Integrate pathway information and protein interaction network to explore possible interactions between genes
Date Issued
2009
Date
2009
Author(s)
Wong, Siao-Han
Abstract
Currently the analysis of microarray data had turned into integrating with prior biological knowledge: pathway analysis interprets transcriptomic data on pathway level and identified predefined groups of genes with dysregulation; network analysis takes gene-gene interactions information into consideration and searches for modules associated to the phenotypes under study. The two analyses have its own advantages respectively and they complement the weaknesses of each other: pathway analysis provides little clues to directly explore new biological knowledge and network analysis usually yields modules including few consistent biological information. In this study an analytical methodology was developed to integrate current pathway analysis method with network analysis methods.Initially, dysregulated pathways are identified by modified pathway analysis method in Tian et al.. Subsequently, a focus-oriented investigation on dysregulated pathways are performed by network analysis following the work of Nacu et al., and this step is using modules within or related to members of the pathways to be further investigated. Several improvements were made, such as the scoring functions and the module identification algorithms.To illustrate the benefits of this methodology, a lung cancer study with 30 paired cancer and normal tissues was explored. The results derived within dysregulated pathways were also identified consistently in another public dataset GSE7670. Furthermore, GO term enrichment analysis was applied to show that the modules have a specialized functionality than the original pathways. In brief, original large modules were reduced from the entire pathway to a smaller size of relevant interconnected members, which are much easier to be manipulated but still remain their biological information. Moreover, the ability of this methodology to explore novel interactions related to pathway members were also demonstrated by extending the module search algorithm beyond the pre-defined pathways. This would not be achieved by traditional pathway analysis methods, which usually don’t include biomolecular interaction information. Yet, modules identified in this methodology were based on dysregulated pathways with specific biological meaning since their members were mainly associated.In conclusion, these data all indicated the advantages to integrate both pathway and network information during microarray analysis: to uncover manageable size of molecular interaction networks important for pathway dysregulation, to focus on interested pathways, functions or even specific regulatory events, and to possess the potential of performing exploratory researches on mechanisms that are not yet well understood. Undoubtedly, this concept could be extensively applied to other array experiments of similar design regardless of the disease under study.
Subjects
microarray
pathway analysis
network analysis
module
SDGs
Type
thesis
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