title: A novel signaling pathway impact analysis.
abstract: BACKGROUND
  Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed (DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by the observed changes. Most of the existing pathway analysis methods focus on either the number of DE genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat the pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe.
  
  
  RESULTS
  We describe a novel signaling pathway impact analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition. A bootstrap procedure is used to assess the significance of the observed total pathway perturbation. Using simulations we show that the evidence derived from perturbations is independent of the pathway enrichment evidence. This allows us to calculate a global pathway significance P-value, which combines the enrichment and perturbation P-values. We illustrate the capabilities of the novel method on four real datasets. The results obtained on these data show that SPIA has better specificity and more sensitivity than several widely used pathway analysis methods.
  
  
  BACKGROUND
  SPIA was implemented as an R package available at http://vortex.cs.wayne.edu/ontoexpress/
authors.affiliation: Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA.
authors.forename: Adi Laurentiu
authors.initials: AL
authors.lastname: Tarca
authors.forename: Sorin
authors.initials: S
authors.lastname: Draghici
authors.forename: Purvesh
authors.initials: P
authors.lastname: Khatri
authors.forename: Sonia S
authors.initials: SS
authors.lastname: Hassan
authors.forename: Pooja
authors.initials: P
authors.lastname: Mittal
authors.forename: Jung-Sun
authors.initials: JS
authors.lastname: Kim
authors.forename: Chong Jai
authors.initials: CJ
authors.lastname: Kim
authors.forename: Juan Pedro
authors.initials: JP
authors.lastname: Kusanovic
authors.forename: Roberto
authors.initials: R
authors.lastname: Romero
mesh_terms: D015179:Colorectal Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007743:Labor, Obstetric / Q000235:genetics; D008957:Models, Genetic; D011247:Pregnancy; D015398:Signal Transduction / Q000235:genetics*