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. 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.
    2. 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.
    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. Michael J. Geuenich & Dae-won Gong & Kieran R. Campbell, 2024. "The impacts of active and self-supervised learning on efficient annotation of single-cell expression data," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. 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.
    5. 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.
    6. 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.
    7. Guocheng Fang & Zhen Qiao & Luqi Huang & Hui Zhu & Jun Xie & Tian Zhou & Zhongshu Xiong & I-Hsin Su & Dayong Jin & Yu-Cheng Chen, 2024. "Single-cell laser emitting cytometry for label-free nucleolus fingerprinting," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    8. Alastair Magness & Emma Colliver & Katey S. S. Enfield & Claudia Lee & Masako Shimato & Emer Daly & David A. Moore & Monica Sivakumar & Karishma Valand & Dina Levi & Crispin T. Hiley & Philip S. Hobso, 2024. "Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    9. Songming Tang & Xuejian Cui & Rongxiang Wang & Sijie Li & Siyu Li & Xin Huang & Shengquan Chen, 2024. "scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    10. 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.
    11. Sandra Tietscher & Johanna Wagner & Tobias Anzeneder & Claus Langwieder & Martin Rees & Bettina Sobottka & Natalie Souza & Bernd Bodenmiller, 2023. "A comprehensive single-cell map of T cell exhaustion-associated immune environments in human breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    12. 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.
    13. Vincent Geldhof & Laura P. M. H. Rooij & Liliana Sokol & Jacob Amersfoort & Maxim Schepper & Katerina Rohlenova & Griet Hoste & Adriaan Vanderstichele & Anne-Marie Delsupehe & Edoardo Isnaldi & Naima , 2022. "Single cell atlas identifies lipid-processing and immunomodulatory endothelial cells in healthy and malignant breast," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    14. 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.
    15. Minhui Chen & Andy Dahl, 2024. "A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    16. 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.
    17. Zhijian Li & Christoph Kuppe & Susanne Ziegler & Mingbo Cheng & Nazanin Kabgani & Sylvia Menzel & Martin Zenke & Rafael Kramann & Ivan G. Costa, 2021. "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    18. Nastaran Mohammadi Ghahhari & Magdalena K. Sznurkowska & Nicolas Hulo & Lilia Bernasconi & Nicola Aceto & Didier Picard, 2022. "Cooperative interaction between ERα and the EMT-inducer ZEB1 reprograms breast cancer cells for bone metastasis," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    19. 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.
    20. George C. Linderman & Jun Zhao & Manolis Roulis & Piotr Bielecki & Richard A. Flavell & Boaz Nadler & Yuval Kluger, 2022. "Zero-preserving imputation of single-cell RNA-seq data," Nature Communications, Nature, vol. 13(1), pages 1-11, 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.