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MENDER: fast and scalable tissue structure identification in spatial omics data

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  • Zhiyuan Yuan

    (Fudan University Pudong Medical Center, Fudan University)

Abstract

Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question in the field. Inspired by the consistent observation of cellular neighborhood structures across various spatial technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), for tissue structure identification. Applied on datasets of 3 brain regions and a whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern complex models while automatically aligning labels across slices, despite using much less running time than the second-fastest. MENDER’s identification power allows the uncovering of previously overlooked spatial domains that exhibit strong associations with brain aging. MENDER’s scalability makes it freely appliable on a million-level brain spatial atlas. MENDER’s discriminative power enables the differentiation of breast cancer patient subtypes obscured by single-cell analysis.

Suggested Citation

  • Zhiyuan Yuan, 2024. "MENDER: fast and scalable tissue structure identification in spatial omics data," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44367-9
    DOI: 10.1038/s41467-023-44367-9
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    1. Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    2. Chunman Zuo & Yijian Zhang & Chen Cao & Jinwang Feng & Mingqi Jiao & Luonan Chen, 2022. "Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Honglei Ren & Benjamin L. Walker & Zixuan Cang & Qing Nie, 2022. "Identifying multicellular spatiotemporal organization of cells with SpaceFlow," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Nea, 2020. "Case law," Nuclear Law Bulletin, OECD Publishing, vol. 2020(1).
    5. Rongbo Shen & Lin Liu & Zihan Wu & Ying Zhang & Zhiyuan Yuan & Junfu Guo & Fan Yang & Chao Zhang & Bichao Chen & Wanwan Feng & Chao Liu & Jing Guo & Guozhen Fan & Yong Zhang & Yuxiang Li & Xun Xu & Ji, 2022. "Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    6. Zhiyuan Yuan & Yisi Li & Minglei Shi & Fan Yang & Juntao Gao & Jianhua Yao & Michael Q. Zhang, 2022. "SOTIP is a versatile method for microenvironment modeling with spatial omics data," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    7. Nea, 2020. "Case Law: 0," Nuclear Law Bulletin, OECD Publishing, vol. 2019(1).
    8. Anjali Rao & Dalia Barkley & Gustavo S. França & Itai Yanai, 2021. "Exploring tissue architecture using spatial transcriptomics," Nature, Nature, vol. 596(7871), pages 211-220, August.
    9. Kangning Dong & Shihua Zhang, 2022. "Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    10. Meng Zhang & Stephen W. Eichhorn & Brian Zingg & Zizhen Yao & Kaelan Cotter & Hongkui Zeng & Hongwei Dong & Xiaowei Zhuang, 2021. "Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH," Nature, Nature, vol. 598(7879), pages 137-143, October.
    11. Benjamin L. Walker & Qing Nie, 2023. "NeST: nested hierarchical structure identification in spatial transcriptomic data," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    12. Nea, 2020. "Case law: 0," Nuclear Law Bulletin, OECD Publishing, vol. 2019(2).
    13. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    14. Chee-Huat Linus Eng & Michael Lawson & Qian Zhu & Ruben Dries & Noushin Koulena & Yodai Takei & Jina Yun & Christopher Cronin & Christoph Karp & Guo-Cheng Yuan & Long Cai, 2019. "Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+," Nature, Nature, vol. 568(7751), pages 235-239, April.
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