Evaluation of statistical techniques to normalize mass spectrometry-based urinary metabolomics data
Evaluation of statistical techniques to normalize mass spectrometry-based urinary metabolomics data
复制标题
DOI：
10.1016/j.jpba.2019.112854
复制
发表时间：
2020-01-01
影响因子：
3.4
通讯作者：
Gamagedara, Sanjeewa
中科院分区：
文献类型：
--
作者：
Cook, Tyler;Ma, Yinfa;Gamagedara, Sanjeewa
由于人体尿液的收集是非侵入性的，因此最近成为一种流行的代谢组学生物标志物发现媒介。有时肾脏尿液稀释在这种类型的尿液生物标志物分析中可能会出现问题。目前，各种归一化技术，如肌酐比、渗透压、比重、干质量、尿量和曲线下面积被用来解释肾脏稀释。然而，这些规范化技术有其自身的缺点。本项目对前列腺癌（n = 56）、膀胱癌（n = 57）和对照组（n = 69）的尿液代谢组学数据采用统计归一化技术进行分析。本文研究的归一化技术包括肌酐比、对数值、线性基线、循环黄土、分位数、概率商、自动缩放、帕累托缩放和方差稳定归一化。使用方差、变异系数和箱线图创建了用于比较归一化技术的适当汇总统计。对于每种归一化技术，进行主成分分析以确定基于癌症类型的聚类。此外，还进行了假设检验，以确定标准化的生物标志物是否可以用于区分癌症类型。结果表明，统计显著性的确定取决于采用哪种归一化方法。因此，应该仔细考虑选择合适的规范化技术，因为没有一种方法具有普遍的优越性能。(C) 2019 Elsevier B.V.版权所有
Human urine recently became a popular medium for metabolomics biomarker discovery because its collection is non-invasive. Sometimes renal dilution of urine can be problematic in this type of urinary biomarker analysis. Currently, various normalization techniques such as creatinine ratio, osmolality, specific gravity, dry mass, urine volume, and area under the curve are used to account for the renal dilution. However, these normalization techniques have their own drawbacks. In this project, mass spectrometry-based urinary metabolomic data obtained from prostate cancer (n = 56), bladder cancer (n = 57) and control (n = 69) groups were analyzed using statistical normalization techniques. The normalization techniques investigated in this study are Creatinine Ratio, Log Value, Linear Baseline, Cyclic Loess, Quantile, Probabilistic Quotient, Auto Scaling, Pareto Scaling, and Variance Stabilizing Normalization. The appropriate summary statistics for comparison of normalization techniques were created using variances, coefficients of variation, and boxplots. For each normalization technique, a principal component analysis was performed to identify clusters based on cancer type. In addition, hypothesis tests were conducted to determine if the normalized biomarkers could be used to differentiate between the cancer types. The results indicate that the determination of statistical significance can be dependent upon which normalization method is utilized. Therefore, careful consideration should go into choosing an appropriate normalization technique as no method had universally superior performance. (C) 2019 Elsevier B.V. All rights reserved.