RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards.
RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards.
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DOI：
10.1371/journal.pone.0179530
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Locasale JW
中科院分区：
文献类型：
--
作者：
Salerno S Jr;Mehrmohamadi M;Liberti MV;Wan M;Wells MT;Booth JG;Locasale JW
随着人们对代谢的兴趣的激增及其在许多生物医学背景下的不同作用的认识，近年来使用液相色谱-质谱联用（LC-MS）方法进行代谢组学研究的数量急剧增加。然而，在代谢组学数据中独立于生物信号和噪声（即批次效应）发生的变化可能是显著的。缺乏允许交叉研究比较的数据标准化标准协议。在这里，我们研究了一些算法的批次效应校正和差异丰度分析，并比较它们的性能。我们表明，线性混合效应模型，占潜在的（即不能直接测量）的因素，产生令人满意的结果，在批量效应的存在下，不需要内部控制或先验知识的性质和来源的不必要的变化代谢组学数据。我们进一步介绍了一种算法RRmix-内的家庭的潜在因素模型，并说明其适用于差分丰度分析存在较强的批量效应。这种分析为系统地标准化代谢组学数据提供了一个框架。
With the surge of interest in metabolism and the appreciation of its diverse roles in numerous biomedical contexts, the number of metabolomics studies using liquid chromatography coupled to mass spectrometry (LC-MS) approaches has increased dramatically in recent years. However, variation that occurs independently of biological signal and noise (i.e. batch effects) in metabolomics data can be substantial. Standard protocols for data normalization that allow for cross-study comparisons are lacking. Here, we investigate a number of algorithms for batch effect correction and differential abundance analysis, and compare their performance. We show that linear mixed effects models, which account for latent (i.e. not directly measurable) factors, produce satisfactory results in the presence of batch effects without the need for internal controls or prior knowledge about the nature and sources of unwanted variation in metabolomics data. We further introduce an algorithm—RRmix—within the family of latent factor models and illustrate its suitability for differential abundance analysis in the presence of strong batch effects. Together this analysis provides a framework for systematically standardizing metabolomics data.