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Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information

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  • Sandra E. Safo
  • Shuzhao Li
  • Qi Long

Abstract

Integrative analysis of high dimensional omics data is becoming increasingly popular. At the same time, incorporating known functional relationships among variables in analysis of omics data has been shown to help elucidate underlying mechanisms for complex diseases. In this article, our goal is to assess association between transcriptomic and metabolomic data from a Predictive Health Institute (PHI) study that includes healthy adults at a high risk of developing cardiovascular diseases. Adopting a strategy that is both data†driven and knowledge†based, we develop statistical methods for sparse canonical correlation analysis (CCA) with incorporation of known biological information. Our proposed methods use prior network structural information among genes and among metabolites to guide selection of relevant genes and metabolites in sparse CCA, providing insight on the molecular underpinning of cardiovascular disease. Our simulations demonstrate that the structured sparse CCA methods outperform several existing sparse CCA methods in selecting relevant genes and metabolites when structural information is informative and are robust to mis†specified structural information. Our analysis of the PHI study reveals that a number of gene and metabolic pathways including some known to be associated with cardiovascular diseases are enriched in the set of genes and metabolites selected by our proposed approach.

Suggested Citation

  • Sandra E. Safo & Shuzhao Li & Qi Long, 2018. "Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information," Biometrics, The International Biometric Society, vol. 74(1), pages 300-312, March.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:1:p:300-312
    DOI: 10.1111/biom.12715
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    1. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108, February.
    2. Wei Pan & Benhuai Xie & Xiaotong Shen, 2010. "Incorporating Predictor Network in Penalized Regression with Application to Microarray Data," Biometrics, The International Biometric Society, vol. 66(2), pages 474-484, June.
    3. Parkhomenko Elena & Tritchler David & Beyene Joseph, 2009. "Sparse Canonical Correlation Analysis with Application to Genomic Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-36, January.
    4. 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.
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    Cited by:

    1. 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.

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