scslat 0.2.0

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

Docker

Dockerfile of scSLAT is available at env/Dockerfile. You can also pull the docker image directly from here by :

docker pull huhansan666666/slat:latest

Development

Installing scSLAT within a new conda environment is recommended. Warning: machine with old NVIDIA driver may raise error, please update NVIDIA driver to the latest version or use Docker.

For development purpose, clone this repo and install:

git clone [e-mail]:gao-lab/SLAT.git
cd SLAT
pip install -e ".[torch]"
pip install -e ".[pyg,dev,doc]"

PyPI (Ongoing)

Fist we create a clean environment and install scSLAT from PyPI:

We need install dependency torch before install pyg.

conda create -n scSLAT python=3.8 -y && conda activate scSLAT
pip install scSLAT[torch]
pip install scSLAT[pyg]

Conda (Ongoing)

We plan to provide a conda package of scSLAT in the near future.

Reproduce manuscript results

1. Please follow the env/README.md to install all dependencies. Please checkout the repository to v0.1.0 before install scSLAT:

git clone [e-mail]:gao-lab/SLAT.git
git checkout tags/v0.2.0
pip install -e ".[torch]"
pip install -e ".[pyg,dev,doc]"

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.