Addressing Sparsity in Metabolomics Data Analysis

项目摘要 样品中的小分子库的全面分析被称为代谢组学，并且正在被研究。 用于解决生物医学研究中的各种科学问题。代谢组学提供了更直接的 个人生理的测量，以及对暴露影响的更直接检查， 营养、吸烟和细菌感染。对于人类健康，代谢组学研究正在用于调查 疾病机制，发现生物标志物，诊断疾病和监测治疗反应。代谢组 越来越多地被认为是精准医疗计划的重要组成部分， 增强收集的基因组数据。这是至关重要的，因为代谢组不能从代谢产物的知识来预测。 基因组、转录组或蛋白质组，但提供关于表型的重要信息。最近的技术 基于质谱的代谢组学的进步使得更全面和灵敏的 代谢物的测量。我们专注于非靶向超高压液相色谱法， 质谱法，这是一种更常用的方法。尽管技术进步， 充分利用代谢组学数据的瓶颈通常是 分析工具和数据库。我们的目标是开发新的统计方法和软件的研究 社区，以提高代谢组学数据的利用。在代谢组学数据中有许多步骤 分析管道，我们将重点放在下游步骤的规范化，和单变量，多变量和 路径分析特别是，我们将解决高水平的稀疏性，这是一个更独特的 与其他组学数据集相比，代谢组学数据的各个方面。对于代谢组学数据， 由于生物学或技术原因，数据缺失的比例很大， 由于代谢产物中的高度共线性和稀疏连接网络， 途径。我们开发的方法和软件将最大限度地发挥代谢组学的潜力， 疾病病因学、诊断和药物开发方面的发现。

Project Summary Comprehensive profiling of the small molecule repertoire in a sample is referred to as metabolomics, and is being used to address a variety of scientific questions in biomedical studies. Metabolomics offers more immediate measures of the physiology of an individual, and more direct examination of the effects of exposures such as nutrition, smoking and bacterial infections. For human health, metabolomics studies are being used to investigate disease mechanisms, discover biomarkers, diagnose disease, and monitor treatment responses. Metabolomics is increasingly recognized as an important component of precision medicine initiatives to complement and enhance collected genomic data. This is critical as the metabolome cannot be predicted from knowledge of the genome, transcriptome or proteome, but provides important information on the phenotype. Recent technological advances in mass spectrometry-based metabolomics have allowed for more comprehensive and sensitive measurements of metabolites. We focus on untargeted ultra-high pressure liquid chromatography coupled to mass spectrometry, which is one of the more commonly used methods. Despite the technological advances, the bottleneck for taking full advantage of metabolomics data is often the paucity and incompleteness of analytical tools and databases. Our goal is to develop novel statistical methods and software for the research community to improve the utilization of metabolomics data. There are many steps in a metabolomics data analysis pipeline, and we will focus on the downstream steps of normalization, and univariate, multivariate and pathway analyses. In particular, we will address the high levels of sparsity, which is one of the more unique aspects of metabolomics data compared to other –omics data sets. For metabolomics data, there is sparsity in individual metabolites due to a large percentage of missing data for biological or technical reasons, and sparsity in connections between metabolites due to high collinearity and sparsely connected networks in metabolic pathways. The methods and software we develop will maximize the potential of metabolomics to provide new discoveries in disease etiology, diagnosis, and drug development.