LAUSR

AbstractIt is well recognized that physiological and environmental factors such as race, age, gender, and diurnal cycles often have a definite influence on metabolic results that statistically manifests as confounding variables. Currently, removal or controlling of confounding effects relies heavily on experimental design. There are no available data processing techniques focusing on the compensation of their effects. We therefore proposed a new method, Metabolic confounding effect elimination (MCEE), to remove the influence of specified confounding factors and make the data more accurate. The method consists of three steps: metabolites grouping, confounder-related metabolites selection, and metabolites modification. Its effectiveness and advantages were evaluated comprehensively by several simulated models and real datasets, and were compared with two typical methods, the principal component analysis (PCA)- and the direct orthogonal signal correction (DOSC)-based methods. MCEE is simple, effective, and safe, and is independent of sample number, association degree, and missing value. Hence, it may serve as a good complement to existing metabolomics data preprocessing methods and aid in better understanding the metabolic and biological status of interest. Graphical AbstractAlgorithm flow and demo performance of MCEE