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

Rodrigo Manjarin, Magdalena A Maj, Michael R La Frano and Hunter Glanz

PLOS ONE, 2020, vol. 15, issue 12, 1-10

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0244013

DOI: 10.1371/journal.pone.0244013

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