'Simple Tidy GeneCoEx': a gene co-expression analysis workflow powered by tidyverse and graph-based clustering in R

Gene co-expression analysis is an effective method to detect groups (or modules) of co-expressed genes that display similar expression patterns, which may function in the same biological processes. Here, we present Simple Tidy GeneCoEx, a gene co-expression analysis workflow written in the R programming language. The workflow is highly customizable across multiple stages of the pipeline including gene selection, edge selection, clustering resolution, and data visualization. Powered by the tidyverse package ecosystem and network analysis functions provided by the igraph package, the workflow detects gene co-expression modules whose members are highly interconnected. Step-by-step instructions with two use case examples as well as source code are available at https://github.com/cxli233/SimpleTidy_GeneCoEx. Core IdeasO_LIAn R-based workflow that performs gene co-expression analysis was developed. C_LIO_LIThe workflow is based on tidyverse packages and graph theory. C_LIO_LIThe workflow is highly customizable, detects tight gene co-expression modules, and generates publication quality figures. C_LIO_LITwo plant gene expression datasets were used to benchmark the workflow. C_LI