bio-gene-regulatory-networks-coexpression-networks

To identify gene co-expression modules and hub genes from RNA-seq data, construct weighted gene co-expression networks using WGCNA, CEMiTool, hdWGCNA, or PyWGCNA. Provide a normalized expression matrix with genes as columns and samples as rows to discover co-regulated modules.

Yes, WGCNA can be applied to single-cell RNA-seq data to find module-trait relationships. The workflow supports hdWGCNA and PyWGCNA specifically for single-cell datasets, allowing you to compute module eigengenes and correlate them with sample traits.

The best way to construct weighted gene co-expression networks for bulk and single-cell datasets is using WGCNA or CEMiTool for bulk, and hdWGCNA or PyWGCNA for single-cell data. Supply a normalized expression matrix and optional trait data to detect modules and hub genes.

You need R to run WGCNA and CEMiTool workflows for coexpression network analysis, but Python alternatives like PyWGCNA are also supported. Choose the environment that fits your pipeline to build networks and export module networks for Cytoscape.

To relate gene co-expression modules to phenotypes using CEMiTool, provide a normalized expression matrix alongside optional trait data. The workflow computes module eigengenes and correlates them with sample traits to identify phenotype-associated modules.

WGCNA requires genes as columns and samples as rows in the expression matrix to correctly compute gene-gene correlations and construct weighted networks. Transposing the matrix ensures the algorithm accurately identifies co-regulated modules and hub genes.