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

针对高维代谢组学数据中缺失值的内聚统计方法

• 批准号: 9433329
• 负责人: Guy Brock
• 金额: $ 16.84万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-20 至 2019-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9433329
• 关键词: Address; Affect; Algorithms; Antibiotic Resistance; Autoimmune Diseases; Bayesian Modeling; Biological; Classification; Communities; Complex; Computer software; Coupled; Data; Data Set; Databases; Detection; Diabetes Mellitus; Disease; Funding; Goals; Guidelines; Health; Human; Laboratories; Literature; Malignant Neoplasms; Methodology; Methods; Modeling; Obesity; Philosophy; Plant Roots; Play; Practice Guidelines; Procedures; Recommendation; Research; Resolution; Role; Sample Size; Sampling; Source; Statistical Data Interpretation; Statistical Models; Structure; Uncertainty; Work; analytical method; base; cohesion; high dimensionality; human disease; improved; metabolomics; method development; novel; outreach; programs; response; simulation; user friendly software; user-friendly; web app; web-accessible

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.

项目摘要 随着涉及代谢组学的人类健康研究数量的快速增长， 解决代谢组学数据分析中的关键分析障碍的方法是至关重要的。 缺失值（MV）是代谢组学中普遍存在且经常被忽视的问题，但MV的处理可以 对不同丰度和其他下游统计分析有重大影响。的mv 代谢组学中的问题是相当具有挑战性的，即因为MV的来源并不总是清楚的， 因为代谢物i）不生物学地存在于样品中，ii）存在于样品中，但以 浓度低于检测下限（LOD），或iii）存在于样品中，但由于以下原因未检出 与样品预处理步骤相关的技术问题（例如峰分辨率）。目前常用的方法 (e.g.,用零、LOD或平均值替换）往往过于简单化， 可能误导的结果。由于文献中明显缺乏估算方法， 正确解释代谢组学数据中不同类型的缺失，迫切需要投资 改进代谢组学特定MV的统计模型。我们最近开发了一种改良的 K-最近邻（KNN）填补算法，其考虑截断点（即，LOD）的数据 （KNN-TN）。基于来自真实的代谢组学研究的模拟，该算法显示出相当大的 与单个值（LOD、平均值、零）相比，插补准确度（均方根误差）提高 插补方法和标准KNN插补。在这个建议中，我们将开发一个替代贝叶斯 一种建模方法，用于解释归因于插补的不确定性，并稳定小样本的估计值 通过在代谢物之间共享信息。此外，我们将评估MV插补对下游的影响， 统计分析的基础上模拟从各种公开可用的数据集，从 代谢组学我们的分析将使我们能够向分析师提出全面的建议 关于哪种插补算法在生物影响方面是最佳的。最后，我们将公开开发 用于执行所有已开发估算方法的现有软件，包括一个可通过网络访问的界面， 扩大外联和影响。这项建议的整体长远目标，是发展方便用户的软件 以及代谢组学数据中插补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。