How to Perform WGCNA on a Pathway of Interest and Identify Hub Genes?
1. Data Preparation
To begin, a high-quality gene expression dataset is required, typically derived from RNA-seq or microarray experiments.
2. Data Preprocessing
Standardize the dataset, eliminate batch effects (if applicable), and filter out genes with minimal expression levels to reduce noise.
3. Selection of Pathways of Interest
Based on the research objectives, select one or more relevant pathways from well-established biological databases such as KEGG and Reactome.
4. Construction of the Expression Matrix
Generate an expression matrix that includes only the genes associated with the selected pathways.
5. WGCNA Analysis
(1) Network Construction: Utilize the WGCNA package in R to construct a gene co-expression network. First, compute the pairwise correlation among all genes and convert the correlation matrix into an adjacency matrix using a power function.
(2) Module Detection: Identify gene modules—clusters of highly co-expressed genes—through hierarchical clustering with an average linkage method, followed by dynamic tree cutting.
(3) Module-Pathway Association Analysis: Assess the relationship between each module and the pathway of interest by quantifying the enrichment of module genes within the pathway.
6. Identification of Hub Genes
(1) Eigengene Analysis: Identify the module that exhibits the strongest association with the pathway of interest.
(2) Network Centrality Analysis: Evaluate the centrality of genes within this module (e.g., degree, betweenness centrality) to pinpoint key hub genes.
7. Experimental Validation
Confirm the biological significance of the identified hub genes within the pathway of interest using experimental approaches such as RT-qPCR, gene knockout, or overexpression assays.
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