PUBLICATION
Selecting biologically informative genes in co-expression networks with a centrality score
- Authors
- Azuaje, F.J.
- ID
- ZDB-PUB-140621-8
- Date
- 2014
- Source
- Biology Direct 9: 12 (Journal)
- Registered Authors
- Keywords
- none
- MeSH Terms
-
- Animals
- Gene Expression Profiling/methods*
- Gene Regulatory Networks
- Heart/physiology*
- Myocardium/metabolism
- Regeneration*
- Zebrafish/genetics*
- Zebrafish/metabolism
- Zebrafish/physiology*
- PubMed
- 24947308 Full text @ Biol. Direct
Citation
Azuaje, F.J. (2014) Selecting biologically informative genes in co-expression networks with a centrality score. Biology Direct. 9:12.
Abstract
Background Measures 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.
Results The 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.
Conclusions A method for selecting biologically informative genes from gene co-expression networks is provided, together with free open software.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping