Rodriguez Jonas, Gomez-Cano Lina, Grotewold Erich, de Leon Natalia
Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA.
Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
Metabolites. 2022 May 12;12(5):435. doi: 10.3390/metabo12050435.
In biological research domains, liquid chromatography-mass spectroscopy (LC-MS) has prevailed as the preferred technique for generating high quality metabolomic data. However, even with advanced instrumentation and established data acquisition protocols, technical errors are still routinely encountered and can pose a significant challenge to unveiling biologically relevant information. In large-scale studies, signal drift and batch effects are how technical errors are most commonly manifested. We developed pseudoDrift, an R package with capabilities for data simulation and outlier detection, and a new training and testing approach that is implemented to capture and to optionally correct for technical errors in LC-MS metabolomic data. Using data simulation, we demonstrate here that our approach performs equally as well as existing methods and offers increased flexibility to the researcher. As part of our study, we generated a targeted LC-MS dataset that profiled 33 phenolic compounds from seedling stem tissue in 602 genetically diverse non-transgenic maize inbred lines. This dataset provides a unique opportunity to investigate the dynamics of specialized metabolism in plants.
在生物学研究领域，液相色谱 - 质谱联用技术（LC-MS）已成为生成高质量代谢组学数据的首选技术。然而，即便有先进的仪器设备和既定的数据采集方案，技术误差仍屡见不鲜，这可能对揭示生物学相关信息构成重大挑战。在大规模研究中，信号漂移和批次效应是技术误差最常见的表现形式。我们开发了pseudoDrift，这是一个具有数据模拟和异常值检测功能的R包，以及一种新的训练和测试方法，用于捕捉并可选择校正LC-MS代谢组学数据中的技术误差。通过数据模拟，我们在此证明，我们的方法与现有方法表现相当，且为研究人员提供了更高的灵活性。作为我们研究的一部分，我们生成了一个靶向LC-MS数据集，该数据集对602个遗传多样的非转基因玉米自交系幼苗茎组织中的33种酚类化合物进行了分析。该数据集为研究植物中特殊代谢的动态变化提供了独特的机会。
