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Examining microbe–metabolite correlations by linear methods

Nature Methods volume 18, pages 37–39 (2021)Cite this article

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Matters Arising to this article was published on 04 January 2021

The Original Article was published on 04 November 2019

arising from J. T. Morton et al. Nature Methods https://doi.org/10.1038/s41592-019-0616-3 (2019)

Analyzing correlative relationships between microbes and metabolites is a timely topic1,2,3 but is complicated by the compositional (i.e., relative) nature of the data4,5. Recently, Morton et al. proposed a neural network architecture called MMvec to predict metabolite abundances from microbe presence6. We do not doubt the usefulness of MMvec but write in defense of simple linear statistics. When used correctly, correlation and proportionality5,7 can be scale invariant and can outperform MMvec in certain conditions.

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Fig. 1: Reanalysis of the simulated data from Morton et al.
Fig. 2: Reanalysis of the sparsified simulated data.

Data availability

Data used in Figs. 1 and 2 are available from https://doi.org/10.5281/zenodo.3610709 and https://doi.org/10.5281/zenodo.3833174, respectively.

Code availability

Scripts used in Figs. 1 and 2 are available from https://doi.org/10.5281/zenodo.3610709 and https://doi.org/10.5281/zenodo.3833174, respectively.

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Acknowledgements

I.E. has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 825835 (BovReg), Secretaria de Universidades e Investigación del Departamento de Economía y Conocimiento de la Generalidad de Cataluña, 2017 SGR 447 (SGR), Agencia Estatal de Investigación (AEI) and FEDER under Project BFU2017-88264-P (Plan Estatal). I.E. also acknowledges the following CRG funding sources: support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, Centro de Excelencia Severo Ochoa, the CERCA Programme / Generalitat de Catalunya and the European Regional Development Fund (ERDF).

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Authors and Affiliations

  1. Applied Artificial Intelligence Institute, Deakin University, Geelong, Victoria, Australia

    Thomas P. Quinn

  2. Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain

    Ionas Erb

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  1. Thomas P. Quinn
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T.P.Q. performed the analysis. T.P.Q. and I.E. drafted the manuscript.

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Correspondence to Thomas P. Quinn.

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Quinn, T.P., Erb, I. Examining microbe–metabolite correlations by linear methods. Nat Methods 18, 37–39 (2021). https://doi.org/10.1038/s41592-020-01006-1

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