title: Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks.
abstract: BACKGROUND
  Biological processes are carried out by coordinated modules of interacting molecules. As clustering methods demonstrate that genes with similar expression display increased likelihood of being associated with a common functional module, networks of coexpressed genes provide one framework for assigning gene function. This has informed the guilt-by-association (GBA) heuristic, widely invoked in functional genomics. Yet although the idea of GBA is accepted, the breadth of GBA applicability is uncertain.
  
  
  RESULTS
  We developed methods to systematically explore the breadth of GBA across a large and varied corpus of expression data to answer the following question: To what extent is the GBA heuristic broadly applicable to the transcriptome and conversely how broadly is GBA captured by a priori knowledge represented in the Gene Ontology (GO)? Our study provides an investigation of the functional organization of five coexpression networks using data from three mammalian organisms. Our method calculates a probabilistic score between each gene and each Gene Ontology category that reflects coexpression enrichment of a GO module. For each GO category we use Receiver Operating Curves to assess whether these probabilistic scores reflect GBA. This methodology applied to five different coexpression networks demonstrates that the signature of guilt-by-association is ubiquitous and reproducible and that the GBA heuristic is broadly applicable across the population of nine hundred Gene Ontology categories. We also demonstrate the existence of highly reproducible patterns of coexpression between some pairs of GO categories.
  
  
  CONCLUSIONS
  We conclude that GBA has universal value and that transcriptional control may be more modular than previously realized. Our analyses also suggest that methodologies combining coexpression measurements across multiple genes in a biologically-defined module can aid in characterizing gene function or in characterizing whether pairs of functions operate together.
authors.affiliation: Children's Hospital Informatics Program and Harvard MIT Division of Health Sciences and Technology, 300 Longwood Avenue, Boston, MA 02115, USA. cecily@soest.hawaii.edu
authors.forename: Cecily J
authors.initials: CJ
authors.lastname: Wolfe
authors.forename: Isaac S
authors.initials: IS
authors.lastname: Kohane
authors.forename: Atul J
authors.initials: AJ
authors.lastname: Butte
mesh_terms: D000818:Animals; D001846:Bone Development / Q000235:genetics; D005247:Feeding Behavior / Q000502:physiology; D020869:Gene Expression Profiling; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D010802:Phylogeny; D011336:Probability; D012372:ROC Curve; D051381:Rats; D018013:Receptors, Neuropeptide / Q000502:physiology; D015203:Reproducibility of Results; D013045:Species Specificity; D026801:Synteny
chemical_list: D018013:Receptors, Neuropeptide