Unsupervised Discovery and Comparison of Structural Families Across Multiple Samples in Untargeted Metabolomics.

ABSTRACT: In untargeted metabolomics approaches, the inability to structurally annotate relevant features and map them to biochemical pathways is hampering the full exploitation of many metabolomics experiments. Furthermore, variable metabolic content across samples result in sparse feature matrices that are statistically hard to handle. Here, we introduce MS2LDA+ that tackles both above-mentioned problems. Previously, we presented MS2LDA, which extracts biochemically relevant molecular substructures ("Mass2Motifs") from a collection of fragmentation spectra as sets of co-occurring molecular fragments and neutral losses, thereby recognizing building blocks of metabolomics. Here, we extend MS2LDA to handle multiple metabolomics experiments in one analysis, resulting in MS2LDA+. By linking Mass2Motifs

SUBMITTER: van der Hooft JJJ 

PROVIDER: S-EPMC5524435 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature