RT Journal
A1 Draghici, Sorin
A1 Khatri, Purvesh
A1 Tarca, Adi Laurentiu
A1 Amin, Kashyap
A1 Done, Arina
A1 Voichita, Calin
A1 Georgescu, Constantin
A1 Romero, Roberto
T1 A systems biology approach for pathway level analysis
JF Genome Research 
JO Genome Research 
YR 2007 
FD October 01 
VO 17 
IS 10 
SP 1537 
OP 1545 
DO 10.1101/gr.6202607 
UL http://genome.cshlp.org/content/17/10/1537.abstract 
AB 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).