network-regulatory

To construct a gene co-expression network from bulk RNA-seq data, use PyWGCNA for automated soft-thresholding and module detection. This process identifies biologically relevant gene modules and hub genes associated with specific traits.

Identifying hub genes associated with a disease phenotype involves filtering expression data, constructing a co-expression network, and performing module-trait correlation. This pinpoints biologically relevant targets driving the specific phenotype.

Yes, you can infer transcription factor activity and regulons from single-cell RNA-seq data. Using decoupler allows deep biological insight into regulatory networks by scoring TF activity within the constructed network topology.

Yes, the network regulatory analysis supports protein-protein interaction (PPI) networks. It integrates PPI network analysis alongside gene regulatory networks to validate network topology and identify biological modules.

Gene co-expression network construction often fails with memory exhaustion when processing large datasets. Applying memory-efficient filtering beforehand prevents this pitfall and ensures reproducible network topology during analysis.