“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 progr...

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Bibliographic Details
Published in:The plant genome Vol. 16; no. 2; pp. e20323 - n/a
Main Authors: Li, Chenxin, Deans, Natalie C., Buell, C. Robin
Format: Journal Article
Language:English
Published: United States John Wiley & Sons, Inc 01.06.2023
Wiley
Subjects:
Cluster Analysis
data visualization
Datasets
Ecosystem biology
ecosystems
Gene Expression
Gene Expression Profiling
Gene Regulatory Networks
genes
Libraries
Metabolism
Metadata
Programming languages
Software
Workflow
ISSN:1940-3372, 1940-3372
Online Access:Get full text
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Description
Summary: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 Ideas An 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.
Bibliography:Assigned to Associate Editor David Edwards.
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ISSN:1940-3372
1940-3372
DOI:10.1002/tpg2.20323