IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-023-44570-8.html
   My bibliography  Save this article

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer

Author

Listed:
  • Xiaoying Wang

    (Shandong University
    College of Medicine, The Ohio State University
    The James Comprehensive Cancer Center, The Ohio State University)

  • Maoteng Duan

    (Shandong University)

  • Jingxian Li

    (Shandong University)

  • Anjun Ma

    (College of Medicine, The Ohio State University
    The James Comprehensive Cancer Center, The Ohio State University)

  • Gang Xin

    (The James Comprehensive Cancer Center, The Ohio State University)

  • Dong Xu

    (University of Missouri
    University of Missouri)

  • Zihai Li

    (The James Comprehensive Cancer Center, The Ohio State University)

  • Bingqiang Liu

    (Shandong University)

  • Qin Ma

    (College of Medicine, The Ohio State University
    The James Comprehensive Cancer Center, The Ohio State University)

Abstract

Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduce MarsGT: Multi-omics Analysis for Rare population inference using a Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperforms existing tools in identifying rare cells across 550 simulated and four real human datasets. In mouse retina data, it reveals unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detects an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identifies a rare MAIT-like population impacted by a high IFN-I response and reveals the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

Suggested Citation

  • Xiaoying Wang & Maoteng Duan & Jingxian Li & Anjun Ma & Gang Xin & Dong Xu & Zihai Li & Bingqiang Liu & Qin Ma, 2024. "MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44570-8
    DOI: 10.1038/s41467-023-44570-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-44570-8
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-44570-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Aashi Jindal & Prashant Gupta & Jayadeva & Debarka Sengupta, 2018. "Discovery of rare cells from voluminous single cell expression data," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    2. Anjun Ma & Xiaoying Wang & Jingxian Li & Cankun Wang & Tong Xiao & Yuntao Liu & Hao Cheng & Juexin Wang & Yang Li & Yuzhou Chang & Jinpu Li & Duolin Wang & Yuexu Jiang & Li Su & Gang Xin & Shaopeng Gu, 2023. "Single-cell biological network inference using a heterogeneous graph transformer," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. Katharina T. Schmid & Barbara Höllbacher & Cristiana Cruceanu & Anika Böttcher & Heiko Lickert & Elisabeth B. Binder & Fabian J. Theis & Matthias Heinig, 2021. "scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    4. Juexin Wang & Anjun Ma & Yuzhou Chang & Jianting Gong & Yuexu Jiang & Ren Qi & Cankun Wang & Hongjun Fu & Qin Ma & Dong Xu, 2021. "scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    5. Botao Fa & Ting Wei & Yuan Zhou & Luke Johnston & Xin Yuan & Yanran Ma & Yue Zhang & Zhangsheng Yu, 2021. "GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    6. Kenji Kamimoto & Blerta Stringa & Christy M. Hoffmann & Kunal Jindal & Lilianna Solnica-Krezel & Samantha A. Morris, 2023. "Dissecting cell identity via network inference and in silico gene perturbation," Nature, Nature, vol. 614(7949), pages 742-751, February.
    7. Eirini Arvaniti & Manfred Claassen, 2017. "Sensitive detection of rare disease-associated cell subsets via representation learning," Nature Communications, Nature, vol. 8(1), pages 1-10, April.
    8. Suoqin Jin & Christian F. Guerrero-Juarez & Lihua Zhang & Ivan Chang & Raul Ramos & Chen-Hsiang Kuan & Peggy Myung & Maksim V. Plikus & Qing Nie, 2021. "Inference and analysis of cell-cell communication using CellChat," Nature Communications, Nature, vol. 12(1), pages 1-20, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yunpei Xu & Shaokai Wang & Qilong Feng & Jiazhi Xia & Yaohang Li & Hong-Dong Li & Jianxin Wang, 2024. "scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data," Nature Communications, Nature, vol. 15(1), pages 1-20, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yunpei Xu & Shaokai Wang & Qilong Feng & Jiazhi Xia & Yaohang Li & Hong-Dong Li & Jianxin Wang, 2024. "scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Jingtao Wang & Gregory J. Fonseca & Jun Ding, 2024. "scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning," Nature Communications, Nature, vol. 15(1), pages 1-27, December.
    3. Anjun Ma & Xiaoying Wang & Jingxian Li & Cankun Wang & Tong Xiao & Yuntao Liu & Hao Cheng & Juexin Wang & Yang Li & Yuzhou Chang & Jinpu Li & Duolin Wang & Yuexu Jiang & Li Su & Gang Xin & Shaopeng Gu, 2023. "Single-cell biological network inference using a heterogeneous graph transformer," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    4. Gregory Farber & Yanhan Dong & Qiaozi Wang & Mitesh Rathod & Haofei Wang & Michelle Dixit & Benjamin Keepers & Yifang Xie & Kendall Butz & William J. Polacheck & Jiandong Liu & Li Qian, 2024. "Direct conversion of cardiac fibroblasts into endothelial-like cells using Sox17 and Erg," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Zhuohan Yu & Yanchi Su & Yifu Lu & Yuning Yang & Fuzhou Wang & Shixiong Zhang & Yi Chang & Ka-Chun Wong & Xiangtao Li, 2023. "Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    6. Yanchuan Li & Huamei Li & Cheng Peng & Ge Meng & Yijun Lu & Honglin Liu & Li Cui & Huan Zhou & Zhu Xu & Lingyun Sun & Lihong Liu & Qing Xiong & Beicheng Sun & Shiping Jiao, 2024. "Unraveling the spatial organization and development of human thymocytes through integration of spatial transcriptomics and single-cell multi-omics profiling," Nature Communications, Nature, vol. 15(1), pages 1-25, December.
