Identifying Biomarkers from Multi-source, Multi-way Data

项目摘要 在医学研究中，越来越多的高内容平台和技术被用于测量糖尿病 诗句，但相关信息。例如对基因组、表观基因组、转录组和 翻译组、代谢物前体fi和成像方式。此外，来自同一高内容平台的数据 通常在多个维度上进行测量，例如多个组织、身体区域或发育时间 积分。我们将通过多个平台或技术测量的数据称为多源数据，将测量的数据称为 在多个维度上作为多路。许多现代生物医学研究收集的数据都是多源的 和多路，这意味着从多个平台收集多路数据。多源多路数据有 捕捉和合成复杂生物系统方方面面的巨大潜力。然而到目前为止， 几乎没有开发出对这类数据进行全面综合分析的方法。我们将专注于发展- 从多源、多路数据中确定临床结果的生物标记物的OP方法。生物标志物是 通常用作疾病进展的替代物或临床试验的终点，因此它们的精确度 捕捉一个特定的医学现象是至关重要的。我们建议开发新的复合生物标记物METH- 识别跨多个数据源和多个维度的模式的消耗臭氧层物质，与 这是一个临床结果。我们的中心假设是，完全整合和多元的方法将产生更多 精确的生物标志物并简化其解释。这个项目的新产品将是一套方法 将常见生物标记物任务扩展到多源多路上下文，包括降维(AIM A)、缺失值推定(目标1b)、高维预测(目标2)和相依假设检验 (目标3)。这项工作的动机是我们参与了几个正在进行的合作翻译项目， 丰富的多源多途径数据，包括发现慢性肺癌发展的生物标志物 阻塞性肺疾病患者，对于神经退行性疾病的进展，如Friedre- ICH的共济失调，以及婴儿的脑铁fi病。我们将申请并严格评估我们的多个来源 在这些应用程序上采取多路方法。所有方法都将以免费、开源和轻松的方式实现 便于其他研究人员和从业者使用的易用软件。

Project Summary In medical research, a growing number of high-content platforms and technologies are used to measure di- verse but related information. Examples include sequencing of the genome, epigenome, transcriptome and translatome, metabolite profiling, and imaging modalities. Moreover, data from the same high-content platform are often measured over multiple dimensions, such as multiple tissues, body regions, or developmental time points. We refer to data measured over multiple platforms or technologies as multi-source, and data measured over multiple dimensions as multi-way. Many modern biomedical studies collect data that are both multi-source and multi-way, meaning multi-way data are collected from multiple platforms. Multi-source multi-way data has enormous potential to capture and synthesize every facet of a complex biological system. However, to date there has been little methodology developed for fully integrative analysis of such data. We will focus on devel- oping methods to identify biomarkers for a clinical outcome from multi-source multi-way data. Biomarkers are often used as a surrogate for disease progression or as an endpoint for clinical trials, and so their precision in capturing a given medical phenomenon is crucial. We propose to develop new composite biomarker meth- ods that identify patterns across multiple sources of data, and multiple dimensions, that are associated with a clinical outcome. Our central hypothesis is that a fully integrated and multivariate approach will yield more precise biomarkers and simplify their interpretation. The novel product of this project will be a suite of methods extending common biomarker tasks to the multi-source multi-way context, including dimension reduction (Aim 1a), missing value imputation (Aim 1b), high-dimensional prediction (Aim 2) and dependent hypothesis testing (Aim 3). This work is motivated by our involvement in several ongoing collaborative translational projects with rich multi-source multi-way data, including biomarker discovery for the development of lung cancer in chronic obstructive pulmonary disease patients, for the progression of neurodegenerative disorders such as Friedre- ich's Ataxia, and for brain iron deficiency in infants. We will apply and rigorously assess our multi-source multi-way approaches on these applications. All methods will be implemented in free, open-source and easily accessible software to facilitate their use by other researchers and practitioners.