Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations

• Main Authors: Gayatri R. Iyer, Janis Wigginton, William Duren, Jennifer L. LaBarre, Marci Brandenburg, Charles Burant, George Michailidis, Alla Karnovsky
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Metabolites
• Subjects: partial correlation networks; differential networks; enrichment analysis; metabolic disorders; metabolomics and lipidomics
• Online Access: https://www.mdpi.com/2218-1989/10/12/479

Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.