Dataset Information

Fusing metabolomics data sets with heterogeneous measurement errors.

ABSTRACT: 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.

SUBMITTER: Waaijenborg S

PROVIDER: S-EPMC5919515 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Fusing metabolomics data sets with heterogeneous measurement errors.

Waaijenborg Sandra S Korobko Oksana O Willems van Dijk Ko K Lips Mirjam M Hankemeier Thomas T Wilderjans Tom F TF Smilde Age K AK Westerhuis Johan A JA

PloS one 20180426 4

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 measure ...[more]

PMID: 29698490

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Project description:Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples.This study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1-36?h pre-centrifugation delay time at 4?°C and 22?°C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22?°C. Moreover, pre-centrifugation delay at 4?°C resulted in large, specific variability at 3?h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22?°C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS.The sampleDrift R package is available at https://gitlab.com/CarlBrunius/sampleDrift.carl.brunius@chalmers.se.Supplementary data are available at Bioinformatics online.

Dataset Information

Fusing metabolomics data sets with heterogeneous measurement errors.

Publications

Fusing metabolomics data sets with heterogeneous measurement errors.

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