bioRxiv2016
Expression analysis and variant calling workflows are employed to identify genes that either exhibit a differential behaviour or have a significant functional impact of mutations. This is always followed by pathway analysis which provides greater insights and simplifies explanation of observed phenotype. The current techniques used towards this purpose have some serious limitations. Only a small number of genes which satisfy certain thresholds are used for pathway analysis. All the shortlisted genes are treated as equal ignoring the differences in p-values and fold changes. These genes are treated as independent entities and interactions among them are ignored for statistical pathway analysis. Hence, there is serious disconnect between the techniques employed and networked nature of the data. Various Pathway data ases have great degree of discordance on structure of pathway graphs. Many of the pathways are still far from complete. Current algorithms do not take into account this uncertainty. In this paper, we propose a theoretical framework Prius to overcome many limitations of current techniques. Prius perturbs the gene expression fold changes through interaction network and generates an ordered list of affected pathways. Thus, it integrates the networked nature of the data and provides facility to weigh each gene differently and in the process we do away with the need of arbitrary cut-offs. This framework is designed to be modular and provides the researchers with flexibility to plug analytical tools of their choice for every component. We also demonstrate effectiveness of our approach for personalized and cohort analysis of cancer gene expression samples with PageRank as one of the modules in the framework. The R package for Prius is available on GitHub.