A Comparison of Various Normalization Methods for LC/MS Metabolomics Data

Abstract

In metabolomics data, like other -omics data, normalization is an important part of the data processing. The goal of normalization is to reduce the variation from non-biological sources (such as instrument batch effects), while maintaining the biological variation. Many normalization techniques make adjustments to each sample. One common method is to adjust each sample by its Total Ion Current (TIC), i.e. for each feature in the sample, divide its intensity value by the total for the sample. Because many of the assumptions of these methods are dubious in metabolomics data sets, we compare these methods to two methods that make adjustments separately for each metabolite, rather than for each sample. These two methods are the following: 1) for each metabolite, divide its value by the median level in bridge samples (BRDG); 2) for each metabolite divide its value by the median across the experimental samples (MED). These methods were assessed by comparing the correlation of the normalized values to the values from targeted assays for a subset of metabolites in a large human plasma data set. The BRDG and MED normalization techniques greatly outperformed the other methods, which often performed worse than performing no normalization at all.