1. Introduction

COSMOS is a computational tool crafted to overcome the challenges associated with integrating spatially resolved multi-omics data. This software harnesses a graph neural network algorithm to deliver cutting-edge solutions for analyzing biological data that encompasses various omics types within a spatial framework. Key features of COSMOS include domain segmentation, effective visualization, and the creation of spatiotemporal maps. These capabilities empower researchers to gain a deeper understanding of the spatial and temporal dynamics within biological samples, distinguishing COSMOS from other tools that may only support single omics types or lack comprehensive spatial integration. The proven superior performance of COSMOS underscores its value as an essential resource in the realm of spatial omics.

Paper: Cooperative Integration of Spatially Resolved Multi-Omics Data with COSMOS, Zhou Y., X. Xiao, L. Dong, C. Tang, G. Xiao*, and L Xu*, 2024.

DOI for the Latest Released Version of the Github repository: 10.5281/zenodo.14114770.

2. Environment setup and code compilation

2.1. Download the package

The package can be downloaded by running the following command in the terminal:

git clone https://github.com/Lin-Xu-lab/COSMOS.git

Then, use

cd COSMOS

to access the downloaded folder.

If the "git clone" command does not work with your system, you can download the zip file from the website https://github.com/Lin-Xu-lab/COSMOS.git and decompress it. Then, the folder that you need to access is COSMOS-main.

2.2. Environment setup

The package has been successuflly tested in a Linux environment of python version 3.8.8, pandas version 1.5.2, and so on. An option to set up the environment is to use Conda (https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html).

You can use the following command to create an environment for COSMOS:

conda create -n cosmos python pandas numpy scanpy matplotlib umap-learn scikit-learn seaborn torch networkx gudhi anndata cmcrameri pytorch-geometric

After the environment is created, you can use the following command to activate it:

conda activate cosmos

Please install Jupyter Notebook from https://jupyter.org/install. For example, you can run

pip install notebook

in the terminal to install the classic Jupyter Notebook.

2.3. Import COSMOS in different directories (optional)

If you would like to import COSMOS in different directories, there is an option to make it work. Please run

python setup.py install --user &> log

in the terminal.

After doing these successfully, you are supposed to be able to import COSMOS when you are using Python or Jupyter Notebook in other folders:

import COSMOS

2.4. Using "pip install" to install the COSMOS package

Please run

pip install COSMOS-LinXuLab

in the terminal.

3. Tutorials

The step-by-step guides for closely replicating the COSMOS results on simulated mouse visual cortex multi-omics data, ATAC-RNA-Seq mouse brain multi-omics data, and DBiT-Seq mouse embryo multi-omics data are accessible at: Tutorials and COSMOS Tutorials on Read the Docs. Furthermore, all the processed data required to reproduce the figures presented in the manuscript can be found at Zenodo under the DOI: 10.5281/zenodo.13932144.