Improved One-Class Modeling of High-Dimensional Metabolomics Data via Eigenvalue-Shrinkage.

ABSTRACT: One-class modelling is a useful approach in metabolomics for the untargeted detection of abnormal metabolite profiles, when information from a set of reference observations is available to model "normal" or baseline metabolite profiles. Such outlying profiles are typically identified by comparing the distance between an observation and the reference class to a critical limit. Often, multivariate distance measures such as the Mahalanobis distance (MD) or principal component-based measures are used. These approaches, however, are either not applicable to untargeted metabolomics data, or their results are unreliable. In this paper, five distance measures for one-class modeling in untargeted metabolites are proposed. They are based on a combination of the MD and five so-called eigenvalue-shrin

SUBMITTER: Brini A 

PROVIDER: S-EPMC8069634 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature