Abstract
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 the above challenges, we introduce CAMEX, a heterogeneous Graph Neural Network (GNN) tool that leverages many-to-many homologous relationships for multi-species integration, alignment, and annotation of scRNA-seq data from multiple species. Notably, CAMEX outperforms state-of-the-art methods 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 enables the detection of 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 single-cell resolution.
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Data availability
The details of all datasets can be found in Supplementary Data 26. We preprocessed all raw data following the pipeline by Scanpy80 and upload the processed data in h5ad format to Google Driver. This dataset is freely accessible without requiring a password: https://drive.google.com/drive/folders/1rwdjEvWFEFw82a0x2JzMi2jXICbUc5eb?usp=sharing and the dataset can also be available at: https://figshare.com/articles/dataset/Dataset_for_CAMEX/31131808. Source data are provided with this paper.
Code availability
The source codes of CAMEX package, along with code and detailed tutorials for reproducibility, are available at https://github.com/zhanglabtools/CAMEX/ under MIT license, and in Zenodo (https://zenodo.org/records/17991379)81.
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Acknowledgements
This work has been supported by the National Key Research and Development Program of China [No. 2021YFA1302500 to S.Z.], the National Natural Science Foundation of China [Nos. 32341013, 12326614 to S.Z., Nos. 62333018, 62372255, 62432013, W2412087, 62402250, 62433001, U22A2039 to D.S.H.], the CAS Project for Young Scientists in Basic Research [No. YSBR-034 to S.Z.], the Zhejiang Province Vanguard Goose-Leading Initiative (No. 2025C01114 to S.Z.), the Natural Science Foundation of Zhejiang Province [No. LMS25F020001 to D.S.H.], the Key Research and Development Program of Ningbo City [Nos. 2024Z112, 2023Z219, 2023Z226 to D.S.H.], the Yongjiang Talent Project of Ningbo, Yongrencaifa [No. 2024-4 to D.S.H.], and the Basic Research Program Project of the Department of Science and Technology of Guizhou Province [No. ZK2024ZD035 to D.S.H)].
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S.Z. conceived and supervised the project. Z. G. collected the datasets and developed the algorithm. Z. G., D. H. and S.Z. performed the analyses and wrote the manuscript. All authors read and approved the final manuscript.
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Guo, ZH., Huang, DS. & Zhang, S. Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69696-3
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DOI: https://doi.org/10.1038/s41467-026-69696-3
