Metabolomics Data Normalization with EigenMS

• Published in: PloS one Vol. 9; no. 12; p. e116221
• Main Authors: Karpievitch, Yuliya V., Nikolic, Sonja B., Wilson, Richard, Sharman, James E., Edwards, Lindsay M.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.12.2014; Public Library of Science (PLoS)
• Subjects: Aged; Bias; Biology and Life Sciences; Blood sugar; Cholesterol; Chromatography; Chromatography, Liquid - methods; Contaminants; Correlation analysis; Data processing; Databases, Factual; Decomposition; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - blood; Diabetes Mellitus, Type 2 - metabolism; Experiments; Gene expression; Heart rate; Hemoglobin; Humans; Laboratories; Liquid chromatography; Mass spectrometry; Mass Spectrometry - methods; Mass spectroscopy; Metabolism; Metabolites; Metabolomics; Metabolomics - methods; Methods; Middle Aged; Permutations; Physical Sciences; Physiology; Proteomics; Reproducibility of Results; Research and Analysis Methods; Scientific imaging; Sensitivity; Sensitivity analysis; Singular value decomposition; Software; Studies; Time dependence; Trends; Urine; Variance analysis; United Kingdom > UK; Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0116221

Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data.