RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards

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.

随着代谢组学研究的关注度持续攀升，学界对其在众多生物医学场景中发挥的多样功能的认知不断深化，近年来采用液相色谱-质谱联用（liquid chromatography coupled to mass spectrometry, LC-MS）技术的代谢组学研究数量急剧增长。然而，代谢组学数据中独立于生物信号与噪声之外产生的变异，即批次效应（batch effects），往往十分显著。目前仍缺乏可支持跨研究比较的标准化数据归一化方案。本研究针对多款用于批次效应校正与差异丰度分析的算法展开系统调研，并对比其性能表现。研究表明，线性混合效应模型能够对潜在（即无法直接测量）因子进行建模，在存在批次效应的场景下无需内部对照，也无需知晓代谢组学数据中非预期变异的本质与来源，即可获得令人满意的分析结果。本研究进一步在潜在因子模型的框架下提出一款名为RRmix的算法，并验证其在强批次效应场景下可适用于差异丰度分析。本研究的整体分析结果为代谢组学数据的系统化标准化提供了一套完整可行的框架。