Centering, scaling, and transformations: improving the biological information content of metabolomics data

Abstract

Background: Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability.

Results: Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis.

Conclusion: Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis).In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.

Figure 1

The different steps between biological sampling and ranking of the most important metabolites.

Figure 2

Experimental design. The fermentations were performed in independent triplicates. Of the third glucose fermentation a sample was taken in duplicate and of G1, N1 and S1 the samples were analyzed in duplicate by GC-MS. The samples of N3, S2 and S3 were not taken into account in this study.

Figure 3

Effect of data pretreatment on the original data. Original data of experiment G2 (A), and the data after centering (B), autoscaling (C), pareto scaling (D), range scaling (E), vast scaling (F), level scaling (G), log transformation (H), and power transformation (I). For units refer to Table 1.

Figure 4

Analytical and biological heteroscedasticity in the data. A: Analytical standard deviation (experiment G1), B: Biological standard deviation (all glucose experiments), and C: Relative biological standard deviation (all glucose experiments), as a function of the metabolite concentration. To obtain a clearer overview, the standard deviations were grouped together based on average mean value of the peak area (Binning, see Jansen et al. [23]). The first bin contained the metabolites whose peak area was below the detection limit.

Figure 5

Effect of data transformation on biological heteroscedasticity. A: power transformed data. B: log transformed data. The standard deviations over all glucose experiments were ordered by the mean value of the peak areas and binned per 10 metabolites. The first bin contained the metabolites whose peak area was below the detection limit.

Figure 6

Effect of data pretreatment on the PCA results. PCA results of range scaled data (6A), centered data (6B), and vast scaled data (6C). For every pretreatment method the score plot (X1) (PC1 vs. PC2) and the loadings of PC 1 (X2) and PC 2 (X3) are shown. D-fructose (F, △), succinate (S, □), D-gluconate (N, ◯), D-glucose (G, *).

Figure 7

Rank of the most important metabolites. The rank was based on the cumulative contributions of the loadings of the first three PCs. Top 10 metabolites are given in white characters with a black background, the top 11 to 20 is given in white characters with dark gray background, the top 21 to 30 is given in black characters with a light gray background.

Figure 8

Relation between the abundance or the fold change of a metabolite and its rank after data pretreatment. The highest ranked metabolite after data pretreatment, based on its cumulative contributions on the loadings of the first three PCs, has position 1 on the X-axis. The metabolite that is ranked at position 1 on the Y-axis has either the highest fold change in concentration (largest standard deviation of the peak area over all the experiments in the clean data (O)); or is most abundant (largest mean concentration (□)) in the clean data.

Figure 9

Stability of the rank of the most important metabolites. The order of the metabolites is based on the average rank.