Abstract
Gene co-expression networks are a powerful type of analysis to construct gene groupings based on transcriptomic profiling. Co-expression networks make it possible to discover modules of genes whose mRNA levels are highly correlated across samples. Subsequent annotation of modules often reveals biological functions and/or evidence of cellular specificity for cell types implicated in the tissue being studied. There are multiple ways to perform such analyses with weighted gene co-expression network analysis (WGCNA) amongst one of the most widely used R packages. While managing a few network models can be done manually, it is often more advantageous to study a wider set of models derived from multiple independently generated transcriptomic data sets (e.g., multiple networks built from many transcriptomic sources). However, there is no software tool available that allows this to be easily achieved. Furthermore, the visual nature of co-expression networks in combination with the coding skills required to explore networks, makes the construction of a web-based platform for their management highly desirable. Here, we present the CoExp Web application, a user-friendly online tool that allows the exploitation of the full collection of 109 co-expression networks provided by the CoExpNets suite of R packages. We describe the usage of CoExp, including its contents and the functionality available through the family of CoExpNets packages. All the tools presented, including the web front- and back-ends are available for the research community so any research group can build its own suite of networks and make them accessible through their own CoExp Web application. Therefore, this paper is of interest to both researchers wishing to annotate their genes of interest across different brain network models and specialists interested in the creation of GCNs looking for a tool to appropriately manage, use, publish, and share their networks in a consistent and productive manner.
Highlights
Gene co-expression network analysis has been widely used to identify biologically important patterns in gene expression in a hypothesis-free and genome-wide manner (Miller et al, 2010; Forabosco et al, 2013; Uk Brain Expression Consortium (UKBEC), et al, 2016; de la Torre-Ubieta et al, 2018; Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al, 2018; Bettencourt et al, 2019; Mencacci et al, 2020)
In order to create all the gene co-expression networks (GCNs), we first follow the standard WGCNA procedure: we identify the smoothing parameter that guarantees scale free topology for the network, we generate an adjacency matrix and the Topology Overlap Matrix (TOM). 1-TOM is used as the distance for hierarchical clustering
The CoExp Web page consists of three separate tabs, corresponding to the three different ways of using the network models: (1) network catalogue browsing, (2) network-based annotation of gene sets, and (3) network module visualization through active graph plots
Summary
Gene co-expression network analysis has been widely used to identify biologically important patterns in gene expression in a hypothesis-free and genome-wide manner (Miller et al, 2010; Forabosco et al, 2013; Uk Brain Expression Consortium (UKBEC), et al, 2016; de la Torre-Ubieta et al, 2018; Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al, 2018; Bettencourt et al, 2019; Mencacci et al, 2020). If we want to study neuro-degenerative diseases at the gene level, and how specific genes behave in terms of their co-expression, it is much more useful to study the gene set of interest across different brain regions (i.e., those vulnerable to disease, in comparison with those which are less or never affected), and in unrelated tissues (e.g., skin) With this in mind, it is tremendously useful to have a tool which enables gene sets to be studied across all conditions in a comparative manner, including predictions based on module membership about the genes’ functions and cellular specificity across the conditions of interest
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