Abstract
BackgroundMeasures of node centrality in biological networks are useful to detect genes with critical functional roles. In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been associated with key disease-related pathways. Although different approaches to estimating gene centrality are available, their potential biological relevance in gene co-expression networks deserves further investigation. Moreover, standard measures of gene centrality focus on binary interaction networks, which may not always be suitable in the context of co-expression networks. Here, I also investigate a method that identifies potential biologically meaningful genes based on a weighted connectivity score and indicators of statistical relevance.ResultsThe method enables a characterization of the strength and diversity of co-expression associations in the network. It outperformed standard centrality measures by highlighting more biologically informative genes in different gene co-expression networks and biological research domains. As part of the illustration of the gene selection potential of this approach, I present an application case in zebrafish heart regeneration. The proposed technique predicted genes that are significantly implicated in cellular processes required for tissue regeneration after injury.ConclusionsA method for selecting biologically informative genes from gene co-expression networks is provided, together with free open software.ReviewersThis article was reviewed by Anthony Almudevar, Maciej M Kańduła (nominated by David P Kreil) and Christine Wells.
Highlights
Measures of node centrality in biological networks are useful to detect genes with critical functional roles
I found that real networks tend to display Weighted node connectivity (WNC) scores that are higher than those obtained from permuted networks
Results from only 5 permuted networks are shown. These results indicate that it is possible to distinguish between the WNC value distributions from real and random networks
Summary
Measures of node centrality in biological networks are useful to detect genes with critical functional roles. The analysis of gene co-expression networks has become an important approach to enabling fundamental and translational biomedical research Such transcriptional association networks have allowed the generation of novel hypotheses about potential functional roles of genes or about their involvement in phenotype-specific cellular processes [1,2,3,4,5]. In these networks, genes and their coexpression relationships are graphically represented as nodes and edges respectively. The resulting networks can be analyzed using Among these techniques, different measures of node centrality have been proposed to identify functionallycritical network components. Genes exhibiting high degree or high betweenness-centrality scores have been proposed as candidate targets in different human and animal models [8,9,10]
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