IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-36134-7.html
   My bibliography  Save this article

Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA

Author

Listed:
  • Zhuohan Yu

    (Jilin University)

  • Yanchi Su

    (Jilin University)

  • Yifu Lu

    (Jilin University)

  • Yuning Yang

    (University of Toronto)

  • Fuzhou Wang

    (City University of Hong Kong)

  • Shixiong Zhang

    (City University of Hong Kong)

  • Yi Chang

    (Jilin University)

  • Ka-Chun Wong

    (City University of Hong Kong)

  • Xiangtao Li

    (Jilin University)

Abstract

Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36134-7
    DOI: 10.1038/s41467-023-36134-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-36134-7
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-36134-7?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. 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.
    2. Xiangjie Li & Kui Wang & Yafei Lyu & Huize Pan & Jingxiao Zhang & Dwight Stambolian & Katalin Susztak & Muredach P. Reilly & Gang Hu & Mingyao Li, 2020. "Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    3. Sara Mostafavi & Anna Goldenberg & Quaid Morris, 2012. "Labeling Nodes Using Three Degrees of Propagation," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-10, December.
    4. William Stephenson & Laura T. Donlin & Andrew Butler & Cristina Rozo & Bernadette Bracken & Ali Rashidfarrokhi & Susan M. Goodman & Lionel B. Ivashkiv & Vivian P. Bykerk & Dana E. Orange & Robert B. D, 2018. "Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    5. Duc Tran & Hung Nguyen & Bang Tran & Carlo La Vecchia & Hung N. Luu & Tin Nguyen, 2021. "Fast and precise single-cell data analysis using a hierarchical autoencoder," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    6. Gökcen Eraslan & Lukas M. Simon & Maria Mircea & Nikola S. Mueller & Fabian J. Theis, 2019. "Single-cell RNA-seq denoising using a deep count autoencoder," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    7. 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)

    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. Yasa Baig & Helena R. Ma & Helen Xu & Lingchong You, 2023. "Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics," Nature Communications, Nature, vol. 14(1), pages 1-17, 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. Zhenchao Tang & Guanxing Chen & Shouzhi Chen & Jianhua Yao & Linlin You & Calvin Yu-Chian Chen, 2024. "Modal-nexus auto-encoder for multi-modality cellular data integration and imputation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. 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.
    6. Qihuang Zhang & Shunzhou Jiang & Amelia Schroeder & Jian Hu & Kejie Li & Baohong Zhang & David Dai & Edward B. Lee & Rui Xiao & Mingyao Li, 2023. "Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    7. Hyun Kim & Won Chang & Seok Joo Chae & Jong-Eun Park & Minseok Seo & Jae Kyoung Kim, 2024. "scLENS: data-driven signal detection for unbiased scRNA-seq data analysis," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    8. Garrett Dunlap & Aaron Wagner & Nida Meednu & Ruoqiao Wang & Fan Zhang & Jabea Cyril Ekabe & Anna Helena Jonsson & Kevin Wei & Saori Sakaue & Aparna Nathan & Vivian P. Bykerk & Laura T. Donlin & Susan, 2024. "Clonal associations between lymphocyte subsets and functional states in rheumatoid arthritis synovium," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Ethan Bahl & Snehajyoti Chatterjee & Utsav Mukherjee & Muhammad Elsadany & Yann Vanrobaeys & Li-Chun Lin & Miriam McDonough & Jon Resch & K. Peter Giese & Ted Abel & Jacob J. Michaelson, 2024. "Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    14. 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.
    15. Kaichen Xu & Yan Lu & Suyang Hou & Kainan Liu & Yihang Du & Mengqian Huang & Hao Feng & Hao Wu & Xiaobo Sun, 2024. "Detecting anomalous anatomic regions in spatial transcriptomics with STANDS," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:14:y:2023:i:1:d:10.1038_s41467-023-36134-7. 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.