    7. Tim Flerlage & Jeremy Chase Crawford & E. Kaitlynn Allen & Danielle Severns & Shaoyuan Tan & Sherri Surman & Granger Ridout & Tanya Novak & Adrienne Randolph & Alina N. West & Paul G. Thomas, 2023. "Single cell transcriptomics identifies distinct profiles in pediatric acute respiratory distress syndrome," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    8. Shirong Cao & Yu Pan & Andrew S. Terker & Juan Pablo Arroyo Ornelas & Yinqiu Wang & Jiaqi Tang & Aolei Niu & Sarah Abu Kar & Mengdi Jiang & Wentian Luo & Xinyu Dong & Xiaofeng Fan & Suwan Wang & Matth, 2023. "Epidermal growth factor receptor activation is essential for kidney fibrosis development," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    9. Christopher Bono & Yang Liu & Alexander Ferrena & Aneesa Valentine & Deyou Zheng & Bernice E. Morrow, 2023. "Single-cell transcriptomics uncovers a non-autonomous Tbx1-dependent genetic program controlling cardiac neural crest cell development," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    10. Lichun Ma & Sophia Heinrich & Limin Wang & Friederike L. Keggenhoff & Subreen Khatib & Marshonna Forgues & Michael Kelly & Stephen M. Hewitt & Areeba Saif & Jonathan M. Hernandez & Donna Mabry & Roman, 2022. "Multiregional single-cell dissection of tumor and immune cells reveals stable lock-and-key features in liver cancer," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    11. Qingnan Liang & Yuefan Huang & Shan He & Ken Chen, 2023. "Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    12. Junyi Chen & Xiaoying Wang & Anjun Ma & Qi-En Wang & Bingqiang Liu & Lang Li & Dong Xu & Qin Ma, 2022. "Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    13. Faith H. Brennan & Yang Li & Cankun Wang & Anjun Ma & Qi Guo & Yi Li & Nicole Pukos & Warren A. Campbell & Kristina G. Witcher & Zhen Guan & Kristina A. Kigerl & Jodie C. E. Hall & Jonathan P. Godbout, 2022. "Microglia coordinate cellular interactions during spinal cord repair in mice," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    14. Sandra Curras-Alonso & Juliette Soulier & Thomas Defard & Christian Weber & Sophie Heinrich & Hugo Laporte & Sophie Leboucher & Sonia Lameiras & Marie Dutreix & Vincent Favaudon & Florian Massip & Tho, 2023. "An interactive murine single-cell atlas of the lung responses to radiation injury," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    15. Ilmatar Rooda & Jasmin Hassan & Jie Hao & Magdalena Wagner & Elisabeth Moussaud-Lamodière & Kersti Jääger & Marjut Otala & Katri Knuus & Cecilia Lindskog & Kiriaki Papaikonomou & Sebastian Gidlöf & Ce, 2024. "In-depth analysis of transcriptomes in ovarian cortical follicles from children and adults reveals interfollicular heterogeneity," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    16. Moujtaba Y. Kasmani & Paytsar Topchyan & Ashley K. Brown & Ryan J. Brown & Xiaopeng Wu & Yao Chen & Achia Khatun & Donia Alson & Yue Wu & Robert Burns & Chien-Wei Lin & Matthew R. Kudek & Jie Sun & We, 2023. "A spatial sequencing atlas of age-induced changes in the lung during influenza infection," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    17. Wei Yang & Li-Bo Liu & Feng-Liang Liu & Yan-Hua Wu & Zi-Da Zhen & Dong-Ying Fan & Zi-Yang Sheng & Zheng-Ran Song & Jia-Tong Chang & Yong-Tang Zheng & Jing An & Pei-Gang Wang, 2023. "Single-cell RNA sequencing reveals the fragility of male spermatogenic cells to Zika virus-induced complement activation," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    18. Luke Simpson & Andrew Strange & Doris Klisch & Sophie Kraunsoe & Takuya Azami & Daniel Goszczynski & Triet Minh & Benjamin Planells & Nadine Holmes & Fei Sang & Sonal Henson & Matthew Loose & Jennifer, 2024. "A single-cell atlas of pig gastrulation as a resource for comparative embryology," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    19. Erick Armingol & Hratch M. Baghdassarian & Cameron Martino & Araceli Perez-Lopez & Caitlin Aamodt & Rob Knight & Nathan E. Lewis, 2022. "Context-aware deconvolution of cell–cell communication with Tensor-cell2cell," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    20. Baptiste Lamarthée & Jasper Callemeyn & Yannick Van Herck & Asier Antoranz & Dany Anglicheau & Patrick Boada & Jan Ulrich Becker & Tim Debyser & Frederik De Smet & Katrien De Vusser & Maëva Eloudzeri , 2023. "Transcriptional and spatial profiling of the kidney allograft unravels a central role for FcyRIII+ innate immune cells in rejection," Nature Communications, Nature, vol. 14(1), pages 1-22, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44570-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.