Identifying Biomarkers from Multi-source, Multi-way Data

项目摘要 在医学研究中，越来越多的高含量平台和技术被用于测量DI。 但相关的信息。实例包括基因组测序、表观基因组测序、转录组测序和基因组测序。 翻译组、代谢物分析和成像模式。此外，来自同一个高内容平台的数据 通常在多个维度上测量，例如多个组织、身体区域或发育时间 点我们将在多个平台或技术上测量的数据称为多源数据， 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 多路，即从多个平台收集多路数据。多源多路数据， 捕捉和合成复杂生物系统的各个方面的巨大潜力。但迄今 目前还没有为全面综合分析这些数据而制定的方法。我们将专注于发展- 希望从多源多途径数据中鉴定临床结果的生物标志物的方法。生物标志物 通常用作疾病进展的替代物或临床试验的终点，因此其精确度 捕捉特定的医学现象至关重要。我们建议开发新的复合生物标志物方法， ODS，它识别跨多个数据源和多个维度的模式，这些模式与 临床结果。我们的中心假设是，一个完全整合和多元的方法将产生更多的 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， 将常见的生物标志物任务扩展到多源多路环境，包括降维（Aim 1a）、缺失值插补（Aim 1b）、高维预测（Aim 2）和相关假设检验 (Aim 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，包括慢性肺癌发展的生物标志物发现， 阻塞性肺病患者，神经退行性疾病的进展，如弗里德- 他的共济失调和婴儿的脑铁缺乏症。我们将应用并严格评估我们的多源 在这些应用程序上的多路方法。所有的方法都将在免费，开源和容易实现 可访问的软件，以方便其他研究人员和从业人员使用。

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.