scSLAT 0.3.0

A graph deep learning based tool to align single cell spatial omics data

scSLAT: single cell spatial alignment tools

scSLAT package implements the SLAT (Spatial Linked Alignment Tool) model to align single cell spatial omics data.

Directory structure

.
├── 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

Tutorial of scSLAT is here, if you have any question please open an issue on github

Installation

PyPI

Note Installing scSLAT within a new conda environment is recommended.

Warning Old NVIDIA driver may raise error.

First, we create a clean environment and install scSLAT from PyPI. Then we also need install dependencies for pyg manually via install_pyg_dependencies.

conda create -n scSLAT python=3.11 -y && conda activate scSLAT
pip install scSLAT
install_pyg_dependencies

Docker

You can pull the docker image directly from Docker Hub or refer to the Dockerfile to build it.

docker pull huhansan666666/slat:latest

Development version

For development purpose, clone this repo and install:

git clone [e-mail]:gao-lab/SLAT.git && cd SLAT
pip install -e ".[dev,docs]"
install_pyg_dependencies

Reproduce manuscript results

1. Please follow the env/README.md to install all dependencies. Please checkout the repository to v0.2.1 before install scSLAT.
2. Download and pre-process data follow the data/README.md.
3. Whole benchmark and evaluation procedure can be found in /benchmark and /evaluation, respectively.
4. Every case study is recorded in the /case directory in the form of jupyter notebook.