Statistical methods for handling unwanted variation in metabolomics data.

ABSTRACT: Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognized need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages an

SUBMITTER: De Livera AM 

PROVIDER: S-EPMC4544854 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature