Bokanizad Behzad, Tagett Rebecca, Ansari Sahar, Helmi B Hoda, Draghici Sorin
Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.
Nucleic Acids Res. 2016 Jun 20;44(11):5034-44. doi: 10.1093/nar/gkw429. Epub 2016 May 18.
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.
通路分析的目标是识别当生物系统受到干扰(例如疾病或药物)时受到显著影响的通路。当前的方法将通路视为独立的实体。然而,许多信号不断地从一条通路发送到另一条通路,实际上将所有通路连接成一个全局的、全系统范围的复合体。在这项工作中,我们基于影响分析提出了一组三种通路分析方法,该方法通过考虑通路之间的所有信号及其重叠来进行系统级分析。简而言之,全局系统以两种方式建模:(i)考虑每个单独通路的通路间相互作用交换,以及(ii)组合所有单独通路以形成全局的、全系统范围的图。第三种分析方法是这两种模型的混合。在涉及12种条件下研究的19种组织的23个GEO数据集上,将新方法与DAVID、GSEA、GSA、PathNet、Crosstalk和SPIA进行了比较。结果表明,当分析考虑通路之间的全系统依赖性和相互作用时,目标通路的排名和P值都有显著改善。