A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate
Padayachee Trishanta (),
Salo Perttu and
Additional contact information
Burzykowski Tomasz: Hasselt University, I-BioStat, Diepenbeek, Belgium
Perola Markus: Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland
Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 2, 13
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.
Keywords: conditional co-expression; conditional correlations; gene modules; general linear models; metabolomics; transcriptomics (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
For access to full text, subscription to the journal or payment for the individual article is required.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:18:y:2019:i:2:p:13:n:3
Ordering information: This journal article can be ordered from
Access Statistics for this article
Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf
More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().