IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-50194-3.html
   My bibliography  Save this article

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing

Author

Listed:
  • Zehua Zeng

    (University of Science and Technology Beijing
    University of Science and Technology Beijing)

  • Yuqing Ma

    (Tsinghua-Berkeley Shenzhen Institute
    Tsinghua Shenzhen International Graduate School)

  • Lei Hu

    (University of Science and Technology Beijing
    Westlake University)

  • Bowen Tan

    (Chinese Academy of Sciences
    University of Science and Technology Beijing)

  • Peng Liu

    (University of Science and Technology Beijing)

  • Yixuan Wang

    (University of Science and Technology Beijing)

  • Cencan Xing

    (University of Science and Technology Beijing
    University of Science and Technology Beijing)

  • Yuanyan Xiong

    (Guangzhou)

  • Hongwu Du

    (University of Science and Technology Beijing
    University of Science and Technology Beijing)

Abstract

Single-cell sequencing is frequently affected by “omission” due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly “omitted” cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of “omitted” cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

Suggested Citation

  • Zehua Zeng & Yuqing Ma & Lei Hu & Bowen Tan & Peng Liu & Yixuan Wang & Cencan Xing & Yuanyan Xiong & Hongwu Du, 2024. "OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50194-3
    DOI: 10.1038/s41467-024-50194-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-50194-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-50194-3?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. Jie Liao & Jingyang Qian & Yin Fang & Zhuo Chen & Xiang Zhuang & Ningyu Zhang & Xin Shao & Yining Hu & Penghui Yang & Junyun Cheng & Yang Hu & Lingqi Yu & Haihong Yang & Jinlu Zhang & Xiaoyan Lu & Li , 2022. "De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    2. Shobana V. Stassen & Gwinky G. K. Yip & Kenneth K. Y. Wong & Joshua W. K. Ho & Kevin K. Tsia, 2021. "Generalized and scalable trajectory inference in single-cell omics data with VIA," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    3. Yanshuo Chen & Yixuan Wang & Yuelong Chen & Yuqi Cheng & Yumeng Wei & Yunxiang Li & Jiuming Wang & Yingying Wei & Ting-Fung Chan & Yu Li, 2022. "Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    4. Xuran Wang & Jihwan Park & Katalin Susztak & Nancy R. Zhang & Mingyao Li, 2019. "Bulk tissue cell type deconvolution with multi-subject single-cell expression reference," Nature Communications, Nature, vol. 10(1), pages 1-9, 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. Bárbara Andrade Barbosa & Saskia D. Asten & Ji Won Oh & Arantza Farina-Sarasqueta & Joanne Verheij & Frederike Dijk & Hanneke W. M. Laarhoven & Bauke Ylstra & Juan J. Garcia Vallejo & Mark A. Wiel & Y, 2021. "Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Keyong Sun & Runda Xu & Fuhai Ma & Naixue Yang & Yang Li & Xiaofeng Sun & Peng Jin & Wenzhe Kang & Lemei Jia & Jianping Xiong & Haitao Hu & Yantao Tian & Xun Lan, 2022. "scRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    3. Chang Su & Zichun Xu & Xinning Shan & Biao Cai & Hongyu Zhao & Jingfei Zhang, 2023. "Cell-type-specific co-expression inference from single cell RNA-sequencing data," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. Vishnu Muraleedharan Saraswathy & Lili Zhou & Mayssa H. Mokalled, 2024. "Single-cell analysis of innate spinal cord regeneration identifies intersecting modes of neuronal repair," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    5. Xiao Zhou & Zhen Cheng & Mingyu Dong & Qi Liu & Weiyang Yang & Min Liu & Junzhang Tian & Weibin Cheng, 2022. "Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. David R. Ghasemi & Konstantin Okonechnikov & Anne Rademacher & Stephan Tirier & Kendra K. Maass & Hanna Schumacher & Piyush Joshi & Maxwell P. Gold & Julia Sundheimer & Britta Statz & Ahmet S. Rifaiog, 