title: VAN: an R package for identifying biologically perturbed networks via differential variability analysis.
abstract: BACKGROUND
  Large-scale molecular interaction networks are dynamic in nature and are of special interest in the analysis of complex diseases, which are characterized by network-level perturbations rather than changes in individual genes/proteins. The methods developed for the identification of differentially expressed genes or gene sets are not suitable for network-level analyses. Consequently, bioinformatics approaches that enable a joint analysis of high-throughput transcriptomics datasets and large-scale molecular interaction networks for identifying perturbed networks are gaining popularity. Typically, these approaches require the sequential application of multiple bioinformatics techniques - ID mapping, network analysis, and network visualization. Here, we present the Variability Analysis in Networks (VAN) software package: a collection of R functions to streamline this bioinformatics analysis.
  
  
  RESULTS
  VAN determines whether there are network-level perturbations across biological states of interest. It first identifies hubs (densely connected proteins/microRNAs) in a network and then uses them to extract network modules (comprising of a hub and all its interaction partners). The function identifySignificantHubs identifies dysregulated modules (i.e. modules with changes in expression correlation between a hub and its interaction partners) using a single expression and network dataset. The function summarizeHubData identifies dysregulated modules based on a meta-analysis of multiple expression and/or network datasets. VAN also converts protein identifiers present in a MITAB-formatted interaction network to gene identifiers (UniProt identifier to Entrez identifier or gene symbol using the function generatePpiMap) and generates microRNA-gene interaction networks using TargetScan and Microcosm databases (generateMicroRnaMap). The function obtainCancerInfo is used to identify hubs (corresponding to significantly perturbed modules) that are already causally associated with cancer(s) in the Cancer Gene Census database. Additionally, VAN supports the visualization of changes to network modules in R and Cytoscape (visualizeNetwork and obtainPairSubset, respectively). We demonstrate the utility of VAN using a gene expression data from metastatic melanoma and a protein-protein interaction network from the Human Protein Reference Database.
  
  
  CONCLUSIONS
  Our package provides a comprehensive and user-friendly platform for the integrative analysis of -omics data to identify disease-associated network modules. This bioinformatics approach, which is essentially focused on the question of explaining phenotype with a 'network type' and in particular, how regulation is changing among different states of interest, is relevant to many questions including those related to network perturbations across developmental timelines.
authors.affiliation: School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia. jean.yang@sydney.edu.au.
authors.forename: Vivek
authors.initials: V
authors.lastname: Jayaswal
authors.forename: Sarah-Jane
authors.initials: SJ
authors.lastname: Schramm
authors.forename: Graham J
authors.initials: GJ
authors.lastname: Mann
authors.forename: Marc R
authors.initials: MR
authors.lastname: Wilkins
authors.forename: Yee Hwa
authors.initials: YH
authors.lastname: Yang
mesh_terms: D019295:Computational Biology; D030562:Databases, Protein; D053263:Gene Regulatory Networks; D014644:Genetic Variation; D006801:Humans; D008545:Melanoma / Q000235:genetics / Q000378:metabolism*; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism*; D009362:Neoplasm Metastasis; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D025941:Protein Interaction Mapping; D012984:Software
chemical_list: D035683:MicroRNAs; D009363:Neoplasm Proteins