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EBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples

Author

Listed:
  • Madsen Tobias

    (Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark)

  • Świtnicki Michał

    (Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark)

  • Juul Malene

    (Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark)

  • Pedersen Jakob Skou

    (Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark)

Abstract

DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types. Here, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets. EBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets.

Suggested Citation

  • Madsen Tobias & Świtnicki Michał & Juul Malene & Pedersen Jakob Skou, 2019. "EBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(6), pages 1-23, December.
    • Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:6:p:23:n:1
    DOI: 10.1515/sagmb-2018-0050
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    References listed on IDEAS

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    1. Jiarui Ding & Melissa K. McConechy & Hugo M. Horlings & Gavin Ha & Fong Chun Chan & Tyler Funnell & Sarah C. Mullaly & Jüri Reimand & Ali Bashashati & Gary D. Bader & David Huntsman & Samuel Aparicio , 2015. "Systematic analysis of somatic mutations impacting gene expression in 12 tumour types," Nature Communications, Nature, vol. 6(1), pages 1-13, December.
    2. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
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