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

By Robert A. van den Berg, Huub C. J. Hoefsloot, Johan A. Westerhuis, Age K. Smilde, M. J. van der Werf. BMC Genomics, 2006

697 citations recorded by the source corpus.

BackgroundExtracting 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.ResultsDifferent 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.ConclusionDifferent 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b40b9d056f2233945dd9597fbefeeea5e49ea922

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Citations in the corpus

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling (2016, methodology): “Different scaling and transformation methods have been assessed for GC–MS datasets (van den Berg et al. 2006) and a comparison between autoscaling and Pareto scaling has been performed for a single UHPLC-MS dataset (Masson et al. 2011).”
Proposed minimum reporting standards for data analysis in metabolomics (2007, background): “…UV (unit variance) or Z- scaling Range scaling Mean-centering followed by dividing row entries of a column through the range within that column van den Berg et al. (2006) Pareto scaling Mean-centering followed by dividing row entries of a column through the square root of the standard…”
metaX: a flexible and comprehensive software for processing metabolomics data (2017, methodology): “Five different scaling approaches are offered in metaX: Pareto scaling, vast scaling, range scaling, autoscaling and level scaling [16].”