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Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis

Author

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  • Waaijenborg Sandra

    (Academic Medical Center / University of Amsterdam)

  • Verselewel de Witt Hamer Philip C.

    (Academic Medical Center / University of Amsterdam)

  • Zwinderman Aeilko H

    (Academic Medical Center / University of Amsterdam)

Abstract

Multiple changes at the DNA level are at the basis of complex diseases. Identifying the genetic networks that are influenced by these changes might help in understanding the development of these diseases. Canonical correlation analysis is used to associate gene expressions with DNA-markers and thus reveals sets of co-expressed and co-regulated genes and their associating DNA-markers. However, when the number of variables gets high, e.g. in the case of microarray studies, interpretation of these results can be difficult. By adapting the elastic net to canonical correlation analysis the number of variables reduces, and interpretation becomes easier, moreover, due to the grouping effect of the elastic net co-regulated and co-expressed genes cluster. Additionally, our adaptation works well in situations where the number of variables exceeds by far the number of subjects.

Suggested Citation

  • Waaijenborg Sandra & Verselewel de Witt Hamer Philip C. & Zwinderman Aeilko H, 2008. "Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-29, January.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:1:n:3
    DOI: 10.2202/1544-6115.1329
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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. Lê Cao Kim-Anh & Rossouw Debra & Robert-Granié Christèle & Besse Philippe, 2008. "A Sparse PLS for Variable Selection when Integrating Omics Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-32, November.
    3. 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.
    4. Melissa G Naylor & Xihong Lin & Scott T Weiss & Benjamin A Raby & Christoph Lange, 2010. "Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants," PLOS ONE, Public Library of Science, vol. 5(5), pages 1-6, May.
    5. Lykou, Anastasia & Whittaker, Joe, 2010. "Sparse CCA using a Lasso with positivity constraints," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3144-3157, December.
    6. 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.
    7. Feng, Qing & Jiang, Meilei & Hannig, Jan & Marron, J.S., 2018. "Angle-based joint and individual variation explained," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 241-265.

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