RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards.

ABSTRACT: With the surge of interest in metabolism and the appreciation of its diverse roles in numerous biomedical contexts, the number of metabolomics studies using liquid chromatography coupled to mass spectrometry (LC-MS) approaches has increased dramatically in recent years. However, variation that occurs independently of biological signal and noise (i.e. batch effects) in metabolomics data can be substantial. Standard protocols for data normalization that allow for cross-study comparisons are lacking. Here, we investigate a number of algorithms for batch effect correction and differential abundance analysis, and compare their performance. We show that linear mixed effects models, which account for latent (i.e. not directly measurable) factors, produce satisfactory results in the presence of ba

SUBMITTER: Salerno S 

PROVIDER: S-EPMC5491020 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature