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Joint association and classification analysis of multi‐view data

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  • Yunfeng Zhang
  • Irina Gaynanova

Abstract

Multi‐view data, which is matched sets of measurements on the same subjects, have become increasingly common with advances in multi‐omics technology. Often, it is of interest to find associations between the views that are related to the intrinsic class memberships. Existing association methods cannot directly incorporate class information, while existing classification methods do not take into account between‐views associations. In this work, we propose a framework for Joint Association and Classification Analysis of multi‐view data (JACA). Our goal is not to merely improve the misclassification rates, but to provide a latent representation of high‐dimensional data that is both relevant for the subtype discrimination and coherent across the views. We motivate the methodology by establishing a connection between canonical correlation analysis and discriminant analysis. We also establish the estimation consistency of JACA in high‐dimensional settings. A distinct advantage of JACA is that it can be applied to the multi‐view data with block‐missing structure, that is to cases where a subset of views or class labels is missing for some subjects. The application of JACA to quantify the associations between RNAseq and miRNA views with respect to consensus molecular subtypes in colorectal cancer data from The Cancer Genome Atlas project leads to improved misclassification rates and stronger found associations compared to existing methods.

Suggested Citation

  • Yunfeng Zhang & Irina Gaynanova, 2022. "Joint association and classification analysis of multi‐view data," Biometrics, The International Biometric Society, vol. 78(4), pages 1614-1625, December.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:4:p:1614-1625
    DOI: 10.1111/biom.13536
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    References listed on IDEAS

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    1. Irina Gaynanova & James G. Booth & Martin T. Wells, 2016. "Simultaneous Sparse Estimation of Canonical Vectors in the ≫ Setting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 696-706, April.
    2. P. Tseng, 2001. "Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization," Journal of Optimization Theory and Applications, Springer, vol. 109(3), pages 475-494, June.
    3. Witten Daniela M & Tibshirani Robert J., 2009. "Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-29, June.
    4. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    1. Kuangnan Fang & Jingmao Li & Qingzhao Zhang & Yaqing Xu & Shuangge Ma, 2023. "Pathological imaging‐assisted cancer gene–environment interaction analysis," Biometrics, The International Biometric Society, vol. 79(4), pages 3883-3894, December.

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