title: Network-based pathway enrichment analysis with incomplete network information.
abstract: Pathway enrichment analysis has become a key tool for biomedical researchers to gain insight into the underlying biology of differentially expressed genes, proteins and metabolites. It reduces complexity and provides a system-level view of changes in cellular activity in response to treatments and/or in disease states. Methods that use existing pathway network information have been shown to outperform simpler methods that only take into account pathway membership. However, despite significant progress in understanding the association amongst members of biological pathways, and expansion of data bases containing information about interactions of biomolecules, the existing network information may be incomplete or inaccurate and is not cell-type or disease condition-specific.
  
  
  
  We propose a constrained network estimation framework that combines network estimation based on cell- and condition-specific high-dimensional Omics data with interaction information from existing data bases. The resulting pathway topology information is subsequently used to provide a framework for simultaneous testing of differences in expression levels of pathway members, as well as their interactions. We study the asymptotic properties of the proposed network estimator and the test for pathway enrichment, and investigate its small sample performance in simulated and real data settings.
  
  
  
  The proposed method has been implemented in the R-package netgsa available on CRAN.
  
  
  
  jinma@upenn.eduSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, PA 19104, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Ma
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98915, USA.
authors.forename: Ali
authors.initials: A
authors.lastname: Shojaie
authors.affiliation: Department of Statistics, University of Florida, Gainesville, FL 32611, USA.
authors.forename: George
authors.initials: G
authors.lastname: Michailidis
mesh_terms: D002450:Cell Communication; D019295:Computational Biology; D016208:Databases, Factual; D060066:Protein Interaction Maps