Open Access%polynova_2way: A SAS macro for implementation of mixed models for metabolomics data
Author(s) -
Rodrigo Manjarín,
Magdalena Maj,
Michael R. La Frano,
Hunter Glanz
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0244013
Subject(s) - macro , metabolomics , metabolite , computer science , bonferroni correction , statistics , computational biology , bioinformatics , data mining , mathematics , biology , biochemistry , programming language
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.