scSLAT package implements the SLAT (Spatial Linked Alignment Tool) model to align single cell spatial omics data.
.
├── scSLAT/ # Main Python package
├── env/ # Extra environment
├── data/ # Data files
├── evaluation/ # SLAT evaluation pipeline
├── benchmark/ # Benchmark pipeline
├── case/ # Case studies in paper
├── docs/ # Documentation files
├── resource/ # Other useful resource
├── pyproject.toml # Python package metadata
└── README.md
Tutorial of scSLAT
is here, if you have any question please open an issue on github
Dockerfile of scSLAT
is available at env/Dockerfile
. You can also pull the docker image directly from here by:
docker pull huhansan666666/slat:latest
Note Installing
scSLAT
within a new conda environment is recommended.
First, we create a clean environment and install scSLAT
from PyPI. We need install dependency torch
before install pyg
.
Warning old NVIDIA driver may raise error, please update NVIDIA driver to the latest version.
conda create -n scSLAT python=3.8 -y && conda activate scSLAT
pip install "scSLAT[torch]"
pip install "scSLAT[pyg]"
Note Some dependencies such as
torch-scatter
need to compile from source, which may take a long time. Please refer our solution to accelerate the install here
For development purpose, clone this repo and install:
git clone [email protected]:gao-lab/SLAT.git
cd SLAT
pip install -e ".[torch]"
pip install -e ".[pyg,dev,doc]"
We plan to provide a conda package of scSLAT
in the near future.
- Please follow the
env/README.md
to install all dependencies. Please checkout the repository to v0.2.0 before installscSLAT
: - Download and pre-process data follow the
data/README.md
- Whole benchmark and evaluation procedure can be found in
/benchmark
and/evaluation
, respectively. - Every case study is recorded in the
/case
directory in the form of jupyter notebook.