IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-36492-2.html
   My bibliography  Save this article

A spectral method for assessing and combining multiple data visualizations

Author

Listed:
  • Rong Ma

    (Stanford University)

  • Eric D. Sun

    (Stanford University)

  • James Zou

    (Stanford University)

Abstract

Dimension reduction is an indispensable part of modern data science, and many algorithms have been developed. However, different algorithms have their own strengths and weaknesses, making it important to evaluate their relative performance, and to leverage and combine their individual strengths. This paper proposes a spectral method for assessing and combining multiple visualizations of a given dataset produced by diverse algorithms. The proposed method provides a quantitative measure – the visualization eigenscore – of the relative performance of the visualizations for preserving the structure around each data point. It also generates a consensus visualization, having improved quality over individual visualizations in capturing the underlying structure. Our approach is flexible and works as a wrapper around any visualizations. We analyze multiple real-world datasets to demonstrate the effectiveness of the method. We also provide theoretical justifications based on a general statistical framework, yielding several fundamental principles along with practical guidance.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36492-2
    DOI: 10.1038/s41467-023-36492-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-36492-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-36492-2?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. Alexander Platzer, 2013. "Visualization of SNPs with t-SNE," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-6, February.
    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. Tetsutaro Hayashi & Haruka Ozaki & Yohei Sasagawa & Mana Umeda & Hiroki Danno & Itoshi Nikaido, 2018. "Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs," Nature Communications, Nature, vol. 9(1), pages 1-16, 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. Zhexuan Liu & Rong Ma & Yiqiao Zhong, 2025. "Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    2. Kai Battenberg & S. Thomas Kelly & Radu Abu Ras & Nicola A. Hetherington & Makoto Hayashi & Aki Minoda, 2022. "A flexible cross-platform single-cell data processing pipeline," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    3. Hinako M Takase & Tappei Mishina & Tetsutaro Hayashi & Mika Yoshimura & Mariko Kuse & Itoshi Nikaido & Tomoya S Kitajima, 2024. "Transcriptomic signatures of WNT-driven pathways and granulosa cell-oocyte interactions during primordial follicle activation," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-26, October.
    4. 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.
    5. 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.
    6. Wanying Wu & Jinyang Zhang & Xiaofei Cao & Zhengyi Cai & Fangqing Zhao, 2022. "Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing," Nature Communications, Nature, vol. 13(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. Andriana Manousidaki & Anna Little & Yuying Xie, 2024. "Clustering and visualization of single-cell RNA-seq data using path metrics," PLOS Computational Biology, Public Library of Science, vol. 20(5), pages 1-19, May.
    9. 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.
    10. Marissa A. Weis & Stelios Papadopoulos & Laura Hansel & Timo Lüddecke & Brendan Celii & Paul G. Fahey & Eric Y. Wang & J. Alexander Bae & Agnes L. Bodor & Derrick Brittain & JoAnn Buchanan & Daniel J., 2025. "An unsupervised map of excitatory neuron dendritic morphology in the mouse visual cortex," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    11. 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.
    12. Ramezani, Zahra & Pourdarvish, Ahmad, 2021. "Transfer learning using Tsallis entropy: An application to Gravity Spy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    13. Yuki Takakura & Moeka Machida & Natsumi Terada & Yuka Katsumi & Seika Kawamura & Kenta Horie & Maki Miyauchi & Tatsuya Ishikawa & Nobuko Akiyama & Takao Seki & Takahisa Miyao & Mio Hayama & Rin Endo &, 2024. "Mitochondrial protein C15ORF48 is a stress-independent inducer of autophagy that regulates oxidative stress and autoimmunity," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    14. Lucy Xia & Christy Lee & Jingyi Jessica Li, 2024. "Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    15. Mohammad Abbasi & Connor R Sanderford & Narendiran Raghu & Mirjeta Pasha & Benjamin B Bartelle, 2023. "Sparse representation learning derives biological features with explicit gene weights from the Allen Mouse Brain Atlas," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-16, March.
    16. Lijun Cheng & Pratik Karkhanis & Birkan Gokbag & Yueze Liu & Lang Li, 2022. "DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data," PLOS Computational Biology, Public Library of Science, vol. 18(4), pages 1-22, April.
    17. Mikhael D. Manurung & Friederike Sonnet & Marie-Astrid Hoogerwerf & Jacqueline J. Janse & Yvonne Kruize & Laura de Bes-Roeleveld & Marion König & Alex Loukas & Benjamin G. Dewals & Taniawati Supali & , 2024. "Controlled human hookworm infection remodels plasmacytoid dendritic cells and regulatory T cells towards profiles seen in natural infections in endemic areas," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    18. Parashar Dhapola & Johan Rodhe & Rasmus Olofzon & Thomas Bonald & Eva Erlandsson & Shamit Soneji & Göran Karlsson, 2022. "Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    19. Wen Jiang & Eric J. Jaehnig & Yuxing Liao & Zhiao Shi & Tomer M. Yaron-Barir & Jared L. Johnson & Lewis C. Cantley & Bing Zhang, 2025. "Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    20. Caitlin E. Carey & Rebecca Shafee & Robbee Wedow & Amanda Elliott & Duncan S. Palmer & John Compitello & Masahiro Kanai & Liam Abbott & Patrick Schultz & Konrad J. Karczewski & Samuel C. Bryant & Caro, 2024. "Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation," Nature Human Behaviour, Nature, vol. 8(8), pages 1599-1615, August.

    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_s41467-023-36492-2. 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.