Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data.

ABSTRACT: Metabolite profiles from biological samples suffer from both technical variations and subject-specific variants. To improve the quality of metabolomics data, conventional data processing methods can be employed to remove technical variations. These methods do not consider sources of subject variation as separate factors from biological factors of interest. This can be a significant issue when performing quantitative metabolomics in clinical trials or screening for a potential biomarker in early-stage disease, because changes in metabolism or a desired-metabolite signal are small compared to the total metabolite signals. As a result, inter-individual variability can interfere subsequent statistical analyses. Here, we propose an additional data processing step using linear mixed-effects mode

SUBMITTER: Wanichthanarak K 

PROVIDER: S-EPMC6506811 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature