A cohesive statistical approach for missing values in high-dimensional metabolomics data

项目总结 随着涉及代谢组学的人类健康研究的数量迅速增加， 解决代谢组学数据分析中的关键分析障碍的方法至关重要。 缺失值(MVS)在代谢组学中是一个普遍存在且经常被忽视的问题，然而MVS的治疗可能 对差异丰度和其他下游统计分析有重大影响。MVS 代谢组学中的问题是相当具有挑战性的，也就是说，MVS的来源并不总是清楚的，而且可以 产生的原因是：i)代谢物在生物上不存在于样品中，ii)存在于样品中，而是在 浓度低于检测下限(LOD)，或iii)样品中存在但由于以下原因而未被检测到 与样品前处理步骤(如峰值分辨率)有关的技术问题。当前常用的方法 (例如，用零、LOD或平均值进行替换)往往过于简单化，并产生次优和 潜在的误导性结果。由于从文献中明显缺乏推论方法， 正确解释代谢组学数据中不同类型的缺失，迫切需要投资 改善代谢组学特有的MVS统计模型。我们最近开发了一种改装的 考虑数据中截断点(即LOD)的K近邻(KNN)填充算法 (KNN-TN)。基于来自真实代谢组学研究的模拟，该算法显示出相当可观的 与单值(LOD、均值、零)相比，推算精度(均方根误差)有所提高 归结方法和标准KNN归结。在这个提案中，我们将开发一种替代的贝叶斯 一种考虑补偿不确定性并稳定小样本估计的建模方法 通过在代谢物之间共享信息。此外，我们将评估MV归责对下游的影响 统计分析基于对各种公开可用的数据集的模拟 代谢组学工作台。我们的分析将使我们能够向分析师提出全面的建议 关于哪种归罪算法(S)在生物影响方面是最优的。最后，我们将公开发展 可用于实施所有开发的归责方法的软件，包括可通过网络访问的界面，以 扩大影响范围和影响力。这项提议的总体长期目标是开发用户友好的软件 和代谢组学数据中归因策略的最佳实践指南，从而提高 下游统计分析和由此产生的生物影响。

PROJECT SUMMARY With the number of human health studies involving metabolomics rising at a rapid rate, the development of methods to address critical analytic barriers in the analysis of metabolomics data is of critical importance. Missing values (MVs) are a pervasive, and often ignored, issue in metabolomics, yet the treatment of MVs can have a substantial impact on differential abundance and other downstream statistical analyses. The MVs problem in metabolomics is quite challenging, namely because the source of MVs is not always clear and can arise because the metabolite is i) not biologically present in the sample, ii) present in the sample but at a concentration below the lower limit of detection (LOD), or iii) present in the sample but undetected due to technical issues related to sample pre-processing steps (e.g. peak resolution). Current commonly used methods (e.g., substitution by zeros, LOD, or the mean value) tend to be overly-simplistic and produce sub-optimal and potentially misleading results. Since there is a noticeable absence of imputation methods from the literature that properly account for the different types of missingness in metabolomics data, there is an urgent need to invest in improving statistical models of MVs that are specific to metabolomics. We have recently developed a modified K-nearest neighbors (KNN) imputation algorithm that accounts for the truncation point (i.e., the LOD) in the data (KNN-TN). Based on simulations derived from real metabolomics studies, this algorithm showed considerable improvement in imputation accuracy (root-mean squared error) compared to single value (LOD, mean, zero) imputation approaches and standard KNN imputation. In this proposal, we will develop an alternative Bayesian modeling approach that accounts for the uncertainty due to imputation and stabilizes estimates for small samples by sharing information across metabolites. Further, we will evaluate the impact of MV imputation on downstream statistical analyses based on simulations from a wide-variety of publicly available datasets from the Metabolomics Workbench. Our analyses will allow us to make comprehensive recommendations to analysts about which imputation algorithm(s) are optimal in terms of biological impact. Lastly, we will develop publicly available software for implementing all developed imputation methods, including a web-accessible interface to broaden outreach and impact. The overall long term goal of this proposal is to develop user-friendly software and best-practices guidelines for imputation strategies in metabolomics data, thereby improving accuracy of downstream statistical analysis and the resulting biological impact.