MetaboGroupS: A Group Entropy-Based Web Platform for Evaluating Normalization Methods in Blood Metabolomics Data from Maintenance Hemodialysis Patients

Because of inevitable and complicated signal variations in LC-MSn-based nontargeted metabolomics, normalization of metabolites data is a highly recommended procedure to assist in improving accuracies in metabolic profiling and discovery of potential biomarkers. Despite various normalization methods having been developed and applied for processing these data sets, it is still difficult to assess their performance. Moreover, such methods are elusive and difficult to choose for users, especially those without bioinformatics training. In this study, we present a powerful and user-friendly web platform, named MetaboGroupS, for comparison and evaluation of seven popular normalization methods and provide an optimal one automatically for end users based on the group entropies of every sample data point. For examination and application of this tool, we analyzed a complex clinical human data set from maintenance hemodialysis patients with erythrin resistance. Metabolite peaks (11 027) were extracted from the experimental data and then imported into this platform; the entire analysis process was completed sequentially within 5 min. To further test the performance and universality of MetaboGroupS, we analyzed two more published data sets including a nuclear magnetic resonance (NMR) data set on this platform. The results indicated that the method with a lower intragroup entropy and higher intergroup entropy would be preferable. In addition, MetaboGroupS can be quite conveniently operated by users and does not require any profound computational expertise or background for scientists in many fields. MetaboGroupS is freely available at https://omicstools. shinyappsio/Metabo GroupSapp/.