PT  - JOURNAL ARTICLE
AU  - Chenxin Li
AU  - C. Robin Buell
TI  - ’Simple Tidy GeneCoEx’: a gene co-expression analysis workflow powered by tidyverse and graph-based clustering in R
AID  - 10.1101/2022.11.11.516131
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.11.11.516131
4099  - http://biorxiv.org/content/early/2022/11/11/2022.11.11.516131.short
4100  - http://biorxiv.org/content/early/2022/11/11/2022.11.11.516131.full
AB  - 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 IdeasAn R-based workflow that performs gene co-expression analysis was developed.The workflow is based on tidyverse packages and graph theory.The workflow is highly customizable, detects tight gene co-expression modules, and generates publication quality figures.Two plant gene expression datasets were used to benchmark the workflow.Competing Interest StatementThe authors have declared no competing interest.ANCOVAanalysis of covarianceANOVAanalysis of varianceFPKMfragments per kilobase exon model per million mapped fragmentsLCMlaser capture micro-dissectionmsqmean sum of squaresPCAprincipal component analysissdstandard deviationTPMtranscripts per millionWGCNAweighted gene co-expression network analysis