Critical comparison of methods for fault diagnosis in metabolomics data.

ABSTRACT: Platforms like metabolomics provide an unprecedented view on the chemical versatility in biomedical samples. Many diseases reflect themselves as perturbations in specific metabolite combinations. Multivariate analyses are essential to detect such combinations and associate them to specific diseases. For this, usually targeted discriminations of samples associated to a specific disease from non-diseased control samples are used. Such targeted data interpretation may not respect the heterogeneity of metabolic responses, both between diseases and within diseases. Here we show that multivariate methods that find any set of perturbed metabolites in a single patient, may be employed in combination with data collected with a single metabolomics technology to simultaneously investigate a large arr

SUBMITTER: Koeman M 

PROVIDER: S-EPMC6362212 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature