IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-37478-w.html
   My bibliography  Save this article

Comparative analysis of dimension reduction methods for cytometry by time-of-flight data

Author

Listed:
  • Kaiwen Wang

    (Southern Methodist University)

  • Yuqiu Yang

    (Southern Methodist University
    University of Texas Southwestern Medical Center)

  • Fangjiang Wu

    (University of Texas Southwestern Medical Center)

  • Bing Song

    (University of Texas Southwestern Medical Center)

  • Xinlei Wang

    (Southern Methodist University
    University of Texas at Arlington
    University of Texas at Arlington)

  • Tao Wang

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

Abstract

While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmark the performances of 21 DR methods on 110 real and 425 synthetic CyTOF samples. We find that less well-known methods like SAUCIE, SQuaD-MDS, and scvis are the overall best performers. In particular, SAUCIE and scvis are well balanced, SQuaD-MDS excels at structure preservation, whereas UMAP has great downstream analysis performance. We also find that t-SNE (along with SQuad-MDS/t-SNE Hybrid) possesses the best local structure preservation. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs.

Suggested Citation

  • Kaiwen Wang & Yuqiu Yang & Fangjiang Wu & Bing Song & Xinlei Wang & Tao Wang, 2023. "Comparative analysis of dimension reduction methods for cytometry by time-of-flight data," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37478-w
    DOI: 10.1038/s41467-023-37478-w
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-37478-w
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-37478-w?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. Dmitry Kobak & Philipp Berens, 2019. "The art of using t-SNE for single-cell transcriptomics," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    2. Chongzhi Zang & Tao Wang & Ke Deng & Bo Li & Sheng’en Hu & Qian Qin & Tengfei Xiao & Shihua Zhang & Clifford A. Meyer & Housheng Hansen He & Myles Brown & Jun S. Liu & Yang Xie & X. Shirley Liu, 2016. "High-dimensional genomic data bias correction and data integration using MANCIE," Nature Communications, Nature, vol. 7(1), pages 1-8, September.
    3. Hartland W. Jackson & Jana R. Fischer & Vito R. T. Zanotelli & H. Raza Ali & Robert Mechera & Savas D. Soysal & Holger Moch & Simone Muenst & Zsuzsanna Varga & Walter P. Weber & Bernd Bodenmiller, 2020. "The single-cell pathology landscape of breast cancer," Nature, Nature, vol. 578(7796), pages 615-620, February.
    4. Wei Vivian Li & Jingyi Jessica Li, 2018. "An accurate and robust imputation method scImpute for single-cell RNA-seq data," Nature Communications, Nature, vol. 9(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. 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.
    2. Juan Du & Junlei Zhang & Lin Wang & Xun Wang & Yaxing Zhao & Jiaoying Lu & Tingmin Fan & Meng Niu & Jie Zhang & Fei Cheng & Jun Li & Qi Zhu & Daoqiang Zhang & Hao Pei & Guang Li & Xingguang Liang & He, 2023. "Selective oxidative protection leads to tissue topological changes orchestrated by macrophage during ulcerative colitis," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. Yunhao Bai & Bokai Zhu & John-Paul Oliveria & Bryan J. Cannon & Dorien Feyaerts & Marc Bosse & Kausalia Vijayaragavan & Noah F. Greenwald & Darci Phillips & Christian M. Schürch & Samuel M. Naik & Edw, 2023. "Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    4. Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    5. Rui Chen & Jiasu Xu & Boqian Wang & Yi Ding & Aynur Abdulla & Yiyang Li & Lai Jiang & Xianting Ding, 2024. "SpiDe-Sr: blind super-resolution network for precise cell segmentation and clustering in spatial proteomics imaging," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    6. Rong Ma & Eric D. Sun & James Zou, 2023. "A spectral method for assessing and combining multiple data visualizations," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. L. Mathur & B. Szalai & N. H. Du & R. Utharala & M. Ballinger & J. J. M. Landry & M. Ryckelynck & V. Benes & J. Saez-Rodriguez & C. A. Merten, 2022. "Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Jing Qi & Yang Zhou & Zicen Zhao & Shuilin Jin, 2021. "SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-20, June.
    9. Stefanie Hiltbrunner & Lena Cords & Sabrina Kasser & Sandra N. Freiberger & Susanne Kreutzer & Nora C. Toussaint & Linda Grob & Isabelle Opitz & Michael Messerli & Martin Zoche & Alex Soltermann & Mar, 2023. "Acquired resistance to anti-PD1 therapy in patients with NSCLC associates with immunosuppressive T cell phenotype," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    10. Ziyan Huang & Myung Chung & Kentaro Tao & Akiyuki Watarai & Mu-Yun Wang & Hiroh Ito & Teruhiro Okuyama, 2023. "Ventromedial prefrontal neurons represent self-states shaped by vicarious fear in male mice," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    11. Md Tauhidul Islam & Jen-Yeu Wang & Hongyi Ren & Xiaomeng Li & Masoud Badiei Khuzani & Shengtian Sang & Lequan Yu & Liyue Shen & Wei Zhao & Lei Xing, 2022. "Leveraging data-driven self-consistency for high-fidelity gene expression recovery," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    12. Darci Phillips & Magdalena Matusiak & Belén Rivero Gutierrez & Salil S. Bhate & Graham L. Barlow & Sizun Jiang & Janos Demeter & Kimberly S. Smythe & Robert H. Pierce & Steven P. Fling & Nirasha Ramch, 2021. "Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    13. Thomas Hu & Mayar Allam & Shuangyi Cai & Walter Henderson & Brian Yueh & Aybuke Garipcan & Anton V. Ievlev & Maryam Afkarian & Semir Beyaz & Ahmet F. Coskun, 2023. "Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    14. Hui Li & Cory R. Brouwer & Weijun Luo, 2022. "A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    15. Xianke Xiang & Yao He & Zemin Zhang & Xuerui Yang, 2024. "Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    16. Hugo Croizer & Rana Mhaidly & Yann Kieffer & Geraldine Gentric & Lounes Djerroudi & Renaud Leclere & Floriane Pelon & Catherine Robley & Mylene Bohec & Arnaud Meng & Didier Meseure & Emanuela Romano &, 2024. "Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-28, December.
    17. Michele Bortolomeazzi & Lucia Montorsi & Damjan Temelkovski & Mohamed Reda Keddar & Amelia Acha-Sagredo & Michael J. Pitcher & Gianluca Basso & Luigi Laghi & Manuel Rodriguez-Justo & Jo Spencer & Fran, 2022. "A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    18. Shengen Shawn Hu & Lin Liu & Qi Li & Wenjing Ma & Michael J. Guertin & Clifford A. Meyer & Ke Deng & Tingting Zhang & Chongzhi Zang, 2022. "Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    19. Yael Amitay & Yuval Bussi & Ben Feinstein & Shai Bagon & Idan Milo & Leeat Keren, 2023. "CellSighter: a neural network to classify cells in highly multiplexed images," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    20. Peng Lu & Karolyn A. Oetjen & Diane E. Bender & Marianna B. Ruzinova & Daniel A. C. Fisher & Kevin G. Shim & Russell K. Pachynski & W. Nathaniel Brennen & Stephen T. Oh & Daniel C. Link & Daniel L. J., 2023. "IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry," Nature Communications, Nature, vol. 14(1), pages 1-16, 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_s41467-023-37478-w. 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.