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%polynova_2way: A SAS macro for implementation of mixed models for metabolomics data

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  • Rodrigo Manjarin
  • Magdalena A Maj
  • Michael R La Frano
  • Hunter Glanz

Abstract

The generation of large metabolomic data sets has created a high demand for software that can fit statistical models to one-metabolite-at-a-time on hundreds of metabolites. We provide the %polynova_2way macro in SAS to identify metabolites differentially expressed in study designs with a two-way factorial treatment and hierarchical design structure. For each metabolite, the macro calculates the least squares means using a linear mixed model with fixed and random effects, runs a 2-way ANOVA, corrects the P-values for the number of metabolites using the false discovery rate or Bonferroni procedure, and calculate the P-value for the least squares mean differences for each metabolite. Finally, the %polynova_2way macro outputs a table in excel format that combines all the results to facilitate the identification of significant metabolites for each factor. The macro code is freely available in the Supporting Information.

Suggested Citation

  • Rodrigo Manjarin & Magdalena A Maj & Michael R La Frano & Hunter Glanz, 2020. "%polynova_2way: A SAS macro for implementation of mixed models for metabolomics data," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-10, December.
    • Handle: RePEc:plo:pone00:0244013
    DOI: 10.1371/journal.pone.0244013
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    1. Kwanjeera Wanichthanarak & Sili Fan & Dmitry Grapov & Dinesh Kumar Barupal & Oliver Fiehn, 2017. "Metabox: A Toolbox for Metabolomic Data Analysis, Interpretation and Integrative Exploration," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-14, January.
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