ABSTRACT
This chapter discusses the utility of gene interaction hierarchy (GIH) analysis for the management of large datasets. This step-wise analysis combines Ingenuity Pathways Analysis (IPA®) with other offline techniques to identify genes that are central to gene expression patterns in the data. Specifically, this example uses IPA®’s functional analysis capabilities to identify genes that are associated with cell growth and proliferation (CGP) following unilateral traumatic brain injury (TBI). Gene expression patterns reveal distinct ipsilateral and contralateral responses. The majority of CGP genes increase ipsilateral to the injury while the inverse is true on the contralateral side of the brain. Network analysis in IPA® identified genes of interest. Subsequently, these genes were interconnected based on direct (first order) interactions in the Ingenuity knowledge base. The number of interactions in that network determined the gene’s placement
in the resultant GIH. After microarray analysis, the original datasets numbered 31099 genes. Out of that large number, the process presented here identified 22 primary and 30 secondary genes on the ipsilateral side and 9 primary and 17 secondary genes on the contralateral side of the brain that were central to the CGP gene response following unilateral TBI. This GIH methodology can also be applied to any dataset where the potential for molecular interaction exists.
