MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery

• Main Authors: Ziling Fan, Yuan Zhou, Habtom W. Ressom
• Format: Article
• Language: English
• Published: MDPI AG 2020-04-01
• Series: Metabolites
• Subjects: multi-omic integration; differential network; metabolomics; transcriptomics
• Online Access: https://www.mdpi.com/2218-1989/10/4/144

The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods.