Installation Guide

For Windows system, we suggest to use the Anaconda powershell to run following code.

Requirements

• [Optional] It is recommended to create a conda environment for CAST.

For example, create a new environment named cast_demo and activate it.

conda create -y -n cast_demo python=3.9
conda activate cast_demo

• [Optional] CAST requires pytorch and dgl

Install Pytorch

Install DGL

Users could use nvcc --version to check the CUDA version for installation.

Here we provide with an example of the CUDA 11.3 installation code.

#### If CPU only ####
conda install pytorch==1.11.0 cpuonly -c pytorch
conda install -c dglteam dgl

#### If GPU available ####
conda install -y -c pytorch pytorch==1.11.0 cudatoolkit=11.3
conda install -y -c dglteam dgl-cuda11.3==0.9.1

Installation

If git is available:

pip install git+https://github.com/wanglab-broad/CAST.git

If git is unavailable:

1. Download the package and unpack it

2. run the code:

cd $package
pip install -e .

Demo

We provide with several demos to demonstrate the functions in CAST package.

Users can use following code to open the Jupyter notebook (We recommend to use Chrome to open the jupyter notebook).

cd $demo_path
jupyter notebook

#### If remote kernel ####
jupyter notebook --ip=0.0.0.0 --port=8800

#### If dead kernel ####
jupyter notebook --NotebookApp.max_buffer_size=21474836480

Demo 1 CAST_Mark

In this demo, CAST_Mark can captures the common spatial features across multiple samples

Users should first replace $demo_path with the CAST demo Path in the first cell.

Demo 2 CAST_Stack_Align_S4_to_S1

In this demo, Stack_Align can align two samples together.

Users should first replace $demo_path with the CAST demo Path in the first cell.

Demo 3 CAST_project

In this demo, CAST_projectiong will project one sample to another one.

Users should first replace $demo_path with the CAST demo Path in the first cell.