Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e. technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

中文翻译：

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使用批次外参考人群改善非目标代谢组学，以筛选先天性代谢错误

非目标代谢组学是实验室诊断先天性代谢错误（IEM）的新兴技术。大量参考样品的分析对于校正由饮食，年龄和性别等因素引起的代谢物浓度变化，以判断代谢物水平是否异常至关重要。但是，大量参比样品需要使用批外样品，这受未靶向代谢组学数据的半定量性质（即批次之间的技术差异）的影响。迫切需要一种方法来合并多批数据并对其进行准确归一化。基于六个指标，我们比较了现有的归一化方法从九个独立处理的批次中减少批次影响的能力。其中许多表现不佳，Metchalizer，一种标准化方法，使用10个稳定的同位素标记的内标和混合效应模型。此外，我们提出了一个回归模型，其中年龄和性别为协变量，拟合了从所有九批产品中获得的参考样本。在对数转换后的数据上应用的Metchalizer在去除批效应方面以及在49个IEM患者样品中检测195种已知生物标志物方面表现出最有前途的性能，其执行效果至少类似于使用15种批内参考样品的方法。此外，我们的回归模型表明，所考虑特征的6.5–37％显示出显着的年龄依赖性变化。我们对归一化方法的全面比较表明，我们的Log-Metchalizer该方法能够使用批外参考样品建立代谢物浓度的临床相关参考值。这些发现打开了在临床环境中使用大规模批外参考样品的可能性，从而提高了通量和检测精度。