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Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data

Author

Listed:
  • Witten Daniela M

    (Stanford University)

  • Tibshirani Robert J.

    (Stanford University)

Abstract

In recent work, several authors have introduced methods for sparse canonical correlation analysis (sparse CCA). Suppose that two sets of measurements are available on the same set of observations. Sparse CCA is a method for identifying sparse linear combinations of the two sets of variables that are highly correlated with each other. It has been shown to be useful in the analysis of high-dimensional genomic data, when two sets of assays are available on the same set of samples. In this paper, we propose two extensions to the sparse CCA methodology. (1) Sparse CCA is an unsupervised method; that is, it does not make use of outcome measurements that may be available for each observation (e.g., survival time or cancer subtype). We propose an extension to sparse CCA, which we call sparse supervised CCA, which results in the identification of linear combinations of the two sets of variables that are correlated with each other and associated with the outcome. (2) It is becoming increasingly common for researchers to collect data on more than two assays on the same set of samples; for instance, SNP, gene expression, and DNA copy number measurements may all be available. We develop sparse multiple CCA in order to extend the sparse CCA methodology to the case of more than two data sets. We demonstrate these new methods on simulated data and on a recently published and publicly available diffuse large B-cell lymphoma data set.

Suggested Citation

  • 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.
  • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:28
    DOI: 10.2202/1544-6115.1470
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    1. Michael Morley & Cliona M. Molony & Teresa M. Weber & James L. Devlin & Kathryn G. Ewens & Richard S. Spielman & Vivian G. Cheung, 2004. "Genetic analysis of genome-wide variation in human gene expression," Nature, Nature, vol. 430(7001), pages 743-747, August.
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    Cited by:

    1. Wang, Wenjia & Zhou, Yi-Hui, 2021. "Eigenvector-based sparse canonical correlation analysis: Fast computation for estimation of multiple canonical vectors," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    2. Zhang Fan & Miecznikowski Jeffrey C. & Tritchler David L., 2020. "Identification of supervised and sparse functional genomic pathways," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(1), pages 1-27, February.
    3. 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.
    4. Sandra E. Safo & Eun Jeong Min & Lillian Haine, 2022. "Sparse linear discriminant analysis for multiview structured data," Biometrics, The International Biometric Society, vol. 78(2), pages 612-623, June.
    5. Efrat Muller & Itamar Shiryan & Elhanan Borenstein, 2024. "Multi-omic integration of microbiome data for identifying disease-associated modules," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. Lee Woojoo & Lee Donghwan & Lee Youngjo & Pawitan Yudi, 2011. "Sparse Canonical Covariance Analysis for High-throughput Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-24, July.
    7. Ronglai Shen & Qianxing Mo & Nikolaus Schultz & Venkatraman E Seshan & Adam B Olshen & Jason Huse & Marc Ladanyi & Chris Sander, 2012. "Integrative Subtype Discovery in Glioblastoma Using iCluster," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
    8. Jose A Seoane & Colin Campbell & Ian N M Day & Juan P Casas & Tom R Gaunt, 2014. "Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-13, October.
    9. Bin Li & Hyunjin Shin & Georgy Gulbekyan & Olga Pustovalova & Yuri Nikolsky & Andrew Hope & Marina Bessarabova & Matthew Schu & Elona Kolpakova-Hart & David Merberg & Andrew Dorner & William L Trepicc, 2015. "Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-20, June.
    10. Nam D Nguyen & Daifeng Wang, 2020. "Multiview learning for understanding functional multiomics," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-26, April.
    11. Dmitry Kobak & Yves Bernaerts & Marissa A. Weis & Federico Scala & Andreas S. Tolias & Philipp Berens, 2021. "Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 980-1000, August.
    12. Langworthy, Benjamin W. & Stephens, Rebecca L. & Gilmore, John H. & Fine, Jason P., 2021. "Canonical correlation analysis for elliptical copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    13. Iaci, Ross & Sriram, T.N., 2013. "Robust multivariate association and dimension reduction using density divergences," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 281-295.
    14. Bayarbaatar Amgalan & Hyunju Lee, 2014. "WMAXC: A Weighted Maximum Clique Method for Identifying Condition-Specific Sub-Network," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
    15. Diptavo Dutta & Yuan He & Ashis Saha & Marios Arvanitis & Alexis Battle & Nilanjan Chatterjee, 2022. "Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    16. Palzer, Elise F. & Wendt, Christine H. & Bowler, Russell P. & Hersh, Craig P. & Safo, Sandra E. & Lock, Eric F., 2022. "sJIVE: Supervised joint and individual variation explained," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    17. Coleman Jacob & Replogle Joseph & Chandler Gabriel & Hardin Johanna, 2016. "Resistant multiple sparse canonical correlation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(2), pages 123-138, April.
    18. Tenenhaus, Arthur & Philippe, Cathy & Frouin, Vincent, 2015. "Kernel Generalized Canonical Correlation Analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 114-131.
    19. Chalise, Prabhakar & Fridley, Brooke L., 2012. "Comparison of penalty functions for sparse canonical correlation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 245-254.
    20. Jung, Sungkyu, 2018. "Continuum directions for supervised dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 27-43.

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