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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
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    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. Alexander Platzer, 2013. "Visualization of SNPs with t-SNE," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-6, February.
    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.
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