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Exploring patterns enriched in a dataset with contrastive principal component analysis

Author

Listed:
  • Abubakar Abid

    (Stanford University)

  • Martin J. Zhang

    (Stanford University)

  • Vivek K. Bagaria

    (Stanford University)

  • James Zou

    (Stanford University
    Chan-Zuckerberg Biohub)

Abstract

Visualization and exploration of high-dimensional data is a ubiquitous challenge across disciplines. Widely used techniques such as principal component analysis (PCA) aim to identify dominant trends in one dataset. However, in many settings we have datasets collected under different conditions, e.g., a treatment and a control experiment, and we are interested in visualizing and exploring patterns that are specific to one dataset. This paper proposes a method, contrastive principal component analysis (cPCA), which identifies low-dimensional structures that are enriched in a dataset relative to comparison data. In a wide variety of experiments, we demonstrate that cPCA with a background dataset enables us to visualize dataset-specific patterns missed by PCA and other standard methods. We further provide a geometric interpretation of cPCA and strong mathematical guarantees. An implementation of cPCA is publicly available, and can be used for exploratory data analysis in many applications where PCA is currently used.

Suggested Citation

  • Abubakar Abid & Martin J. Zhang & Vivek K. Bagaria & James Zou, 2018. "Exploring patterns enriched in a dataset with contrastive principal component analysis," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04608-8
    DOI: 10.1038/s41467-018-04608-8
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    Cited by:

    1. Sheng Zhang & Xinyuan Xie & Haibin Qu, 2023. "A data-driven workflow for evaporation performance degradation analysis: a full-scale case study in the herbal medicine manufacturing industry," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 651-668, February.

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