Abstract
MotivationDifferential coexpression—the alteration of gene coexpression patterns observed in different biological conditions—has been proposed to be a mechanism for revealing rewiring of transcription regulatory networks. Despite wide use of methods for differential coexpression analysis, the phenomenon has not been well-studied. In particular, in many applications, differential coexpression is confounded with differential expression, that is, changes in average levels of expression across conditions. This confounding, despite affecting the interpretation of the differential coexpression, has rarely been studied.ResultsWe constructed high-quality coexpression networks for five human tissues and identified coexpression links (gene pairs) that were specific to each tissue. Between 3 and 32% of coexpression links were tissue-specific (differentially coexpressed) and this specificity is reproducible in an external dataset. However, we show that up to 75% of the observed differential coexpression is substantially explained by average expression levels of the genes. ‘Pure’ differential coexpression independent from differential expression is a minority and is less reproducible in external datasets. We also investigated the functional relevance of pure differential coexpression. Our conclusion is that to a large extent, differential coexpression is more parsimoniously explained by changes in average expression levels and pure links have little impact on network-based functional analysis.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Coexpression networks have been adopted as a convenient representation of the pairwise similarities between gene RNA levels in transcriptomic datasets (Gillis and Pavlidis, 2011; Lee et al, 2004)
We built Tissue aggregated networks (TAN) for each tissue by aggregating binary coexpression networks constructed from each dataset for that tissue, retaining links that were observed in multiple data sets, where the threshold minimum number of dataset networks was set to control the false discovery rate (FDR < 10À4, Fig. 2, Methods 2.2)
Each TAN has a different number of links (Supplementary Table S6), but this was not significantly correlated with the number of genes present in the networks or the count of datasets for each tissue
Summary
Coexpression networks have been adopted as a convenient representation of the pairwise similarities between gene RNA levels in transcriptomic datasets (Gillis and Pavlidis, 2011; Lee et al, 2004). A key feature of coexpression is that pairs of coexpressed genes have a tendency to be functionally related (Brown et al, 2000; Eisen et al, 1998; Wen et al, 1998) For this reason coexpression networks have been widely used in computational function prediction frameworks, based on the Guilt By Association (GBA) principle (Mostafavi et al, 2008; Pavlidis and Gillis, 2013; Quackenbush, 2003) and various types of coexpression analysis are prominent features of many transcriptomic studies (Amar et al, 2013; de la Fuente, 2010; Gillis and Pavlidis, 2009; Langfelder and Horvath, 2008).
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