Abstract:
High-throughput technologies have revolutionized scientific research by producing copious amounts of data at a low cost. These large-scale biological data offers tremendous opportunities to understand the molecular mechanisms of biological systems. One of the biggest challenges is to interpret these large-scale biological “omics” data in order to understand their interrelation and the functioning in the larger systems. Pathways analysis is the key component for gaining insight into the underlying biological mechanisms. In our study, we have used a statistical approach based on Functional Class Scoring (FCS) scheme to understand the advantages of multi-omics datasets in the pathway analysis. The study completely focuses on pathway analysis method in the context of multi-omics datasets. We have used NCI-60 cancer cell lines transcriptome and proteome datasets for the analysis which has 9 different cancer datasets. The results were computed using three different databases such as Kegg, Reactome db and Gene Ontology. All statistically significant pathways were ranked on the basis of multi-omics z-score. In contrast to their single omics zscores we found that multi-omics improve the ability to detect the significant pathways.
