Strategies for Comparing Metabolic Profiles: Implications for the Inference of Biochemical Mechanisms from Metabolomics Data.

• Type of Material: Article
• Authors: Zhen Qi, Emory University; Eberhard Voit, Emory University
• Language: English
• Date: 2017-11
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: Copyright 2016 IEEE
• Final Published Version (URL): http://dx.doi.org/10.1109/TCBB.2016.2586065
• Title of Journal or Parent Work: IEEE/ACM Transactions on Computational Biology and Bioinformatics
• ISSN: 1545-5963
• Volume: 14
• Issue: 6
• Start Page: 1434
• End Page: 1445
• Grant/Funding Information: This work was supported in part by a grant from the National Institutes of Health (1P30ES019776-01A1, Gary W. Miller, PI) and an endowment from the Georgia Research Alliance (EOV, PI).
• Keywords: cancer mechanisms; distance-based metrics; pathway analysis; metabolomics; computational systems biology; purine metabolism
• Research Categories: Engineering, Biomedical; Health Sciences, General

Abstract

BACKGROUND: Large amounts of metabolomics data have been accumulated in recent years and await analysis. Previously, we had developed a systems biology approach to infer biochemical mechanisms underlying metabolic alterations observed in cancers and other diseases. The method utilized the typical Euclidean distance for comparing metabolic profiles. Here, we ask whether any of the numerous alternative metrics might serve this purpose better. METHODS AND FINDINGS: We used enzymatic alterations in purine metabolism that were measured in human renal cell carcinoma to test various metrics with the goal of identifying the best metrics for discerning metabolic profiles of healthy and diseased individuals. The results showed that several metrics have similarly good performance, but that some are unsuited for comparisons of metabolic profiles. Furthermore, the results suggest that relative changes in metabolite levels, which reduce bias toward large metabolite concentrations, are better suited for comparisons of metabolic profiles than absolute changes. Finally, we demonstrate that a sequential search for enzymatic alterations, ranked by importance, is not always valid. CONCLUSIONS: We identified metrics that are appropriate for comparisons of metabolic profiles. In addition, we constructed strategic guidelines for the algorithmic identification of biochemical mechanisms from metabolomics data.