Department of Animal Science; California Polytechnic State University, San Luis Obispo, California, United States of America.
Department of Biological Sciences, California Polytechnic State University, San Luis Obispo, California, United States of America.
PLoS One. 2020 Dec 15;15(12):e0244013. doi: 10.1371/journal.pone.0244013. eCollection 2020.
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.
大型代谢组学数据集的产生对能够逐个代谢物拟合统计模型的软件提出了很高的要求，而软件的适用对象是数百种代谢物。我们提供了 SAS 中的 %polynova_2way 宏，用于识别具有双向因子处理和层次设计结构的研究设计中差异表达的代谢物。对于每个代谢物，宏使用具有固定和随机效应的线性混合模型计算最小二乘均值，运行双向 ANOVA，使用错误发现率或 Bonferroni 程序对代谢物数量进行 P 值校正，并计算每个代谢物的最小二乘均值差异的 P 值。最后，%polynova_2way 宏以 excel 格式输出一个表格，其中包含所有结果，便于识别每个因素的显著代谢物。宏代码可在支持信息中免费获得。
