title: An inferential framework for biological network hypothesis tests.
abstract: BACKGROUND
  Networks are ubiquitous in modern cell biology and physiology. A large literature exists for inferring/proposing biological pathways/networks using statistical or machine learning algorithms. Despite these advances a formal testing procedure for analyzing network-level observations is in need of further development. Comparing the behaviour of a pharmacologically altered pathway to its canonical form is an example of a salient one-sample comparison. Locating which pathways differentiate disease from no-disease phenotype may be recast as a two-sample network inference problem.
  
  
  RESULTS
  We outline an inferential method for performing one- and two-sample hypothesis tests where the sampling unit is a network and the hypotheses are stated via network model(s). We propose a dissimilarity measure that incorporates nearby neighbour information to contrast one or more networks in a statistical test. We demonstrate and explore the utility of our approach with both simulated and microarray data; random graphs and weighted (partial) correlation networks are used to form network models. Using both a well-known diabetes dataset and an ovarian cancer dataset, the methods outlined here could better elucidate co-regulation changes for one or more pathways between two clinically relevant phenotypes.
  
  
  CONCLUSIONS
  Formal hypothesis tests for gene- or protein-based networks are a logical progression from existing gene-based and gene-set tests for differential expression. Commensurate with the growing appreciation and development of systems biology, the dissimilarity-based testing methods presented here may allow us to improve our understanding of pathways and other complex regulatory systems. The benefit of our method was illustrated under select scenarios.
authors.affiliation: Pfizer Global Research and Development, Groton, CT, USA. pyatesgene@gmail.com
authors.forename: Phillip D
authors.initials: PD
authors.lastname: Yates
authors.forename: Nitai D
authors.initials: ND
authors.lastname: Mukhopadhyay
mesh_terms: D000465:Algorithms; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics / Q000378:metabolism; D005260:Female; D053263:Gene Regulatory Networks; D006801:Humans; D008954:Models, Biological; D010051:Ovarian Neoplasms / Q000235:genetics / Q000378:metabolism; D049490:Systems Biology / Q000379:methods; D059467:Transcriptome