GWENA: Gene Co-expression Networks Analysis and Extended Modules Characterization in a Single Bioconductor Package

Abstract

Background: Network-based analysis of gene expression through co-expression networks can be used to investigate modular interactions occurring between genes toward different biological functions. An extended description of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.Results: Here we present GWENA, a new R package that integrates gene co-expression network construction and a whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performances, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. We successfully prioritized a gene whose involvement was unknown in the muscle development and growth. We also gave new insight about the variations in patterns of co-expression as the already known age-dependent loss of connectivity was found coupled to a genes interactions reorganization leading to the expression of other functions involved in aging. Conclusion: GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as conditions comparison, it eases the understanding of genes interactions involved in diseases or phenotypes. Going beyond actual packages to perform co-expression analysis, GWENA includes new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

Identification of modules and key genes associated with breast cancer subtypes through network analysis

“…Hub genes are characterized by being genes whose degree value is higher than the average of the threshold network degree value 84 . Many studies have shown that the relationship between connectivity and node significance carries important biological information 47 .…”

Identification of modules and key genes associated with Breast Cancer subtypes through network analysis

“…The hub genes are characterized by being genes whose degree value is higher than the average of the threshold network degree value 43 . Many studies have shown that the relationship between connectivity and node signi cance carries important biological information 49 .…”

Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep

“…Genes expressed in less than half of the samples were removed and strong outlier samples were removed in order to get a better fit to a scale-free topology. We tested two network construction pipelines: 1) The pipeline proposed by the authors of Gene Whole co-Expression Network Analysis (GWENA) (Lemoine et al, 2021), which consists of applying the variance stabilizing transformation (VST) from DESeq2 (Love et al, 2014) and using spearman correlations, and 2) counts adjusted with trimmed mean of M-values (TMM) factors followed by asinh transformation, Pearson correlations and network transformation by context likelihood of relatedness (CLR) (Johnson and Krishnan, 2022). Before creating the correlation matrices, normalised gene expression was corrected for covariates with limma's removeBatchEffect function (Ritchie et al, 2015) to account for the effect of the interaction of tissue type and BioProject ID, as those were the main drivers of the groupings seen in the exploratory analysis.…”

“…The 30% less variable genes were removed for network construction. Co-expression networks were constructed with GWENA (Lemoine et al, 2021) R package, which implements the Weighted Correlation Network Analysis (WGCNA) (Langfelder and Horvath, 2008) R package.…”

“…Gene co-expression networks were constructed providing valuable information about the expression relationships of lncRNAs with PCGs and allowing the inference of their putative functions by guilt-by-association. We tested two different network construction pipelines and selected the one proposed by the authors of GWENA (Lemoine et al, 2021), as it produced networks with better fit to a scale-free topology and most of the genes could be associated to an expression module. Covariate correction for tissue type and source project enabled the construction of unbiased networks (Supplementary Figure S9.10).…”