| It is challenging to analyze genomics data in the context of number of regulatory and signaling pathways in order to assess the corresponding impact on cellular functions and processes at the fundamental level for a given experiment. The commonly used classical analysis approach employs one of the several statistical models to list the most impacted pathways in a given experiment. We developed a new impact analysis approach that not only includes classical statistical analysis but also extends it. It includes important factors such as gene's expression change, topology of the pathway, type and strength of interaction between genes on that pathway, etc. Thus, compared to existing techniques, the newly developed impact analysis is much more aware of pathway nature of the cellular biology. This thesis describes the novel impact analysis approach and also the design, implementation and features of the tool that implements this new approach, Pathway-Express. The results classical analysis on various explanative datasets can be misleading while the impact analysis using Pathway-Express provides biologically significant results and thus validates our approach. |
