Equating, or Correction for Between-Block Effects with Application to Body Fluid LC−MS and NMR Metabolomics Data Sets

Abstract

Combination of data sets from different objects (for example, from two groups of healthy volunteers from the same population) that were measured on a common set of variables (for example, metabolites or peptides) is desirable for statistical analysis in "omics" studies because it increases power. However, this type of combination is not directly possible if nonbiological systematic differences exist among the individual data sets, or "blocks". Such differences can, for example, be due to small analytical changes that are likely to accumulate over large time intervals between blocks of measurements. In this article we present a data transformation method, that we will refer to as "quantile equating", which per variable corrects for linear and nonlinear differences in distribution among blocks of semiquantitative data obtained with the same analytical method. We demonstrate the successful application of the quantile equating method to data obtained on two typical metabolomics platforms, i.e., liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. We suggest uni- and multivariate methods to evaluate similarities and differences among data blocks before and after quantile equating. In conclusion, we have developed a method to correct for nonbiological systematic differences among semiquantitative data blocks and have demonstrated its successful application to metabolomics data sets.

Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families

“…To correct the data for practically unavoidable nonbiological differences between the blocks, we applied the method of 'quantile equating' as described in one of our previous publications. 16 Although we showed in that paper that the correction by this method was successful at the metabolite level, our results in the current paper in addition suggest that after application of this method the clustering of family members is largely retained.…”

“…17 Characterization of participants, collection of fasting blood and urine samples, and sample preparation were performed as described previously. 14,16,18 Participants completed a number of surveys; for the current study, we used answers to questions regarding current use of any medication, recent subjective health, current and earlier smoking habits and whether participants currently lived together. Female participants reported the day of their menstrual cycle at the time of blood sampling.…”

“…14,16 In brief, lipidomics profiling was performed using an LC-MS method targeted at the analysis of lipids that become positively charged during ionization in the mass spectrometer. These measurements were carried out in two consecutive 'blocks' , denoted as B1 and B2, respectively.…”

“…16 In quantile equating, the distributions of the observations for the same metabolite in different data sets are averaged, assuming that all observations originate from the same population. This corrects the data for practically unavoidable slight technical differences between measurement blocks.…”

“…Therefore, we applied the method of 'quantile equating' to make the data from both measurement blocks combinable. 16 The inclusion in the current study of more DZ twin pairs and more nontwin siblings allowed for observation of the effect of age on similarity of lipidomics profiles, in addition to observation of the influences of genes and environment. Furthermore, the current paper demonstrates that the application of quantile equating to make combinable metabolomics data sets indeed causes biological effects to be visible in the combined data set, rather than nonbiological differences between the data from different measurement blocks.…”