Title: Hierarchical structured component analysis for integrative analysis of multi-omics data
Authors: Taesung Park - Seoul National University (Korea, South) [presenting]
Yongkang Kim - Seoul national University (Korea, South)
Abstract: Identification of multi-markers is one of most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many studies have been developed to identify appropriate markers for each omics data, not many methods are available to identify integrated markers for various omics data. We propose a hierarchical structured component analysis of integrative multi-omics data. As an illustration, we consider miRNA-mRNA integration analysis. Many recent studies have shown that miRNAs are related to the pathogenesis of cancer and that miRNAs would be triggers of cancer initiation. It is well known that miRNAs affect phenotype only indirectly by regulating mRNA expression or protein translation. Although many researches have tried to use inhibition information of miRNAs to mRNAs, they could not use the information how much mRNA expression is regulated by miRNA to identify specific disease. Thus, we suggest an integration model which accounts for this biological relationship in the structured component and provides the integrated markers efficient. Through an application to pancreatic cancer data, our proposed model is shown to identify well the integrated markers of miRNA and mRNA for early diagnosis with better biological interpretation.