A biplot correlation range for group-wise metabolite selection in mass spectrometry.

ABSTRACT:

Background

Analytic methods are available to acquire extensive metabolic information in a cost-effective manner for personalized medicine, yet disease risk and diagnosis mostly rely upon individual biomarkers based on statistical principles of false discovery rate and correlation. Due to functional redundancies and multiple layers of regulation in complex biologic systems, individual biomarkers, while useful, are inherently limited in disease characterization. Data reduction and discriminant analysis tools such as principal component analysis (PCA), partial least squares (PLS), or orthogonal PLS (O-PLS) provide approaches to separate the metabolic phenotypes, but do not offer a statistical basis for selection of group-wise metabolites as contributors to metabolic phenotypes.

Met

SUBMITTER: Park YH 

PROVIDER: S-EPMC6360680 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature