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A Sparse PLS for Variable Selection when Integrating Omics Data

Author

Listed:
  • Lê Cao Kim-Anh

    (INRA UR 631 and Université de Toulouse)

  • Rossouw Debra

    (University of Stellenbosch)

  • Robert-Granié Christèle

    (INRA UR 631)

  • Besse Philippe

    (Université de Toulouse)

Abstract

Recent biotechnology advances allow for multiple types of omics data, such as transcriptomic, proteomic or metabolomic data sets to be integrated. The problem of feature selection has been addressed several times in the context of classification, but needs to be handled in a specific manner when integrating data. In this study, we focus on the integration of two-block data that are measured on the same samples. Our goal is to combine integration and simultaneous variable selection of the two data sets in a one-step procedure using a Partial Least Squares regression (PLS) variant to facilitate the biologists' interpretation. A novel computational methodology called ``sparse PLS" is introduced for a predictive analysis to deal with these newly arisen problems. The sparsity of our approach is achieved with a Lasso penalization of the PLS loading vectors when computing the Singular Value Decomposition.Sparse PLS is shown to be effective and biologically meaningful. Comparisons with classical PLS are performed on a simulated data set and on real data sets. On one data set, a thorough biological interpretation of the obtained results is provided. We show that sparse PLS provides a valuable variable selection tool for highly dimensional data sets.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:1:n:35
    DOI: 10.2202/1544-6115.1390
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    References listed on IDEAS

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    1. Shen, Haipeng & Huang, Jianhua Z., 2008. "Sparse principal component analysis via regularized low rank matrix approximation," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1015-1034, July.
    2. Mevik, Björn-Helge & Wehrens, Ron, 2007. "The pls Package: Principal Component and Partial Least Squares Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i02).
    3. Lê Cao Kim-Anh & Gonçalves Olivier & Besse Philippe & Gadat Sébastien, 2007. "Selection of Biologically Relevant Genes with a Wrapper Stochastic Algorithm," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-23, November.
    4. 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.
    5. 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.
    6. 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.
    7. Boulesteix Anne-Laure, 2004. "PLS Dimension Reduction for Classification with Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-32, November.
    8. Trendafilov, Nickolay T. & Jolliffe, Ian T., 2006. "Projected gradient approach to the numerical solution of the SCoTLASS," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 242-253, January.
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    4. 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.
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    6. Michael Gutkin & Ron Shamir & Gideon Dror, 2009. "SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-12, July.
    7. Marttinen Pekka & Gillberg Jussi & Havulinna Aki & Corander Jukka & Kaski Samuel, 2013. "Genome-wide association studies with high-dimensional phenotypes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 413-431, August.
    8. Cemal Erdem & Sean M. Gross & Laura M. Heiser & Marc R. Birtwistle, 2023. "MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    9. Perrin, Augustine & Cristobal, Magali San & Milestad, Rebecka & Martin, Guillaume, 2020. "Identification of resilience factors of organic dairy cattle farms," Agricultural Systems, Elsevier, vol. 183(C).
    10. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
    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. Chung Dongjun & Keles Sunduz, 2010. "Sparse Partial Least Squares Classification for High Dimensional Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-32, March.
    13. Minji Lee & Zhihua Su, 2020. "A Review of Envelope Models," International Statistical Review, International Statistical Institute, vol. 88(3), pages 658-676, December.
    14. Hernandez Roig, Harold Antonio & Aguilera Morillo, María del Carmen & Aguilera, Ana M. & Preda, Cristian, 2023. "Penalized function-on-function partial leastsquares regression," DES - Working Papers. Statistics and Econometrics. WS 37758, Universidad Carlos III de Madrid. Departamento de Estadística.

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