de Abreu E Lima Francisco, Leifels Lydia, Nikoloski Zoran
Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.
Methods Mol Biol. 2018;1778:321-327. doi: 10.1007/978-1-4939-7819-9_23.
Bridging metabolomics with plant phenotypic responses is challenging. Multivariate analyses account for the existing dependencies among metabolites, and regression models in particular capture such dependencies in search for association with a given trait. However, special care should be undertaken with metabolomics data. Here we propose a modeling workflow that considers all caveats imposed by such large data sets.
将代谢组学与植物表型反应联系起来具有挑战性。多变量分析考虑了代谢物之间现有的相关性，特别是回归模型在寻找与给定性状的关联时捕捉到了这种相关性。然而，对于代谢组学数据应格外小心。在此，我们提出一种建模工作流程，该流程考虑了此类大数据集带来的所有注意事项。
