Fig. 2. Correlation values within and across datasets.

a Proportion of significant correlations between biomolecules within and across datasets. For every dataset, the proportion of significant correlation coefficients within each dataset is substantially larger than across datasets. Consequently, statistical methods that depend on correlations will be biased towards intra-dataset interactions in a multi-omics setting. b Example correlations between two molecules measured on the sample blood samples using two similar metabolomics platforms, Metabolon Plasma HD2 and Metabolon Plasma HD4. Valine on the HD2 platform correlated stronger with Leucine measured on the same platform than with Valine on the HD4 platform. This further illustrates the tendency for stronger correlations within a dataset than between datasets. c Dataset distribution in the correlation-based hierarchical structure formed on the QMDiab dataset. Strong intra-dataset correlations can be seen for lipids (brown) and to a lesser extent for proteomics (light green), as these two datasets have dense regions where they segregate from the other -omics datasets which are otherwise thought to be well integrated.