A gene co-expression network (GCN) can be constructed by looking for pairs of genes which show a similar expression pattern across samples, since the transcript levels of two co-expressed genes rise and fall together across samples. They are typically represented as undirected graphs, where each node corresponds to a gene, and a pair of nodes are connected with an edge if there is a significant co-expression relationship between them. Gene co-expression networks are of biological interest since co-expressed genes are likely to be controlled by the same transcriptional regulatory program, functionally related, or members of the same pathway or protein complex.

Our standard service provides the following deliverables and assumes that expression data is already processed and possibly normalized.

  • (As needed: data normalization)
  • Thresholding of data to minimize noise
  • Determination of optimal parameters for scale-free network topology
  • Preparation of metadata for downstream trait correlation analysis
  • Running iterativeWGCNA until convergence
  • Reporting on clusters and what genes they contain
  • Trait-to-cluster correlation analysis
  • Functional gene enrichment analysis for each cluster

Extended services are available, which supplement our standard/fixed workflow described above:

  • Construction of gene correlation network graphs via Cytoscape
  • Community detection analysis for meta-module detection (clusters of clusters)
  • Acquiring data from 3rd party services, such as ArrayExpress or GEO
  • Data processing from FastQ files
  • Iterative customization of results and plots for presentations, publications, grants, etc...

Service info

  • Category:Bioinformatics
  • Skills:R, Python, Statistics
  • Inquire: Email us