Installation

Important software Dependencies

• scanpy==1.9.3

• squidpy==1.3.0

• pytorch==1.13.0(cuda==11.6)

• torch_geometric==2.3.1(cuda==11.6)

• R==3.5.1

• mclust==5.4.10

Setup by Docker (Recommended)

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

   docker run --gpus all --name your_container_name -idt hannshu/stmsa: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 stMSA.

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

• Details of the container

/root
|-- stMSA           # The stMSA source code
|-- stMSA_paras     # The model parameters of stMSA for each experiment
`-- stMSA_results   # The embedding result of each experiment

Setup by Anaconda

1. Install Anaconda.

2. Clone the stMSA repository from GitHub:

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

3. Download the dataset repository:

   git submodule init
   git submodule update
   

4. Import the conda environment:

   conda env create -f environment.yml
   

5. Write a Python script to run stMSA.

• Note: If you need to generate clustering result by mclust, you need to install mclust package to the R environment in your conda environment.

• If the environment.yml file not fit your system or device, please try the Docker container we provided.