Multi-group covariance estimation for metabolomic analysis with applications to neurodegenerative disease

循环代谢物的水平以及这些代谢物之间的相互作用是变化的函数。 在基因、蛋白质和环境中。因此，代谢组提供了一个非常详细的轮廓 细胞的生理状态。通过整合对这些物质的同一性和丰度的测量 代谢物，我们可以对疾病的原因和后果建立更全面的图景 不能仅用遗传学来解释。因此，人们越来越认识到代谢组将成为一种 系统级生物医学研究的关键要素。然而，分析新陈代谢数据是例外的 困难，因为测量本身就有噪声，并且涉及复杂的高维相互作用 样本量相对较小。要理解这个丰富的数据源，我们需要新的、更强大的 统计工具。这里提出的工作将产生一套估计相关性的统计方法 在多组测量中，一种我们称之为“多组尖峰协方差模型”的方法 (MSCov)。这些模型通过利用数据中的已知关系(例如， 疾病状态、基因和年龄)，以评估代谢物之间的相关性。我们的方法也限制了 通过对所有被估计的价值包括精确的不确定性量化来发现错误的发现。使用 MSCov，我们建立了一个强大的框架，可以用来推断新陈代谢的差异变化 跨临床组的路径，以确定用于疾病预测的潜在生物标记物并提出靶点 为了治疗药物。为了说明MSCov的实用性，这个项目将分析一个新的代谢组数据集 以及180名受试者的脑脊液中的脂肪组学特征，其中一半人被诊断为 神经退行性疾病，包括载脂蛋白ε4阳性和载脂蛋白ε4阴性的个体。当应用于 这些数据，这里开发的方法应该能识别相关代谢途径的新成分 患有阿尔茨海默氏症和帕金森氏症。这些新方法和数据将提供给 研究社区。这个项目的重点是更好地理解代谢学原因和 神经退行性疾病的后果。然而，这里开发的工具将增强我们的能力 比较任何组之间的代谢谱。

The levels of circulating metabolites, and the interactions between these metabolites, are a function of variation in genes, proteins and the environment. As such, the metabolome provides an extremely detailed profile of the physiological state of cells. By integrating measurements of the identities and abundances of these metabolites, we can build a more comprehensive picture of the causes and consequences of disease that cannot be explained by genetics alone. Thus, it is increasingly recognized that the metabolome will become a crucial element in systems level biomedical research. However, analyzing metabolomic data is exceptionally difficult, because the measurements are inherently noisy, and involve complex high-dimensional interactions and relatively small sample sizes. To make sense of this rich data source, we need new, more powerful statistical tools. The work proposed here will generate a suite of statistical methods for estimating correlations across multiple groups of measurements, an approach we term “Multi-group spiked covariance models” (MSCov). These models achieve unprecedented accuracy by utilizing known relationships in the data (e.g., disease status, genotype, and age) to estimate correlations between metabolites. Our approach also limits false discoveries by including precise uncertainty quantification for all of the values being estimated. With MSCov, we establish a powerful framework that can be used to infer differential changes in metabolic pathways across clinical groups, to identify potential biomarkers for disease prediction and to propose targets for therapeutic drugs. To illustrate the utility of MSCov, this project will analyze a new dataset of metabolomic and lipidomic profiles from the cerebrospinal fluid of 180 human subjects, half of whom are diagnosed with neurodegenerative disease, including both APO-ε4-positive and APO-ε4-negative individuals. When applied to these data, the methods developed here should identify novel components of metabolic pathways associated with Alzheimer's disease and Parkinson's disease. These new methods and data will be made available to the research community. This project focuses on tools for better understanding the metabolomic causes and consequences of neurodegenerative disease. However, the tools developed here will enhance our ability to compare metabolomic profiles among any groups.