Installation

Software Dependencies

• python==3.10.13

• numpy==1.26.4

• pandas==2.2.2

• matplotlib==3.8.2

• scanpy==1.10.1

• squidpy==1.4.1

• faiss==1.7.4

• igraph==0.11.5

• scikit-learn==1.5.0

• scikit-misc==0.3.1

• louvain==0.8.2

• scipy==1.12.0

• tqdm==4.66.1

• pytorch==2.3.1+cu121

• torch_geometric==2.5.0

• rpy2==3.2.2

• R==3.6.1

• mclust==6.1.1

Setup by Docker (Recommended)

1. Download the stSCI image from DockerHub and setup a container:

   docker run --gpus all --name your_container_name -idt hannshu/stsci:latest
   

2. Access the container:

   docker start your_container_name
   docker exec -it your_container_name /bin/bash
   

3. Write a python script to run stSCI

The anaconda environment for stSCI will be automatically activate in the container. The stSCI source code is located at /root/stSCI, please run git pull to update the codes before you use. All dependencies of stSCI have been properly installed in this container, including the mclust R package, and the conda environment stSCI will automatically activate when you run the container.

• Note: Please make sure NVIDIA Container Toolkit is properly installed on your host device. (Or follow this instruction to setup NVIDIA Container Toolkit first)

Manually setup

We suggest you to use the Docker to setup and run stSCI. If you want to manually setup stSCI, we recommend you to use Anaconda to build the runtime environment.

1. Clone this repository from Github:

   git clone https://github.com/hannshu/stSCI.git
   

2. Download dataset repository:

   git submodule init
   git submodule update
   

3. Build the Anaconda environment, the package version of the essential dependencies noted at Software Dependencies section.

4. Write a python script to run stSCI