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 me

SUBMITTER: Waaijenborg S 

PROVIDER: S-EPMC5919515 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature