IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-37168-7.html
   My bibliography  Save this article

A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics

Author

Listed:
  • Haoyang Li

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Juexiao Zhou

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Zhongxiao Li

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Siyuan Chen

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Xingyu Liao

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Bin Zhang

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Ruochi Zhang

    (Syneron Technology)

  • Yu Wang

    (Syneron Technology)

  • Shiwei Sun

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xin Gao

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

Abstract

Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction of tissue architecture. Due to the existence of low-resolution spots in recent spatial transcriptomics technologies, uncovering cellular heterogeneity is crucial for disentangling the spatial patterns of cell types, and many related methods have been proposed. Here, we benchmark 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods. We compare these methods comprehensively using different metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. In terms of performance, CARD, Cell2location, and Tangram are the best methods for conducting the cellular deconvolution task. To refine our comparative results, we provide decision-tree-style guidelines and recommendations for method selection and their additional features, which will help users easily choose the best method for fulfilling their concerns.

Suggested Citation

  • Haoyang Li & Juexiao Zhou & Zhongxiao Li & Siyuan Chen & Xingyu Liao & Bin Zhang & Ruochi Zhang & Yu Wang & Shiwei Sun & Xin Gao, 2023. "A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37168-7
    DOI: 10.1038/s41467-023-37168-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-37168-7
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-37168-7?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. Brendan F. Miller & Feiyang Huang & Lyla Atta & Arpan Sahoo & Jean Fan, 2022. "Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Emelie Berglund & Jonas Maaskola & Niklas Schultz & Stefanie Friedrich & Maja Marklund & Joseph Bergenstråhle & Firas Tarish & Anna Tanoglidi & Sanja Vickovic & Ludvig Larsson & Fredrik Salmén & Chri, 2018. "Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    3. Patrick Danaher & Youngmi Kim & Brenn Nelson & Maddy Griswold & Zhi Yang & Erin Piazza & Joseph M. Beechem, 2022. "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Franziska Hildebrandt & Alma Andersson & Sami Saarenpää & Ludvig Larsson & Noémi Van Hul & Sachie Kanatani & Jan Masek & Ewa Ellis & Antonio Barragan & Annelie Mollbrink & Emma R. Andersson & Joakim L, 2021. "Spatial Transcriptomics to define transcriptional patterns of zonation and structural components in the mouse liver," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    5. 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.
    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. Tianci Song & Charles Broadbent & Rui Kuang, 2023. "GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations," Nature Communications, Nature, vol. 14(1), pages 1-17, 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. S. Vickovic & B. Lötstedt & J. Klughammer & S. Mages & Å Segerstolpe & O. Rozenblatt-Rosen & A. Regev, 2022. "SM-Omics is an automated platform for high-throughput spatial multi-omics," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Wei Liu & Xu Liao & Ziye Luo & Yi Yang & Mai Chan Lau & Yuling Jiao & Xingjie Shi & Weiwei Zhai & Hongkai Ji & Joe Yeong & Jin Liu, 2023. "Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. 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.
    4. Xinrui Zhou & Wan Yi Seow & Norbert Ha & Teh How Cheng & Lingfan Jiang & Jeeranan Boonruangkan & Jolene Jie Lin Goh & Shyam Prabhakar & Nigel Chou & Kok Hao Chen, 2024. "Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. 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.
    6. Yuchen Liang & Guowei Shi & Runlin Cai & Yuchen Yuan & Ziying Xie & Long Yu & Yingjian Huang & Qian Shi & Lizhe Wang & Jun Li & Zhonghui Tang, 2024. "PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    7. Zixiang Zhou & Yunshan Zhong & Zemin Zhang & Xianwen Ren, 2023. "Spatial transcriptomics deconvolution at single-cell resolution using Redeconve," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    8. Johannes Wirth & Nina Huber & Kelvin Yin & Sophie Brood & Simon Chang & Celia P. Martinez-Jimenez & Matthias Meier, 2023. "Spatial transcriptomics using multiplexed deterministic barcoding in tissue," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    9. 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.
    10. Maja Marklund & Niklas Schultz & Stefanie Friedrich & Emelie Berglund & Firas Tarish & Anna Tanoglidi & Yao Liu & Ludvig Bergenstråhle & Andrew Erickson & Thomas Helleday & Alastair D. Lamb & Erik Son, 2022. "Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    11. Kalen Clifton & Manjari Anant & Gohta Aihara & Lyla Atta & Osagie K. Aimiuwu & Justus M. Kebschull & Michael I. Miller & Daniel Tward & Jean Fan, 2023. "STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    12. Zhaohui Cao & Wenlong Zuo & Lanxiang Wang & Junyu Chen & Zepeng Qu & Fan Jin & Lei Dai, 2023. "Spatial profiling of microbial communities by sequential FISH with error-robust encoding," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    13. 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.
    14. Ian Covert & Rohan Gala & Tim Wang & Karel Svoboda & Uygar Sümbül & Su-In Lee, 2023. "Predictive and robust gene selection for spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    15. Ziyang Tang & Zuotian Li & Tieying Hou & Tonglin Zhang & Baijian Yang & Jing Su & Qianqian Song, 2023. "SiGra: single-cell spatial elucidation through an image-augmented graph transformer," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    16. Thomas M. Goralski & Lindsay Meyerdirk & Libby Breton & Laura Brasseur & Kevin Kurgat & Daniella DeWeerd & Lisa Turner & Katelyn Becker & Marie Adams & Daniel J. Newhouse & Michael X. Henderson, 2024. "Spatial transcriptomics reveals molecular dysfunction associated with cortical Lewy pathology," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    17. Linhua Wang & Mirjana Maletic-Savatic & Zhandong Liu, 2022. "Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    18. Beibei Ru & Jinlin Huang & Yu Zhang & Kenneth Aldape & Peng Jiang, 2023. "Estimation of cell lineages in tumors from spatial transcriptomics data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    19. 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.
    20. 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.

    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_s41467-023-37168-7. 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.