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Evaluation of Normalization Methods for Analysis of LC-MS Data

机译：评估LC-MS数据分析的标准化方法

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The purpose of normalization of data generated by liquid chromatography coupled with mass spectrometry (LC-MS) is to reduce bias due to differences in sample collection, biomolecule extraction, and instrument variability. In this paper several normalization methods are reviewed and evaluated based on LC-MS data acquired from experimental and quality control (QC) samples. Specifically, LC-MS data from a metabolomic study aimed at discovering liver cancer biomarkers are analyzed to evaluate the performance of the normalization methods. ANOVA models are used for identification of ions with statistically significant peak intensities between liver cancer and cirrhotic controls. Also, LC-MS data from QC samples are analyzed to assess the ability of the normalization methods in decreasing the variability of ion intensity measurements in multiple runs. Significant run to run variability is observed despite normalizing the LC-MS data by various methods. Thus, it is important to select a suitable normalization method for each data set, as it is difficult to find a method that is applicable for all types of LC-MS data.
机译：由质谱（LC-MS）偶联的液相色谱法产生的数据的标准化是由于样品收集，生物分子提取和仪器变异性的差异，减少了偏差。本文基于从实验和质量控制（QC）样本中获取的LC-MS数据进行审查和评估几种归一化方法。具体地，分析来自旨在发现肝癌生物标志物的代谢组研究的LC-MS数据被分析，评价了归一化方法的性能。 ANOVA模型用于鉴定离子，肝癌和肝硬化控制之间具有统计显着的峰值强度的离子。此外，分析了来自QC样本的LC-MS数据，以评估归一化方法在多次运行中降低离子强度测量的可变性的能力。尽管通过各种方法使LC-MS数据标准化，但观察到运行变异性的显着运行。因此，重要的是为每个数据集选择合适的归一化方法，因为难以找到适用于所有类型的LC-MS数据的方法。

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