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On the Use of Correlation and MI as a Measure of Metabolite—Metabolite Association for Network Differential Connectivity Analysis

Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
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Metabolites 2020, 10(4), 171; https://doi.org/10.3390/metabo10040171
Received: 25 March 2020 / Revised: 15 April 2020 / Accepted: 22 April 2020 / Published: 24 April 2020
Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite—metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson’s or Spearman’s correlation when the application is to quantify and detect differentially connected metabolites. View Full-Text
Keywords: biological networks; data simulation; dynamic model; metabolomics; network analysis; nonlinearity; Pearson’s correlation coefficient; permutation test; Spearman’s correlation coefficient; Toeplitz correlation biological networks; data simulation; dynamic model; metabolomics; network analysis; nonlinearity; Pearson’s correlation coefficient; permutation test; Spearman’s correlation coefficient; Toeplitz correlation
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Jahagirdar, S.; Saccenti, E. On the Use of Correlation and MI as a Measure of Metabolite—Metabolite Association for Network Differential Connectivity Analysis. Metabolites 2020, 10, 171.

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