A novel signaling pathway impact analysis.
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/
Tarca
Adi Laurentiu
AL
Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA.
Draghici
Sorin
S
Khatri
Purvesh
P
Hassan
Sonia S
SS
Mittal
Pooja
P
Kim
Jung-Sun
JS
Kim
Chong Jai
CJ
Kusanovic
Juan Pedro
JP
Romero
Roberto
R
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*