/adata.h5ad')
model.register_data(adata)
# Fit DECIPHER model
model.fit_omics()
# Clustering disentangled embeddings
adata.obsm['X_center'] = model.center_emb # intracellular molecular embedding
adata.obsm['X_nbr'] = model.nbr_emb # spatial context embedding
adata = scanpy_viz(adata, ['center', 'nbr'], rapids=False)
# Plot
adata.obsm['X_umap'] = adata.obsm['X_umap_center'].copy()
sc.pl.umap(adata, color=['cell_type'])
adata.obsm['X_umap'] = adata.obsm['X_umap_nbr'].copy()
sc.pl.umap(adata, color=['region'])Please check documentation for all tutorials.
| Name | Description | Colab |
|---|---|---|
| Basic Model Tutorial | Tutorial on how to use DECIPHER |  |
| Multi-slices with Batch Effects | Tutorial on how to apply DECIPHER to multiple slices with batch effects | |
| Identify Localization-related LRs | Tutorial on how to identify ligand-receptors which related wtih cells’ localization based on DECIPHER embeddings | Insufficient resources |
| Multi-GPUs Training | Tutorial on how to use DECIPHER with multi-GPUs on spatial atlas | Insufficient resources |
| More technologies | Tutorial on how to use DECIPHER with multiple spatial technologies | Insufficient resources |
DECIPHER for learning disentangled cellular embeddings in large-scale heterogeneous spatial omics data (Nature Communications)
If you want to repeat our benchmarks and case studies, please check the benchmark and experiments folder.
Please open a new github issue if you meet problem.
- Visium or ST data
DECIPHER is designed for single cell resolution data. As for Visium or ST, you can still use DECIPHER after obtaining single-cell resolution through deconvolution or spatial mapping strategies.
We thank the following great open-source projects for their help or inspiration: