Fusing metabolomics data sets with heterogeneous measurement errors

Combining different metabolomics platforms can contribute significantly to the discovery of complementary processes expressed under different conditions. However, analysing the fused data might be hampered by the difference in their quality. In metabolomics data, one often observes that measurement...

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Bibliographic Details
Published in:PloS one 2018-04, Vol.13 (4), p.e0195939-e0195939
Main Authors: Waaijenborg, Sandra, Korobko, Oksana, Willems van Dijk, Ko, Lips, Mirjam, Hankemeier, Thomas, Wilderjans, Tom F, Smilde, Age K, Westerhuis, Johan A
Format: Article
Language:English
Subjects:
Amino acids
Bioinformatics
Biology and Life Sciences
Classification
Data analysis
Data processing
Datasets
Diabetes
Diabetes mellitus
Economic models
Error analysis
Error correction
Filtration
Gastrointestinal surgery
Gene expression
Genetic transformation
Genomics
Heterogeneity
Information management
Life sciences
Medicine and Health Sciences
Metabolism
Metabolites
Metabolomics
Methods
Modelling
Obesity
Physical Sciences
Platforms
Principal components analysis
Proteomics
Quality control
Quantitative psychology
Research and Analysis Methods
Transformation
Variables
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Summary:Combining different metabolomics platforms can contribute significantly to the discovery of complementary processes expressed under different conditions. However, analysing the fused data might be hampered by the difference in their quality. In metabolomics data, one often observes that measurement errors increase with increasing measurement level and that different platforms have different measurement error variance. In this paper we compare three different approaches to correct for the measurement error heterogeneity, by transformation of the raw data, by weighted filtering before modelling and by a modelling approach using a weighted sum of residuals. For an illustration of these different approaches we analyse data from healthy obese and diabetic obese individuals, obtained from two metabolomics platforms. Concluding, the filtering and modelling approaches that both estimate a model of the measurement error did not outperform the data transformation approaches for this application. This is probably due to the limited difference in measurement error and the fact that estimation of measurement error models is unstable due to the small number of repeats available. A transformation of the data improves the classification of the two groups.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0195939