CAMEX: Leveraging Heterogeneous Graph Neural Network for Multi-Species scRNA-seq data integration, alignment and annotation
- CAMEX integrates liver scRNA-seq dataset across four species
- CAMEX integrates testis scRNA-seq dataset across 11 species
- CAMEX aligns various development stages of seven organs across seven different species
- CAMEX could achieve more accurate integration and annotation performance in both relatives and distant species
- CAMEX facilitates the discovery of new populations and markers in Primate dlPFC
Overview of CAMEX
a. Single-cell RNA-seq (scRNA-seq) data from multiple species present remarkable opportunities to explore cellular origins and evolution. However, integrating and annotating scRNA-seq data across different species remains challenging due to the variations in sequencing techniques, ambiguity of homologous relationships, and limited biological knowledge. To tackle above challenges, we introduce CAMEX, a heterogeneous Graph Neural Network (GNN) tool which leverages many-to-many homologous relationships for integration, alignment and annotation of scRNA-seq data from multiple species. Notably, CAMEX outperforms state-of-the-art (SOTA) methods in terms of integration on various cross-species benchmarking datasets (ranging from one to eleven species). Besides, CAMEX facilitates the alignment of diverse species across different developmental stages, significantly enhancing our understanding of organ and organism origins. Furthermore, CAMEX makes it easier to detect species-specific cell types and marker genes through cell and gene embedding. In short, CAMEX holds the potential to provide invaluable insights into how evolutionary forces operate across different species at the single cell resolution.
Installation
It’s recommended to create a separate conda environment for running CAMEX:
#create an environment called CAMEX
conda create -n CAMEX python==3.9
#activate your environment
conda activate CAMEX
Install all the required packages.
conda install cudatoolkit=11.6 -c conda-forge
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install dgl-cu116 -f https://data.dgl.ai/wheels/repo.html
The other versions of pytorch and dgl can be installed from [torch](https://pytorch.org/) and [dgl](https://www.dgl.ai/pages/start.html).
Clone the repository.
git clone https://github.com/zhanglabtools/CAMEX.git
cd CAMEX-main
cd CAMEX
python setup.py bdist_wheel sdist
cd dist
pip install CAMEX-0.0.2.tar.gz
Citation
CAMEX: Leveraging Heterogeneous Graph Neural Network for Multi-Species scRNA-seq data integration, alignment and annotation