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http://hdl.handle.net/1942/21416
Title: | The Detection of Metabolite-Mediated Gene Module Co-Expression Using Multivariate Linear Models | Authors: | PADAYACHEE, Trishanta KHAMIAKOVA, Tatsiana SHKEDY, Ziv Perola, Markus Salo, Perttu BURZYKOWSKI, Tomasz |
Issue Date: | 2016 | Source: | PLoS One, 11 (2) | Abstract: | Investigating whether metabolites regulate the co-expression of a predefined gene module is one of the relevant questions posed in the integrative analysis of metabolomic and transcriptomic data. This article concerns the integrative analysis of the two high-dimensional datasets by means of multivariate models and statistical tests for the dependence between metabolites and the co-expression of a gene module. The general linear model (GLM) for correlated data that we propose models the dependence between adjusted gene expression values through a block-diagonal variance-covariance structure formed by metabolicsubset specific general variance-covariance blocks. Performance of statistical tests for the inference of conditional co-expression are evaluated through a simulation study. The proposed methodology is applied to the gene expression data of the previously characterized lipid-leukocyte module. Our results show that the GLM approach improves on a previous approach by being less prone to the detection of spurious conditional co-expression. | Document URI: | http://hdl.handle.net/1942/21416 | ISSN: | 1932-6203 | e-ISSN: | 1932-6203 | DOI: | 10.1371/journal.pone.0150257 | ISI #: | 000371274400111 | Rights: | Copyright: © 2016 Padayachee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2017 |
Appears in Collections: | Research publications |
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journal.pone.0150257.PDF | Published version | 3.75 MB | Adobe PDF | View/Open |
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