Multi-omics Approach for Estimating Metabolic Networks Using Low-Order Partial Correlations
Two typical purposes of metabolome analysis are to estimate metabolic pathways and to understand the regulatory systems underlying the metabolism. A powerful source of information for these analyses is a set of multi-omics data for RNA, proteins, and metabolites. However, integrated methods that analyze multi-omics data simultaneously and unravel the systems behind metabolisms have not been well established. We developed a statistical method based on low-order partial correlations with a robust correlation coefficient for estimating metabolic networks from metabolome, proteome, and transcriptome data. Our method is defined by the maximum of low-order, particularly first-order, partial correlations (MF-PCor) in order to assign a correct edge with the highest correlation and to detect the factors that strongly affect the correlation coefficient. First, through numerical experiments with real and synthetic data, we showed that the use of protein and transcript data of enzymes improved the accuracy of the estimated metabolic networks in MF-PCor. In these experiments, the effectiveness of the proposed method was also demonstrated by comparison with a correlation network (Cor) and a Gaussian graphical model (GGM). Our theoretical investigation confirmed that the performance of MF-PCor could be superior to that of the competing methods. In addition, in the real data analysis, we investigated the role of metabolites, enzymes, and enzyme genes that were identified as important factors in the network established by MF-PCor. We then found that some of them corresponded to specific reactions between metabolites mediated by catalytic enzymes that were difficult to be identified by analysis based on metabolite data alone.
statistics computational molecular biology