title: A systems biology approach for pathway level analysis.
abstract: A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions taking place on various signaling pathways. A statistical approach using various models is universally used to identify the most relevant pathways in a given experiment. Here, we show that the existing pathway analysis methods fail to take into consideration important biological aspects and may provide incorrect results in certain situations. By using a systems biology approach, we developed an impact analysis that includes the classical statistics but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. The impact analysis is an attempt to a deeper level of statistical analysis, informed by more pathway-specific biology than the existing techniques. On several illustrative data sets, the classical analysis produces both false positives and false negatives, while the impact analysis provides biologically meaningful results. This analysis method has been implemented as a Web-based tool, Pathway-Express, freely available as part of the Onto-Tools (http://vortex.cs.wayne.edu).
authors.affiliation: Karmanos Cancer Institute, Wayne State University, Detroit, Michigan 48202, USA. sod@cs.wayne.edu
authors.forename: Sorin
authors.initials: S
authors.lastname: Draghici
authors.forename: Purvesh
authors.initials: P
authors.lastname: Khatri
authors.forename: Adi Laurentiu
authors.initials: AL
authors.lastname: Tarca
authors.forename: Kashyap
authors.initials: K
authors.lastname: Amin
authors.forename: Arina
authors.initials: A
authors.lastname: Done
authors.forename: Calin
authors.initials: C
authors.lastname: Voichita
authors.forename: Constantin
authors.initials: C
authors.lastname: Georgescu
authors.forename: Roberto
authors.initials: R
authors.lastname: Romero
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics; D001777:Blood Coagulation / Q000187:drug effects / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D002460:Cell Line; D003167:Complement Activation / Q000187:drug effects / Q000235:genetics; D030541:Databases, Genetic; D005260:Female; D022001:Focal Adhesions / Q000235:genetics; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods* / Q000706:statistics & numerical data; D022781:Hepatocytes / Q000187:drug effects / Q000378:metabolism; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D019308:Palmitic Acid / Q000494:pharmacology; D012984:Software; D049490:Systems Biology / Q000379:methods* / Q000706:statistics & numerical data