Abstract
SummaryGene co-expression networks can be constructed in multiple different ways, both in the use of different measures of co-expression, and in the thresholds applied to the calculated co-expression values, from any given dataset. It is often not clear which co-expression network construction method should be preferred. COGENT provides a set of tools designed to aid the choice of network construction method without the need for any external validation data.Availability and implementation https://github.com/lbozhilova/COGENT.Supplementary information Supplementary information is available at Bioinformatics online.
Highlights
Gene expression data are a powerful resource for understanding genetic function under different conditions
Genes are represented by nodes and highly co-expressed gene pairs are connected by edges
Network construction commonly consists of three steps—the data are pre-processed, a measure of co-expression is calculated for every pair of genes and a score cut-off is applied
Summary
Gene expression data are a powerful resource for understanding genetic function under different conditions. A common way of exploring this data is through gene co-expression networks (Lee et al, 2004). In these networks, genes are represented by nodes and highly co-expressed gene pairs are connected by edges. Genes are represented by nodes and highly co-expressed gene pairs are connected by edges Such networks have been used in many ways, including for gene function prediction and the identification of disease- or tissue-relevant gene modules (van Dam et al, 2017). Network construction commonly consists of three steps—the data are pre-processed, a measure of co-expression is calculated for every pair of genes and a score cut-off is applied. Weighted networks can be analysed, in which edge weights correspond to levels of coexpression (Langfelder and Horvath, 2008)
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