WGCNA Explained: Analysis, Tutorial & Online Tools for Omics Research

Weighted Gene Co-expression Network Analysis (WGCNA) is a powerful method for identifying gene modules, relating them to traits, and discovering hub genes in large-scale omics datasets. This article serves as both an in-depth guide and a step-by-step tutorial for conducting WGCNA analysis—whether you use the R package or online platforms like Metware Cloud.

1. What is WGCNA?
2. When and why is WGCNA used?
3. Step-by-Step WGCNA Tutorial
4. How to interpret WGCNA results?
5. FAQs about WGCNA
6. WGCNA Online with Metware Cloud Platform

01 What is WGCNA?

WGCNA, short for Weighted Gene Co-expression Network Analysis, is a commonly used tool for analyzing gene co-expression networks. It is often translated as weighted correlation network analysis. Weighted Correlation network analysis, particularly through the WGCNA R package, is applied to examine correlation structures in high-dimensional datasets, such as gene expression and proteomics data. For a deeper dive into interpreting WGCNA in research articles, see our Understanding WGCNA Analysis in Publications。

02 When and why is WGCNA used?

WGCNA is applied to analyze gene expression data in complex transcriptome data with multiple samples, particularly in the study of developmental regulation across different organs/tissues and stages.  Differential network analysis is crucial for identifying changes in connectivity patterns under varying conditions. Additionally, it is utilized to investigate response mechanisms to biotic and abiotic stresses at various time points. Common scenarios include linking gene modules to phenotypic traits, identifying key metabolic regulators, and exploring developmental or stress-response networks. WGCNA is also increasingly applied in proteomics, metabolomics, and multi-omics integration to reveal biological mechanisms.

03 Step-by-Step WGCNA Tutorial

1. Data preparation & quality control – Ensure your expression matrix passes basic QC checks, removing low-expression genes and problematic samples.
2. Choosing soft-threshold power – Select β using scale-free topology criteria; this step determines network construction.
3. Constructing the network & detecting modules – Build the adjacency matrix, transform to TOM, and identify modules via dynamic tree cutting.
4. Relating modules to traits – Calculate module eigengenes and correlate them with phenotypic data to find biologically relevant modules.
5. Identifying hub genes – Use connectivity measures (KME, TOM) or literature evidence to find key regulators.
6. Functional enrichment & visualization – Apply GO/KEGG enrichment and create visual module-trait maps.

For a code-free experience, try the Metware Cloud WGCNA Online Analysis Tool.

04 How to interpret WGCNA results?

4.1 Identifying gene co-expression network sets

Based on pairwise correlation of gene expression data across all samples, genes with similar expression patterns are grouped into modules. This categorization condenses thousands of differentially expressed genes into several modules, typically a dozen or more. Using transcriptome data from tomato fruit development research as an example, WGCNA analysis identified 12 gene modules. The commonly used representation in literature is depicted in the figure below, where the upper part of the evolutionary tree displays each branch representing a gene, and the different colors below represent various modules.

Consider using interactive visualization tools for WGCNA to explore module relationships, especially in large datasets. Such tools can help zoom into specific branches, inspect module membership, and link module eigengenes to traits in real time, making it easier to identify biologically meaningful patterns.

4.2 Filtering Key Modules for Functional enrichment analysis

Method 1: Filtering based on module characteristic expression patterns.

After identifying modules, WGCNA calculates a module characteristic value (Epigene) for each module, representing the expression status of all genes in the module. Analyzing the abundance of module characteristic values in various samples helps filter modules closely related to the samples. For instance, in tomatoes, the "brown" module shows higher characteristic value expression (positive) in samples from the first period, making it a key module for subsequent analysis. This process requires the input of gene expression data to accurately determine the module characteristic expression patterns.

Method 2: Filtering through module-sample or phenotype correlation analysis. Calculating the correlation coefficient between modules and sample or phenotype data identifies modules highly correlated with specific samples or phenotypes. In tomato data, a specific correlation between JS3 and the "pink" module suggests special attention to this module. If there are statistical data on relevant phenotypes during tomato fruit development, such as tomato lycopene content, modules with the highest correlation to lycopene content can also be selected.

Method 3: Filtering through module gene function enrichment. Conducting functional enrichment analysis, like Gene Ontology (GO), for each module helps identify modules corresponding to biological processes related to the traits of interest. For example, in tomato fruit development, processes like carotenoid metabolism and ethylene signaling are relevant to fruit ripening, prompting focus on modules enriched with relevant GO terms. Additionally, network visualization can be enhanced by plotting the connectivity distribution of the entire network, where the y-axis depicts the logarithm of the corresponding frequency distribution.

Method 4: Filtering modules through target gene selection. Considering research objectives, previous findings, and published literature, modules containing target genes of interest can be directly selected for further analysis. In tomato fruit development, key genes like PG2A and PL1 involved in pectin degradation found in the "yellow" module make it a candidate for further investigation.

4.3 Identifying key genes

After filtering down to candidate modules through the aforementioned analyses, analyzing the internal composition of the modules is crucial. Identifying key genes within the modules, often referred to as Hub genes, is essential. This can be achieved through analyzing intra-modular gene connectivity (TOM values, KME, or KIM), selecting genes with higher connectivity in the network. RNA-Seq datasets from the Gene Expression Omnibus (GEO) are invaluable for such transcriptomics research, providing comprehensive data for various species and biological sample groups. Additionally, attention can be directed towards genes with regulatory functions, such as transcription factors, as they generally act as regulators in the upstream part of the module regulatory network.

How do I find hub genes? Hub genes are typically those with high connectivity within a module (e.g., high KME or TOM values) and are often biologically central. Combining statistical measures with literature evidence improves reliability, and follow-up experimental validation—such as RT-qPCR—is recommended for WGCNA hub gene validation.

05 FAQs about WGCNA

Q1: What is WGCNA used for?

To detect clusters (modules) of highly correlated genes and relate them to external traits.

Q2: How to choose soft-threshold power?

Use the scale-free topology fit index plot; pick the lowest β that reaches R² ≥ 0.8.

Q3: Can I run WGCNA online without coding?

Yes. Platforms like Metware Cloud provide automated workflows.

Q4: How to interpret module-trait correlation heatmaps?

Red/blue intensity reflects strength and direction of correlation; focus on modules with high |r| and low p-values.

Q5: How to find hub genes in WGCNA?

Filter by high connectivity and trait correlation; validate with literature and biological experiments.

06 WGCNA Online with Metware Cloud Platform

• Omics Data Analysis Series
• Beyond Single-Omics: A Guide to Multi-Omics Association Analysis
• Mastering Pearson Correlation: A Step-by-Step Guide to Analyzing Data Relationships
• Step-by-Step Guide to Multi-Omics Association Analysis in Metabolomics and Microbiomics
• Guide to Multi-Omics Association Analysis in Metabolomics and Transcriptomics