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Title: | A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate | Authors: | PADAYACHEE, Trishanta KHAMIAKOVA, Tatsiana SHKEDY, Ziv Salo, Perttu Perola, Markus BURZYKOWSKI, Tomasz |
Issue Date: | 2019 | Publisher: | WALTER DE GRUYTER GMBH | Source: | Statistical applications in genetics and molecular biology, 18(2) (Art N° 20180008) | Abstract: | A way to enhance our understanding of the development and progression of complex diseases is to investigate the influence of cellular environments on gene co-expression (i.e. gene-pair correlations). Often, changes in gene co-expression are investigated across two or more biological conditions defined by categorizing a continuous covariate. However, the selection of arbitrary cut-off points may have an influence on the results of an analysis. To address this issue, we use a general linear model (GLM) for correlated data to study the relationship between gene-module co-expression and a covariate like metabolite concentration. The GLM specifies the gene-pair correlations as a function of the continuous covariate. The use of the GLM allows for investigating different (linear and non-linear) patterns of co-expression. Furthermore, the modeling approach offers a formal framework for testing hypotheses about possible patterns of co-expression. In our paper, a simulation study is used to assess the performance of the GLM. The performance is compared with that of a previously proposed GLM that utilizes categorized covariates. The versatility of the model is illustrated by using a real-life example. We discuss the theoretical issues related to the construction of the test statistics and the computational challenges related to fitting of the proposed model. | Notes: | [Padayachee, Trishanta; Khamiakova, Tatsiana; Shkedy, Ziv; Burzykowski, Tomasz] Hasselt Univ, I BioStat, Diepenbeek, Belgium. [Salo, Perttu; Perola, Markus] Natl Inst Hlth & Welf, Unit Publ Hlth Genom, Helsinki, Finland. | Keywords: | conditional co-expression;conditional correlations;gene modules;general linear models;metabolomics;transcriptomics | Document URI: | http://hdl.handle.net/1942/28738 | ISSN: | 2194-6302 | e-ISSN: | 1544-6115 | DOI: | 10.1515/sagmb-2018-0008 | ISI #: | 000463667100003 | Rights: | 2019 Walter de Gruyter GmbH, Berlin/Boston | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2020 |
Appears in Collections: | Research publications |
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