2024. "Compartments in medulloblastoma with extensive nodularity are connected through differentiation along the granular precursor lineage," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    7. Zhenzhen Xun & Xinyu Ding & Yao Zhang & Benyan Zhang & Shujing Lai & Duowu Zou & Junke Zheng & Guoqiang Chen & Bing Su & Leng Han & Youqiong Ye, 2023. "Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    8. Maja Olecka & Alena Bömmel & Lena Best & Madlen Haase & Silke Foerste & Konstantin Riege & Thomas Dost & Stefano Flor & Otto W. Witte & Sören Franzenburg & Marco Groth & Björn Eyss & Christoph Kaleta , 2024. "Nonlinear DNA methylation trajectories in aging male mice," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. Bryce Rowland & Ruth Huh & Zoey Hou & Cheynna Crowley & Jia Wen & Yin Shen & Ming Hu & Paola Giusti-Rodríguez & Patrick F Sullivan & Yun Li, 2022. "THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data," PLOS Genetics, Public Library of Science, vol. 18(3), pages 1-18, March.
    10. Jingyang Qian & Jie Liao & Ziqi Liu & Ying Chi & Yin Fang & Yanrong Zheng & Xin Shao & Bingqi Liu & Yongjin Cui & Wenbo Guo & Yining Hu & Hudong Bao & Penghui Yang & Qian Chen & Mingxiao Li & Bing Zha, 2023. "Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    11. Stefano Berto & Alex H. Treacher & Emre Caglayan & Danni Luo & Jillian R. Haney & Michael J. Gandal & Daniel H. Geschwind & Albert A. Montillo & Genevieve Konopka, 2022. "Association between resting-state functional brain connectivity and gene expression is altered in autism spectrum disorder," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    12. Zhiyuan Liu & Dafei Wu & Weiwei Zhai & Liang Ma, 2023. "SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Joschka Hey & Michelle Paulsen & Reka Toth & Dieter Weichenhan & Simone Butz & Jolanthe Schatterny & Reinhard Liebers & Pavlo Lutsik & Christoph Plass & Marcus A. Mall, 2021. "Epigenetic reprogramming of airway macrophages promotes polarization and inflammation in muco-obstructive lung disease," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    14. Jie Liao & Jingyang Qian & Yin Fang & Zhuo Chen & Xiang Zhuang & Ningyu Zhang & Xin Shao & Yining Hu & Penghui Yang & Junyun Cheng & Yang Hu & Lingqi Yu & Haihong Yang & Jinlu Zhang & Xiaoyan Lu & Li , 2022. "De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    15. Jolene S. Ranek & Wayne Stallaert & J. Justin Milner & Margaret Redick & Samuel C. Wolff & Adriana S. Beltran & Natalie Stanley & Jeremy E. Purvis, 2024. "DELVE: feature selection for preserving biological trajectories in single-cell data," Nature Communications, Nature, vol. 15(1), pages 1-26, December.
    16. Gavin J. Sutton & Daniel Poppe & Rebecca K. Simmons & Kieran Walsh & Urwah Nawaz & Ryan Lister & Johann A. Gagnon-Bartsch & Irina Voineagu, 2022. "Comprehensive evaluation of deconvolution methods for human brain gene expression," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    17. Michael T. H. Ng & Rowie Borst & Hamez Gacaferi & Sarah Davidson & Jessica E. Ackerman & Peter A. Johnson & Caio C. Machado & Ian Reekie & Moustafa Attar & Dylan Windell & Mariola Kurowska-Stolarska &, 2024. "A single cell atlas of frozen shoulder capsule identifies features associated with inflammatory fibrosis resolution," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    18. Daniel Charytonowicz & Rachel Brody & Robert Sebra, 2023. "Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    19. Kai Kang & Qian Meng & Igor Shats & David M Umbach & Melissa Li & Yuanyuan Li & Xiaoling Li & Leping Li, 2019. "CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-18, December.
    20. Kobe Ridder & Huiwen Che & Kaat Leroy & Bernard Thienpont, 2024. "Benchmarking of methods for DNA methylome deconvolution," Nature Communications, Nature, vol. 15(1), pages 1-17, 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_s41467-024-50194-3. 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.