AI Article Synopsis
- - The goal of pathway analysis is to pinpoint which biological pathways are affected when a system experiences changes, like diseases or treatments, but existing methods treat pathways separately rather than as interconnected.
- - This study introduces three new pathway analysis methods that evaluate pathways in a more holistic way by analyzing communication and overlaps between them at a system level.
- - When tested against other established methods on multiple datasets, the new approaches significantly improved the ranking and statistical significance of the affected pathways by considering their interdependencies.
Article Abstract
The goal of pathway analysis is to identify the pathways that are significantly impacted when a biological system is perturbed, e.g. by a disease or drug. Current methods treat pathways as independent entities. However, many signals are constantly sent from one pathway to another, essentially linking all pathways into a global, system-wide complex. In this work, we propose a set of three pathway analysis methods based on the impact analysis, that performs a system-level analysis by considering all signals between pathways, as well as their overlaps. Briefly, the global system is modeled in two ways: (i) considering the inter-pathway interaction exchange for each individual pathways, and (ii) combining all individual pathways to form a global, system-wide graph. The third analysis method is a hybrid of these two models. The new methods were compared with DAVID, GSEA, GSA, PathNet, Crosstalk and SPIA on 23 GEO data sets involving 19 tissues investigated in 12 conditions. The results show that both the ranking and the P-values of the target pathways are substantially improved when the analysis considers the system-wide dependencies and interactions between pathways.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4914126 | PMC |
| http://dx.doi.org/10.1093/nar/gkw429 | DOI Listing |
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