Journal
GENOME RESEARCH
Volume 17, Issue 10, Pages 1537-1545Publisher
COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT
DOI: 10.1101/gr.6202607
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Funding
- NCI NIH HHS [1U01CA117478-01, 1R21CA100740-01, R21 CA100740, U01 CA117478, P30 CA022453, 2P30 CA022453-24] Funding Source: Medline
- NCRR NIH HHS [1S10 RR017857-01, S10 RR017857] Funding Source: Medline
- NHGRI NIH HHS [R01 HG003491, 1R01HG003491-01A1] Funding Source: Medline
- NIBIB NIH HHS [5R21EB00090-03, R21 EB000990] Funding Source: Medline
- NINDS NIH HHS [1R01NS045207-01, R01 NS045207] Funding Source: Medline
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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).
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