Khalili, Bita Tomasoni, Mattia Mattei, Mirjam Mallol Parera, Roger Sonmez, Reyhan Krefl, Daniel Rueedi, Rico Bergmann, Sven Automated Analysis of Large-Scale NMR Data Generates Metabolomic Signatures and Links Them to Candidate Metabolites Identification of metabolites in large-scale <sup>1</sup>H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we tested the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of covarying features derived from principal component analysis (PCA), the iterative signature algorithm (ISA), and averaged correlation profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. On the basis of the number and quality of the matches, we concluded that ISA and ACP can robustly identify ten and nine metabolites, respectively, half of which were shared, while PCA did not produce any signatures with robust matches. 1 H NMR data;urine samples;set;Large-Scale NMR Data Generates Metabolomic Signatures;PCA;metabolite signatures;metabomatching method;correlation profiles;Candidate Metabolites Identification;ISA;968 NMR profiles;component analysis;analysis tools;covarying features;NMR spectra;iterative signature algorithm;STOCSY approach;Automated Analysis;ACP 2019-08-01
    https://acs.figshare.com/articles/journal_contribution/Automated_Analysis_of_Large-Scale_NMR_Data_Generates_Metabolomic_Signatures_and_Links_Them_to_Candidate_Metabolites/9205784
10.1021/acs.jproteome.9b00295.s001