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EBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples.
Madsen, Tobias; Switnicki, Michal; Juul, Malene; Pedersen, Jakob Skou.
Affiliation
  • Madsen T; Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark.
  • Switnicki M; Bioinformatics Research Centre, Aarhus University, C.F. Møllers Alle 8 DK-8000 Aarhus C, Denmark.
  • Juul M; Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark.
  • Pedersen JS; Department of Molecular Medicine, Aarhus University, Palle Juul-Jensens Boulevard 99, DK-8200 Aarhus N, Denmark.
Stat Appl Genet Mol Biol ; 18(6)2019 11 16.
Article in En | MEDLINE | ID: mdl-31734658
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.
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Full text: 1 Database: MEDLINE Main subject: Bayes Theorem / Computational Biology / DNA Methylation / Gene Expression Profiling / Epigenomics Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Stat Appl Genet Mol Biol Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2019 Type: Article Affiliation country: Denmark

Full text: 1 Database: MEDLINE Main subject: Bayes Theorem / Computational Biology / DNA Methylation / Gene Expression Profiling / Epigenomics Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Stat Appl Genet Mol Biol Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2019 Type: Article Affiliation country: Denmark