Abstract
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.
Highlights
Along with nuclear magnetic resonance, liquid chromatography coupled to mass spectrometry (LC-MS) has become one of the most common analytical platforms for studying cell, tissue or body fluid metabolomes [1,2,3,4,5,6,7]
Time-dependent trends in LC-MS metabolomics datasets typically result from analyte retention time drift due to changes in LC column performance or variations in signal intensity caused by fluctuations in MS sensitivity
We report here the application of a singular value decomposition-based method, called EigenMS, to remove systematic biases from metabolomics data in the presence of missing observations [9]
Summary
Along with nuclear magnetic resonance, liquid chromatography coupled to mass spectrometry (LC-MS) has become one of the most common analytical platforms for studying cell, tissue or body fluid metabolomes [1,2,3,4,5,6,7]. Time-dependent trends in LC-MS metabolomics datasets typically result from analyte retention time drift due to changes in LC column performance or variations in signal intensity caused by fluctuations in MS sensitivity While these issues can be addressed in part by careful experimental design and the use of quality control samples, there remains a need for robust post-acquisition data normalization. We report here the application of a singular value decomposition-based method, called EigenMS, to remove systematic biases from metabolomics data in the presence of missing observations [9] This normalization method, previously shown to be effective in normalizing LC-MS proteomics data [9], improved downstream differential analysis and increased correlation of the metabolite peak intensities with corresponding physiological measurements of what we call clinical biochemistry
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