Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: 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
Citation: 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.
URI: http://hdl.handle.net/1942/21416
DOI: 10.1371/journal.pone.0150257
ISI #: 000371274400111
ISSN: 1932-6203
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
published version3.75 MBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.